Real-time identification of partnership for a new generation of vehicles battery model parameters based on the model reference adaptive system - 文章详情页

作者：

Lin, Peng (Lin, Peng.) | Jin, Peng (Jin, Peng.) | Zou, Aixiao (Zou, Aixiao.) | Wang, Zhenpo (Wang, Zhenpo.)

收录：

EI SCIE

摘要：

Partnership　for　a　new　generation　of　vehicles　(PNGV)　model　is　a　conventional　battery　equivalent　circuit　model　(ECM).　However,　identifying　the　best　parameters　for　this　model　is　a　challenge.　In　this　study,　the　PNGV　model　is　transformed　into　a　directly　identifiable　difference　equation　to　identify　its　parameters.　Subsequently,　the　model　reference　adaptive　system　(MRAS)　is　used　to　realize　the　real-time　identification　of　the　model　parameters.　The　identification　accuracy　of　the　MRAS　is　found　to　be　superior　to　that　of　the　recursive　extended　least　square　algorithm.　For　a　single　hybrid　pulse　power　characterization　(HPPC),　the　PNGV　model　identified　by　the　MRAS　can　achieve　a　high-precision　terminal　voltage　estimation.　For　lithium　iron　phosphate,　lithium　titanate,　and　nickel-metal　hydride　batteries,　the　root　mean　square　errors　are　0.024,　0.048,　and　0.020　V,　respectively.　Besides,　the　real-time　state　of　charge　(SOC)　estimation　can　be　realized　by　the　identified　open-circuit　voltage　(OCV).　The　average　errors　of　the　three　batteries　are　only　−0.02,　−0.01,　and　−0.01,　respectively.　Since　the　PNGV　model　has　a　capacity　of　describing　the　change　of　OCV　with　the　current　accumulation　effect,　the　model　is　only　suitable　for　simulating　single　HPPC　or　positive-negative　pulses　with　equal　amplitude　and　not　for　other　current　pulses.　This　is　a　major　drawback　of　the　PNGV　model.　The　real-time　PNGV　model　parameters　identification　method　proposed　in　this　study　can　provide　a　solid　foundation　for　various　state　estimation　of　a　battery.　Novelty　Statement:　Transformation　of　the　PNGV　model　into　a　difference　equation　that　can　be　directly　identified.　Real-time　identification　of　the　PNGV　model　parameters　via　the　MRAS.　The　identified　OCV　realized　the　real-time　SOC　estimation　and　discussed　the　deficiencies　of　the　PNGV　model.　©　2021　John　Wiley　&　Sons　Ltd

关键词：

Battery management systems Charging (batteries) Difference equations Equivalent circuits Hydrides Iron compounds Lithium compounds Lithium-ion batteries Mean square error Model reference adaptive control Nickel compounds Nickel metal hydride batteries Open circuit voltage Parameter estimation State estimation

作者机构：

[ 1 ] [Lin, Peng]National Engineering Laboratory for Electric Vehicles, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
[ 2 ] [Lin, Peng]North China University of Technology, Collaborative Innovation Center of Electric Vehicle in Beijing, Beijing, China
[ 3 ] [Jin, Peng]National Engineering Laboratory for Electric Vehicles, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
[ 4 ] [Jin, Peng]North China University of Technology, Collaborative Innovation Center of Electric Vehicle in Beijing, Beijing, China
[ 5 ] [Jin, Peng]School of Electrical and Control Engineering, North China University of Technology, Beijing, China
[ 6 ] [Zou, Aixiao]Faculty of Information Technology, Beijing University of Technology, Beijing, China
[ 7 ] [Wang, Zhenpo]National Engineering Laboratory for Electric Vehicles, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
[ 8 ] [Wang, Zhenpo]North China University of Technology, Collaborative Innovation Center of Electric Vehicle in Beijing, Beijing, China

通讯作者信息：

[lin, peng]north china university of technology, collaborative innovation center of electric vehicle in beijing, beijing, china;;[lin, peng]national engineering laboratory for electric vehicles, school of mechatronical engineering, beijing institute of technology, beijing, china

电子邮件地址：

linpengstar@bit.edu.cnemailwangzhenpo@bit.edu.cnemaillinpengstar@bit.edu.cnemailwangzhenpo@bit.edu.cn) Wang, Zhenpo(wangzhenpo@bit.edu.cnemaillinpengstar@bit.edu.cnemailwangzhenpo@bit.edu.cn) Lin, Peng(linpengstar@bit.edu.cnemailwangzhenpo@bit.edu.cn) Wang, Zhenpo(wangzhenpo@bit.edu.cn

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