储能科学与技术 ›› 2020, Vol. 9 ›› Issue (1): 145-151.doi: 10.19799/j.cnki.2095-4239.2019.0209

• 储能系统与工程 • 上一篇    下一篇

基于改进EKF算法变温度下的动力锂电池SOC估算

蒋聪(), 王顺利(), 李小霞, 熊鑫   

  1. 西南科技大学信息工程学院，四川 绵阳 621010
  • 收稿日期:2019-09-20 修回日期:2019-10-09 出版日期:2020-01-05 发布日期:2019-10-24
  • 作者简介:蒋聪（1995—），硕士研究生，主要研究方向为电池荷电状态估算，E-mail：jcong-@outlook.com；|王顺利，讲师，主要研究方向为新能源测控，E-mail：wangshunli@swust.edu.cn。
  • 基金资助:
    国家自然科学基金项目(61801407);四川科技厅重点研发项目(2019YFG0427)

Estimation method of SOC for power lithium battery based on improved EKF algorithm adaptive to various temperature

JIANG Cong(), WANG Shunli(), LI Xiaoxia, XIONG Xin   

  1. School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, Sichuan, China
  • Received:2019-09-20 Revised:2019-10-09 Online:2020-01-05 Published:2019-10-24

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摘要：

动力锂电池的荷电状态（SOC）准确估算是电池安全可靠有效使用的关键。而温度对动力锂电池的性能有较大影响，在综合分析动力锂电池SOC估算的各种因素的基础上，结合各种现有的SOC估算方法，比较了优缺点。考虑到-10～40 ℃的温度变化，对中航三元锂50 A·h锂电池在不同温度下进行混合脉冲功率性能测试试验（HPPC）以进行锂电池参数辨识，探究锂电池参数随温度变化特性，建立适应温度变化的Thévenin等效电路模型，运用平方根分解改进扩展卡尔曼滤波（EKF）算法进行SOC估算，以避免由于计算机字长引起的计算误差导致的滤波发散。对锂电池在变温度下进行参考汽车工况进行实验，导出电池数据在Simlink进行算法估算效果仿真验证。结果表明基于所使用变温度下的Thévenin等效电路模型运用改进EKF进行SOC估算的最大误差小于1.5％，平均误差为0.37％。上述方法可以校正SOC初始值的误差，不依赖于初始值的准确性，能实现不同温度下的SOC估算。

关键词: 动力锂电池, SOC估算, 温度, 参数辨识, Thévenin模型, 改进扩展卡尔曼滤波

Abstract:

Accurate estimation of the state-of-charge （SOC） with respect to the power in a lithium battery is the key to its safe and reliable use. Temperature significantly influences the usage of the lithium power battery. The advantages and disadvantages are compared by comprehensively analyzing the impact of various factors on the SOC for lithium batteries combined with various existing SOC estimation methods. Considering a temperature change from -10 ℃ to 40 ℃, a hybrid pulse power characteristic was obtained with respect to an AVIC 50 A·h lithium battery at intervals of 10 ℃. Based on the experimental data, the battery’s parameters were identified according to the least squares principle. The characteristics of the battery were explored with different temperature parameters, and the Thévenin equivalent circuit model （adapted for temperature changes） was established. The error variance matrix may gradually lose its positive definiteness or symmetry, resulting in filter divergence, because all the computer algorithm programs may be affected by word limitations or calculation errors. To solve this problem, the extended Kalman filter （EKF） algorithm is improved by square root decomposition of the EKF algorithm to accurately estimate the SOC. Through the simulation of the vehicle operating conditions of the China National Aviation’s ternary lithium battery, the simulation verification algorithm is used to estimate the effect under variable temperature conditions. The results reveal that the maximum error of SOC estimation based on the Thévenin equivalent circuit model at the considered variable temperature is less than 1.5% with an average error of 0.37%, which is better than that of the EKF algorithm. The improved EKF algorithm based on square root decomposition can rectify the error of the initial value of SOC and realize SOC estimation at different temperatures without depending on the accuracy of the initial value.

Key words: power Lithium-ion battery, SOC estimation, temperature, parameter identification, Thévenin model, improved extended Kalman filter

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