储能科学与技术 ›› 2020, Vol. 9 ›› Issue (6): 1933-1939.doi: 10.19799/j.cnki.2095-4239.2020.0166

• 储能测试与评价 • 上一篇    下一篇

基于BMS的锂离子电池建模方法综述

梁新成1(), 张 勉1, 黄国钧2   

  1. 1.西南大学工程技术学院
    2.西南大学人工智能学院,重庆 400715
  • 收稿日期:2020-05-05 修回日期:2020-06-14 出版日期:2020-11-05 发布日期:2020-10-28
  • 作者简介:联系人:梁新成(1978—),男,博士,研究方向为汽车系统动力学及锂电池的建模与控制,E-mail:dylb1978@126.com

Review on lithium-ion battery modeling methods based on BMS

Xincheng LIANG1(), Mian ZHANG1, Guojun HUANG2   

  1. 1.School of Engineering and Technology, Southwest University
    2.School of Artificial Intelligence, Southwest University, Chongqing 400715, China
  • Received:2020-05-05 Revised:2020-06-14 Online:2020-11-05 Published:2020-10-28

摘要:

电池管理系统(battery management system, BMS)是电动车辆的技术核心,而精确的电池模型是实现BMS的关键。电池模型的精度与材料、环境温度、工作模式、老化程度等密切相关,而在建模时完整地包含上述因素是非常困难的。本文在简单介绍BMS功能和结构的基础上,通过对近几年锂离子电池建模文献的整理,着重介绍了电学特性模型、热模型及电-热耦合模型的建模方法。由于电-热模型综合了其他两种方法的优点,模型相对简单且在实际中使用较多。在此基础上阐述了三种模型在电池内部状态如电池荷电状态(state of charge, SOC)、电池健康状态(state of health, SOH)、温度等参数估计中的应用。特别是SOH的状态估计,除受电流、温度、SOC等因素影响外,还与机械振动及过电势等密切相关。考虑到状态估计变量之间的相互耦合如电池的SOC、内部温度等,故需进一步提高耦合参数的估计精度以确保BMS工作的可靠性。在未来,还需要继续对模型进行降阶,以实现BMS工作的实时性。

关键词: 锂电池, 电池管理系统, 建模方法

Abstract:

Beset by energy shortage and environmental pollution with the sharp rise in car ownership, electric vehicles (EVs) are currently receiving high praise from people. However, the energy used in EVs must be supplied by hundreds of cells when they are driven for a limited energy density of the lithium ion; as a result, the battery management system (BMS) can be viewed as the core technology for EVs. Unluckily, some parameters cannot be measured directly and are obtained only through model estimation. The appropriate model of a lithium-ion battery is the key to the efficiency, precision, and stability of the BMS. Several factors, including material, ambient temperature, work mode, and aging degree, are well known to be closely related to the lithium-ion battery model; nevertheless, having such elements during modeling is difficult. This study introduces the function and structure of the BMS, enabling the listing of concerned references regarding the battery model in the recent years. Three sorts of model (i.e., electrical characteristic, thermal, and electric-thermal coupling models) are then discussed separately. Although the first and second models can clearly reveal the work mechanism of the lithium-ion battery, the large amount of calculation makes it hard to be accepted in engineering considering the current situation. Adversely, the third model combines the advantages of the two models and is widely used for its relative simplicity. Herein, the applications of such models on internal states, such as state of charge (SOC), state of health (SOH), and inner temperature of battery, are described based on a correlative discussion. The SOC variable of EV is as significant as the oil gauge of the internal combustion engine vehicle; thus, it is relatively mature. The SOH is influenced by the current, temperature, and SOC and is very relevant to mechanical vibration and overpotential. Meanwhile, the internal temperature is vital to the capacity, discharging efficiency, span life, and safety of the lithium-ion battery; therefor, how to maintain the proper temperature is very crucial. Considering the coupling of estimation variables (e.g., SOC and inner temperature variable), the estimation accuracy should be greatly enhanced to ensure the BMS reliability. In the future, the lithium-ion battery model is expected to be simplified continually, thereby enabling the invariable satisfaction of a real-time BMS.

Key words: lithium battery, battery management system, modeling method

中图分类号: