储能科学与技术

• 储能XXX •    

考虑锂电池温度和老化的荷电状态估算

陈峥1(), 杨博1, 赵志刚2, 申江卫1, 肖仁鑫1, 夏雪磊1()   

  1. 1.昆明理工大学交通工程学院，云南 昆明 650500
    2.北京航天发射技术研究所，北京 100000
  • 收稿日期:2024-02-23 修回日期:2024-03-11
  • 通讯作者: 夏雪磊 E-mail:chen@kust.edu.cn;xxl92@stu.kust.edu.cn
  • 作者简介:陈峥（1982—），男，教授，研究方向：动力电池管理与控制，E-mail：chen@kust.edu.cn；
  • 基金资助:
    国家自然科学基金项目(52267022)

State of charge estimation considering lithium battery temperature and aging

Zheng CHEN1(), Bo YANG1, Zhi-gang Zhao2, Jiang-wei SHEN1, Ren-xin XIAO1, Xue-lei XIA1()   

  1. 1.Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, Yunnan China
    2.Beijing Institute of Space Launch Technology, Beijing, 100000, China
  • Received:2024-02-23 Revised:2024-03-11
  • Contact: Xue-lei XIA E-mail:chen@kust.edu.cn;xxl92@stu.kust.edu.cn

摘要：

针对锂离子动力电池工作环境复杂且电池老化导致内部参数辨识精度低与荷电状态估计误差大的难题，本文提出了一种多新息最小二乘法与平方根容积卡尔曼滤波估计锂离子电池荷电状态的联合算法，实现动力电池在全服役周期内多温度条件下的状态估算。首先，为解决传统最小二乘法对历史数据利用率低的问题，在最小二乘法中融入多新息理论，采用一阶RC等效电路建立电池模型，利用多新息最小二乘法对电池内部参数进行参数辨识；然后，采用平方根容积卡尔曼滤波的方法估算电池SOC；最后，通过多温度全寿命的电池实验数据对本文所提算法进行验证，并且与扩展卡尔曼滤波、容积卡尔曼滤波算法进行对比，证明本文提出算法的有效性。实验结果表明：本文提出的多新息最小二乘法-平方根容积卡尔曼滤波算法在多温度全寿命条件下，能够准确反映动力电池内部参数和精确估算电池SOC，电压平均绝对误差不超过40mV，SOC的估算误差控制在2%范围内。

关键词: 锂电池, 多新息最小二乘法, 平方根容积卡尔曼滤波, 多温度, 荷电状态

Abstract:

Aiming at the problems of low internal parameter identification accuracy and large charge state estimation error caused by complex working environment and aging of lithium-ion power batteries, this paper proposed a combined algorithm of multi-innovation least square method and square root cubature kalman filter to estimate the charge state of lithium-ion batteries, and realized the state estimation of power batteries under multi-temperature conditions during the full lifetime. Firstly, in order to solve the problem of low utilization rate of historical data by traditional least squares method, the multi-innovation theory was incorporated into the least squares method, a first-order RC equivalent circuit was used to establish the battery model, and the internal parameters of the battery were identified by multi-innovation least squares method. After that, the SOC was estimated by the square root cubature kalman filter. Finally, the proposed algorithm is verified by the experimental data of multi-temperature battery, and compared with the extended kalman filter and cubature kalman filter algorithms, the effectiveness of the proposed algorithm is proved. The experimental results show that the proposed algorithm can accurately reflect the internal parameters of the power battery and accurately estimate the SOC of the battery under the condition of multi-temperature lifetime. The average absolute voltage error is less than 40mV, and the SOC estimation error is controlled within 2%.

Key words: lithium-ion battery, multi-innovation least square algorithm, square root cubature Kalman filter, multi-temperature, state of charge

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