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
  • [1] XIE Shaobo, LIU Tong, LI Huiling, Et al., A study on predictive energy management strategy for a plug-in hybrid electric bus based on Markov chain, Automotive Engineering, 40, 8, pp. 871-877, (2018)
  • [2] DENG Tao, LUO Junlin, LI Junying, Et al., A study on adaptive rule energy management control for HEV based on approximate ECMS, Automotive Engineering, 39, 6, pp. 607-614, (2017)
  • [3] WANG Qian, LI Dinggen, MIAO Huachun, Research on energy management strategy of fuel cell vehicle based on fuzzy logic control, Automotive Engineering, 41, 12, pp. 1347-1355, (2019)
  • [4] BIASINI R, ONORI S, RIZZONI G., A near-optimal rule-based energy management strategy for medium duty hybrid truck, International Journal of Powertrains, 2, 2, (2013)
  • [5] HEMI H, GHOUILI J, CHERITI A., A real time fuzzy logic power management strategy for a fuel cell vehicle, Energy Conversion and Management, 80, pp. 63-70, (2014)
  • [6] SONG Z, LI J, HAN X, Et al., Multi-objective optimization of a semi-active battery/supercapacitor energy storage system for electric vehicles, Applied Energy, 135, pp. 212-224, (2014)
  • [7] YANG Ye, ZHANG Youtong, ZHANG Biao, Et al., Adaptive energy management strategy for plug-in hybrid electric bus based on equivalent factor optimization, Automotive Engineering, 42, 3, pp. 292-298, (2020)
  • [8] ZHANG S, XIONG R, CAO J., Battery durability and longevity based power management for plug-in hybrid electric vehicle with hybrid energy storage system, Applied Energy, 179, pp. 316-328, (2016)
  • [9] VAN K T, GILLOT J, DE J B, Et al., Solution for state constrained optimal control problems applied to power split control for hybrid vehicles, Automatica, 50, 1, pp. 187-192, (2014)
  • [10] ZHENG C, XU G, XU K, Et al., An energy management approach of hybrid vehicles using traffic preview information for energy saving, Energy Conversion and Management, 105, pp. 462-470, (2015)
12