Energy Management of Tracked Vehicles Based on Battery Life Prediction Control

被引：0
|
作者：
Han L. [1 ,2 ]
Liu H. [1 ,2 ]
Liu C. [1 ,2 ]
Liu B. [1 ,2 ]
Zhang C. [1 ,2 ]
机构：
[1] School of Mechanical Engineering, Beijing Institute of Technology, Beijing
[2] Institute of Advanced Technology, Beijing Institute of Technology, Jinan
来源：
Qiche Gongcheng/Automotive Engineering | 2021年 / 43卷 / 05期
关键词：
Battery life; Energy management; NMPC; Series hybrid tracked vehicles;
D O I：
10.19562/j.chinasae.qcgc.2021.05.003
中图分类号：
学科分类号：
摘要：
In order to improve the fuel economy and power performance of series hybrid tracked vehicles in complex driving environment, an energy management strategy based on nonlinear model predictive control (NMPC) considering the influence of battery life is proposed in this paper. Firstly, considering the influence of different output power on the battery temperature, the second-order RC model, thermoelectric coupling model and life model of the battery are established. Then, based on the second-order RC model of the battery, the prediction model is established to describe the future dynamics of the vehicle front power chain. At the same time, considering the influence of the battery life, an energy management strategy based on the nonlinear model predictive control is designed. A calculation method of conversion factor between power consumption and fuel consumption is proposed to make the conversion factor adaptive to different driving conditions and energy management strategies of the vehicle. Finally, the simulation and hardware in-loop test platform are built to verify the effectiveness of the proposed energy management strategy under three typical working conditions. © 2021, Society of Automotive Engineers of China. All right reserved.
引用
收藏
页码：657 / 666
页数：9
相关论文
共 17 条
  • [11] WANG H, HUANG Y, KHAJEPOUR A, Et al., Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle, Applied Energy, 182, pp. 105-114, (2016)
  • [12] ZHAO Yong, XIE Jinfa, SHI Jiawei, Et al., Energy management strategy of fuel cell hybrid electric vehicle based on gentic algorithm-support vector machine condition recognition, Science Technology and Engineering, 20, 14, pp. 5820-5827, (2020)
  • [13] GENG Wenran, LOU Diming, ZHANG Tong, Multi-objective energy management strategy for hybrid electric vehicle based on particle swarm optimization, Journal of Tongji University (Natural Science), 48, 7, pp. 1030-1039, (2020)
  • [14] WU J, ZHANG C H, CUI N X., Fuzzy energy management strategy for a hybrid electric vehicle based on driving cycle recognition, International Journal of Automotive Technology, 13, 7, pp. 1159-1167, (2012)
  • [15] DENG Y W, WANG B J, ZHANG S A, Et al., Optimization of energy management strategy of PHEV based on chaos-genetic algorithm, Journal of Hunan University, 40, 4, pp. 42-48, (2013)
  • [16] SHU H, QIN D T, HU J J., Research on current situation and trend of control strategies for hybrid electric vehicles, Journal of Chongqing University(Nature Science Edition), 6, pp. 28-31, (2001)
  • [17] FATHABADI H., A novel design including cooling media for Lithium-ion batteries pack used in hybrid and electric vehicles, Journal of Power Sources, 245, 1, pp. 495-500, (2014)
12