Energy Management System for Battery/Ultracapacitor Electric Vehicle with Particle Swarm Optimization

Transcription

1 Proceedngs of the Internatonal Conference on Recent Advances n Electrcal Systems, Tunsa, 06 Energy Management System for Battery/Ultracapactor Electrc Vehcle wth Partcle Swarm Optmzaton Selm Koroglu Akf Demrcal Selam Kesler Peter Sergeant Erkan Ozturk Mustafa Tumbek Dept. of Electrcal and Electroncs Eng., Pamukkale Unversty, 0070, Knkl, Denzl, Turkey Department of Electrcal Energy, Systems and Automaton, Ghent Unversty, Gent, Belgum Department of Automotve Engneerng, Pamukkale Unversty, 0070, Knkl, Denzl, Turkey Abstract Energy usage and envronment polluton n the transportaton are major problems of today s world. Although electrc vehcles are promsng solutons to these problems, ther energy management methods are complcated and need to be mproved for the extensve usage. In ths work, a heurstc optmzaton approach, Partcle Swarm Optmzaton s used to provde an optmal energy management system for a battery/ultracapactor powered electrc vehcle wthout pror knowledge of the drve cycle. The proposed scheme has been smulated n Matlab and appled on the ECE drvng cycle. Results show the effectveness of the appled method for the energy management problem of the mult-source electrc vehcles wth the lowest possble energy usage. Index Terms Battery, electrc vehcle, energy management, partcle swarm optmzaton, ultracapactor.. INTRODUCTION In recent years, depleton of petroleum resources, global warmng and clmate change has caused an ncreased nterest about effectve usage of avalable energy resources. Electrc vehcles (EVs) are promsng solutons about transportaton to those problems because of hgh effcency of electrc motors and almost zero emsson of drvetrans. Improvements on power converters and control technques lead to ncrease of usablty and drvablty of them. However, there are some drawbacks and unresolved problem that are research subjects of many researchers. Energy management of energy sources s one of these problems because of only one type of energy source could not provde the needs of the entre drve profle. It s a common soluton to use mult energy storage devces to overcome the dsadvantage of each source and takng beneft of every source n an optmal way. Batteres, fuel cells, ultracapactors (UC) and flywheels are the most researched storage solutons for the electrc vehcle energy source []. Energy storage systems n electrc vehcles need to have hgh specfc energy, hgh specfc power, long cycle lfe and safe operaton n all road condtons []. Fuel cells and flywheels are not suffcent yet to supply all needs of vehcles due to lmted storage capablty, safety and operatonal constrants []. Batteres provde a hgh specfc energy but ther specfc powers are not enough to meet the vehcle nstant power need most of the tme. Therefore, ntegraton of UC wth batteres s an accepted soluton because of the ablty of UC to provde or absorb hgh powers []-[]. Integraton and management of these two sources are studed by usng several methods n lterature. Fuzzy logc [], smulated annealng [4], partcle swarm optmzaton (PSO) [], model predctve control [5] methods are some of them. Despte the dfference between them, t s generally not possble to compare the effectveness or usefulness of these methods because nearly all of the methods are appled to dfferent drvetrans and topologes. In ths work, a partcle swarm optmzaton based energy management strategy s appled to the Alatay-EV whose general connecton topology s shown n Fg.. S D Inverter Motor / Generator Acceleratng Brakng Steerng Torque controller Chargng Unt Battery Pack + - DC/DC Converter Ultracapactor Global Energy Management System Torque controller D Inverter Motor / Generator S Power lne Control lne Sensor lne Command nput Fg.. General connecton topology of Alatay-EV ISBN: (9) Edtors: Tarek Bouktr & Rafk Nej

2 Proceedngs of the Internatonal Conference on Recent Advances n Electrcal Systems, Tunsa, 06 Optmzaton of the energy management strategy (EMS) s acheved n two stages. The frst stage s to restrct the search space of the optmzaton method accordng to condtons of storage devces and power demand of the vehcle. After determnaton and restrcton of the search space, the power sharng optmzaton s mplemented by usng the PSO ntroduced by Kennedy and Eberthart [6]. Ths paper s structured as follows. The frst secton states the needs for ths work and gves an ntroducton to the subject. The second secton presents the structure of the EMS and gves detaled nformaton about the optmzaton method. Secton represents the results of smulatons studes, and dscusson of the obtaned results. Fnally, conclusons are gven n Secton 4.. ENERGY MANAGEMENT STRATEGY In ths work, power losses are neglected to provde smplcty. Only power sharng s consdered as llustrated n Fg. []. Here, the battery provdes the contnuous power whle the UC provdes peak powers. For acceptng hgh regeneratve powers and sometmes to provde hgh power to accelerate the vehcle, there s a power exchange between battery and UC. Ths exchange results n a more effcent use of energy storage devces and by consequence longer the drvng range. The energy exchange s mplemented accordng to some rules. These rules are formed by consderng the mnmum and maxmum capactes of storage devces, demanded power and maxmum obtanable power of battery. These rules and relevant actons are descrbed n detals n [4]. Formng the rules s mplemented accordng to workng constrants and operatonal needs of the vehcle and storage devces. Ultracapactors Pload=Pbat+Puc Battery Energy Exchange Fg.. General power sharng scheme [] Peak Powers Load Power Contnuous Powers In electrc vehcles, demanded power and suppled powers from battery and UC must be n equlbrum n any case and n the whole tme nterval as descrbed n (). P ( t) P ( t) P ( t) t () dem bat UC where the demanded power s calculated accordng to (), and the constants and parameters used n ths equaton are gven n Table I. Pdem m. av.. Cd.. V Kr. mv. m. g.sn( ). V () Ths power equlbrum s subject to certan restrctons n terms of mnmum and maxmum chargng/dschargng powers as (). P P ( t) P, t bat,mn bat bat,max P P ( t) P, t UC,mn UC UC,max where mnmum and maxmum powers are as descrbed n (4). Here, mnmum power represents the maxmum chargng power, whle the maxmum power s the maxmum dschargng power from the storage unts. P 0 P, { bat, UC} (4),mn,max The objectve functon to be mnmzed can be expressed as n (5) as descrbed n [], [4]. () t N dem Bat bat,max UC UC,max (5) k J mn { P ( t) ( w ( t)* P ( t) w ( t)* P ( t))} where N s the tme nterval of the chosen drve profle and wbat and wuc s the weghtng factors of battery and UC, respectvely. Also the weghtng factors have restrctons as n (6). w Bat, w [,] UC P ( t) w ( t) * P ( t) bat Bat bat,max P ( t) w ( t) * P ( t) UC UC UC,max Here, the objectve s to provde optmal sharng of power among battery and UC by determnng the weghtng factors of them. In ths paper, for the determnaton and optmzaton of these factors, PSO s used. Detals of the PSO method are gven n the next subsecton. TABLE I Assumptons and constants used n the smulaton Name Value Unt Mass (m) 400 kg G 9.8 m/s^ Kr 0.0 Θ 0 Degree Ρ. kg/m^ cd 0. Front Area (A).64 m^ Number of Battery Cell Battery Cell Nomnal Voltage. V Battery Cell Nomnal 6 Ah Capacty Battery Nomnal Dscharge.6 A Current Number of UC Cell 0 UC Cell Nomnal Voltage.7 V UC Cell Nomnal Capacty 0.44 Ah (6) ISBN: (0) Edtors: Tarek Bouktr & Rafk Nej

3 Proceedngs of the Internatonal Conference on Recent Advances n Electrcal Systems, Tunsa, 06 In the mplementaton phase some threshold values for battery and UC must be determned to avod damage of the storage devces. For ths purpose, the battery state of charge (SOC) level s restrcted between 5% and 95%. In a same way, UC mnmum and maxmum SOC are lmted between 80% and 90% to show effectve operaton of the algorthm. Also, the operatng voltages of battery and UC cells are (.8-.7) and (0-.85) respectvely. Detaled specfcatons and some constants about vehcle, battery and UC are gven n Table I. A. Partcle Swarm Optmzaton PSO s a populaton based evolutonary computaton technque, nspred from the socal behavor of brd flockng and optmzes a functon by utlzng a populaton of partcles that fly through the soluton hyperspace [6]. Partcles are ntalzed randomly and seekng for optmal soluton s carred out by updatng ts velocty and poston at each teraton. Updatng polcy for each partcle consst of best experence of ts own and the entre populaton. Fg. shows a flow chart of fndng optmal weghtng factors wbat and w UC. The steps of the program flow are descrbed below: Step : Parameters of PSO lke swarm sze, acceleraton coeffcents, nerta weght, maxmum velocty and teraton are defned. Postons of each partcle are randomly ntalzed wthn a predefned range. Step : Objectve functon ftness s are calculated for each partcle. Intalzaton of partcle postons Objectve Functon Calculaton Fndng partcle s mnmum and global mnmum Updatng postons wth calculated veloctes Is stopng crtera satsfed? Yes Optmal parameters Fg.. Flow chart of PSO algorthm.. RESULTS AND DISCUSSIONS The proposed energy management strategy s appled on ECE drvng cycle shown n Fg. 4 [7]. Drve cycle data gves nformaton of the speed of the vehcle. The demanded power accordng to ths drve profle s calculated accordng to (). No Step : From ftness evaluaton, partcle best poston (pbest) and global best poston (gbest) are obtaned. Step 4: Accordng to evaluated ftness s, veloctes of partcles wll be calculated and postons wll be updated. The velocty formula s gven below: v ( t ) wv ( t) c R ( t)( pbest ( t) p ( t)) c R ( t)( gbest ( t) p ( t)) p ( t ) p ( t) v ( t ) (7) where c and c are acceleraton coeffcents, w s weght factor, R and R are random varable, and p s poston of partcle. Step 5: If the ftness reached a specfed value or maxmum teraton exceeded, algorthm exts from the loop. Step 6: PSO algorthm s termnated and optmal parameters are obtaned. d Fg. 4. ECE Drvng cycle. Some setup results of the management method s gven n Fg. 5 and Fg. 6 to show the optmzaton process. In the frst setup, battery and UC ntal SOCs are determned as 80% and 50% respectvely. From Fg. 5 a), energy exchange between battery and UC can be seen. For example, n the frst 8 seconds power demand s zero and UC SOC s below the threshold value. So, battery charges the UC n ths case. Ths ncrement provdes to rse UC SOC for the later use to accelerate the vehcle. In Fg. 5 b), demanded power and suppled power from each source are llustrated. Power exchanges between sources can be seen ISBN: () Edtors: Tarek Bouktr & Rafk Nej

4 Proceedngs of the Internatonal Conference on Recent Advances n Electrcal Systems, Tunsa, 06 clearly n ths fgure. As a result, power equalty s gven n Fg. 5 c). Ths fgure shows good matchng of demanded and suppled powers. approxmately kw (.6 A contnuous dscharge current and 96 V nomnal voltage). However maxmum demanded power about kw for the ECE drvng cycle. In the other smulaton setup battery and UC ntal SOCs are determned as 80% and 00% respectvely, and the results are shown n Fg. 6. (a) (a) (b) (b) (c) Fg. 5. Battery ntal SOC: %80, UC ntal SOC: %50, a) battery and UC SOC values, b) battery, UC and demanded powers, c) demanded and suppled powers. In Alatay-EV vehcle batteres have much more power densty. It results to demand power s met by battery power most of the tme. Used batteres can supply (c) Fg. 6. Battery ntal SOC: %80, UC ntal SOC: %00, a) battery and UC SOC values, b) battery, UC and demanded powers, c) demanded and suppled powers. ISBN: () Edtors: Tarek Bouktr & Rafk Nej

5 Proceedngs of the Internatonal Conference on Recent Advances n Electrcal Systems, Tunsa, 06 In ths work, the power losses of gear box, motors and nverters are neglected to provde smplcty and avod from the computatonal effort. Demanded power from the drve cycle s consdered as demanded power from energy storage devces. Also the DC-DC converter between battery and UC must be ncluded for realstc and correct results. Every energy exchange between these devces cannot be effcent n every tme. 4. CONCLUSION Electrc vehcle technology s a growng ssue wth the concerns about future of the petroleum resources and clmate change. One of the major components of these technology s the energy management of used storage devces n vehcle because of none of the current energy storage devces s enough the entre need of the vehcle. For ths purpose, optmal power sharng and energy management of a battery/uc powered electrc vehcle s studed n ths work. Optmzaton of power sharng s acheved wth an effectve PSO technque. It s concluded that the PSO technque can be used effectvely for the energy management problem of the mult-source electrc vehcles wth the less energy usage. ACKNOWLEDGMENT Ths work was supported by the Scentfc and Technologcal Research Councl of Turkey, the Fund of Scentfc Research Flanders under Grant 4E0, and the specal research fund of Ghent Unversty. REFERENCES [] J. P. Trovão, C. H. Antunes, "A comparatve analyss of metaheurstc methods for power management of a dual energy storage system for electrc vehcles", Energy Converson and Management, vol. 95, pp. 8 96, 05. [] Y. Wang, W. Wang, Y. Zhao, L. Yang, W. Chen, "A fuzzy-logc power management strategy based on Markov random predcton for hybrd energy storage systems", Energes, vol. 9, no., Artcle number:5,06. [] L. C. Rosaro, "Power and Energy Management of Multple Energy Storage Systems n Electrc Vehcles", PhD. dssertaton, Cranfeld Unversty, Unted Kngdom, 007. [4] J. P. Trovão, P. G. Perernha, H. M. Jorge, C. H. Antunes, "A mult-level energy management system for mult-source electrc vehcles - An ntegrated rule-based meta-heurstc approach", Appled Energy, vol. 05, pp. 04-8, 0. [5] R. T. Meyer, R. A. DeCarlo, S. Pekarek, " Hybrd model predctve power management of a battery-supercapactor electrc vehcle", Asan Journal of Control, vol. 8, no., pp , Jan. 06. [6] J. Kennedy, R. Eberhart, "Partcle swarm optmzaton", Proceedngs IEEE Internatonal Conference on Neural Networks, Perth, Australa, vol.4, Nov/Dec, 995, pp [7] W. Courtos, Dynamometer Drve Schedules [Onlne] Avalable: Access date: ISBN: () Edtors: Tarek Bouktr & Rafk Nej