Braking energy optimization control for four in-wheel motors electric vehicles considering battery life

被引：1
|
作者：
Xu W. [1 ,2 ]
Chen H. [1 ,2 ,3 ]
Zhao H.-Y. [1 ,2 ]
机构：
[1] State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130025, Jilin
[2] College of Communication Engineering, Jilin University, Changchun, 130025, Jilin
[3] Clean Energy Automotive Engineering Center, Tongji University, Shanghai
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2019年 / 36卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Battery life; Braking energy optimization; Electric vehicles; Torque distribution;
D O I：
10.7641/CTA.2019.90532
中图分类号：
学科分类号：
摘要：
The braking energy recovery system can convert kinetic energy into electrical energy and store it in the battery, which effectively improves the driving range of the electric vehicle. However, the frequent braking will cause the frequent charging of the battery. The external work environment of the battery, such as the charge and discharge rates, the battery state of charge (SOC), and the work temperature are directly related to the battery life. The simple braking energy recovery system does not consider the impact of the regenerative braking time point and length and the braking strength on the battery aging, which shouldn’t be ignored. There are two braking modes in electric vehicle braking process: motor braking mode and hydraulic braking mode. In this paper, the energy consumption and recovery model and battery life depletion model of the braking process are established for the four in-wheel motors electric vehicle, a coordination and optimization controller of the two braking modes is designed to take into account both the energy recovery and the battery life. Based on AMESim/Simulink co-simulation platform, the simulations are carried out to analyse the impact of braking energy recovery on battery life first, then the proposed control method is verified compared with the strategy without considering battery life, finally the effects of the initial battery SOC and different braking strengths for the optimization are simulated and analyzed. © 2019, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
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页码：1942 / 1951
页数：9
相关论文
共 18 条
  • [1] Richardson P., Flynn D., Keane A., Optimal charging of electric vehicles in low-voltage distribution systems, IEEE Transactions on Power Systems, 27, 1, pp. 268-279, (2012)
  • [2] Siang F.T., Chee W.T., A review of energy sources and energy management system in electric vehicles, Renewable and Sustainable Energy Reviews, 20, pp. 82-102, (2013)
  • [3] Guo L., Gao B., Chen H., Optimal ecodriving control of vehicles, Scientia Sinica Informationis, 46, 5, pp. 560-570, (2016)
  • [4] Wu J.C., Hu J.J., Ai X., Et al., Multi-time scale energy management of electric vehicle model-based prosumers by using virtual battery model, Applied Energy, 251, (2019)
  • [5] Zhang J.Z., Li Y.T., Lu C., Et al., New regenerative braking control strategy for rear-driven electrified minivans, Energy Conversion and Management, 82, pp. 135-145, (2014)
  • [6] Xu W., Chen H., Wang J.M., Et al., Velocity optimization for braking energy management of in-wheel motor electric vehicles, IEEE Access, 7, pp. 66410-66422, (2019)
  • [7] Gao Y., Jiang J.C., Zhang C.P., Et al., Lithium-ion battery aging mechanisms and life model under different charging stresses, Journal of Power Sources, 356, pp. 103-114, (2017)
  • [8] Wang J., Liu P., Hicks-Garner J., Et al., Cycle-life model for graphite-LiFePO4 cells, Journal of Power Sources, 196, pp. 3942-3948, (2011)
  • [9] Tang L., Rizzoni G., Onori S., Energy management strategy for HEVs including battery life optimization, IEEE Transactions on Transportation Electrification, 1, 3, pp. 211-222, (2015)
  • [10] Wang C., Zhao Q., Qin X., Et al., Life prediction method of lithium battery based on improved relevance vector machine, Journal of Beijing University of Aeronautics and Astronautics, 44, 9, pp. 1998-2003, (2018)
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