储能科学与技术 ›› 2020, Vol. 9 ›› Issue (6): 1948-1953.doi: 10.19799/j.cnki.2095-4239.2020.0165

• 储能测试与评价 • 上一篇    下一篇

基于分布估计算法LSSVM的锂电池SOC预测

成文晶(), 潘庭龙()   

  1. 江南大学物联网工程学院，江苏 无锡 214122
  • 收稿日期:2020-05-02 修回日期:2020-05-12 出版日期:2020-11-05 发布日期:2020-10-28
  • 通讯作者: 潘庭龙 E-mail:2091982189@qq.com;tlpan@jiangnan.edu.cn
  • 作者简介:成文晶（1996—），女，硕士研究生，研究方向为梯次利用动力电池均衡控制策略，E-mail：2091982189@qq.com；
  • 基金资助:
    国家自然科学基金项目(61672266)

Prediction for SOC of lithium-ion batteries by estimating the distribution algorithm with LSSVM

Wenjing CHENG(), Tinglong PAN()   

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, Jiangsu, China
  • Received:2020-05-02 Revised:2020-05-12 Online:2020-11-05 Published:2020-10-28
  • Contact: Tinglong PAN E-mail:2091982189@qq.com;tlpan@jiangnan.edu.cn

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摘要：

基于最小二乘支持向量机（LSSVM）的锂电池荷电状态（SOC）预测模型收敛速度快且得到的是全局最优解，具有较强的预测能力，然而最小二乘支持向量机的参数选择会对预测结果产生较大的影响，因此提出了基于分布估计算法（EDA）最小二乘支持向量机的锂电池SOC预测方法。以锂电池工作电压、电流以及温度为输入量，电池SOC为输出量使用LSSVM建立非线性系统模型，并利用分布估计算法对模型正则化参数λ和径向基核宽度μ进行优化，从而得到最优模型。仿真结果表明，与常规的锂电池SOC预测模型相比，本文提出的EDA-LSSVM方法具有较高的SOC预测精度。

关键词: 锂电池, 荷电状态预测, 分布估计算法, 最小二乘支持向量机

Abstract:

The prediction of the state of charge (SOC) of lithium-ion batteries by the least squares support vector machine (LSSVM) shows a faster convergence speed and gives an extraordinary method for the global optimal solution. The prediction ability is enhanced more than ever before. However, the parameter selection of the LSSVM will greatly affect the prediction result. A prediction method for the SOC of lithium-ion batteries by estimating the distribution algorithm (EDA) with an LSSVM is proposed herein. The operating voltage, current, and temperature of the lithium-ion batteries are used as the input quantities. The SOC of the batteries is used as the output quantity. Moreover, a non-linear system model is built using the LSSVM. The EDA is designed to optimize the regularization parameter and the radial basis kernel width of the model. We then obtain the optimal model. The simulation results show that compared with the conventional prediction model for the SOC of lithium-ion batteries, the proposed EDA-LSSVM method has a higher prediction accuracy for the SOC.

Key words: lithium-ion battery, prediction for state of charge, estimation of distribution algorithm, least squares support vector machine

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