WP3. D.3.1 Attributional Life Cycle Analysis report

Transcription

1 WP3 D.3.1 Attributional Life Cycle Analysis report Date: 10th December 2014 Authors : Therese Daxner, Alexander Forell, Paola Gamarra, Sophie Kieselbach, Adolf Merl, Daniel Mühlbach, Alexander Stoffregen WP3/D3.1 1

2 Table of Contents Table of Contents... 2 List of Figures... 3 List of Tables... 6 Acronyms Scope of Attributional LCA Attributional Life Cycle Assessment Methodology System Description and Boundaries The Functional Unit Selection of Impact Assessment Categories Main Indicators Optional elements of LCIA Production of Vehicles Use Phase Electricity supply in Fuel supply Use-Phase Fuel consumption & vehicle emissions End of Life Cut off Criteria Overall Data Quality and Representativeness Precision and completeness Consistency and Reproducibility Geographical coverage and representativeness Time coverage and representativeness Technological coverage and representativeness Software and Databases Results Results NEDC (reference) Results of electricity grid mix scenarios Results of ARTEMIS driving cycles Sensitivity analysis of the battery pack Life cycle interpretation Conclusions and recommendations WP3/D3.1 2

3 List of Figures Figure 1 Gasoline vehicle: Material composition by mass...22 Figure 2 Diesel vehicle: Material composition by mass...23 Figure 3 CNG vehicle: Material composition by mass...23 Figure 4 HEV: Material composition by mass...24 Figure 5 PHEV: Material composition by mass...24 Figure 6 Electric vehicle: Material composition by mass...25 Figure 7 Energy carrier share of 2030 electricity scenarios...30 Figure 8 Projection of natural gas supply in the EU (based on EC 2013A & IEA 2013)...33 Figure 9 Demand and domestic production of crude oil in the EU (EC 2013A)...34 Figure 10 Demand of refined products in the EU (EC 2013A)...35 Figure 11 GaBi model: Plan of highest hierarchy...42 Figure 12 GaBi model: Plan for car assembly (exemplary for gasoline vehicle)...43 Figure 13 GaBi model: Plan for the glider as vehicle component (exemplary for gasoline vehicle)...44 Figure 14 GaBi model: Plan for the use phase (exemplary for gasoline vehicle) Figure 15 Abiotic depletion of elements of different vehicle types - totals (CML )...49 Figure 16 Abiotic depletion of elements over the life cycle phases of different vehicle types (CML )...49 Figure 17 Abiotic depletion of fossil resources of different vehicle types - totals (CML )...50 Figure 18 Abiotic depletion of fossil resources over the life cycle phases of different vehicle types (CML )...50 Figure 19 Acidification potential generated of different vehicle types - totals (CML )...51 Figure 20 Acidification potential generated over the life cycle phases of different vehicle types (CML )...52 Figure 21 Eutrophication potential of different vehicle types - totals (CML )...52 Figure 22 Eutrophication potential generated over the life cycle phases of different vehicle types (CML )...53 Figure 23 Global warming potential of different vehicle types totals (CML )...53 WP3/D3.1 3

4 Figure 24 Global warming potential generated over the life cycle phases of different vehicle types (CML )...54 Figure 25 Ozone layer depletion potential of different vehicle types - totals (CML )...55 Figure 26 Ozone layer depletion potential generated over the life cycle phases of different vehicle types (CML )...55 Figure 27 Photochemical ozone creation potential of different vehicle types - totals (CML )...56 Figure 28 Photochemical ozone creation potential generated over the life cycle phases of different vehicle types (CML )...56 Figure 29 Primary energy demand from non renewable resources of different vehicle types - totals...57 Figure 30 Primary energy demand from non renewable resources over the life cycle phases of different vehicle types...58 Figure 31 Primary energy demand from renewable resources of different vehicle types totals...58 Figure 32 Primary energy demand from renewable resources over the life cycle phases of different vehicle types...59 Figure 33 Primary energy demand from renewable and non renewable resources of different vehicle types totals...59 Figure 34 Primary energy demand from renewable and non renewable resources over the life cycle phases of different vehicle types...60 Figure 35 Particulate matter formation resources of different vehicle types totals...60 Figure 36 Particulate matter formation over the life cycle phases of different vehicle types...61 Figure 37 Environmental impact contribution of 1 kg of Lithium Ion Cell...62 Figure 38 Environmental impacts of 1 kwh of electricity from the grid referring to different policy scenarios (S00, S01, S09, S17, and S25)...63 Figure 39 Global warming potential of different vehicle types assuming ARTEMIS driving cycles (1)...64 Figure 40 Global warming potential of different vehicle types assuming ARTEMIS driving cycles (2)...65 Figure 41 Acidification potential of different vehicle types assuming ARTEMIS driving cycles (1)...66 Figure 42 Acidification potential of different vehicle types assuming ARTEMIS driving cycles (2)...66 WP3/D3.1 4

5 Figure 43 Global warming potential of electric vehicle production & End of Life comparing a LiFePO4 cell based vehicle with a LiNiCoMn cell based one (CML )...67 Figure 44 Eutrophication potential of electric vehicle production & End of Life comparing a LiFePO4 cell based vehicle with a LiNiCoMn cell based one (CML )...68 Figure 45 Acidification potential of electric vehicle production & End of Life comparing a LiFePO4 cell based vehicle with a LiNiCoMn cell based one (CML )...68 WP3/D3.1 5

6 List of Tables Table 1 System boundary inclusions and exclusions...14 Table 2 Life cycle impact assessment categories & indicators...16 Table 3 Main Vehicle Characteristics...18 Table 4 Material Composition for a Gasoline Car...19 Table 5 Final composition of the six vehicle types in Table 6: Efficiencies of power plants and transmission losses...31 Table 7: Emission factors of LCP per type of fuel...32 Table 8 NEDC fuel consumption according to vehicle type (IFPEN, 2014)...36 Table 9 ARTEMIS fuel consumption according to vehicle type and use (conventional vehicles)...36 Table 10 ARTEMIS fuel consumption according to vehicle type and use (electric vehicles)...36 Table 10 Threshold values for fuel emission of vehicles category Euro 6 (European Union, 2014)...37 Table 11 End of Life Scenarios for different vehicle materials...39 Table 12: Global warming potential associated to different vehicle types (conventional vehicles)...48 Table 13: Global warming potential associated to different vehicle types (EVs)...48 Table 14: Global warming potential associated to different vehicle types (PHEVs)...48 Table 15: Premises of calculated 2030 electricity mix scenarios...80 WP3/D3.1 6

7 Acronyms Abbreviation ABS ADP ADPE ADPF A-LCA AP ARTEMIS BEV BOF C CCS CFC CHP C-LCA CML CNG CO CO2 CTUe CTUh EAF ELCD EM EoL EP EU27 EV GaBi GHG GJ GWP HC HEV ICE ICEV IEA IFPEN ILCD Explanation Acrylonitrile butadiene styrene Abiotic Depletion Potential Abiotic Depletion Potential (elementary) Abiotic Depletion Potential (fossil) Attributional Life Cycle Assessment Acidification Potential Assessment and Reliability of Transport Emission Models and Inventory Systems Battery Electric Vehicles Blast Oxygen Furnace Consumption Carbon Capture and Storage Chlorofluorocarbon Combined heat and power Consequential Life Cycle Assessment Centre of Environmental Science at Leiden Compressed Natural Gas Carbon monoxide Carbon Dioxide Comparative Toxic Unit for Ecosystems Comparative Toxic Unit for Humans Electric Arc Furnace European Life Cycle Database Emissions End-of-Life Eutrophication Potential European Union with 27 member states Electric vehicle Ganzheitliche Bilanzierung (German for holistic balancing) Greenhouse Gas Giga joule Global Warming Potential Hydrocarbons Hybrid Electric Vehicle Internal combustion engine Internal combustion engine vehicle International energy agency IFP energies nouvelles International Life Cycle Data System WP3/D3.1 7

8 ISO JRC IMPRO LCA LCI LCIA LCP LFP LNG MJ NEDC NMC NMVOC NOx ODP OEMs PCB PE PE PET PHEV PM POCP PP ppm PU PVC S00 S01 S09 S17 S25 SOx TRACI USGS UV VOC WIP WP WTW International Organization for Standardization Joint research centre Environmental improvement potential of passenger cars Life Cycle Assessment Life Cycle Inventory Life Cycle Impact Assessment Large Combustion Plant Lithium iron phosphate Liquefied natural gas Mega joule New European Driving Cycle Nickel managenese cobalt Non-methane Volatile Organic Compound Nitrogen Oxide Ozone Depletion Potential Original equipment manufacturers polychlorinated biphenyl PE International Polyethylene Polyethylene terephthalate Plug-in Hybrid Electric Vehicles Particulate matter Photochemical Ozone Creation Potential polypropylene Parts Per Million polyurethane polyvinyl chloride Baseline 40% GHG reduction with nuclear phase out 40% GHG reduction with nuclear promotion 80% GHG reduction with nuclear phase out 80% GHG reduction with nuclear promotion Sulphur oxide An impact assessment methodology developed by the United States Environmental Protection Agency United States Geological Survey Ultraviolet Volatile Organic Compound Waste Incineration Plant Work Package Well to Wheels WP3/D3.1 8

9 1 Introduction to Life Cycle Assessment Life Cycle Assessment (LCA) is a tool for analysing and assessing the environmental impacts resulting from the production, use and disposal/recycling of products. LCA considers a range of different environmental impact categories and, as such, does not always produce clear-cut straight forward assertions but may give diverse and complex results illustrating the trade-offs associated with different choices. This LCA study complies with the requirements of the ISO standards 14040, and the Greenhouse Gas Protocol (product standard) /Erreur! Source du renvoi introuvable./. According to the standards LCA is comprised of four main stages. In the first step, the goal and scope of the life cycle assessment are defined. An LCA study may consider the whole life cycle of a product from cradle to grave or have a more limited scope. The second step is the calculation of the life cycle inventory (LCI). This is a list of all the inputs and outputs associated with the life cycle of the product (e.g., kg of crude oil extracted, kg of carbon dioxide emissions, etc.). This list is developed based on primary data related to the product life cycle (e.g., specifying electricity consumption, or mass and type of materials used) linked to background data contained on the emissions and resource consumption associated with the generation of electricity, production of fuels and different materials. The third step is the life cycle impact assessment (LCIA), in which data relating to environmental impacts of each flow listed in the LCI (which normally contains several hundred input and output flows) are evaluated, and these impacts are aggregated into a few key impact categories. An example of an impact category is the climate change impact category in which all greenhouse gas emissions are combined into a single carbon footprint value reported in terms of kg carbon dioxide equivalents. This impact category is a main category for decision support. In the fourth step, the results are evaluated and interpreted according to the objectives and the goals of the study. In most cases, it is possible to determine hot spots in the life cycle of the product system and draw conclusions for further investigations and optimisation. WP3/D3.1 9

10 2 Methodology The environmental impacts induced by the spreading of Plug-in Hybrid Electric Vehicles (PHEV) and Battery Electric Vehicles (BEV) in Europe in 2030 shall be assessed in work package 3 of the SCelecTRA project using two different LCA approaches. Firstly, the impacts of individual vehicle types are analysed via a classical (or so-called attributional) LCA using GaBi LCA software (task 3.1). Then, task 3.2 consists in comparative assessments of different scenarios for the overall passenger car fleet with a consequential LCA (C-LCA) using two modeling tools: a passenger fleet simulation tool and PET36 energy system modeling tool. Environmental impacts are assessed according to standard Life Cycle Impact Assessment (LCIA) methods for both approaches. From these findings, the relevance and respective limitations of each LCA approach will be discussed in accordance with the different questions that can be addressed on the environmental benefits of e-mobility deployment in Europe. Part of this discussion will provide thoughts on the feasibility of developing methods to standardize environmental burden evaluations of electric vehicles at least in terms of GHG emissions and Cumulative Energy Demand. (task 3.4). Finally, external costs associated with some scenarios regarding the future EU passenger fleet will be assessed. Considered scenarios as well as corresponding data (on fleet composition and energy mixes) are derived from WP4 work (same input data than for C-LCA assessments). Resulting external costs will be put into perspective with the policy associated costs (done in WP4). Task 3.1 consists of an attributional life cycle assessment (A-LCA) aiming to assess the Well To Wheel (WTW) impacts of the most relevant vehicles in Europe for Therefore some discussions and presentations have been carried out in order to understand and agree on the A-LCA scope but also to perceive the outcomes (scope and format of results) as essential for the subsequent tasks. In addition, the life cycle inventory of six different car types was setup including a gasoline, diesel, electric, hybrid and plug-in hybrid vehicle. The A-LCA task is split and carried out in parallel between two areas of PEs expertise: LCA Energy supply; LCA Automotive sector. A complete description of what has been carried out until now is described in the sections below. As a mode to summarize them, the most important activities carried out are: definition of the system and boundaries; WP3/D3.1 10

11 identification of the vehicle technologies to be assessed; definition of the functional unit; definition of vehicle segment in Europe; definition of vehicle lifetime; define the overall weight of the vehicles in order to determine overall performance in terms of energy consumption; define the material composition of the different components in the vehicle; definition of the electric motor power and ICE power; definition of battery pack (high voltage) type/capacity; definition of fuel consumption based on NEDC and Artemis (IFPEN); definition of a lighter profile of the vehicles or driven system for the future; estimation of scenarios of the electricity mix for 2030 in Europe; estimation of fuel supply for 2030 in Europe; Extensive research of information was carried out in order to find latest published data sources (see list of references). On the other hand many details were not easily found as some technologies (electric vehicles) are still under development. For example there is uncertainty about the batteries which will be used in 2030 and suitable end of life strategies. As a result, a complete life cycle inventory was set up providing the basis for modelling the environmental impacts created by car manufacture, use phase and end-of-life. The GaBi model was represents six vehicle types. This report presents result analysis and interpretation of the attributional LCA. 2.1 Scope of Attributional LCA PE CEE is leading WP3 and conducting a Life Cycle Assessment (LCA) in accordance with ISO 14040/44 for Gasoline Vehicles, Diesel Vehicles, Compressed Natural Gas Vehicles (CNG), Hybrid Electric Vehicles, Plug-in Hybrid Electric Vehicles (PHEV) and Battery Electric Vehicles (BEV) in Europe in The attributional LCA for the vehicle technologies covers an analysis of the entire car of each vehicle technology proposed, which in total are six vehicles. The following section describes the general scope of the project that has been set to achieve the stated goals. This includes the identification of vehicles to be assessed, the supporting product systems, the boundary of the study, the cut-off criteria etc. 2.2 Attributional Life Cycle Assessment Methodology Life-cycle assessment represents a standard method to assess environmental impacts associated with all product's life cycle stages from-cradle-to-grave (i.e., from raw material extraction to materials processing, manufacture, distribution, use, repair and maintenance, and disposal or recycling). Attributional LCAs seek to establish the burdens associated WP3/D3.1 11

12 with the production and use of a product, or specific service or process, at a point in time (typically the recent past). The attributional LCA will follow exclusively the ISO 14040/44 set of standards. The study is based on secondary data, publicly available (see list of references) but also information provided by IFPEN obtained through their research activities and simulation models for the automobile sector completed by expert knowledge provided by the sector experts of PE International. The LCA models are created using the GaBi 6 Software system for life cycle engineering, developed by PE International. The GaBi database developed by PE International provides the life cycle inventory data for materials, vehicle materials and components, fuels and energy obtained from the background system. GaBi databases are updated on a yearly basis. The GaBi datasets used for this study are based on the GaBi database Results will be presented for different impact categories, such as global warming potential and acidification, which are selected according to their significance for the vehicle industry and best available methodology. No critical review process will be carried out. 2.3 System Description and Boundaries It is important to note that six technologies for C-segment vehicles are considered for the study; Gasoline vehicle Diesel vehicle Compressed natural gas (CNG) vehicle Gasoline HEV Plug-in gasoline HEV Electric vehicle The first three are compiled within the so called conventional vehicle group. Hybrids are differentiated by a low (Gasoline HEV) and a higher e-drive due to external battery charging (grid) pattern (Plug-in gasoline HEV). On the other hand, the electric vehicle is referring to a pure electric drive technology. At the moment six vehicle technologies are included in the GaBi model. This analysis focuses on C-segment vehicles only. Small urban electric vehicles represent a car segment of which functionality strongly differs from C-segment vehicles. As a result, the comparison of only C-segment vehicles with one small urban electric vehicle is not consistent in terms of functional unit. An extension of scope including small urban electric vehicles would need to accentuate the differences in functionality and thus there is the need for further analysis on how to arrive at a common functional unit. One way to allow such a comparison would be to include social patterns in the definition of the functionality of the system (work distances, vehicle driving cycles, temporal variations, range, speed, etc.). As such an extension of scope would increase the uncertainty of the study significantly, only C-segment vehicles are included in this study. WP3/D3.1 12

13 The system under study is a cradle-to-grave system covering process steps from the manufacture of the vehicle, to its end of life: Extraction of raw materials; Car production including automobile coating; All fuel transformation processes upstream to fuel consumption (Well to Tank); Electricity production process upstream to power consumption (Well to Tank); Fuel and electricity consumption for car driving (Tank to Wheel); Use phase of vehicle over a defined lifetime; End of life of vehicle (according to the EU EoL Vehicle directive including shredding process); Recovery and recycling of battery/battery pack components, metal components and electronic equipment Elements excluded from the system are the production of capital equipment, human labour and commuting. These elements are traditionally excluded from the product-lcas as they are assumed to fall far below the cut-off criteria. Table 1 below gives examples of the industry activities included and excluded in the assessment. Impacts from vehicle assembly are assumed to be equal to each vehicle and are thus excluded from the analysis (see section 3.1 In addition, different charging patterns for plug-in hybrid and electric vehicles were not specifically analysed in this study. The electricity consumption of the plug in gasoline HEV and the two electric vehicles was calculated and communicated by IFPEN. Energy for the assembly of the different car components is also excluded from this study due to a number of reasons given in chapter 2.1. The assessed scenario includes the end of life of all materials used in the different vehicles except zinc oxide and lead, which were cut-off due to a minor importance concerning mass and a lack of suitable background datasets and information of the recycling process (see 2.2.4). WP3/D3.1 13

14 Table 1 System boundary inclusions and exclusions Cradle-to-grave system of the study included examples Production of raw materials Production of materials like steel, aluminium different plastics etc. Production of auxiliary materials Production of lubricants/fuels for equipment. Energy production Production of electricity, and fuels needed for running the vehicles. excluded examples Construction of capital equipment Production of different machinery. Construction of buildings and plant manufacturing (power plants are included though),charging pattern (grid), gasoline stations etc. Human labour and employee transport Production of residues from employees (food, waste water). Employees commuting to work. 2.4 The Functional Unit The functional unit (FU) quantifies the performance/function of a product system for use as a reference unit. It is very much linked to the type of question which is addressed and resulting choices regarding system boundaries. Since boundaries of C-LCA assessments will be very different (wider) than that of A-LCA assessments, the functional unit will also differ. For C-LCA, the boundaries will cover all sectors and countries represented in the PET model (a TIMES paradigm model). Therefore two functional units will be considered: A-LCA: One vehicle-life time taking into account the average life time of a vehicle of 150,000 km. C-LCA: To satisfy the energy and energy service demand (household and industry demands for electricity and heat + mobility demand) in Europe (36 countries) from 2007 to The functional unit of a life time of 150,000 km is used in the A-LCA study in order to account for an equivalent functionality of different systems. Next to the vehicle life time factors such as vehicle range, charging infrastructure, new technologies or social effects play an important role determining its functionality. As this study aims at the assessment of realistic developments until 2030, increase of battery capacity for electric vehicles represents an important issue addressing the functionality of this technology. Development in WP3/D3.1 14

15 this sector is certainly to expect, but major technological breakthroughs (e.g. lithium-air cells) are difficult to predict. This study assesses C-class vehicles referring to a comparable vehicle size and a comparable amount of persons transported. In order to stick to this comparability, the A-LCA study does not take into account small EVs which otherwise would result in a difference of functionality between the vehicle types. As a result, it analyses vehicles of a comparable size. If e.g. also busses were analysed, the evaluation of a vehicle life time could lead to mistakable results, due to the significant difference in number of persons transported between these two modes of transport. The analysis of vehicles of the same size does not show such obvious deviations. Thus, differences in the number of people depend of various factors and are difficult to predict for Therefore, a definition of an average passenger load for each vehicle technology based on sound scientific findings would have to be set up. Beyond that, social factors such as car sharing, behavioral aspects and differences in urban and rural areas influence the number of people transported. In addition, economic parameters such as emerging incentive systems, fuel prices, etc. have direct effects on consumer choices and can influence the number of people transported. To sum up, the functional unit of a vehicle life time which is applied in this study represents a frequently applied reference in automotive studies and aims at the generation of results which are easily tangible and interpretable. The consideration of person kilometres would imply a high uncertainty and would be based on value choices, which may would have misleading effects on the results of this study. 2.5 Selection of Impact Assessment Categories Main Indicators A comprehensive set of environmental impact categories has been investigated. The choice of categories is made based on the recommendations of the ILCD Handbook (ILCD Handbook, 2010). The study includes the following inventory flows and environmental categories at midpoint level: global warming potential, photochemical oxidant creation potential (smog formation), acidification potential, eutrophication potential, stratospheric ozone depletion potential, particulate matter and demand for abiotic resources (elementary and fossil). In addition, primary energy demand (total and non-renewable sources) is included in the study due to its broad acceptance in the field of LCA. These impact categories have a classification of I (recommended and satisfactory) or II (recommended but in need of some improvements) in the ILCD handbook (2010). Some impact categories with an I/II rating will not be included. In the selected impact categories the CML indicators were calculated. Only the evaluation of particulate matter formation follows the ReCiPe methodology. The details of each impact category and its indicator are shown in Table 2. The indicators chosen for this study are latest CML indicators (CML method from 2001, factors updated 2013)and for particulate matter ReCiPe respectively. WP3/D3.1 15

16 Table 2 Life cycle impact assessment categories & indicators LCIA categories and indicators used in cradle-to-grave vehicle systems Category Indicator Impact category Description Unit Reference Energy Use Primary Energy Demand (PE) A measure of the total amount of primary energy extracted from the earth. PE is expressed in energy demand from non-renewable resources (e.g. petroleum, natural gas, uranium, etc.) and energy demand from renewable resources (e.g. hydropower, wind energy, solar, etc.). Efficiencies in energy conversion (e.g. power, heat, steam, etc.) are taken into account. MJ Guinée et al., 2001, factors updated in 2010 Climate Change Global Warming Potential* (GWP) A measure of greenhouse gas emissions, such as CO 2 and methane. These emissions are causing an increase in the absorption of radiation emitted by the earth, magnifying the natural greenhouse effect. kg CO 2 equivalent IPCC, 2006, 100 year GWP is used Eutrophication Eutrophication Potential (CML) A measure of emissions that cause eutrophying effects to the environment. The eutrophication potential is a stoichiometric procedure, which identifies the equivalence between N and P for both terrestrial and aquatic systems kg Phosphate equivalent Guinée et al., 2001, factors updated in 2010 Acidification Acidification Potential (CML) A measure of emissions that cause acidifying effects to the environment. The acidification potential is assigned by relating the existing S-, N-, and halogen atoms to the molecular weight. kg SO2 equivalent Guinée et al., 2001, factors updated in 2010 Ozone creation in troposphere Photochemical Ozone Creation Potential (POCP) A measure of emissions of precursors that contribute to low level smog, produced by the reaction of nitrogen oxides and VOC s under the influence of UV light. kg Ethene equivalent Guinée et al., 2001, factors updated in 2010 Stratospheric Ozone Depletion Ozone Depletion Potential (ODP) Refers to the thinning of the stratospheric ozone layer as a result of emissions. This effect causes a greater fraction of solar UV-B radiation to reach the surface earths, with potentially harmful impacts to human and animal health, terrestrial and aquatic ecosystems etc. referring trichlorofluoromethane, also called freon-11 or CFC 11 Kg CFC-11 equivalent or trichlorofluoromethane, also called freon-11 or R11 Guinée et al., 2001, factors updated in 2010 Particulate matter (Midpoint) Particulate matter (PM) Refers to fine particulate matter of less than 10 µm (PM10). This complex mixture of organic and inorganic substances cause health problems reaching the upper part of the airways and lungs. Kg PM10 equivalent ReCiPe, 2008 * The terminology potential is defined by ISO and used by CML to clearly indicate that LCIA shows potential impacts in the future. For example for climate change the Global Warming Potential represents the potential impact of GHG emissions related to the reference unit CO Optional elements of LCIA Optional elements of the ISO 14040/44 normalization, grouping, and weighting, will not be applied as they involve value-choices and/or were not necessary for the defined goal and scope. The additional LCIA data quality analysis will be performed and will include contribution analysis (identification of the greatest contribution to the indicator result), and sensitivity analysis (identification of how changes in data and methodological choices affect the results of the LCIA). WP3/D3.1 16

17 3 Data Collection and Overview of Modelling Assumptions The following assumptions have been made for developing the life cycle inventory and life cycle assessment models. Due to the complexity of the scope especially into a future scenario prediction, technology/parameters (vehicle and energy supply) characterization and data collection represented an iterative process. The following life cycle inventory is now the result of a combination of literature data and expert judgment. PE International represents the leading LCA practitioner in the field of car technology due to the long lasting collaboration with car manufacturers including Renault, VW, Daimler, Porsche, Audi, BMW, Ford, Toyota, etc. In addition, the participation in various projects including Super Light Car, HyFLEET:CUTE and ZeroWIN enabled PE International to provide expert judgment data for the modelling of the different vehicle technologies assessed in this study. A whitepaper representing an example for PE s involvement in studies in the automotive sector is presented on the company s homepage ( providing information for the recycling of lightweight automobiles. 3.1 Production of Vehicles The following sections will present further information concerning vehicle technology characteristics and base vehicle specification. Vehicle Technology Characteristics The vehicles in this study have been limited to C-Segment type only. This choice can be confirmed as this size of vehicle has come to dominate new car sales figures in Europe (ICCT, 2011). It is assumed in this study that this trend will hold for the foreseeable future and as such, an analysis limited to C-Segment vehicles is justifiable. The main characteristics of the vehicles are defined for the selected technologies in Table 3 below: WP3/D3.1 17

18 Table 3 Main Vehicle Characteristics Approximate Weight (kg) Gasoline vehicle Diesel vehicle CNG vehicle Gasoline HEV Plug-in gasoline HEV Electric vehicle ICE power (kw) Electric motor power (kw) Type of fuel injection Gasoline Direct injection Diesel High pressure injection CNG injection Gasoline Direct injection Gasoline Direct injection Turbocompressor yes yes yes yes yes no Stop&Start yes yes yes no no no High Voltage Battery Type of high voltage batteries Battery capacity (kwh) Fuel tanks Gasoline Diesel Post-treatment no no no yes yes yes 3 way catalyst DOC + DPF + SCR Gasoline + CNG 3 way catalyst Li-ion Li-ion Li-ion Gasoline 3 way catalyst Gasoline 3 way catalyst Particulate filter yes yes no yes yes no The average lifespan of a car in Europe is between 12 and 15 years. Evidently the life span varies between countries and vehicle technologies. In order to provide a comparable functional unit in this study an average lifetime mileage 150,000 km was assumed over the car s life-cycle. Base Vehicle Specification The base vehicle is based on the material composition of the average European gasoline ICE passenger car from the JRC IMPRO-car study (EC JRC, 2008) as presented below: WP3/D3.1 18

19 Table 4 Material Composition for a Gasoline Car Materials Mass contribution [%] Total content of ferrous and non-ferrous metals 66.05% Steel BOF 40.32% Steel EAF 19.52% Total content of iron and steel 59.84% Aluminium primary 3.39% Aluminium secondary 2.10% Total content of aluminium 5.48% Cu 0.73% Mg 0.04% Pt 0.00% Pl 0.00% Rh 0.00% Glass 3.23% Paint 2.90% Total content of plastics 16.13% PP 9.19% PE 2.98% PU 2.42% ABS 0.73% PA 0.48% PET 0.32% Other 2.18% Miscellaneous (textile, etc.) 1.85% Tyres (in total 4 units) 2.50% Rubber 0.32% Carbon black 0.16% Steel 0.08% Textiles 0.03% Zinc oxide 0.01% Sulphur 0.01% Additives 0.08% Battery 1.13% Lead 0.73% PP 0.06% Sulphuric acid 0.32% PVC 0.02% Fluids 4.03% Transmission fluid 0.56% Engine coolant 0.97% Engine oil 0.24% Petrol 1.85% Brake fluid 0.08% Refrigerant 0.07% WP3/D3.1 19

20 Water 0.16% Windscreen cleaning agent 0.04% Total weight % This basic composition published by the JRC IMPRO-car study is used as starting point for the vehicle composition used in this analysis. In order to gain realistic assumptions, this reference composition has been adjusted to realistic conditions in 2010 and subsequently to 2030 in several iterations. As a result, the basic vehicle material composition choices above have been altered in order to align with the six drive train technologies being assessed reflecting the year WP3/D3.1 20

21 Table 5 Final composition of the six vehicle types in 2030 Material [kg] Gasoline Diesel CNG HEV PHEV EV ABS Aluminium (prim) Aluminium (sec) Capacitor Carbon black ,00 Cast iron Copper Ethylene glycol Glass lead Lithium-Ion-Cell Lubricants PCB PET Polyester Polyethylene Polypropylene PU PVC Rhodium Rotor magnet Rubber Stainless steel Steel forging part steel sheet (BOF) Sulphur (tyres) sulphuric acid Switch transformer Tyre Additives Water Zinc -oxide ,40 total Masses were determined based on a target weight which was set by IFPEN. These input masses need to correlate for both models used in the applied assessment tools in order to ensure their compatibility (GaBi & fleet simulation tool & PET). As not all the masses proposed by IFPEN matched with the generated masses of PE, the overall weight of the gasoline HEV and the plug-in HEV was adapted in accordance with IFPEN. Furthermore, different sources of literature have been collected and have been consulted as shown in the list of references. In addition, the resulting values were compared to the environmental certificates of state of the art conventional vehicles. As a result, the values used in this study do not show high deviations from the vehicle composition of passenger cars which are introduced to the market currently. WP3/D3.1 21

22 All vehicles are based on the average mid-sized petrol car as detailed in the 2008 JRC IMPRO-car study (EC JRC, 2008) (which corresponds to the C-Segment focus of this project). The vehicles have been differentiated by adding or subtracting relevant components based on the drive train in question with all other common components (such as the vehicle glider) assumed to remain the same e.g. the electric vehicles have no ICE and no catalytic converter but will include an appropriately sized e-motor, power electronics and battery pack (high voltage battery). Alternatively, the petrol ICEV will have a catalytic converter, engine oil/lubricants but no e-motor or battery pack. Subsequently, the material composition of each vehicle type is shown. 55% 1% 4% Gasoline 7% 1% 6% 6% 1% 1% 1% 3% 1% 8% 2% 2% 2% ABS Aluminium (prim) Aluminium (sec) Capacitor Carbon black Cast iron Copper Ethylene glycol Glass lead Lithium-Ion-Cell Lubricants PCB PET Polyester Polyethylene Polypropylene PU PVC Rhodium Rotor magnet Rubber Stainless steel Steel forging part steel sheet (BOF) Sulphur (tyres) sulphuric acid Switch Figure 1 Gasoline vehicle: Material composition by mass WP3/D3.1 22

23 55% 1% 4% Diesel 7% 6% 1% 6% 1% 1% 3% 1% 8% 1% 2% 2% 2% ABS Aluminium (prim) Aluminium (sec) Capacitor Carbon black Cast iron Copper Ethylene glycol Glass lead Lithium-Ion-Cell Lubricants PCB PET Polyester Polyethylene Polypropylene PU PVC Rhodium Rotor magnet Rubber Stainless steel Steel forging part steel sheet (BOF) Sulphur (tyres) sulphuric acid Switch Figure 2 Diesel vehicle: Material composition by mass 59% 1% 4% CNG 1% 6% 6% 1% 1% 2% 1% 1% 2% 6% 2% 6% 1% ABS Aluminium (prim) Aluminium (sec) Capacitor Carbon black Cast iron Copper Ethylene glycol Glass lead Lithium-Ion-Cell Lubricants PCB PET Polyester Polyethylene Polypropylene PU PVC Rhodium Rotor magnet Rubber Stainless steel Steel forging part steel sheet (BOF) Sulphur (tyres) sulphuric acid Figure 3 CNG vehicle: Material composition by mass WP3/D3.1 23

24 51% HEV 1% 5% 1% 6% 7% 7% 6% 2% 1% 1% 1% 2% 1% 3% 1% 2% ABS Aluminium (prim) Aluminium (sec) Capacitor Carbon black Cast iron Copper Ethylene glycol Glass lead Lithium-Ion-Cell Lubricants PCB PET Polyester Polyethylene Polypropylene PU PVC Rhodium Rotor magnet Rubber Stainless steel Steel forging part steel sheet (BOF) Sulphur (tyres) sulphuric acid Figure 4 HEV: Material composition by mass 48% PHEV 1% 5% 0% 6% 6% 1% 1% 2% 1% 10% 1% 7% 2% 5% 1% 2% ABS Aluminium (prim) Aluminium (sec) Capacitor Carbon black Cast iron Copper Ethylene glycol Glass lead Lithium-Ion-Cell Lubricants PCB PET Polyester Polyethylene Polypropylene PU PVC Rhodium Rotor magnet Rubber Stainless steel Steel forging part steel sheet (BOF) Sulphur (tyres) Figure 5 PHEV: Material composition by mass WP3/D3.1 24

25 45% EV 1% 1% 0% 5% 6% 7% 6% 1% 2% 1% 1% 2% 0% 17% 1% 2% ABS Aluminium (prim) Aluminium (sec) Capacitor Carbon black Cast iron Copper Ethylene glycol Glass lead Lithium-Ion-Cell Lubricants PCB PET Polyester Polyethylene Polypropylene PU PVC Rhodium Rotor magnet Rubber Stainless steel Steel forging part steel sheet (BOF) Sulphur (tyres) sulphuric acid Switch Figure 6 Electric vehicle: Material composition by mass As a result, the standard units (such as the glider, power electronics, tires, etc.) remain the same for every vehicle type. Vehicle specific parts, such as the e-motor, power electronics of the e-motor and battery, have been configured and thus changes of masses stem from different weights of the battery, motor and different fluids. For the CNG the higher weight stems from the steel tank, which is needed to store compressed natural gas. The PHEV shows a smaller conventional engine which is accompanied by a decrease of weight. On the other hand, due to the weight of the e-motor the PHEV results in an increase of mass in comparison to the conventional gasoline and diesel vehicles. In general, the composition of all vehicles types is dominated by steel with over 60% (steel sheets and steel forging parts) for the conventional vehicle types. In addition, aluminium is included in the vehicles with a percentage of roughly 11% respectively 6% in the EV. There is still a debate on the light weighting of different components as could be expected for the year 2030 which will have an influence on overall vehicle weight and thus on overall performance (energy consumption). Intense discussions with IFPEN and research was carried out in order to agree on a consistent approach. Hence, for light weighting in 2030, the materials considered are aluminium and high strength steel (AHSS) (light weighting of vehicles by replacing materials i.e. increasing the amounts of advanced high strength steel and aluminium in the car body and reducing mild steel amounts present accordingly). For the engine block, there is a change of mass assumed due to the fact that a replacement of 1,5kg steel to 1kg secondary aluminium can be expected for the future. WP3/D3.1 25

26 The use of composites and other alternative materials was as well considered and evaluated, but do not appear to be used in 2030 in a high extent. Light weighting is considering for both non-drive train components i.e. the vehicle glider (which determines overall vehicle size thus overall performance in terms of energy consumption) as well as components of the propulsion system In general, one must bear in mind that there are multiple factors (e.g. vehicle security) to include in vehicle configuration in the future being not only driven by environmental impact categories. Owing to that fact the precise development of material substitution and the decrease of vehicle weight cannot be predicted without a high grade of uncertainty. Vehicle Material and Component Data for the Year 2030 GaBi software and related databases developed by PE International represent the basis for the modelling of the product system. In general, the GaBi software and databases are widely used by OEMs in the automotive sectors who engage in LCA activities. European GaBi 2013 datasets (GaBi 6, 2013B) are used to account for component manufacturing processes such as stamping and bending metal sheets, casting and machining metal parts, injection moulding plastic parts, etc. Furthermore, it is assumed that vehicle manufacture is in Europe (average situation for a European context) and prevailing fuel and electricity grid mix conditions are applied. Where it is not possible to exactly match the material composition of vehicle components, the best available GaBi datasets were used as proxies. The choice of proxies/estimation is based on PE International s professional experience and judgment. Where steel is used in the vehicle, the modelled datasets differentiate between steel forging parts and steel sheets. Steel forging parts are produced via the electric arc furnace (EAF) route followed by a drop-forging step and part machining. Steel sheets follow the blast oxygen furnace (BOF) route followed by multi-level deep drawing. Primary aluminium is produced by deep drawing of aluminium ingots. For Plastics such as polyamide 6.6, polyethylene, polyethylene terephthalate, polypropylene and ABS part manufacturing with injection moulding of granulate is modelled. In addition, a parameterized GaBi plan of automobile coating was adapted to the vehicle specific properties. In addition, attempts to predict future changes for 2030 in technology and emission intensities of principal and significant raw materials in vehicle manufacture as well as energy consumption and profiles were discussed. Finally, the emissions from energy consumption in the supply chain of vehicle assembly are held constant in the model due to a number of reasons. First, the impact of the supply chain and production phase relative to the use phase of conventional vehicles is expected to be rather low. This assumption is based on published findings of car LCA studies such as the environmental certificate of the Mercedes C-Class (Daimler Chrysler AG, 2009) and Volkswagen Passat (Volkswagen AG, 2007). The LCA analysis of the Passat shows a contribution of 30% of the production phase and 70% of the use phase emissions to global warming potential. For the Mercedes C-class the use phase accounts for 86% of the CO 2 emissions (Daimler Chrysler AG, 2009). For electric vehicles the higher proportion of the production phase relative to the use phase mainly stems from upstream supply chains of the lithium ion cell. WP3/D3.1 26

27 Secondly, there is a high uncertainty in the energy demand for the production of the raw materials as some of the manufacturers rely on internal energy production technologies which are pulled on a higher level using European averages. Especially looking at the main contributors to the environmental impacts associated with material production represented by aluminium and steel, the influence of an exchange of electricity data is expected to be low. Aluminium producers commonly supply themselves independently using mainly hydropower. Iron production is dominated by thermal energy demand, thus the impact of a change of electricity mix is expected to only have a marginal influence on the results generated. Of course these assumptions need to be reflected with caution, as a change in the vehicle fleet and its fuel consumption respectively is expected to have significant impacts on the fuel supply of all sectors. As a result, the assumption of aluminium producers to supply themselves using hydropower and the low contribution of thermal energy demand in iron production may change until All these implications with regard to a systemic approach covering the whole economy in terms of energy supply and demand need to be in focus of the subsequent work in this project. High Voltage Battery for Hybrid and Electric Vehicles The modelling of the high voltage lithium ion batteries comprises the manufacturing processes of the lithium iron phosphate cell itself regarding the production of used materials and raw material extraction, upstream chains of energy generation and associated wastes until it reaches the factory gate (cradle to gate). The production phase includes cathode production, anode production, putting, packing, filling and sealing. Exchange parts of plants, engines, transports and packaging for transportation are excluded. The use of a lithium iron phosphate cell is assumed to represent the state of the art technology in the mobility sector in Other forms of lithium ion cells of different composition (e.g. cobalt as active material) or lithium air cells are under discussion at the moment but cannot be expected to be ready for the market by Currently applied technologies include NMC-batteries (lithium, nickel, manganese, cobalt) which are expected to be replaced by lithium iron phosphate cells by Lithium iron phosphate cells show appealing features such as high capacity, lower cost than comparable cell types (e.g. LiCoO 2 ), a two-phase electrochemical process and most importantly a high intrinsic safety. On the other hand, LFP shows a high intrinsic resistance and thus requires special electrode preparation. As a result, carbon coated LFP represents a promising material for application where high power performance is required. Lithium iron phosphate batteries are already in commercial use especially for small vehicles and plug-in hybrid electric vehicles (Scrosati & Garche, 2010). There is expected to be a high potential for lithium iron phosphate cells to dominate the market in terms of electric vehicle battery technology Due to the rapid development of lithium-ion-cells, no precise prediction for 2030 can be given. Thus, this study applies the most likely scenario which can be expected with the information available at this time. WP3/D3.1 27

28 Vehicle Assembly In reality the exact requirements for vehicle assembly may not significantly vary for the six drive train technologies considered in this study. Past studies show that vehicle assembly is a very minor contributor to overall life cycle CO 2 e impacts. In addition, assembly line technologies are expected to be quite similar across the drive trains considered. Thus, the assembly of the car is neglected in the study due to a lack of publically available data and low importance of this process step. The C-LCA study will include the energy demand for vehicle assembly (as part of the energy demand of the industrial sector). Transport of Vehicle Components and Materials The study will not include information or modelling steps on raw material or component transportation. As such, no transports of materials, vehicle components or delivery of assembled vehicle to the end user will be included in this study. This is due to the fact, that transportation across Europe represents a highly variable factor. In addition, the contribution of the transports to the overall impact resulting out of different vehicle types is expected to be low. 3.2 Use Phase The rate of wear and tear /maintenance is assumed to be the relatively similar for all vehicles assessed. Due to this fact and the low contribution of wear and tear /maintenance to the environmental impact of the differing vehicle types it is excluded from this analysis. Replacement of the battery pack of advance drive train technologies is not considered under wear and tear/maintenance. This is because for electrified vehicles, manufacturers currently provide a typical warranty on battery packs for up to 160,000km (or 8 years). As lifetime mileage in this study is set to 150,000km, it is assumed that the original battery pack suffices for the full life cycle. The base scenario applies NEDC (new European driving cycle) for all vehicle types in In addition, IFPEN simulates requirements for fuel consumption with Artemis cycles. Results applying NEDC are presented in section The results for Artemis cycles are given in section Electricity supply in 2030 Approach for scenario development For a comparison between conventional propulsion systems and those vehicles powered with electricity, it is essential to perform a comprehensive analysis of the supplied electricity. Although electric vehicles do not emit any emissions during their use, important amounts of emissions can be released during electricity generation. Three main aspects have been identified that need to be addressed for the generation of life cycle inventory data sets for the electricity supply in 2030: Share of energy carrier used to generate electricity in 2030 WP3/D3.1 28

29 Technology development of electricity generating technologies, i.e. emissions or efficiency Supply of energy carrier, i.e. supply of natural gas, coal or other energy carrier Energy carrier mix The share of the energy carrier used to generate electricity in 2030 is calculated by the PET model. Based on the existing electricity supply system in the EU (reference year 2010) and the actual policy in the individual Member States (e.g. promotion and development of renewable energies, phase-out of nuclear power etc.), a cost minimized baseline scenario for 2030 has been calculated (S00) by the PET model. The baseline scenario assumes a very moderate climate policy by In addition to the baseline scenario, several alternative scenarios have been defined using the following premises: Reduction of greenhouse gases by 40% in 2050 (reference year 2010) Reduction of greenhouse gases by 80% in 2050 (reference year 2010) Promotion of new nuclear power stations vs. phase out of nuclear power, i.e. no new nuclear power plants after 2020 apart from those already under construction Further promotion of renewable energy use for electricity generation, i.e. optimistic techno-economic assumptions for renewable energy resources Promotion of energy efficiency, i.e. decrease of energy intensity by ~20% compared to reference year of 2010 An additional premise with regard to CCS (carbon capture and storage) use for fossil power plants has been discussed but not seen as realistic until 2030, which is in line with the assumption made for the reference scenario of the EU energy trends to 2050 (EC 2013A). A combination of the premises results in 16 scenarios for which the PET model has calculated cost minimized energy carrier mixes for the 2030 electricity supply in the EU. Figure 7 illustrates the outcome from the PET calculation. The premises for additional promotion of renewable energies and energy efficiency has only limited impact on the scenarios, therefore only a selection is presented here. Results for all scenarios are given in the annex. It should be noted that the scenarios are based on existing policy, a cost minimizing model and the defined premises, i.e. they do not necessarily consider whether the underlying assumptions would find political support in the EU Member States. The assumptions for the baseline scenario result in an increase of renewable energies from 24.7% to 36.2% substituting mainly nuclear power and natural gas. Due to cost advantages, coal increases by 4 percent. Scenario S01 and S09 assume that the greenhouse gases in the EU are reduced by 40% in 2050: whereas S01 assumes a nuclear power phase-out, S09 allows a further promotion of nuclear power in the cost minimized model. As a consequence, the share of renewable energies in the S01 scenario increases to 54.4%. The incremental share of renewable resources is mostly covered by hydro power, wind power and biomass. The use of natural gas increases from 14.7% to 26.7% and nuclear power decreases to 2.4% in In the S09 scenario, nuclear power covers 39% of the electricity supply in the EU compared to 31.3% in Within the 80% greenhouse gas reduction scenario S17, a nuclear phase out is assumed resulting in 64.6% of renew- WP3/D3.1 29

30 able energy in the electricity mix in 2030 and 28% natural gas. S25 includes, similar as S09, a promotion of nuclear power to fulfil the 80% reduction target. Figure 7 Energy carrier share of 2030 electricity scenarios In subsequent task of the SCelecTRA project, new 2030 scenarios will be considered (based on policy scenarios defined in WP2 notably). Therefore, 2030 energy mixes considered in C-LCA assessments will differ from those used in the present analysis (A-LCA). Development of electricity generating technologies Depending on the underlying assumption for the calculation of the 2030 electricity mix scenarios, parameters such as efficiency and share of CHP change within the different scenarios due to exchange/modernization of existing power plants or additions of new power plants using combustible fuels. A higher share of renewable energies within the electricity mix scenarios influences the transmission losses due to longer transport distances (location of generation farther away from location of consumption, increase exchange between countries to compensate fluctuation in production due to weather conditions etc.). Table 6 lists the development of the efficiencies and the share of CHP per fuel and the transmission losses of the network system of the scenarios compared to 2010 as reference year. WP3/D3.1 30

31 Table 6: Efficiencies of power plants and transmission losses S00 S00 S01 S09 S17 S25 Biomass Share of CHP 52.5% 81.2% 58.0% 83.1% 63.6% 82.5% Overall electrical Efficiency 32.3% 31.5% 33.1% 31.7% 32.9% 31.9% Coal Share of CHP 16.4% 14.4% 25.7% 26.0% 71.4% 65.1% Natural gas Overall electrical Efficiency 32.7% 35.1% 37.0% 34.8% 37.8% 36.1% Share of CHP 23.1% 21.5% 6.5% 20.3% 7.2% 24.5% Overall electrical Efficiency 45.5% 48.0% 53.0% 49.7% 53.1% 47.2% Oil Share of CHP 8.8% 22.1% 5.6% 10.6% 0.6% 8.4% Overall electrical Efficiency Share of transmission losses 35.9% 35.8% 36.7% 36.5% 37.0% 36.5% 5.6% 5.8% 6.1% 6.8% 6.5% 7.5% The combustion of fossil fuels, such as coal or natural gas, and biomass lead to combustion emissions. Beside carbon dioxide pollutants like sulphur dioxide or carbon monoxide but also heavy metals or volatile organic compounds are emitted. In the last decades, flue gas treatment technologies have been increasingly installed in large combustion plants to reduce especially sulphur dioxide (SO 2 ) nitrous oxides (NO x ) and dust. The Large Combustion Plant (LCP) Directive of the EU (EC 2001) has set limit values for these pollutants and. The limit values are binding for combustion plants with a thermal capacity over 50MW since Operators of existing plants have the possibility to operate 20,000 hours until 2015 without fulfilling the limit values. After 2023 the emission limits in the LCP will be further tightened by the Directive on Industrial Emissions (EC 2010b). Actual emission values of all European large combustion plants (>50MW) for SO 2, NO x and PM are reported under the LCP Directive (EEA 2012 and EEA 2015). To calculate average emission factors for power plants in 2030, it has been assumed that all power plants comply at least with the emission limits set in the Industrial Emissions Directive. Table 7 presents the development of the emission factors between 2008 and 2012 as well as the resulting emission factors after WP3/D3.1 31

32 Table 7: Emission factors of LCP per type of fuel [kg/tj of fuel burnt] SO2 NOX PM , ,9 Hard coal > , , ,7 Lignite > , , ,9 Fuel oil > , ,9 73 4, ,3 67 4,3 Biomass > ,6 63 3, ,1 35 0, ,1 33 0,5 Natural gas > ,7 22 0,3 For biomass and natural gas power plants, the analysis of the emission factors in 2012 illustrates that no important improvement should be expected anymore. Natural gas power plants are very clean compared to coal or fuel due to the very low sulphur content (odorant) and no ash content. The reported emission factors for dust and SO 2 are also influenced by co-combusted fuel oil. For the 2030 electricity scenarios, the calculated emission factors after 2023 (for all power plants above the limit values in the Industrial Emission Directive, the emission limit is assumed for 2030) were used for SO 2, NO x and dust. The reduction of SO 2 and dust emissions is achieved by a higher appliance of desulphurization scrubbers and dust filters (or a higher efficiency of the equipment) which has also a reducing impact on various other emissions, especially for heavy metals. All other emissions (e.g. carbon monoxide, methane, nitrous oxide etc.) have been remained constant for the 2030 scenarios compared to 2010 as reference year. There are no changes in the existing LCA models (reference year 2010) in the 2030 scenarios for non-combustible energy carriers (renewable energy resources, such as hydro, wind, solar or geothermal, and nuclear power). Supply of energy carrier The possible supply mix of natural gas and crude oil is discussed within chapter for the supply of fuels. The supply mixes of hard coal and lignite have been remained unchanged representing reference year For lignite no relevant changes in the mix are expected, as lignite is normally combusted close to the mining site and not imported. Therefore 100% domestic production within the EU is assumed. WP3/D3.1 32

33 The share of imported hard coal at the total supply will further increase in the EU. Unfortunately the scenarios in the EU Energy Trends to 2050 (EC 2013A) do not contain any information about the origin of imports, while the IEA World Energy Outlook does not provide a split up of coal into lignite and hard coal. It is difficult to calculate a meaningful import share of hard coal in 2030 and therefore the hard coal supply mix for 2010 has been used for the 2030 electricity scenarios Fuel supply 2030 CNG For the supply of compressed natural gas (CNG) the possible natural gas supply in the European Union for the reference year 2030 was estimated based on the EU energy, transport and GHG emissions trends to 2050 (EC 2013A) and the IEA World Energy Outlook 2013 (IEA 2013). According to the EU energy, transport and GHG emissions trend to 2050, the demand for natural gas is projected to remain relatively stable around 500 billion cubic meter (bcm) between 2010 and 2030 (474 bcm in 2012, 501 bcm in 2020 and 486 bcm in 2030). In the same time, the share of domestic production in the EU will further decrease from 34.1% in 2012 to 27.4% in historic projection bcm LNG Import North Africa Russia/Caspian Norway Domestic Production Figure 8 Projection of natural gas supply in the EU (based on EC 2013A & IEA 2013) As outlined in Figure 8, the decreasing domestic supply of the EU is mainly compensated by higher LNG imports. In 2030, around 50% of the LNG would be imported from Qatar; the second 50% would be supplied mainly from North Africa (Algeria & Egypt), Nigeria as well as North America and Trinidad and Tobago. The baseline scenario S00 has been used as electricity supply for the compression at the filling station. WP3/D3.1 33

34 Diesel & Gasoline Oil supply Based on projections of the European Commission and the IEA (EC 2013A, IEA 2013), the production of crude oil from the North Sea will further decrease. The decreasing production from the North Sea would be partly compensated by a lower demand in 2030 and imports from the Caspian region. The share of domestic production at the total crude oil supply in the EU would go down from approximately 16% in 2010, to 10% in 2030 (see Figure 9). In addition, imports from Norway would be reduced by around 20% due to lower production. million toe Domestic production Imports Figure 9 Demand and domestic production of crude oil in the EU (EC 2013A) Refinery The changes in the origin of crude oil used in European refineries will not significantly affect the crude slate and related properties (net calorific value, sulphur content and API gravity or share of atmospheric residue). Therefore no changes in the LCA model have been done. Whereas the demand for middle distillates will even slightly increase (higher demand from air traffic, higher share of diesel passenger cars etc.), the overall demand for refined products is projected to decrease by 10% until 2030 (see Figure 10) due to new car fuel economy, energy efficiency measures in other sectors and a switch to alternative fuels (natural gas, electricity etc.). Especially the market for heavy fuels will be affected by a sharp decrease due to the sulphur limits for marine fuels in the sulphur emission control areas (SECA). The ongoing trend for diesel passenger vehicles will result in an ongoing decrease in motor gasoline demand. As a consequence the increasing ratio between middle distillates and gasoline and the decrease in heavy fuel demand would require important investments in new refinery process units and additional hydrogen production facilities. In addition, the energy consump- WP3/D3.1 34

35 tion is expected to increase by 5 % to provide the necessary hydrogen (CONCAWE 2013) for the incremental residue desulphurization and distillate hydrocracking within the refineries. At the same time, it is expected that the historic annual energy efficiency improvements of 0.5% per year will compensate the increasing energy demand due to a changing product slate towards lighter products. Therefore, the energy demand has been kept constant in the LCA refinery model, the same holds true for specific refinery emissions. Figure 10 Demand of refined products in the EU (EC 2013A) Biofuels The Renewable Energy Directive (EC 2009) formulates a 10% target for renewable energies in transport fuels in In 2012, the European Commission proposed an amendment of the directive (EC 2012), including a 5% cap for food crop-derived biofuels. The remaining 5% should be realized by advanced or waste based biofuels or other renewable energies. The advanced biofuels should be counted as quadrupled and waste based biofuels as doubled, resulting effectively in 7-8% biofuels for road fuels. After a first reading in the European Parliament, the proposal has still not passed. Due to the ongoing discussions to limit the usage of crop-based biofuels, a blend of 8% biodiesel for diesel and 8% bioethanol for gasoline by energetic content is used for the 2030 scenarios. This would result in an E12 blend for gasoline (12% ethanol by volumetric content) and B9 for diesel (9% biodiesel by volumetric content). WP3/D3.1 35

36 3.2.3 Use-Phase Fuel consumption & vehicle emissions The calculation of use phase vehicle emissions is based on the consumption of each vehicle type given by IFPEN. In addition, emissions standards of the European Union were used for the assessment of the emissions of carbon monoxide, nitrous oxides, hydrocarbons and particulate matters completed by the calculation of carbon dioxide and sulphur dioxide emissions based on fuel carbon and sulphur content. Table 8 depicts the consumption of each vehicle type according to its propulsion system according to the NEDC cycles. Table 8 NEDC fuel consumption according to vehicle type (IFPEN, 2014) Electricity Gasoline Diesel CNG (combination) Electricity (only) kg/km kg/km kg/km MJ/km MJ/km Gasoline Diesel CNG Gasoline HEV PHEV EV Table 9 and Table 10 presents the vehicle consumption differentiating between urban, road and highway transport. These calculations represent ARTEMIS European driving cycles. Table 9 ARTEMIS fuel consumption according to vehicle type and use (conventional vehicles) Gasoline Diesel CNG kg/km kg/km kg/km urban road highway urban road highway urban road highway Gasoline V Diesel V CNG V Gasoline HEV PHEV Table 10 ARTEMIS fuel consumption according to vehicle type and use (electric vehicles) Electricity (combi) kg/km Electricity (only) kg/km urban road highway urban road highway PHEV EV The use-phase fuel consumption differs depending on the driving cycle applied. A comparison of NEDC and ARTEMIS cycles clearly show higher consumptions with the AR- WP3/D3.1 36

37 TEMIS approach. The values applied for the NEDC are quite similar to the road transport scenario of the ARTEMIS cycles, which show the lowest values for road transport. Consumption for urban and highway transport is clearly higher. The following table shows the threshold values set by the European Union for vehicles type Euro 6. These values represent the basis for the calculation of CO, HC, NO x and PM emissions during use phase. Table 11 Threshold values for fuel emission of vehicles category Euro 6 (European Union, 2014) CO NOx HC PM g/km g/km g/km g/km Gasoline Diesel CNG Carbon dioxide and sulphur dioxide are calculated separately in the model. The calculation of sulphur dioxide emissions follows equation 1, while equation 2 and 3 describe the calculation of carbon dioxide emissions differentiating between natural gas, diesel and gasoline. = _ h (,, ) _ h h (1) _ = _ _ (,,, ) _ h (2) _ = _ _ h _ h h (3) WP3/D3.1 37

38 Compressed natural gas mainly consists of methane (>91%) which is taken into account in equation 3. The comparably low content of ethane, propane and butane is not specifically calculated as the final composition of CNG depends on the specification of the referring product used. The emissions during the use phase of the vehicle are assessed over its entire life cycle respectively a mileage of 150,000 km and thus scaled to the functional unit of 1 vehicle life time. WP3/D3.1 38

39 3.3 End of Life The following table shows the end of life scenarios which were applied in the model for each material: Table 12 End of Life Scenarios for different vehicle materials Material ABS Aluminium (prim) Aluminium (sec) Capacitor Carbon black Cast iron Copper Ethylene glycol Glass Lead Lithium-Ion-Cell Lubricants PCB PET Polyester Polyethylene Polypropylene PU PVC Rotor magnet Rubber Stainless steel Steel forging part steel sheet (BOF) Sulphur (tyres) sulphuric acid Switch Transformer Tyre Additives Water Zinc oxide End of Life Scenario Waste incineration (plastics) Recycling (incl burdens for remelting of Al) Recycling (incl burdens for remelting of Al) Waste incineration (electronics) Waste incineration (municipal waste) Recycling Recycling Waste incineration (municipal waste) Landfill for inert matter no recycling scenario available (cut-off) Recycling (hydrometallurgical process) Waste incineration (municipal waste) Waste incineration (electronics) Waste incineration (plastics) Waste incineration (plastics) Waste incineration (plastics) Waste incineration (plastics) Waste incineration (plastics) Waste incineration (plastics) Recycling Waste incineration (plastics) Recycling Recycling Recycling Waste incineration Cut-off (not regarded) Waste incineration (electronics) Waste incineration (electronics) Waste incineration (municipal waste) Waste water treatment Cut-off (not regarded) EoL Li-Ion-Cell Little knowledge about recycling potentials of lithium iron phosphate cells is available at the moment. The recycling of lithium ion cells for electric vehicles still represents a challenge for state of the art recycling schemes in terms of process efficiency and profitability. As a result, recycling companies mainly focus on recycling potentials of valuable metals such as cobalt in order to increase profitability (Sullivan & Gaines, 2010). There are sev- WP3/D3.1 39

40 eral possibilities to recover Lithium-ion-cells including hydrometallurgical, pyrometallurgical, mechanical separation processes and combinations of them. As electric vehicles are not yet fully implemented in the market, there is still a lack of information concerning realistically applied collection and recycling concepts. Most of the processes which are known today concentrate on recycling processes for portable electronics and include data gaps concerning lithium recycling and recovery rates. The modelled recycling process represents an estimation based on the LCA project LithoRec. The LithoRec study focuses on a recycling process for average NMC and LFP cells. It works with generic average masses and cell compositions, which were adapted to the cell composition of this study in the model. In practice, lithium ion cell recycling faces a high diversity of cell compositions and thus needs to be capable of dealing with a variation of materials (Buchert et al., 2011). The recycling process includes the following process steps (in concordance to Buchert et al., 2011): discharging and separation of the battery system cell separation cathode separation hydrometallurgical treatment 3.4 Cut off Criteria Generally the decision on the exclusion of materials, energy and emissions data are as follows: Mass If a flow is less than 2% of the cumulative mass of the respective gate-to-gate model inventory, it may be excluded, providing its environmental relevance is not a concern; Energy If a flow is less than 2% of the cumulative energy of the model, it may be excluded, providing its environmental relevance is not a concern; Environmental relevance: if a flow meets the above criteria for exclusion, yet it is thought potentially to have a significant environmental impact, it will be included. Material flows which leave the system (emissions) and whose environmental impact is greater than 2% of the whole impact of an impact category that has been considered in the assessment must be covered. This judgment will be made based on experience and documented as necessary. The sum of the neglected material flows must not exceed 5% of mass, energy or environmental relevance of the system inventory. Section 1.3 contains the list of elements excluded from the system boundary (like buildings or human labour). These elements are assumed to fall far below the cut-off criteria and no estimation is provided on them. WP3/D3.1 40

41 3.5 Overall Data Quality and Representativeness Precision and completeness All relevant process steps for each scenario are considered and modelled to represent each specific situation. The process chain is considered sufficiently complete with regard to the goal and scope of this study. Neglected material and energy flows are described in chapter Consistency and Reproducibility To ensure consistency, all primary data were collected with the same level of detail, while all background data were sourced from the GaBi databases. Allocation and other methodological choices were applied consistently throughout the model Geographical coverage and representativeness Geographical scope of the study includes as previously described the European context EU27. The following aspects are considered: European average data for raw materials that reflect the real world sourcing situation. European data for power grid mix for vehicle assembly, use phase, etc. European average data will be used for component manufacturing process proxies. This is because components may be sourced from several countries. EoL will be based on European average situation Time coverage and representativeness GaBi datasets are used for majority of the background data/upstream systems in the model The models are being flexed to carry out the required scenarios for 2030, as examples: Light weighting of vehicles by replacing materials i.e. increasing the amounts of advanced high strength steel and aluminium in the car body and reducing mild steel amounts present accordingly; Change in the bioethanol feedstock source split Change in the bioethanol gasoline volumetric blend; Change in the emission intensities of power grid mix; Improvements in ICE technology leading to better fuel economy/performance. Fuel savings from light weighting are being modelled along IFPEN simulation model (NEDC & Artemis). WP3/D3.1 41

42 3.5.5 Technological coverage and representativeness All primary and secondary data were modelled to be specific to the technologies or technology mixes under study. Where technology-specific data were unavailable, proxy data were used. Technological representativeness is considered to be good. 3.6 Software and Databases The LCA model was created using the GaBi 6 Software system for life cycle engineering, developed by PE International AG. The GaBi LCI database (GaBi 2013) provides the life cycle inventory data for several of the raw and process materials obtained from the background system. Last update of the database was The following screenshots show the LCA model exemplary for a gasoline vehicle. They cover the LCA model which represents the plan level of highest hierarchy combining the assembly of the car with the use phase. EU-27 LCA Vehicle (2030) GaBi process plan:reference quantities EU-27 Assembly car p 1 pcs. EU-27 Use phase (2030) px Figure 11 GaBi model: Plan of highest hierarchy Subsequently the GaBi plan for car assembly is presented (Figure 12). E-motor, power electronics for the e-motor and the battery charger are not included as it is exemplary shown for a gasoline vehicle. WP3/D3.1 42

43 EU-27 Assembly car GaBi process plan:reference quantities p EU-27: Engine (Baseline) p 1 pcs. EU-27: Car Assembly PE <u-so> px EU-27: Gearbox p 1 pcs. EU-27: Glider p 1 pcs. EU-27: Tyres p 1 pcs. EU-27: Conventional car battery p 1 pcs. EU-27: Fluids p 1 pcs. EU-27: E-Motor p 0 pcs. EU-27 Power electronics E-Motor p 0 pcs. EU-27: Automotive battery charger 0 pcs. EU-27: LiFePO4 batteryp 1 pcs. Figure 12 GaBi model: Plan for car assembly (exemplary for gasoline vehicle) In addition, Figure 13 shows the materials which are used for the glider of the gasoline vehicle. WP3/D3.1 43

44 EU-27: Glider p GaBi process plan:reference quantities EU-27: Steel sheet (BOF) 465 kg EU-27: Glider (PHEV) PE <u-bb> px EU-27: Cast Iron Part EU-27: Steel forging part 63,9 kg 63,9 kg EU-27: Mixer Cast Iron PE <u-so> 128 kg EU-27: Aluminium deep drawing (prim) 39,3 kg EU-27: Aluminium Cast Machine (sec) 4,37 kg EU-27: Copper 6,81 kg EU-27: Magnesium Cast Machine 0,47 kg Cut: Recycling von Pt, Pd, RH - kleine Mengen Recycling von Glas (Wiederaufbereit ung gleicher Aufwand) EU-27: Platinum EU-27: Palladium EU-27: Rhodium 0 kg 0 kg 0 kg EU-27: Glass 37,5 kg DE: Automobile coating SCelecTRA 1 pcs. EU-27: PP Injection Moulding 106 kg EU-27: PE Injection Moulding 31,9 kg EU-27: PU flexible Foam 28,1 kg EU-27: ABS Injection Moulding 8,43 kg EU-27: PA 6.6 Injection Moulding 5,62 kg EU-27: PET Injection Moulding 3,75 kg EU-27: Miscellaneous 21,5 kg Figure 13 GaBi model: Plan for the glider as vehicle component (exemplary for gasoline vehicle) For each material its production, manufacturing processes and the end of life were specifically modelled. In the end of life stage, this includes the shredding, thermal treatment and recycling strategies. WP3/D3.1 44

45 EU-27 Use phase (2030) GaBi process plan:reference quantities p Assumption: km driven during the life-cycle of one vehicle EU-27: Compressed natural gas (CNG, 2030 Scelectra) 0 kg EU-27: Use Phase PE <u-so> px EU-27: Electricity grid mixer (2030) p 0 MJ EU-27: Gasoline mix (regular) at refinery PE 4,65E003 kg EU-27: Diesel mix at filling station (2030) 0 kg Figure 14 GaBi model: Plan for the use phase (exemplary for gasoline vehicle). The use phase calculates the fuel consumption of each vehicle and referring emissions for a lifetime mileage of 150,000km, which is assumed to be one vehicle life time. The model switches between different electricity grid mixes for the year 2030 depending on the scenario calculated. WP3/D3.1 45

46 3.7 Results This chapter investigates the results of the A-LCA modelling of a set of propulsion systems regarding their environmental performance. Therefore, the reference scenario using NEDC is presented in section Beyond that, section presents the results of the electricity grid mix scenarios which were developed during the project and section highlights the influence of fuel consumption (Artemis cycles) assumption on the environmental performance of the different vehicles under investigation Results NEDC (reference) The following section presents the results for different vehicle types in terms of their contributing factors. In addition, it describes the key findings of this study. All results refer to the vehicle life time of different vehicle types over a lifetime mileage of 150,000 km driven. These results are presented referring to the total environmental impact associated with each vehicle type, as well as each of the modules of this study including production of raw materials, car parts, vehicle use and the burdens and credits resulting from end of life. Chapter 3.2 presents a detailed description of all assumptions used for the modelling of fuel supply and electricity grid mix scenarios. IFPEN provided different policy scenarios for the year 2030 and the referring impacts on the electricity grid mix. As a result, these scenarios are included in the model. The following graphs differentiate between the different vehicles technologies in the year ) Diesel vehicle (no change in fuel supply between 2010/2030) 2) Gasoline vehicle (no change in fuel supply between 2010/2030) 3) PHEV including five scenarios for electricity generation - baseline scenario (S00) - 40% GHG reduction with nuclear phase out (S01) - 40% GHG reduction with nuclear promotion (S09) - 80% GHG reduction with nuclear phase out (S17) - 80% GHG reduction with nuclear promotion (S25) 4) HEV (no change in fuel supply between 2010/2030) 5) Electric vehicle including five scenarios for electricity generation - baseline scenario (S00) - 40% GHG reduction with nuclear phase out (S01) - 40% GHG reduction with nuclear promotion (S09) - 80% GHG reduction with nuclear phase out (S17) - 80% GHG reduction with nuclear promotion (S25) The implications of various scenarios on the global warming potential of the different vehicle types are presented in WP3/D3.1 46

47 Table 13 and Table 14. WP3/D3.1 47

48 Table 13: Global warming potential associated to different vehicle types (conventional vehicles) vehicle km CML Apr Global Warming Potential (GWP 100 years) [kg CO2-Equiv.] Diesel Gasoline CNG HEV Production Use Phase End of Life Credits End of Life Total Table 14: Global warming potential associated to different vehicle types (EVs) vehicle km CML Apr Global Warming Potential (GWP 100 years) [kg CO2-Equiv.] EV S00 EV S01 EV S09 EV S17 EV S25 Production Use Phase End of Life Credits End of Life Total Table 15: Global warming potential associated to different vehicle types (PHEVs) vehicle km CML Apr Global Warming Potential (GWP 100 years) [kg CO2-Equiv.] PHEV S00 PHEV S01 PHEV S09 PHEV S17 PHEV S25 Production Use Phase End of Life Credits End of Life Total A full set of result tables including all environmental indicators which are focus of this analysis are presented in 0. Results presented in this chapter show the environmental performance of each vehicle technology as well as the influence of various scenarios on the impact stemming from the electricity used for electric vehicles (PHEV and EV). In a further step, a variation of fuel consumption depending on the driving pattern differentiating between road, urban and highway used is displayed (see section 3.7.3). WP3/D3.1 48

49 CML2001 -Apr Abiotic Depletion (ADP elements) [kg Sb-Equiv.] kg Sb-Equiv./ vehicle km 4,00E-01 3,50E-01 3,00E-01 2,50E-01 2,00E-01 1,50E-01 1,00E-01 5,00E-02 0,00E+00 0,13 0,13 0,14 0,24 0,32 0,32 0,32 0,32 0,32 0,34 0,34 0,34 0,34 0,34 Figure 15 Abiotic depletion of elements of different vehicle types - totals (CML ) The total abiotic depletion of elements of the six different vehicles types is highest for the electric vehicles (EV). All scenarios show a rather similar picture as the abiotic depletion potential of elements is mainly dominated by production phase emissions as indicated in the subsequent figure. CML2001 -Apr Abiotic Depletion (ADP elements) [kg Sb-Equiv.] kg Sb-Equiv./ vehicle km 6,00E-01 5,00E-01 4,00E-01 3,00E-01 2,00E-01 1,00E-01 0,00E+00-1,00E-01-2,00E-01 Production Use Phase End of Life Credits End of Life Figure 16 Abiotic depletion of elements over the life cycle phases of different vehicle types (CML ) The elementary abiotic depletion potential is highest for the electric vehicles when focusing on the environmental impact created by production phase emissions. This high impact mainly results out of the production of the lithium ion cell (60%) being dominated by the copper used for anode production. Also the steel and copper for glider production contributes to the elementary abiotic depletion potential with a percentage of 16% of the production phase. In the conventional vehicles the glider (50-60%), the engine (25%) and the WP3/D3.1 49

50 conventional car battery (17%) mainly dominate the abiotic depletion of elements. The use phase does not significantly contribute to the abiotic depletion potential of elements. 350,0 CML Apr Abiotic Depletion (ADP fossil) [GJ] GJ/ vehicle km 300,0 250,0 200,0 150,0 100,0 50,0 0,0 227,4 273,3 285,7 246,6 189,2 185,4 170,7 175,8 160,2 154,0 147,4 121,6 130,6 103,3 Figure 17 Abiotic depletion of fossil resources of different vehicle types - totals (CML ) Abiotic depletion of fossil fuels shows the highest values for the conventional vehicles respective to the gasoline and the CNG vehicle. GJ/ vehicle km 350,0 300,0 250,0 200,0 150,0 100,0 50,0 0,0-50,0 CML Apr Abiotic Depletion (ADP fossil) [GJ] Production Use Phase End of Life Credits End of Life Figure 18 Abiotic depletion of fossil resources over the life cycle phases of different vehicle types (CML ) This category is mainly influenced by the use phase of the vehicle in which fuel is burnt. As gasoline largely represents a fossil fuel it results in a high abiotic depletion potential (98% stemming of the upstream supply chain of gasoline). Analogously, diesel use during vehicle operation generates the abiotic depletion potential of fossil fuels for the diesel ve- WP3/D3.1 50

51 hicle. The performance of electric vehicles in this context is strongly dependent on the regional electricity grid mix and the share of fossil fuels in it. Among the vehicle parts, glider and engine show the highest contribution to the ADP fossil being mainly dominated by steel and iron production. The abiotic depletion potential created in the production phase of electric vehicles is higher than the one from conventional vehicles due to the comparably high contribution of the lithium ion cell. kg SO2-Equiv./ vehicle km CML Apr Acidification Potential (AP) [kg SO2- Equiv.] 45,0 40,0 35,0 30,0 25,0 20,0 15,0 10,0 5,0 0,0 31,7 33,4 23,9 39,3 41,8 40,7 39,5 39,3 38,1 37,3 35,2 33,2 32,9 30,8 Figure 19 Acidification potential generated of different vehicle types - totals (CML ) Figure 19 and Figure 20 depict the acidification potential of the different vehicle types over their life time of 150,000 km driven. Total acidification potential of different vehicle types is highest for the PHEV referring to scenario 00 which represents the baseline scenario. The CNG vehicle shows a comparable low potential for acidification. CML2001 -Apr Acidification Potential kg SO2-Equiv./ vehicle km 60,0 50,0 40,0 30,0 20,0 10,0 0,0-10,0-20,0 Production Use Phase End of Life Credits End of Life WP3/D3.1 51

52 Figure 20 Acidification potential generated over the life cycle phases of different vehicle types (CML ) In this category the electric vehicles show higher values generated from the production phase than the conventional ones. This phase is mainly determined by the production of the lithium ion battery followed by materials production for the glider. The acidification potential associated with the production of the lithium ion battery is mainly formed by the production of the anode (graphite production) and cathode materials (production of lithium iron phosphate). The use phase plays a vital role in the life cycle of the hybrid electric, diesel and gasoline vehicles. 72% of the impacts generated in the use phase of the HEV stems from the production of gasoline. As a result, the production of gasoline and thus the consumption of gasoline and diesel mainly determine the order of magnitude of the acidification potential of the different conventional vehicle types. Also the gasoline and the diesel vehicle s acidification potential in the use phase is mainly formed by fuel production (above 60%). The residual part of the acidification potential generated in the use phase of the vehicles stems from tailpipe emissions (under 40%). CML2001 -Apr Eutrophication Potential kg Phosphate-Equiv./ vehicle km 7,00 6,00 5,00 4,00 3,00 2,00 1,00 0,00 6,02 4,68 3,00 5,11 5,26 5,09 4,95 4,93 4,77 3,64 3,34 3,09 3,06 2,77 Figure 21 Eutrophication potential of different vehicle types - totals (CML ) The eutrophication potential of different vehicle types is illustrated in Figure 21 (totals) and Figure 22 (life cycle phase differentiation). Diesel driven vehicles and hybrid electric vehicles create the highest values in this impact category arriving at 6, respectively 5,3 kg Phosphate equivalents. WP3/D3.1 52

53 CML2001 -Apr Eutrophication Potential kg Phosphate-Equiv./ vehicle km 7,00 6,00 5,00 4,00 3,00 2,00 1,00 0,00-1,00-2,00 Production Use Phase End of Life Credits End of Life Figure 22 Eutrophication potential generated over the life cycle phases of different vehicle types (CML ) The differentiation between different vehicle life cycle phases shows the dominance of the use phase for conventional vehicles. In contrast, the production phase of conventional vehicles creates by far lower values than the production of material components of electric vehicles. Still, electric vehicles perform better in terms of use phase emissions being again dependent on the present electricity grid mix. In the eutrophication potential of the use phase of diesel vehicles production of diesel and the use phase emissions during operation play both an important role. The production phase of electric vehicles is mainly dominated by the lithium ion battery pack. Analogously to the acidification potential, the production phase of the lithium ion cell is determined by the upstream supply chains for the cathode and anode materials. CML2001 -Apr Global Warming Potential t CO2-Equiv./ vehicle km 25,0 20,0 15,0 10,0 5,0 0, Figure 23 Global warming potential of different vehicle types totals (CML ) WP3/D3.1 53

54 Figure 23 illustrates higher global warming results of conventional vehicles compared to the one associated with the life cycle of electric vehicles. GWP is highest for gasoline vehicles followed by CNG and HEV. EVs show lower results. The changes between different scenarios are linear to variations of the GWP generated through electricity production in the use phase. t CO2-Equiv./ vehicle km 25,0 20,0 15,0 10,0 5,0 0,0-5,0 CML2001 -Apr Global Warming Potential Production Use Phase End of Life Credits End of Life Figure 24 Global warming potential generated over the life cycle phases of different vehicle types (CML ) The global warming potential of the gasoline, CNG, HEV and diesel vehicles is dominated by use phase impacts. In the use phase the emissions associated with the combustion of fossil fuels represents the largest contributor in this context. For the electric vehicles again the production phase emissions are higher than the ones from the use phase. The use phase is mainly determined by the electricity grid mix scenarios which are applied for As a result scenario 25 which shows the highest proportion of nuclear energy of all assessed scenarios creates the lowest values of global warming potential. In addition, during the end of life electric vehicles receive higher credits for global warming potential due to the recycling of the lithium ion batteries. The recycling step provides precious metals which were used in the lithium ion batteries. As these metals can potentially be reused and thus would substitute the production of virgin material, credits are assigned for the hydrometallurgical treatment. WP3/D3.1 54

55 CML2001 -Apr Ozone Layer Depletion Potential kg R11-Equiv./ vehicle km 8,00E-05 7,00E-05 6,00E-05 5,00E-05 4,00E-05 3,00E-05 2,00E-05 1,00E-05 0,00E+00 2,E-072,E-076,E-071,E-054,E-054,E-055,E-054,E-055,E-057,E-057,E-057,E-057,E-058,E-05 Figure 25 Ozone layer depletion potential of different vehicle types - totals (CML ) The ozone layer depletion potential generated from conventional vehicles is significantly low in comparison to electric vehicles including hybrid electric vehicles and plug-in hybrid vehicles. Again, the ODP in the use phase is dependent on the electricity grid mix which is assumed to be realistic in kg R11-Equiv./ vehicle km CML2001 -Apr Ozone Layer Depletion Potential 8,00E-05 7,00E-05 6,00E-05 5,00E-05 4,00E-05 3,00E-05 2,00E-05 1,00E-05 0,00E+00-1,00E-05 Production Use Phase End of Life Credits End of Life Figure 26 Ozone layer depletion potential generated over the life cycle phases of different vehicle types (CML ) Figure 26 clearly shows the predominance of the production phase of the potential for ozone depletion associated to the vehicles. The ODP is generated resulting out of the production of the lithium iron phosphate cell. Polyvinylidene fluoride used as binder material represents the main driver of the ODP resulting out of the production of the battery pack. WP3/D3.1 55

56 CML2001 -Apr Photochem. Ozone Creation Potential kg Ethene-Equiv./ vehicle km 8,00 7,00 6,00 5,00 4,00 3,00 2,00 1,00 0,00 1,25 5,92 5,30 6,30 6,68 6,70 6,48 6,60 6,36 5,19 5,24 4,85 5,07 4,65 Figure 27 Photochemical ozone creation potential of different vehicle types - totals (CML ) Photochemical ozone creation potential significantly varies between the different vehicle types. The gasoline, HEV and PHEV show the highest values for POCP followed by electric and CNG vehicles. Diesel vehicles clearly assert the lowest contribution to POCP. CML2001 -Apr Photochem. Ozone Creation Potential kg Ethene-Equiv./ vehicle km 9,00 8,00 7,00 6,00 5,00 4,00 3,00 2,00 1,00 0,00-1,00-2,00 Production Use Phase End of Life Credits End of Life Figure 28 Photochemical ozone creation potential generated over the life cycle phases of different vehicle types (CML ) As indicated in Figure 28 the use phase represents the main contributor of POCP for the gasoline, the CNG and the HEV vehicle. In the use phase mainly the supply chain for gasoline production contributes to POCP. On the contrary, the POCP generated from the electric vehicle scenarios and the diesel vehicle is dominated by the production phase. Next to the production of the lithium ion cell also the glider shows high values for POCP during production of the electric vehicles. During cell manufacturing the active material in the cathode represents the main contributor to POCP. WP3/D3.1 56

57 The large difference between the diesel and the gasoline vehicle can be explained with the tailpipe emissions resulting out of the combustion of each fuel and its referring emissions. In this context, comparably low values for diesel can be explained with the combination of various factors: On the one hand, emissions of carbon monoxide and hydrocarbons are higher for gasoline vehicles resulting in a higher impact. On the other hand diesel vehicles show higher emissions of nitrogen monoxide. According to CML methodology nitrogen monoxide emissions have a negative characterisation factor leading to a negative impact on photochemical ozone creation. In general, this impact category is highly dependent on regional conditions (e.g. time of the day, radiative conditions). Volatile organic compounds show varying reactivity and thus may have positive or negative effects on POCP. Summer-smog is formed during daytime due to a reaction of NO 2 and NO combined with direct solar radiation. This reaction is reversible during night or also during daytime in regions with excess of NO. As a result, NO 2 and O 3 react with each other leading to a generation of NO 2 and O 2. This reaction is thus associated with a decrease of POCP. 350,0 Primary energy demand from non ren. resources GJ / vehicle km 300,0 250,0 200,0 150,0 100,0 50,0 0,0 232,9 278,7 295,5 253,6 220,9 196,6 218,9 187,0 218,4 204,2 161,7 200,8 144,9 199,8 Figure 29 Primary energy demand from non renewable resources of different vehicle types - totals Primary energy demand from non renewable resources over the life cycle of the six different vehicle types is highest for the CNG vehicle followed by gasoline, HEV and diesel. The non renewable primary energy demand of the electric vehicle scenarios is dependent on the scenario applied. Scenario S17 includes an 80% GHG reduction with nuclear phase out and S01 a 40% reduction of GHG emissions and nuclear phase out. The impact of the proportion of nuclear power in the electricity grid mix combined with a partly switch to renewable energy carriers with low GHG emissions is clearly visible. WP3/D3.1 57

58 MJ / vehicle km Primary energy demand from non ren. resources 350,0 300,0 250,0 200,0 150,0 100,0 50,0 0,0-50,0-100,0 Production Use Phase End of Life Credits End of Life Figure 30 Primary energy demand from non renewable resources over the life cycle phases of different vehicle types Primary energy demand from non renewable resources of conventional vehicles is dominated by the use phase in which fossil fuels are used. The demand over the entire life cycle of the electric vehicles shows a higher contribution of the production phase, higher burdens associated with end of life processes and variation of use phase impacts dependent on the policy scenario in place. 120,0 Primary energy from renewable resources GJ / vehicle km 100,0 80,0 60,0 40,0 20,0 0,0 23,4 18,5 9,7 18,2 41,0 57,5 43,7 67,9 45,3 58,2 87,1 62,9 105,2 65,7 Figure 31 Primary energy demand from renewable resources of different vehicle types totals Primary energy from renewable resource is mainly dependent on the policy scenario in place having direct impacts on the electricity grid mix used for the charging of the electric vehicles. In general, electric vehicles show higher values than the conventional ones due to the proportion of renewable energy in the electricity grid mix. Conventional vehicles use fossil WP3/D3.1 58

59 resources contributing to non renewable resource demand and thus showing relatively low values in renewable energy demand. 120,0 Primary energy from renewable resources GJ / vehicle km 100,0 80,0 60,0 40,0 20,0 0,0-20,0 Production Use Phase End of Life Credits End of Life Figure 32 Primary energy demand from renewable resources over the life cycle phases of different vehicle types 350,0 Primary energy demand from ren. and non ren. resources GJ / vehicle km 300,0 250,0 200,0 150,0 100,0 50,0 0, Figure 33 Primary energy demand from renewable and non renewable resources of different vehicle types totals Figure 33 depicts the totals for renewable and non renewable energy demand of all vehicle types. In total, gasoline and CNG show highest values. All vehicle types except PHEV lie in a small range between 249 and 305 GJ per vehicle life time. WP3/D3.1 59

60 GJ / vehicle km 400,0 350,0 300,0 250,0 200,0 150,0 100,0 50,0 0,0-50,0-100,0 Primary energy demand from ren. and non ren. resources Production Use Phase End of Life Credits End of Life Figure 34 Primary energy demand from renewable and non renewable resources over the life cycle phases of different vehicle types As a result, Figure 34 shows the predominance of the use phase primary energy consumption. The analysis of the split between cumulative primary energy demand of production and use phase shows 17%-26% of the impact for the production phase of conventional vehicles and 26-46% for the production of electric vehicles depending on the scenario applied. 16,00 ReCiPe 1.08 Midpoint (E) -Particulate matter formation kg PM10 equ / vehicle km 14,00 12,00 10,00 8,00 6,00 4,00 2,00 0,00 Figure 35 Particulate matter formation resources of different vehicle types totals Figure 35 depicts total particulate matter formation for the vehicle types under investigation. The sum of all life cycle stages shows relatively high values for gasoline and HEV vehicles whereas diesel and CNG assert comparably low impacts. WP3/D3.1 60

61 20,00 ReCiPe 1.08 Midpoint (E) -Particulate matter formation kg PM10 equ / vehicle km 15,00 10,00 5,00 0,00-5,00-10,00 Production Use Phase End of Life Credits End of Life Figure 36 Particulate matter formation over the life cycle phases of different vehicle types The analysis of various life cycle stages of different propulsion systems (Figure 36) shows a dominance of the production phase for particulate matter formation for all vehicles expect gasoline. Particulate matter formation in the use phase of electric vehicles is dependent on the electricity grid mix resulting of various scenarios for This impact during production phase mainly stems from the lithium ion cell (production of active anode and cathode materials) and the glider. High particulate matter formation during the use phase of gasoline vehicles can be traced back to the upstream supply chain of gasoline production. A comparison of the upstream supply chain particulate matter formation of gasoline and diesel vehicles shows three times higher values for gasoline than for diesel production. In general, differences between the vehicles in the production phase can be mainly traced back to the presence of the lithium ion cell in the vehicle and its contribution in terms of mass. Owing to that fact, Figure 37 further depicts the main drivers of the environmental impact associated to 1 kg of lithium iron phosphate cell. WP3/D3.1 61

62 Figure 37 Environmental impact contribution of 1 kg of Lithium Ion Cell In most of the impact categories, cathode production represents a main contributor in the environmental impact of the lithium ion cell. As illustrated in Figure 37, the ADP is mainly formed by anode production whereas, for example, the ODP is strongly dependent on cathode production. In all other categories cathode production, anode production and the manufacturing process of the lithium ion cell represents the important drivers in terms of CML impact categories. A detailed look at the global warming potential of the lithium iron phosphate cell reveals the dominance of the environmental impact created by the lithium phosphate in the cathode material and the graphite powder used for the anode Results of electricity grid mix scenarios Figure 38 investigates the differences in the use phase emissions due to various policy scenarios and their implications on the electricity grid mix with a comparison of the impact of the production of 1 kwh of electricity. WP3/D3.1 62

63 SCelecTRA Figure 38 Environmental impacts of 1 kwh of electricity from the grid referring to different policy scenarios (S00, S01, S09, S17, and S25) WP3/D3.1 63