插电式混合动力汽车能量管理策略研究综述

doi:10.3901/JME.2025.04.195

机械工程学报 ›› 2025, Vol. 61 ›› Issue (4): 195-218.doi: 10.3901/JME.2025.04.195

扫码分享

插电式混合动力汽车能量管理策略研究综述

尤国贵^1,2, 卢剑伟¹, 张国军³, 苏俊收⁴, 蔡泽民²

1. 合肥工业大学汽车与交通工程学院合肥 230009;
2. 比亚迪汽车新技术研究院深圳 518000;
3. 清华大学汽车安全与节能国家重点实验室北京 100084;
4. 徐工集团高端工程机械智能制造国家重点实验室徐州 221000

收稿日期:2024-02-15 修回日期:2024-07-16 出版日期:2025-02-20 发布日期:2025-04-14
作者简介:尤国贵，男，1990年出生，博士研究生。主要研究方向为混合动力汽车控制策略开发、整车节能技术研究。E-mail：guogui11@163.com
卢剑伟(通信作者)，男，1975年出生，博士，教授，博士研究生导师。主要研究方向为动力学与控制、振动噪声与可靠性。E-mail：jwlu75@163.com
张国军，男，1987年出生，博士后。主要研究方向为汽车动力学与控制。E-mail：hfuta6@tsinghua.edu.cn
基金资助:
国家重点研发计划(2021YFE0116600)、合肥市自然科学基金(2022014)和青岛市关键技术攻关及产业化(23-1-2-qljh-11-gx)资助项目。

Review of Research on Energy Management Strategy for Plug-in Hybrid Electric Vehicles

YOU Guogui^1,2, LU Jianwei¹, ZHANG Guojun³, SU Junshou⁴, CAI Zemin²

1. School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009;
2. Automotive New Technology Research Institute of BYD, Shenzhen 518000;
3. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084;
4. State Key Laboratory of Intelligent Manufacturing of Advanced Construction Machinery, Xuzhou Construction Machinery Group, Xuzhou 221000

Received:2024-02-15 Revised:2024-07-16 Online:2025-02-20 Published:2025-04-14

摘要/Abstract

摘要： 节能减排是汽车工业发展永恒的主题。插电式混合动力电动汽车(Plug-in hybrid electric vehicle，PHEV)为减少汽车尾气排放和燃料置换提供了解决方案，实现插电式混合动力汽车节能减排的核心技术是整车的能量管理。首先对插电式混合动力汽车几种能量管理策略的研究现状和应用情况进行总结；其次分析基于确定规则、优化算法和人工智能等能量管理策略算法的优点与不足；然后讨论未来PHEV能量管理控制策略开发过程中可能存在的技术难点和挑战，对人工智能算法、云计算和智能网联等前沿技术在PHEV能量管理的应用做了详细的论述，重点探讨基于人工智能技术的PHEV能量管理策略研究方向；最后对未来PHEV能量管理策略发展的趋势进行了展望，可为PHEV节能降耗技术研究方向的选择和更为深入的研究提供一定的参考。

关键词: 插电式混合动力电动汽车, 能量管理策略, 优化算法, 人工智能

Abstract: Energy conservation and emission reduction is the eternal theme of the development of the automobile industry. Plug in hybrid electric vehicles(PHEV) provide solutions for energy conservation and emission reduction, the core technology to achieve energy conservation and emission reduction of Plug in hybrid electric vehicles is the vehicle energy management technology.The research status and application of several energy management strategies for plug-in hybrid vehicles are summarized; The advantages and disadvantages of energy management strategies based on deterministic rules, optimization algorithms, and artificial intelligence are analyzed; The technical difficulties and challenges in the development of energy management control strategy for PHEV in the future are discussed, and the application of advanced technologies such as artificial intelligence algorithm, cloud computing and intelligent networking in PHEV energy management is analyzed in detail,with emphasis on the research direction of PHEV energy management based on artificial intelligence technology; The development trend of PHEV energy management strategy in the future is prospected, which can provide a certain reference for the selection of research directions and deeper research on PHEV energy conservation and consumption reduction technology.

Key words: plug-in hybrid electric vehicle, energy management strategy, optimization algorithm, artificial intelligent

中图分类号:

TH156

尤国贵, 卢剑伟, 张国军, 苏俊收, 蔡泽民. 插电式混合动力汽车能量管理策略研究综述[J]. 机械工程学报, 2025, 61(4): 195-218.

YOU Guogui, LU Jianwei, ZHANG Guojun, SU Junshou, CAI Zemin. Review of Research on Energy Management Strategy for Plug-in Hybrid Electric Vehicles[J]. Journal of Mechanical Engineering, 2025, 61(4): 195-218.

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.2025.04.195

http://www.cjmenet.com.cn/CN/Y2025/V61/I4/195

参考文献

[1] Torres J L，Gonzalez R，Gimenez A，et al. Energy management strategy for plug-in hybrid electric vehicles. A comparative study[J]. Applied Energy，2014，113:816-824.
[2] Tang X，Jia T，Hu X，et al. Naturalistic data-driven predictive energy management for plug-in hybrid electric vehicles[J]. IEEE Transactions on Transportation Electrification，2020，7(2):497-508.
[3] Miller J M. Propulsion systems for hybrid vehicles[M]. 2nd ed. Beijing:China Machine Press，2016.
[4] Wang X，He H，Sun F，et al. Comparative study on different energy management strategies for plug-in hybrid electric vehicles[J]. Energies，2013，6(11):5656-5675.
[5] Martínez C，Hu X，Cao D，et al. Energy management in plug-in hybrid electric vehicles:Recent progress and a connected vehicles perspective[J]. IEEE Transactions on Vehicular Technology，2017，66(6):4534-4549.
[6] 刘伟. 混合动力汽车系统建模与控制[M]. 北京:机械工业出版社，2015. Liu Wei. Introduction to hybrid vehicle system modeling and control[M]. Beijing:China Machine Press，2015.
[7] QIANG P，WU P，PAN T，et al. Real-time approximate equivalent consumption minimization strategy based on the single-shaft parallel hybrid powertrain[J]. Energies，2021，14(23):7919.
[8] Neffati A，Marzouki A. Local energy management in hybrid electrical vehicle via fuzzy rules system[J]. AIMS Energy，2020，8(3):421-437.
[9] Sarvestani A S，Safavi A A. A novel optimal energy management strategy based on fuzzy logic for a hybrid electric vehicle[C]//2009 IEEE International Conference on Vehicular Electronics and Safety (ICVES). New York:IEEE，2009:141-145.
[10] Cezar B，Onea A. A rule-based energy management strategy for parallel hybrid vehicles with super capacitors[C]//15th International Conference on System Theory，Control and Computing. New York:IEEE，2011:1-6.
[11] Wang X，He H，Sun F，et al. Application study on the dynamic programming algorithm for energy management of plug-in hybrid electric vehicles[J]. Energies，2015，8(4):3225-3244.
[12] Borhan H，Vahidi A，Phillips A M，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.
[13] Kim N，Rousseau A. Sufficient conditions of optimal control based on Pontryagin’s minimum principle for use in hybrid electric vehicles[J]. Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering，2012，226(9):1160-1170.
[14] 洪金龙，高炳钊，董世营，等. 智能网联汽车节能优化关键问题与研究进展[J]. 中国公路学报，2021，34(11):29. HONG Jinlong，GAO Bingzhao，DONG Shiying，et al. Key problems and research progress of energy saving optimization for intelligent connected vehicles[J]. China Journal of Highway and Transport，2021，34(11):29.
[15] Lockwood O，Si M. Playing Atari with hybrid quantum-classical reinforcement learning[C]//NeurIPS 2020 Workshop on Pre-registration in Machine Learning. PMLR，2021:285-301.
[16] 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 Journal of the Transportation Research Board，2016，2572:1-8.
[17] Wu Y，Zhang Y，LI G，et al. A predictive energy management strategy for multi-mode plug-in hybrid electric vehicles based on multi neural networks[J]. Energy，2020，208:118366.
[18] Yang N，Han L，Xiang C，et al. An indirect reinforcement learning based real-time energy management strategy via high-order Markov chain model for a hybrid electric vehicle[J]. Energy，2021，236:121337.
[19] Zhang H，Peng J，Tan H，et al. A deep reinforcement learning based energy management framework with lagrangian relaxation for plug-in hybrid electric vehicle[J]. IEEE Transactions on Transportation Electrification，2020，7:1146-1160.
[20] GONDER J，MARKEL T. Energy management strategies for plug-in hybrid electric vehicles[R]. SAE Paper 2007-01-0290，2007.
[21] 王钦普，游思雄，李亮，等. 插电式混合动力汽车能量管理策略研究综述[J]. 机械工程学报，2017，53(16):1-19. Wang Qinpu，You Sixiong，LI Liang，et al. Survey on energy management strategy for plug-in hybrid electric vehicles[J]. Journal of Mechanical Engineering，2017，53(16):1-19.
[22] Tie S F，Tan C W. A review of power and energy management strategies in electric vehicles[C]//International Conference on Intelligent & Advanced Systems. New York:IEEE，2012.
[23] 苏岭，曾育平，秦大同. 插电式混合动力汽车能量管理策略研究现状和发展趋势[J]. 重庆大学学报，2017，40(2):10-15. SU Ling，ZENG Yuping，QIN Datong. Current situation and development trend of plug-in hybrid electric vehicle’s energy management strategies[J]. Journal of Chongqing University，2017，40(2):10-15.
[24] 谢海明，黄勇，王静，等. 插电式混合动力汽车能量管理策略综述[J]. 重庆理工大学学报(自然科学)，2015，29(7):1-9. XIE Haiming，HUANG Yong，WANG Jing，et al. Review of energy management strategies for plug-in hevs[J]. Journal of Chongqing University of Technology(Natural Science)，2015，29(7):1-9.
[25] Rohith J，Mahesha G. Review of energy management strategies in plug-in hybrid-electric vehicles[M]. Berlin:Springer，2022.
[26] Li W，Cui H，Nemeth T，et al. Deep reinforcement learning-based energy management of hybrid battery systems in electric vehicles[J]. Journal of Energy Storage，2021，36:102355.
[27] XUN Gong，GUO Yaohui，FENG Yiheng，et al. Evaluation of the energy efficiency in a mixed traffic with automated vehicles and human controlled vehicles[C]//2018 IEEE International Conference on Intelligent Transportation Systems (ITSC). 2018.
[28] 叶晓. 并联混合动力汽车控制策略研究[D]. 北京:清华大学，2013. Ye Xiao. Research on the control strategy of a paralell hybrid electric vehicle[D]. Beijing:Tsinghua University，2013.
[29] TANG X，CHEN J，PU H，et al. Double deep reinforcement learning-based energy management for a parallel hybrid electric vehicle with engine start-stop strategy[J]. IEEE Transactions on Transportation Electrification，2021，8(1):1376-1388.
[30] Lee H D，Koo E S. Torque control strategy for a parallel-hybrid vehicle using fuzzy logic[J]. IEEE Industry Applications Magazine，2000，6(6):33-38.
[31] 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.
[32] Hofman T，Steinbuch M，van Druten R M，et al. Rule-based energy management strategies for hybrid vehicle drivetrains:A fundamental approach in reducing computation time[J]. IFAC Proceedings Volumes，2006，39(16):740-745.
[33] Zhou S，Chen Z，Huang D，et al. Model prediction and rule based energy management strategy for a plug-in hybrid electric vehicle with hybrid energy storage system[J]. IEEE Transactions on Power Electronics，2021，36(5):5926-5940.
[34] Gao Y，Ehsani M. Design and control methodology of plug-in hybrid electric vehicles[J]. IEEE Transactions on Industrial Electronics，2010，57(2):633-640.
[35] Naritomo H，Yoshihiro S，Masashi T，et al. Development of a new two-motor plug-in hybrid system[J]. SAE International Journal of Alternative Powertrains，2013，2(1):135-145.
[36] Ding N，Prasad K，Lie T T. Design of a hybrid energy management system using designed rule-based control strategy and genetic algorithm for the series-parallel plug-in hybrid electric vehicle[J]. International Journal of Energy Research，2020，45(2):1627-1644.
[37] 崔胜民. 新能源汽车技术解析[M]. 北京:化学工业出版社，2016. Cui Shengmin. Analysis of new energy vehicle technology[M]. Beijing:Chemical Industry Press，2016.
[38] Denis N，Dubois M R，Desrochers A. Fuzzy-based blended control for the energy management of a parallel plug-in hybrid electric vehicle[J]. Intelligent Transport Systems IET，2015，9(1):30-37.
[39] Yang J，Zhang T，Hong J，et al. Research on driving control strategy and fuzzy logic optimization of a novel mechatronics-electro-hydraulic power coupling electric vehicle[J]. Energy，2021，233:121221.
[40] Li S G，Sharkh S M，Walsh F C，et al. Energy and battery management of a plug-in series hybrid electric vehicle using fuzzy logic[J]. IEEE Transactions on Vehicular Technology，2011，60(8):3571-3585.
[41] DEY S，KAMLU S，MISHRA S K. Optimization of fuel consumption and emission for hybrid electric vehicle[C]//Artificial Intelligence and Evolutionary Computations in Engineering Systems:Proceedings of ICAIECES 2015. Springer，2016:715-722.
[42] Opila D F，Wang X，Mcgee R，et al. Real-time implementation and hardware testing of a hybrid vehicle energy management controller based on stochastic dynamic programming[J]. Journal of Dynamic Systems Measurement & Control，2013，135(2):021002.
[43] Vagg C，Akehurst S，Brace C J，et al. Model-based optimal control of a hybrid electric vehicle using stochastic dynamic programming[C]//6th Conference on Simulation and Testing for Automotive Electronics. 2014.
[44] HERRERA V I，SAEZ-DE-IBARRA A，MILO A，et al. Optimal energy management of a hybrid electric bus with a battery-supercapacitor storage system using genetic algorithm[C]//2015 International Conference on Electrical Systems for Aircraft，Railway，Ship Propulsion and Road Vehicles (ESARS). New York:IEEE，2015:1-6.
[45] WU X，HU X，YIN X，et al. Convex programming energy management and components sizing of a plug-in fuel cell urban logistics vehicle[J]. Journal of Power Sources，2019，423:358-366.
[46] BRAHMA A，GUEZENNEC Y，RIZZONI G. Optimal energy management in series hybrid electric vehicles[C]//Proceedings of the 2000 American Control Conference ACC (IEEE CAT No 00CH36334). New York:IEEE，2000，1:60-64.
[47] Ramaswamy N，Sadegh N. Optimization of fuel economy of hybrid electric vehicles using set based dynamic programming[C]//Dynamic Systems and Control Conference. American Society of Mechanical Engineers，2014，46193:V002T20A004.
[48] Chen R，ZOU Y，HOU S. Energy management strategy for hybrid electric tracked vehicle based on dynamic programming[C]//Electrical Engineering and Control:Selected Papers from the 2011 International Conference on Electric and Electronics (EEIC 2011) in Nanchang，China on June 20-22，2011，Volume 2. Berlin Heidelberg:Springer，2011:843-851.
[49] Li L，Yang C，Zhang Y，et al. Correctional DP-based energy management strategy of plug-in hybrid electric bus for city-bus route[J]. IEEE Transactions on Vehicular Technology，2014，64(7):2792-2803.
[50] Chen Z ，Liu W ，Yang Y ，et al. Online energy management of plug-in hybrid electric vehicles for prolongation of all-electric range based on dynamic programming[J]. Mathematical Problems in Engineering Theory Methods & Applications，2015，368769:1-11.
[51] Luo Y，Chen T，Zhang S，et al. Intelligent hybrid electric vehicle acc with coordinated control of tracking ability，fuel economy，and ride comfort[J]. IEEE Transactions on Intelligent Transportation Systems，2015，16(4):2303-2308.
[52] Li G，Goerges D. Fuel-efficient gear shift and power split strategy for parallel hevs based on heuristic dynamic programming and neural networks[J]. IEEE Transactions on Vehicular Technology，2019，68(10):9519-9528.
[53] Nuesch T，Elbert P，Flankl M，et al. Convex optimization for the energy management of hybrid electric vehicles considering engine start and gearshift costs[J]. Energies，2014，7(2):834-856.
[54] Xu F，Jiao X，Wang Y，et al. Battery-lifetime-conscious energy management strategy based on SP-SDP for commuter plug-in hybrid electric vehicles[J]. IEEJ Transactions on Electrical and Electronic Engineering，2018，13(3):472-479.
[55] Moura S J，Callaway D S，Fathy H K，et al. Tradeoffs between battery energy capacity and stochastic optimal power management in plug-in hybrid electric vehicles[J]. Journal of Power Sources，2010，195(9):2979-2988.
[56] Wang Y，Jiao X，Sun Z，et al. Energy management strategy in consideration of battery health for PHEV via stochastic control and particle swarm optimization algorithm[J]. Energies，2017，10(11):1894.
[57] Faiz J，Moayed-Zadeh K. Design optimization of switched reluctance machines for starter/generator of hybrid electric vehicle by genetic algorithm[J]. European Transactions on Electrical Power，2013，19(2):302-312.
[58] Ho P，Klang E. Intelligent energy distribution for series hevs using determined optimal driving patterns via a genetic algorithm[C]. SAE Technical Papers，2013-01-0572，2013.
[59] Zheng C，Mi C C，Rui X，et al. Energy management of a power-split plug-in hybrid electric vehicle based on genetic algorithm and quadratic programming[J]. Journal of Power Sources，2014，248(7):416-426.
[60] Trovao J P F，Santos V D N，PEREIRINHA P G，et al. A simulated annealing approach for optimal power source management in a small EV[J]. IEEE Transactions on Sustainable Energy，2013，4(4):867-876.
[61] Bashash S，Moura S J，Forman J C，et al. Plug-in hybrid electric vehicle charge pattern optimization for energy cost and battery longevity[J]. Journal of Power Sources，2011，196(1):541-549.
[62] Boehme T J，Frank B，Schori M，et al. Multi-objective optimal powertrain design of parallel hybrid vehicles with respect to fuel consumption and driving performance[C]//European Control Conference (ECC). New York:IEEE，2014:1017-1023.
[63] Zhang Y，Liu H，Guo Q. Varying-domain optimal management strategy for parallel hybrid electric vehicles[J]. IEEE Transactions on Vehicular Technology，2014，63(2):603-616.
[64] Fang L，Qin S，Xu G，et al. Simultaneous optimization for hybrid electric vehicle parameters based on multi-objective genetic algorithms[J]. Energies，2011，4(3):532-544.
[65] Yuan Z，Teng L，Sun F，et al. Comparative study of dynamic programming and pontryagin’s minimum principle on energy management for a parallel hybrid electric vehicle[J]. Energies，2013，6(4):2305-2318.
[66] Ren Y，Wu Z. Research on the energy management strategy of hybrid vehicle based on Pontryagin’s minimum principle[C]//201810th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). New York:IEEE，2018，2:356-361.
[67] Liu T，Yuan Z，Liu D X，et al. Real-time control for a parallel hybrid electric vehicle based on Pontryagin’s minimum principle[C]//IEEE Transportation Electrification Conference and Expo Asia-Pacific. Beijing:National Engineering Lab for Electric Vehicles，2014:1-5.
[68] Song Z，Zhang X，Li J，et al. Component sizing optimization of plug-in hybrid electric vehicles with the hybrid energy storage system[J]. Energy，2018，144:393-403.
[69] Sharma O P，Onori S，Guezennec Y. Analysis of Pontryagin’s minimum principle-based energy management strategy for PHEV applications[C]//Dynamic Systems and Control Conference. American Society of Mechanical Engineers，2012，45295:145-150.
[70] Xie S，Hu X，Xin Z，et al. Pontryagin’s minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus[J]. Applied Energy，2019，236:893-905.
[71] KIM N，ROUSSEAU A，LEE D. A jump condition of pmp-based control for phevs[J]. Journal of Power Sources，2011，196(23):10380-10386.
[72] Onori S，Tribioli L. Adaptive pontryagin’s minimum Principle supervisory controller design for the plug-in hybrid GM chevrolet volt[J]. Applied Energy，2015，147:224-234.
[73] Ngo V，Hofman T，Steinbuch M，et al. Optimal control of the gearshift command for hybrid electric vehicles[J]. IEEE Transactions on Vehicular Technology，2012，61(8):3531-3543.
[74] Paganelli G，Delprat S，Guerra T M，et al. Equivalent consumption minimization strategy for parallel hybrid powertrains[C]//Vehicular Technology Conference IEEE 55th Vehicular Technology Conference VTC Spring 2002(Cat No 02CH37367). New York:IEEE，2002，4:2076-2081.
[75] Liu X，Qin D，Wang S. Minimum energy management strategy of equivalent fuel consumption of hybrid electric vehicle based on improved global optimization equivalent factor[J]. Energies，2019，12(11):2076.
[76] 陈渠. 基于SOC优化轨迹的插电式混合动力汽车能量管理算法研究[D]. 上海:上海交通大学，2020. CHEN Qu. Research on energy management algorithm of plug-in hybrid electric vehicle based on soc optimal trajectory[D]. Shanghai:Shanghai Jiao Tong University，2020.
[77] Chasse A，Sciarretta A. Supervisory control of hybrid powertrains:An experimental benchmark of offline optimization and online energy management[J]. Control Engineering Practice，2011，19(11):1253-1265.
[78] Larsson V，Johannesson L，Egardt B，et al. Benefit of route recognition in energy management of plug-in hybrid electric vehicles[C]//2012 American Control Conference (ACC). New York:IEEE，2012:1314-1320.
[79] Zhang P，Wu X，Du C，et al. Adaptive equivalent consumption minimization strategy for hybrid heavy-duty truck based on driving condition recognition and parameter optimization[J]. Energies，2020，13(20):5407.
[80] Onori S，Serrao L，Rizzoni G. Adaptive equivalent consumption minimization strategy for hybrid electric vehicles[C]//Dynamic Systems and Control Conference. 2010，44175:499-505.
[81] Yang S，Wang W，Zhang F，et al. Driving-style-oriented adaptive equivalent consumption minimization strategies for hevs[J]. IEEE Transactions on Vehicular Technology，2018，67(10):9249-9261.
[82] LEI Z，QIN D，HOU L，et al. An adaptive equivalent consumption minimization strategy for plug-in hybrid electric vehicles based on traffic information[J]. Energy，2020，190:116409.
[83] Zhang J，He H，Wang X. Model predictive control based energy management strategy for a plug-in hybrid electric vehicle[C]//20153rd International Conference on Mechanical Engineering and Intelligent Systems. Atlantis Press，2015:875-879.
[84] Bruni G，Cordiner S，Mulone V，et al. A study of energy management in domestic micro-grids based on model predictive control strategies[J]. Energy Procedia，2014，61:1012-1016.
[85] 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.
[86] Rezaei A，Burl J B. Effects of time horizon on model predictive control for hybrid electric vehicles[J]. IFAC Papersonline，2015，48(15):252-256.
[87] Liu R，Wang J，Zhang B. High definition map for automated driving:Overview and analysis[J]. The Journal of Navigation，2020，73(2):324-341.
[88] Cairano S D，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，2014，22(3):1018-1031.
[89] Sciarretta A，Guzzella L. Control of hybrid electric vehicles[J]. Control Systems IEEE，2007，27(2):60-70.
[90] Wang H，Huang Y，Khajepour A，et al. Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle[J]. Applied Energy，2016，182(15):105-114.
[91] Homchaudhuri B，Vahidi A，Pisu P. A fuel economic model predictive control strategy for a group of connected vehicles in urban roads[C]//American Control Conference. New York:IEEE，2015:2741-2746.
[92] Homchaudhuri B，Vahidi A，Pisu P. Fast model predictive control-based fuel efficient control strategy for a group of connected vehicles in urban road conditions[J]. IEEE Transactions on Control Systems Technology，2017，25(2):760-767.
[93] Guo L，Gao B，Ying G，et al. Optimal energy management for HEVs in eco-driving applications using Bi-level MPC[J]. IEEE Transactions on Intelligent Transportation Systems，2017，18(8):2153-2162.
[94] He H，Zhang J，Li G. Model predictive control for energy management of a plug-in hybrid electric bus[J]. Energy Procedia，2016，88:901-907.
[95] ZHANG Shuo，XIONG Rui，SUN Fengchun. 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.
[96] XIANG C L，DING F，WANG D W，et al. MPC-based energy management with adaptive Markov-chain prediction for a dual-mode hybrid electric vehicle[J]. Science China(Technological Sciences)，2017，5(60):93-104.
[97] 张风奇，胡晓松，许康辉，等. 混合动力汽车模型预测能量管理研究现状与展望[J]. 机械工程学报，2019，55(10):100-122. ZHANG Fengqi，HU Xiaosong，XU Kanghui，et al. Current status and prospects for model predictive energy management in hybrid electric vehicles[J]. Journal of Mechanical Engineering，2019，55(10):100-122.
[98] Park J，Chen Z，Kiliaris L，et al. Intelligent vehicle power control based on machine learning of optimal control parameters and prediction of road type and traffic congestion[J]. IEEE Transactions on Vehicular Technology，2009，58(9):4741-4756.
[99] Khayyam H，Ba B-Hadiashar A. Adaptive intelligent energy management system of plug-in hybrid electric vehicle[J]. Energy，2014，69:319-335.
[100] Feng T，Hu Y，Lin Y. A neural network model to calculate the energy demand of the vehicle based on traffic features[C]//2014 Seventh International Symposium on Computational Intelligence and Design. 2014.

插电式混合动力汽车能量管理策略研究综述

Review of Research on Energy Management Strategy for Plug-in Hybrid Electric Vehicles

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	秦曈, 孟庆尧, 魏威, 陈立为, 王紫扬, 李莉. 基于数据驱动的运载火箭总体回路设计研究[J]. 机械工程学报, 2026, 62(7): 15-24.
[2]	王树新, 魏静, 陈思宇, 唐明海. AI赋能的机械传动技术范式重构：瓶颈突破与自主创新体系演进[J]. 机械工程学报, 2026, 62(7): 150-168.
[3]	罗仕鉴, 郭和睿, 李庆龄, 易珮琦. 人智协同设计中的信任问题：挑战、进展和展望[J]. 机械工程学报, 2026, 62(7): 324-342.
[4]	刘辉, 马小康, 韩立金, 项昌乐. 基于深度强化学习和模型预测控制的混合动力电动汽车实时能量管理策略[J]. 机械工程学报, 2026, 62(6): 302-313.
[5]	张涛, 何家豪, 施永平, 赵萍, 汪键, 王伟. 基于改进Stribeck摩擦的协作机器人动力学参数辨识[J]. 机械工程学报, 2026, 62(5): 151-167.
[6]	刘素艳, 董一林, 乔一鸣, 马增强. 基于改进蜣螂优化算法的轴承数字孪生模型的动态故障注入研究[J]. 机械工程学报, 2026, 62(3): 446-457.
[7]	王培旭, 裴凤雀, 刘检华, 郭昊鑫, 庄存波. 考虑多技能水平工人分配的复杂产品装配调度问题[J]. 机械工程学报, 2025, 61(4): 389-402.
[8]	宁方伟, 鲁嘉星, 王一轩, 马玉山, 黎磊, 李赫然, 石岩. 智能生成式设计——一种新的机械设计理念[J]. 机械工程学报, 2025, 61(24): 267-284.
[9]	肖乾, 黄文龙, 陈道云, 孙守光, 李强, 石晓玲. 考虑非线性累积损伤模型修正的转向架构架疲劳可靠性评估[J]. 机械工程学报, 2025, 61(22): 237-249.
[10]	张传亮, 刘瑞, 宋权斌, 王子艺. 基于B样条的气膜冷却孔参数化及性能优化研究[J]. 机械工程学报, 2025, 61(20): 165-173.
[11]	孟德彪, 杨恒飞, 杨世源, 苏晓燕, 朱顺鹏. 自适应多保真度Kriging模型辅助的一阶可靠性分析方法[J]. 机械工程学报, 2025, 61(18): 344-365.
[12]	邵海东, 肖一鸣, 钟翔, 韩特. 面向可信机械故障诊断的模型校准方法[J]. 机械工程学报, 2025, 61(17): 114-123.
[13]	杨超, 杜雪龙, 王伟达, 杨刘权, 项昌乐. 基于端云协同优化的混合动力车辆实时能量管理策略研究[J]. 机械工程学报, 2025, 61(16): 227-238.
[14]	张书锋, 宋国锋, 李兴格, 陈循. 含AI模型机电系统环境适应性试验与评估方法[J]. 机械工程学报, 2025, 61(16): 358-373.
[15]	高金吉. 人工自愈开辟智能流程制造向安健发展新途径[J]. 机械工程学报, 2025, 61(14): 117-129.