DYNAMIC ENERGY AND EMISSIONS MANAGEMENT (DEEM) WORKSHOP

Transcription

1 DYNAMIC ENERGY AND EMISSIONS MANAGEMENT (DEEM) WORKSHOP February 15, 2018 Matthew Barth Yeager Families Professor CE-CERT Director

2 AGENDA (MORNING): 10:15AM 10:30AM: OPENING STATEMENTS AND INTRODUCTION Welcome by Sharon Walker, Interim Dean, Bourns College of Engineering, UCR Matthew Barth, Director and Professor, UCR Center for Environmental Research and Technology (CE-CERT) Setting the Scene: What is Dynamic Energy and Emissions Management (DEEM) and what are the potential regulatory and policy issues? 10:30AM 11:00AM: THE ROLE OF PORTABLE EMISSION MEASUREMENT SYSTEMS (PEMS) Kent Johnson, Research Faculty, UCR CE-CERT PEMS: state-of-the-art and state-of-practice, and the role of PEMS in DEEM 11:00AM 12:30PM: TECHNICAL PANEL DISCUSSION Facilitator: Kanok Boriboonsomsin, Research Faculty, UCR CE-CERT Roundtable (short 5-minute intro talks each, then robust discussion with workshop participants): Ralph Morris, Ramboll; Pascal Amar, Volvo; Bill Robertson, CARB 12:30PM 12:45PM: BREAK 12:45PM 1:30PM: LUNCH SPEAKER: Angelo Logan, Moving Forward Network, Occidental College

3 AGENDA (AFTERNOON): 12:45PM 1:30PM: LUNCH SPEAKER: Angelo Logan, Moving Forward Network, Occidental College 1:30PM 3:00PM: POLICY PANEL DISCUSSION Facilitator: Alberto Ayala, Sacramento AQMD Roundtable (short 5-minute intro talks each, then robust discussion with workshop participants): Matt Spears, EMA Karl Tasik, Volvo Chris Cannon, POLA 3:00PM 3:30PM: CLOSING REMARKS, DISCUSSION, AND NEXT STEPS Kent Johnson, Research Faculty, UCR CE-CERT

4 CHATHAM HOUSE RULES: We want to encourage open discussion and discourse Participants are free to use the information received, but neither the identity nor the affiliation of the speaker(s), nor that of any other participant, may be attributed All telecon participants need to identify themselves on the zoom web interface (name and affiliation)

5 EMISSIONS AND THEIR IMPACT

6 HISTORY OF VEHICLE ENGINE/EMISSIONS CONTROLS 1960s: positive crankcase ventilation; carburetors 1970s: Light duty emission standards go in to place (1966) Catalytic converter introduced (1975) Exhaust gas recirculation; Evaporative emission controls 1980s: Heavy duty emission standards go in to place Fuel injection, microprocessors used in controls 1990s: Light Duty OBD Heavy duty in-use Not-to-Exceed standards 2000s: Heavy duty compliance testing protocol developed Heavy duty OBD Heavy Duty Standards

7 ENERGY VS. EMISSIONS TRADEOFF Various manufacturers (both heavy-duty and light-duty) have modified engine and emissions controls to boost fuel economy at the expense of higher emissions Heavy-Duty: consent decree Light-Duty: diesel-gate

8 2000 S: LOCATION-AWARE AND CONNECTED VEHICLES Platooning Location-aware vehicle Connected vehicle Connected Environment

9 DYNAMIC ENERGY AND EMISSIONS MANAGEMENT (DEEM) Managing Energy Consumption and Emissions in Real-Time Dynamic in terms of both spatially and temporally Management from both industry and regulatory perspectives Can be coupled with real-time reporting Can be applied to many types of emissions: greenhouse gases criterial pollutants air toxics

10 DEEM - SPATIAL APPLICATION (AKA, GEOFENCING) For California, focus on disadvantaged communities. 10

11 Ship Emission Control Areas (ECAs) SHIP EMISSIONS CONTROLS: EXAMPLE OF DEEM Controls emissions (and fuel sulfur content) in regions near coastlines Based on size and age of ship California: 24 nautical miles of coastline Also tied to sewage and garbage disposal

12 DEEM - TEMPORAL APPLICATION Based on realtime or historical air quality patterns. Figures show modeled fine particle concentration from on-road mobile sources in Riverside, California (a) March 2012, AM period (b) March 2012, PM period (c) August 2012, AM period (d) August 2012, PM period

13 DECISION FLOW CHART IN A VEHICLE EQUIPPED WITH DEEM M period Where am I? (Is this area highly populated and highly (a) polluted?)* March 2012, AM period (b) March 2012, PM period (b) March 2012, PM period Tradeoffs are optimized: Fuel Use, Compliance Cost, GHG Emissions, & Air pollution AM period (d) August 2012, PM period Yes, highly polluted area Vehicle operates with near zero or zero criteria and toxic emissions (a) March 2012, AM period (c) August 2012, AM period No, no local health risks Vehicle operates to maximize fuel and GHG reductions (b) March 2012, PM period (d) August 2012, PM period Transparency in Real-time: Vehicle automatically reports to regulators about compliant operations Exposure to harmful pollutants in areas of poor air quality is immediately reduced Vehicle emissions do not result in increased exposure *Red represents high PM, Ozone or Toxic concentrations

14 MODELING EXERCISE: A TRUCK TRAVELING TO AND FROM COACHELLA VALLEY case name CO2 CO HC NOx NOx fuel (basin)* 1 baseline case 2 100% NOx control 3 100% fuel economy 4 advanced control % fuel economy advantage in fuel economy mode 35% NOx reductions in NOx control mode Barth, M., Scora, G. and Younglove, T. (2003). Intelligent off-board management of vehicle operating parameters, IEEE Intelligent Transportation Systems Conference, Shanghai, China.

15 Transportation System Level: Routing and navigation Lower speed limits (aka, intelligent speed adaptation or speed harmonization) DEEM STRATEGIES CAN HAPPEN AT MANY LEVELS Engine/Powertrain Level: Energy management for HEVs and PHEVs Engine tuning Aftertreatment tuning Vehicle/Driver Level: Eco-driving Environmentally Friendly Intelligent Transportation Systems (ITS)

16 TRUCK ECO-ROUTING Calculate route that minimize fuel consumption or a specific emission. Account for real-time traffic, road grade, and combined vehicle weight. Simulation shows tradeoff between fuel consumption and travel time. 9%-18% fuel savings with 16%-36% travel time penalty.

17 LOW HUMAN EXPOSURE TRUCK ROUTING Route HDDTs in such a way that lowers impact of their emissions on local air quality and population exposure. Consider how emissions disperse into the nearby communities and inhaled by residents, especially sensitive population groups. 17

18 For PHEVs and HEVs ADVANCED ENERGY MANAGEMENT SYSTEM Optimize energy flow between ICE and motors using predictive analytics based on machine learning algorithms S-A(0.8671) SOC S-L(0.8805) C-D(0.8790) B-A(0.9748) C-U(0.8967) Time(s)

19 Speed ARPA-E NEXTCAR RESEARCH PROGRAM INTEGRATED POWERTRAIN AND VEHICLE DYNAMIC CONTROLS UCR Connected ECO-BUS: ARPA-E NextCar program > 20% fuel & emission savings dynamic parameter selection potential level-2 automation Traffic and Road Grade Info: Vehicle Dynamics controls: Eco-Stop or Eco-Cruise Eco-Approach and Departure a b c d e Powertrain controls: 19 Distance

20 MOVING FORWARD Let s talk about technical solutions Let s think about policy solutions Think of Next Steps and Action Items based on today s workshop