储能科学与技术 ›› 2021, Vol. 10 ›› Issue (2): 695-704.doi: 10.19799/j.cnki.2095-4239.2020.0397

• 储能测试与评价 • 上一篇    下一篇

基于Thevenin模型和改进扩展卡尔曼的特种机器人锂离子电池SOC估算方法

熊然(), 王顺利(), 于春梅, 夏黎黎   

  1. 西南科技大学信息工程学院，四川 绵阳 621010
  • 收稿日期:2020-12-07 修回日期:2020-12-25 出版日期:2021-03-05 发布日期:2021-03-05
  • 作者简介:熊然（1997―），男，硕士研究生，研究方向为新能源测控技术，E-mail：438394119@qq.com|王顺利，教授，研究方向为信号检测与估计、抗干扰处理、控制策略、人工智能和智能计算研究，针对特殊环境机器人安全可靠供能等典型工况需求，进行全寿命周期动力锂电池组的状态测控理论探索及产业化应用，E-mail：497420789@qq.com。
  • 基金资助:
    国家自然科学基金项目(61801407);四川省科技厅重点研发项目(2018GZ0390);四川省教育厅科研项目(17ZB0453);西南科技大学素质类教改（青年发展研究）专项项目(18xnsu12)

An estimation method for lithium-ion battery SOC of special robots based on Thevenin model and improved extended Kalman

Ran XIONG(), Shunli WANG(), Chunmei YU, Lili XIA   

  1. School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, Sichuan, China
  • Received:2020-12-07 Revised:2020-12-25 Online:2021-03-05 Published:2021-03-05

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摘要：

特种机器人由于其复杂的工作环境，要求使用精度高、追踪能力强的荷电状态（state of charge，SOC）估算方法对特种机器人锂离子电池进行实时状态监测及安全控制。SOC是电池管理系统中最重要的参数之一。由于特种机器人工作环境具有强烈的非线性特征，考虑到常用的安时积分法十分依赖初期SOC准确性且在估算后期会积累误差。因此，针对特种机器人的工作特性，以三元锂离子电池为研究对象，在Thevenin等效电路模型及多种工况实验的基础上，利用一种改进扩展卡尔曼滤波（improved extended Kalman filter，IEKF）算法在10、25以及35 ℃下对锂离子电池进行SOC估算。在MATLAB/Simulink中搭建仿真模型并结合多种工况数据进行性能分析。实验结果表明，利用IEKF算法估算三元锂离子电池的SOC值有较好的追踪与收敛效果，且收敛时间在80 s以内。在不同温度下，IEKF算法收敛后HPPC工况和BBDST工况的最大估算误差分别低于2.235%及3.004%，小于扩展卡尔曼滤波（extended Kalman filter，EKF）算法对应最大估算误差（9.067%和4.654%）。本研究验证了IEKF算法在估算锂离子电池SOC时具有较高的精度，为有效解决特种机器人锂离子电池SOC值无法精准估算的问题提供了实验依据。

关键词: 特种机器人, 锂离子电池, Thevenin等效电路模型, 荷电状态, 扩展卡尔曼滤波算法, 不同温度

Abstract:

Because of the complex working environment of special robots, a state of charge (SOC) estimation method with high precision and strong tracking ability is required for real-time state monitoring and safety control of lithium-ion batteries of special robots. SOC is one of the most important parameters in battery management systems. The working environment of special robots has strong nonlinear characteristics, considering that the commonly used ampere-hour integration method depends heavily on the accuracy of the initial SOC and accumulates errors in the later stages of estimation. Therefore, considering the working characteristics of special robots, a ternary lithium-ion battery is taken as the research object based on the Thevenin equivalent circuit model and experiments under various operating conditions. An improved extended Kalman filter (IEKF) algorithm is used to estimate the SOC of lithium-ion batteries at 10 ℃, 25 ℃, and 35 ℃. The simulation model is built in MATLAB/Simulink and combined with various working condition data for performance analysis. The experimental results show that using the IEKF algorithm to estimate the SOC value of ternary lithium-ion batteries has a good tracking and convergence effect, and the convergence time is within 80 s. At different temperatures, the maximum estimation errors of the HPPC and BBDST conditions are less than 2.235% and 3.004%, respectively, after convergence, which 9.067% and 4.654% less than those of the corresponding maximum estimation errors of the extended Kalman filter (EKF) algorithm. This study verifies that the IEKF algorithm has high accuracy in estimating the SOC of lithium-ion batteries, and it provides an experimental basis for effectively resolving the inaccurate estimation of the SOC values of lithium-ion batteries of special robots.

Key words: special robots, lithium-ion batteries, Thevenin equivalent circuit model, state of charge, improved extended Kalman filter algorithm, different temperatures

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