混合动力电动汽车能量管理技术研究综述

姓名
邮箱
手机号码
标题
留言内容
验证码

doi:10.15918/j.tbit1001-0645.2022.161

何洪文^{1, 2,},
孟祥飞^{1, 2,,}

1.
bob手机在线登陆电动车辆国家工程研究中心, 北京　100081
2.
bob手机在线登陆机械与车辆学院, 北京　100081

基金项目:国家自然科学基金资助项目（52172377）

详细信息

作者简介:
何洪文（1975-），男，教授，博士生导师，E-mail:hwhebit@bit.edu.cn

通讯作者:
孟祥飞（1992-），男，博士生，E-mail:mengxiangfei@bit.edu.cn

中图分类号:U469.7
计量
- 文章访问数:433
- HTML全文浏览量:105
- PDF下载量:93
- 被引次数:0
出版历程
- 收稿日期:2022-07-13

A Review on Energy Management Technology of Hybrid Electric Vehicles

HE Hongwen^{1, 2,},
MENG Xiangfei^{1, 2,,}

1.
National Engineering Research Center of Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
2.
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

摘要

摘要:能量管理策略对于提高混合动力电动汽车的燃油经济性、保护系统的健康状态、以及减少温室气体排放具有至关重要的作用，但由于动力系统复杂的非线性结构以及在线应用的实时性要求，开发高效的能量管理策略仍是一项极具挑战性的任务. 为此，本文对能量管理技术的研究进展进行了全面的总结. 首先，综述目前混合动力电动汽车广泛采用的机电耦合系统，总结各类系统的拓扑结构与运行特点；其次，综合分析近年来能量管理策略的研究进展以及发展趋势；同时从最优性以及实时性等关键技术指标出发，评价各类方法的技术优势与不足，为进一步的工程应用提供参考；最后，展望能量管理技术未来的研究方向，希望为能量管理策略在智能网联环境下的发展提供借鉴.
- 混合动力电动汽车/
- 能量管理/
- 机器学习/
- 模型预测控制/
- 能量管理策略
Abstract:Energy management strategy (EMS) is critical to improving the fuel economy, protecting the health status, and reducing the greenhouse gas emissions of hybrid electric vehicles (HEVs). However, due to the complex nonlinear structure of the power system and the real-time requirements of online applications, it is still a challenge task to develop an efficient EMS. Therefore, the evolution of energy management technology was summarized completely in this paper. Firstly, the electro-mechanical coupled systems used widely in HEVs and their topological structures and functional features were surveyed. Secondly, the research progress and development trend of EMS were analyzed synthetically. In addition, the technical advantages and disadvantages of various methods were evaluated based on fundamental technical indicators such as optimality and real-time performance, providing a reference for further engineering applications. Finally, the future research trend of EMS was prospected, hoping to provide a reference for the development of EMS under the intelligent connected environment.
- hybrid electric vehicle/
- energy management/
- machine learning/
- model predictive control/
- energy management strategy (EMS)

HTML全文

图 1电耦合动力系统

Figure 1.Electrical coupled powertrain

下载: 全尺寸图片幻灯片

图 2机械耦合动力系统

Figure 2.Mechanical coupled powertrain

下载: 全尺寸图片幻灯片

图 3机械-电耦合动力系统

Figure 3.Mechanical-electrical coupled powertrain

下载: 全尺寸图片幻灯片

图 4能量管理技术分类

Figure 4.Classification of energy management strategies

下载: 全尺寸图片幻灯片

图 5CD-CS控制策略

Figure 5.The logic structure of CD-CS

下载: 全尺寸图片幻灯片

图 6模糊逻辑控制策略

Figure 6.Schematic diagram of fuzzy logic strategy

下载: 全尺寸图片幻灯片

图 7基于动态规划的规则型策略参数校准

Figure 7.Dynamic programming-based parameter recalibration of the rule-based strategies

下载: 全尺寸图片幻灯片

图 8预测ECMS策略示意图

Figure 8.Schematic diagram of the predictive ECMS strategy

下载: 全尺寸图片幻灯片

图 9机器学习方法

Figure 9.Machine learning methods

下载: 全尺寸图片幻灯片

图 10基于驾驶工况识别的模糊逻辑控制

Figure 10.Driving cycle recognition based fuzzy logic control

下载: 全尺寸图片幻灯片

图 11基于强化学习的能量管理策略

Figure 11.Reinforcement learning based EMS

下载: 全尺寸图片幻灯片

图 12基于DDPG与计算机视觉的能量管理策略

Figure 12.DDPG with computer vision based EMS

下载: 全尺寸图片幻灯片

参考文献 (72)

[1]	CHEN X, LIU Y, WANG Q, et al. Pathway toward carbon-neutral electrical systems in China by mid-century with negative CO₂abatement costs informed by high-resolution modeling[J]. Joule, 2021, 5(10):2715 − 2741.doi:10.1016/j.joule.2021.10.006
[2]	曾小华, 崔臣, 宋大凤, 等. 行星混联混合动力汽车节油因素分析[J]. bob手机在线登陆学报, 2019, 39(7):699 − 705.doi:10.15918/j.tbit1001-0645.2019.07.007 ZENG Xiaohua, CUI Chen, SONG Dafeng, et al. Analysis on fuel-saving factors of planetary hybrid electric vehicles[J]. Transactions of Beijing Institute of Technology, 2019, 39(7):699 − 705. (in Chinese)doi:10.15918/j.tbit1001-0645.2019.07.007
[3]	TRAN D, VAFAEIPOUR M, BAGHDADI M, et al. Thorough state-of-the-art analysis of electric and hybrid vehicle powertrains: Topologies and integrated energy management strategies[J]. Renewable and Sustainable Energy Reviews, 2020, 119:109596.doi:10.1016/j.rser.2019.109596
[4]	KIM M, JUNG D, MIN K. Hybrid thermostat strategy for enhancing fuel economy of series hybrid intracity bus[J]. IEEE Transactions on Vehicular Technology, 2014, 63(8):3569 − 3579.doi:10.1109/TVT.2013.2290700
[5]	WU X, DU J, HU C, et al. The economic analysis of a plug-in series hybrid electric vehicle in different energy management strategy[C]//Proceedings of 2013 IEEE Vehicle Power and Propulsion Conference (VPPC). [S. l.]: IEEE, 2013: 1 − 5.
[6]	DAVIS K, HAYES J. Fuel cell vehicle energy management strategy based on the cost of ownership[J]. IET Electrical Systems in Transportation, 2019, 9(4):226 − 236.doi:10.1049/iet-est.2019.0021
[7]	PENG C, FENG F, XIAO Y, et al. Multi-working points power follower based energy management strategy for series hybrid electric vehicle[J]. Journal of Physics:Conference Series, 2020, 1601:022039.doi:10.1088/1742-6596/1601/2/022039
[8]	TROVAO J, PEREIRINHA P. Control scheme for hybridised electric vehicles with an online power follower management strategy[J]. IET Electrical Systems in Transportation, 2015, 5(1):12 − 23.doi:10.1049/iet-est.2013.0038
[9]	刘楠, 于博轩, 郭爱, 等. 燃料电池混合动力的功率跟随管理策略分析[J]. 西南交通大学学报, 2020, 55(6):1147 − 1154.doi:10.3969/j.issn.0258-2724.20180733 LIU Nan, YU Boxuan, GUO Ai, et al. Analysis of power tracking management strategy for fuel cell hybrid system[J]. Journal of Southwest Jiaotong University, 2020, 55(6):1147 − 1154. (in Chinese)doi:10.3969/j.issn.0258-2724.20180733
[10]	SONG K, LI F, HU X, et al. Multi-mode energy management strategy for fuel cell electric vehicles based on driving pattern identification using learning vector quantization neural network algorithm[J]. Journal of Power Sources, 2018, 389:230 − 239.doi:10.1016/j.jpowsour.2018.04.024
[11]	XU L, LI J, OUYANG M, et al. Multi-mode control strategy for fuel cell electric vehicles regarding fuel economy and durability[J]. International Journal of Hydrogen Energy, 2014, 39:2374 − 2389.doi:10.1016/j.ijhydene.2013.11.133
[12]	LI Q, SU B, PU Y, et al. A state machine control based on equivalent consumption minimization for fuel cell/supercapacitor hybrid tramway[J]. IEEE Transactions on Transportation Electrification, 2019, 5(2):552 − 564.doi:10.1109/TTE.2019.2915689
[13]	ASENSIO E M, MAGALLAN G. A, ANGELO C H, et al. Energy management on battery/ultracapacitor hybrid energy storage system based on adjustable bandwidth filter and sliding-mode control[J]. Journal of Energy Storage, 2020, 30:101569.doi:10.1016/j.est.2020.101569
[14]	FU Z, LI Z, SI P, et al. A hierarchical energy management strategy for fuel cell/battery/supercapacitor hybrid electric vehicles[J]. International Journal of Hydrogen Energy, 2019, 44(39):22146 − 22159.doi:10.1016/j.ijhydene.2019.06.158
[15]	BAU Y, HE H, LI J, et al. Battery anti-aging control for a plug-in hybrid electric vehicle with a hierarchical optimization energy management strategy[J]. Journal of Cleaner Production, 2019, 237:117841.doi:10.1016/j.jclepro.2019.117841
[16]	RIFAI N, SABOR J, ALAOUI C. Energy management strategy of a fuel-cell electric vehicle based on wavelet transform[C]//Proceedings of International Conference on Artificial Intelligence & Industrial Applications. Springer, Cham, 2020: 220 − 235.
[17]	WANG C, YANG R, YU Q. Wavelet transform based energy management strategies for plug-in hybrid electric vehicles considering temperature uncertainty.[J]. Applied Energy, 2019, 256:113928.doi:10.1016/j.apenergy.2019.113928
[18]	WANG C, XIONG R, HE H, et al. Comparison of decomposition levels for wavelet transform based energy management in a plug-in hybrid electric vehicle[J]. Journal of Cleaner Production, 2019, 210:1085 − 1097.doi:10.1016/j.jclepro.2018.11.082
[19]	GAO D, JIN Z, LU Q. Energy management strategy based on fuzzy logic for a fuel cell hybrid bus[J]. Journal of Power Sources, 2008, 185(1):311 − 317.doi:10.1016/j.jpowsour.2008.06.083
[20]	ZDRAVKOVIC S, VUJANOVIC D, STOKIC M, et al. Evaluation of professional driver’s eco-driving skills based on type-2 fuzzy logic model[J]. Neural Computing and Applications, 2021, 33(18):11541 − 11554.doi:10.1007/s00521-021-05823-z
[21]	GUO Q, ZHAO Z, SHEN P, et al. Adaptive optimal control based on driving style recognition for plug-in hybrid electric vehicle[J]. Energy, 2019, 186:115824.doi:10.1016/j.energy.2019.07.154
[22]	LANGARI R, WON J S, Intelligent energy management agent for a parallel hybrid vehicle - Part I: System architecture and design of the driving situation identification process[J]. IEEE Transactions on Vehicular Technology, 2005, 54(3): 925 − 934.
[23]	秦大同, 杨官龙, 胡明辉, 等. 基于驾驶意图的插电式混合动力汽车能量管理策略[J]. 吉林大学学报(工学版), 2015, 45(6):1743 − 1750. QIN Datong, YANG Guanlong, HU Minghui, et al. Energy management strategy of plug-in hybrid electric system based on driving intention[J]. Journal of Jilin University (Engineering and Technology Edition), 2015, 45(6):1743 − 1750. (in Chinese)
[24]	QI W. Fuzzy control strategy of pure electric vehicle based on driving intention recognition.[J]. Journal of Intelligent & Fuzzy Systems, 2020, 39(4):5131 − 5139.
[25]	XU S, ZHAO X, YANG N, et al. Control strategy of braking energy recovery for range-extended electric commercial vehicles by considering braking intention recognition and electopneumatic braking compensation[J]. Energy Technology, 2020, 8(9):2000407.doi:10.1002/ente.202000407
[26]	ZHANG Q, FU X. A neural network fuzzy energy management strategy for hybrid electric vehicles based on driving cycle recognition.[J]. Applied Sciences, 2020, 10(2):696.doi:10.3390/app10020696
[27]	连静, 常静, 李琳辉, 等. 基于模糊在线识别的并联混合动力客车自适应控制策略[J]. bob手机在线登陆学报, 2016, 36(3):264 − 270. LIAN Jing, CHANG Jing, LI Huilin, et al. Adaptive control strategy based on inline fuzzy recognition for parallel hybrid electric vehicle[J]. Transactions of Beijing Institute of Technology, 2016, 36(3):264 − 270. (in Chinese)
[28]	LI P, JIAO X, LI Y. Adaptive real-time energy management control strategy based on fuzzy inference system for plug-in hybrid electric vehicles[J]. Control Engineering Practice, 2021, 107:104703.doi:10.1016/j.conengprac.2020.104703
[29]	PENG H, XIE J. Energy management strategy for plug-in hybrid electric vehicles based on genetic-fuzzy control strategy[C]//Proceedings of 2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC). [S. l.]: IEEE, 2017: 1053-1056.
[30]	ZHANG R, TAO J. GA-based fuzzy energy management system for FC/SC-powered HEV considering H 2 consumption and load variation[J]. IEEE Transactions on Fuzzy Systems, 2017, 26(4):1833 − 1843.
[31]	WU Jingda, HE Hongwen, PENG Jiankun, et al. Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus[J]. Applied Energy, 2018, 222:799 − 811.doi:10.1016/j.apenergy.2018.03.104
[32]	ATUAHENE S, BAO Y, ZIGGAH Y, et al. Short-term electric power forecasting using dual-stage hierarchical wavelet-Particle swarm optimization-adaptive neuro-fuzzy inference system PSO-ANFIS approach based on climate change[J]. Energies, 2018, 11(10):2822.doi:10.3390/en11102822
[33]	SCHMID R, BURGER J, BAJCINCA N. A comparison of PMP-based energy management strategies for plug-in-hybrid electric vehicles[J]. IFAC-PapersOnLine, 2019, 52(5):592 − 597.doi:10.1016/j.ifacol.2019.09.094
[34]	吴铁洲, 王越洋, 许玉姗, 等. 基于PMP算法的HEV能量优化控制策略[J]. 自动化学报, 2018, 44(11):2092 − 2102. WU Tiezhou, WANG Yueyang, XU Yushan, et al. Energy optimal control strategy of HEV with PMP algorithm[J]. Acta Automatica Sinica, 2018, 44(11):2092 − 2102. (in Chinese)
[35]	张承宁, 周维, 李军求, 等. 基于极小值原理的增程式电动车辆在线能量管理控制策略[J]. bob手机在线登陆学报, 2015, 35(9):931 − 935. ZHANG Chengning, ZHOU Wei, LI Junqiu, et al. An onlin energy management strategy for extended range electric vehicles based on pontryagin ’s minimum principle[J]. Transactions of Beijing Institute of Technology, 2015, 35(9):931 − 935. (in Chinese)
[36]	BERNARD J, DELPRAT S, BUECHI F, et al. Global Optimisation in the power management of a Fuel Cell Hybrid Vehicle (FCHV)[C]//Proceedings of 2006 IEEE vehicle power and propulsion conference. [S. l.]: IEEE, 2006: 1 − 6.
[37]	SONG K, WAN X, LI F, et al. Pontryagin ’s minimum principle-based real-time energy management strategy for fuel cell hybrid electric vehicle considering both fuel economy and power source durability[J]. Energy, 2020, 205:118064.doi:10.1016/j.energy.2020.118064
[38]	XU L, OUYANG M, LI J, et al. Application of pontryagin's minimal principle to the energy management strategy of plugin fuel cell electric vehicles[J]. International Journal of Hydrogen Energy, 2013, 38(24):10104 − 10115.doi:10.1016/j.ijhydene.2013.05.125
[39]	DU J, ZHANG X, WANG T, et al. Battery degradation minimization oriented energy management strategy for plug-in hybrid electric bus with multi-energy storage system[J]. Energy, 2018, 165:153 − 163.
[40]	YU H, LIU L, BAI B, et al. A dynamic programming-based control strategy with optimum efficiency of hybrid energy storage system for HEV[J]. Advanced Materials Research, 2015, 1092:165 − 168.
[41]	SANTUCCI A, SORNIOTTO A, LEKAKOU C. Power split strategies for hybrid energy storage systems for vehicular applications[J]. Journal of Power Sources, 2014, 258:395 − 407.doi:10.1016/j.jpowsour.2014.01.118
[42]	HE H, TANG H, WANG X. Global optimal energy management strategy research for a plug-in series-parallel hybrid electric bus by using dynamic programming[J]. Mathematical Problems in Engineering, 2013, 2013:708261.
[43]	RIBAU J, VIEGAS R, ANGELINO A, et al. A new offline optimization approach for designing a fuel cell hybrid bus[J]. Transportation Research Part C:Emerging Technologies, 2014, 42:14 − 27.doi:10.1016/j.trc.2014.02.012
[44]	PENG J, HE H, XIONG R. Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming[J]. Applied Energy, 2017, 185:1633 − 1643.doi:10.1016/j.apenergy.2015.12.031
[45]	ZHANG S, XIONG R, SUN F. Model predictive control for power management in a plug-in hybrid electric vehicle with a hybrid energy storage system[J]. Applied Energy, 2017, 185:1654 − 1662.doi:10.1016/j.apenergy.2015.12.035
[46]	BORHAN, H, VAHIDI A, PHILLIPS A, et al. MPC-based energy management of a power-split hybrid electric vehicle[J]. IEEE Transactions on Control Systems Technology, 2012, 20(3):593 − 603.
[47]	ZENG X, WANG J. A parallel hybrid electric vehicle energy management strategy using stochastic model predictive control with road grade preview[J]. IEEE Transactions on Control Systems Technology, 2015, 23(6):2416 − 2423.doi:10.1109/TCST.2015.2409235
[48]	DI C S, BERNARDINI D, BEMPORAD A, et al. Stochastic MPC with learning for driver-predictive vehicle control and its application to HEV energy management[J]. IEEE Transactions on Control Systems Technology, 2013, 22(3):1018 − 1031.
[49]	XIANG C, DING F, WANG W, et al. Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control[J]. Applied Energy, 2017, 189:640 − 653.doi:10.1016/j.apenergy.2016.12.056
[50]	ZHANG S, LUO Y, WANG J, et al. Predictive energy management strategy for fully electric vehicles based on preceding vehicle movement[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(11):3049 − 3060.doi:10.1109/TITS.2017.2672542
[51]	DENG T, HAN H, LUO J. Improved ECMS energy management control of HEVs based on DP algorithm[J]. China Mechanical Engineering, 2018, 29(3):326 − 332.
[52]	司远, 钱立军, 邱利宏, 等. 基于等效油耗最小的四驱混合动力汽车能量管理[J]. 中国机械工程, 2017, 28(9):1112 − 1117.doi:10.3969/j.issn.1004-132X.2017.09.019 SI Yuan, QIAN Lijun, QIU Lihong, et al. Energy management of a 4WD hybrid electric vehicle based on ECMS[J]. China Mechanical Engineering, 2017, 28(9):1112 − 1117. (in Chinese)doi:10.3969/j.issn.1004-132X.2017.09.019
[53]	LI L, HUANG H, LIAN J, et al. Research of ant colony optimized adaptive control strategy for hybrid electric vehicle[J]. Mathematical Problems in Engineering, 2014, 2014:239130.
[54]	VAN K, DE B, SERRARENS A, et al. Optimal energy management in hybrid electric trucks using route information[J]. Oil & Gas Science and Technology–Revue de l’Institut Franç ais du Pétrole, 2010, 65(1):103 − 113.
[55]	PEI D, LEAMY M J. Dynamic programming-informed equivalent cost minimization control strategies for hybrid-electric vehicles[J]. Journal of Dynamic Systems, Measurement, and Control, 2013, 135(5):051013.doi:10.1115/1.4024788
[56]	BOUWMAN K R, PHAM T H, Wilkins S, et al. Predictive energy management strategy including traffic flow data for hybrid electric vehicles[J]. IFAC-PapersOnLine, 2017, 50(1):10046 − 10051.doi:10.1016/j.ifacol.2017.08.1775
[57]	SUN C, HU X, MOURA S, et al. Velocity predictors for predictive energy management in hybrid electric vehicles[J]. IEEE Transactions on Control Systems Technology, 2014, 23(3):1197 − 1204.
[58]	WU J, ZHANG C, CUI N. Fuzzy energy management strategy for a hybrid electric vehicle based on driving cycle recognition[J]. International journal of automotive technology, 2012, 13(7):1159 − 1167.doi:10.1007/s12239-012-0119-z
[59]	GRELLE C, IPPOLITO L, LOIA V, et al. Agent-based architecture for designing hybrid control systems[J]. Information Sciences, 2006, 176(9):1103 − 1130.doi:10.1016/j.ins.2005.07.018
[60]	MONTAZERI G, FOTOUHI A, NADERPOUR A. Driving patterns clustering based on driving features analysis[J]. Proceedings of the Institution of Mechanical Engineers, Part C:Journal of Mechanical Engineering Science, 2011, 225(6):1301 − 1317.doi:10.1177/2041298310392599
[61]	FINESSO R, SPESSA E, VENDITTI M. An unsupervised machine-learning technique for the definition of a rule-based control strategy in a complex HEV[J]. SAE International Journal of Alternative Powertrains, 2016, 5(2):308 − 327.doi:10.4271/2016-01-1243
[62]	VENDITTI M. Analysis of the performance of different machine learning techniques for the definition of rule-based control strategies in a parallel HEV[J]. Energy Procedia, 2016, 101:685 − 692.doi:10.1016/j.egypro.2016.11.087
[63]	HU X, LIU T, QI X, et al. Reinforcement learning for hybrid and plug-in hybrid electric vehicle energy management: recent advances and prospects[J]. IEEE Industrial Electronics Magazine, 2019, 13(3):16 − 25.doi:10.1109/MIE.2019.2913015
[64]	YUE S, WANG Y, XIE Q, et al. Model-free learning-based online management of hybrid electrical energy storage systems in electric vehicles[C]//Proceedings of IECON 2014-40th Annual Conference of the IEEE Industrial Electronics Society. [S. l.]: IEEE, 2014: 3142 − 3148.
[65]	FANG Y, SONG C, XIA B, et al. An energy management strategy for hybrid electric bus based on reinforcement learning[C]//Proceedings of The 27th Chinese control and decision conference (2015 CCDC). [S. l.]: IEEE, 2015: 4973 − 4977.
[66]	QI X, WU G, BORIBOONSOMSIN K, et al. Data-driven reinforcement learning–based real-time energy management system for plug-in hybrid electric vehicles[J]. Transportation Research Record, 2016, 2572(1):1 − 8.doi:10.3141/2572-01
[67]	LIU T, ZOU Y, LIU D, et al. Reinforcement learning of adaptive energy management with transition probability for a hybrid electric tracked vehicle[J]. IEEE Transactions on Industrial Electronics, 2015, 62(12):7837 − 7846.doi:10.1109/TIE.2015.2475419
[68]	LIU T, ZOU Y, LIU D, et al. Reinforcement learning–based energy management strategy for a hybrid electric tracked vehicle[J]. Energies, 2015, 8(7):7243 − 7260.doi:10.3390/en8077243
[69]	ZOU Y, LIU T, LIU D, et al. Reinforcement learning-based real-time energy management for a hybrid tracked vehicle[J]. Applied energy, 2016, 171:372 − 382.doi:10.1016/j.apenergy.2016.03.082
[70]	WU J, HE H, PENG J, et al. Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus[J]. Applied energy, 2018, 222:799 − 811.doi:10.1109/TVT.2019.2926472
[71]	LI Y, HE H, PENG J, et al. Deep reinforcement learning-based energy management for a series hybrid electric vehicle enabled by history cumulative trip information[J]. IEEE Transactions on Vehicular Technology, 2019, 68(8):7416 − 7430.
[72]	WANG Y, TAN H, WU Y, et al. Hybrid electric vehicle energy management with computer vision and deep reinforcement learning[J]. IEEE Transactions on Industrial Informatics, 2020, 17(6):3857 − 3868.