储能科学与技术 ›› 2020, Vol. 9 ›› Issue (1): 117-123.doi: 10.12028/j.issn.2095-4239.2019.0127

• 储能系统与工程 • 上一篇    下一篇

基于IFA-EKF的锂电池SOC估算

张远进1,2(), 吴华伟1,2(), 叶从进1,2   

  1. 1. 湖北文理学院纯电动汽车动力系统设计与测试湖北省重点实验室
    2. 湖北文理学院汽车与交通工程学院，湖北 襄阳 441053
  • 收稿日期:2019-06-09 修回日期:2019-07-01 出版日期:2020-01-05 发布日期:2020-01-10
  • 作者简介:张远进（1992—）男，硕士，主要研究方向为混合动力汽车能量管理，E-mail：394296412@qq.com；联系人：吴华伟，副教授，主要研究方向为新能源汽车电驱控制及故障诊断，E-mail：9438043@qq.com。|张远进（1992—）男，硕士，主要研究方向为混合动力汽车能量管理，E-mail：394296412@qq.com；联系人：吴华伟，副教授，主要研究方向为新能源汽车电驱控制及故障诊断，E-mail：9438043@qq.com。
  • 基金资助:
    湖北省技术创新专项重大项目(2017AAA133);“机电汽车”湖北省优势特色学科群开放基金(XKQ2019010);中央引导地方科技发展财政专项（鄂财政2017[80]号文）

Estimation of SOC of lithium batteries based on IFA-EKF

ZHANG Yuanjin1,2(), WU Huawei1,2(), YE Congjin1,2   

  1. 1. Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle
    2. Hubei University of Arts and Science, School of Automotive and Traffic Engineering, Xiangyang 441053, Hubei, China
  • Received:2019-06-09 Revised:2019-07-01 Online:2020-01-05 Published:2020-01-10

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摘要：

以研究电动汽车锂电池荷电状态(SOC)估算为背景，针对EKF算法中的状态误差协方差矩阵和测量噪声协方差矩阵难以取得最佳值的问题。建立二阶等效电池模型，结合脉冲功率特性测试实验，对电池模型进行有效地辨识。提出了一种基于改进的萤火虫优化扩展卡尔曼滤波(IFA-EKF)算法的电池SOC估算方法。基于动态工况和静态工况下的仿真实验，结果表明，IFA-EKF算法比EKF算法具有更精确的估计效果和更小的误差。

关键词: 锂电池, 荷电状态, 萤火虫算法, 扩展卡尔曼滤波

Abstract:

As the key technology associated with the battery management systems of electric vehicles, the state of charge (SOC) of lithium-ion batteries describes the residual capacity and indicates the remaining mileage of electric vehicles. An extended Kalman filter (EKF), which is optimized by the improved Firefly algorithm, is proposed to research the estimation of the SOC of lithium-ion batteries for electric vehicles. The state-space representation of the battery model is estimated based on the second-order resistor-capacitor (RC) equivalent circuit model, which uses a pulse power characteristic test experiment to rapidly estimate the model parameters. Subsequently, the Firefly algorithm is applied to optimize the covariance of the system noise matrix and measurement matrix in the EKF to improve the SOC estimation accuracy. After performing the simulation experiments under dynamic and static conditions, the results denote that an algorithm for the estimation of SOC based on IFA–EKF results in a lower absolute maximum error and average absolute error when compared with those obtained via the EKF algorithm. Furthermore, the proposed algorithm offers improved accuracy and practicality.

Key words: lithium battery, charged state, firefly algorithm, extended Kalman filter

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