• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2019, Vol. 55 ›› Issue (20): 28-35.doi: 10.3901/JME.2019.20.028

• 特邀专栏:动力电池系统关键技术 • 上一篇    

基于电热耦合模型和多参数约束的动力电池峰值功率预测

王春雨, 崔纳新, 李长龙, 张承慧   

  1. 山东大学控制科学与工程学院 济南 250061
  • 收稿日期:2019-03-08 修回日期:2019-09-26 发布日期:2020-01-07
  • 通讯作者: 崔纳新(通信作者),女,1968年出生,博士,教授,博士研究生导师。主要研究方向为电动汽车驱动系统优化控制、能量管理及电池管理系统等。E-mail:cuinx@sdu.edu.cn
  • 作者简介:王春雨,女,1991年出生,博士研究生。主要研究方向为电动汽车电池管理系统。E-mail:wangchunyu09@163.com
  • 基金资助:
    国家自然科学基金资助项目(61633015,U1864205,61527809,U1764258)。

State of Power Prediction Based on Electro-thermal Battery Model and Multi-parameter Constraints for Lithium-ion Battery

WANG Chunyu, CUI Naxin, LI Changlong, ZHANG Chenghui   

  1. School of Control Science and Engineering, Shandong University, Jinan 250061
  • Received:2019-03-08 Revised:2019-09-26 Published:2020-01-07

摘要: 锂离子动力电池的峰值功率(State of power,SOP)直接影响电动汽车的加速爬坡性能以及回馈制动的能量回收能力,然而其不能直接测量,且准确估计十分困难。这源自于电池内部复杂的电化学特性,尤其是电池运行是一个电热特性相互耦合的过程,过高的充放电功率可能引起电池过热,进而导致电池寿命加速衰减甚至引发安全事故,因此,引入电池温度作为峰值功率的重要约束条件之一,综合电池温度、电压、荷电状态(State of charge,SOC)等多参数约束实现峰值功率预测。首先建立电池电热耦合模型,准确描述电池电、热动态特性;进而在多参数约束条件下预测电池峰值功率;最后,改进了电池热模型的参数辨识方法,并在不同温度环境和动态工况下试验验证电池建模和峰值功率预测方法的有效性,试验结果表明该方法可有效预测电池充放电功率,提高电池使用的安全性。

关键词: 电动汽车, 锂离子动力电池, 峰值功率, 电热耦合模型, 多参数约束

Abstract: The state of power of lithium-ion battery directly determines the acceleration performance and the braking energy recovery rate of electric vehicles. The state of power cannot be measured directly, so the accurate prediction of which is crucial and difficult. This is due to the complex electrochemical characteristics inside the battery, especially the operation of the battery is a coupled process of electro-thermal characteristics. Excessive charging and discharging power can cause overheating of batteries, which may lead to accelerated degradation of battery life and even cause safety accidents. Therefore, battery temperature is introduced as one of the most important constraints of peak power. Peak power prediction is realized by combining the constraints of battery temperature, voltage and SOC. Firstly, The electro-thermal battery model is established to accurately describe the electrical and thermal dynamic characteristics of the battery. Then the state of power can be predicted with multi-constraints of terminal voltage, state of charge and temperature of battery. Finally, an improved parameter identification method of battery thermal model is proposed. The performance of battery model and power prediction mothed are demonstrated by experiments under different temperatures and dynamic characterization schedules. The experimental results show that the proposed method can effectively predict the power of battery charging and discharging and improve the safety of battery system.

Key words: electric vehicle, lithium-ion battery, state of power, electro-thermal model, multi-parameter constraints

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