吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (6): 1273-1280.doi: 10.13229/j.cnki.jdxbgxb20210020

• 车辆工程·机械工程 • 上一篇    

基于曲线压缩和极限梯度提升算法的锂离子电池健康状态估计

刘兴涛1,2(),刘晓剑1,武骥1,2(),何耀3,刘新天3   

  1. 1.合肥工业大学 汽车与交通工程学院，合肥 230009
    2.合肥工业大学 安徽省智慧交通车路协同工程研究中心，合肥 230009
    3.合肥工业大学 汽车工程技术研究院，合肥 230009
  • 收稿日期:2021-01-11 出版日期:2022-06-01 发布日期:2022-06-02
  • 通讯作者: 武骥 E-mail:xingtao.liu@hfut.edu.cn;wu.ji@hfut.edu.cn
  • 作者简介:刘兴涛（1985-），男，副研究员，博士. 研究方向：锂离子电池建模与状态估计. E-mail：xingtao.liu@hfut.edu.cn
  • 基金资助:
    国家自然科学基金项目(61803138);安徽省自然科学基金项目(2008085QF301);安徽省科协2020年青年科技人才托举计划项目(RCTJ202008);安徽高校协同创新项目(GXXT-2019-002)

State of health estimation method for lithium⁃ion battery based on curve compression and extreme gradient boosting

Xing-tao LIU1,2(),Xiao-jian LIU1,Ji WU1,2(),Yao HE3,Xin-tian LIU3   

  1. 1.School of Automotive and Transportation Engineering，Hefei University of Technology，Hefei 230009，China
    2.Engineering Research Center for Intelligent Transportation and Cooperative Vehicle-Infrastructure of Anhui Province，Hefei University of Technology，Hefei 230009，China
    3.Automotive Research Institute，Hefei University of Technology，Hefei 230009，China
  • Received:2021-01-11 Online:2022-06-01 Published:2022-06-02
  • Contact: Ji WU E-mail:xingtao.liu@hfut.edu.cn;wu.ji@hfut.edu.cn

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摘要：

为了精确地估计锂离子电池的健康状态（SOH），提出了一种基于道格拉斯-普克算法和极限梯度提升（XGBoost）算法的方法。首先对每组电压数据进行预处理，利用道格拉斯-普克算法对每次循环的恒流充电电压曲线进行矢量压缩；在此数据的基础上，运用XGBoost算法建立锂离子电池退化过程模型并估计SOH。对比实验结果表明，所提方法可有效压缩电池电压曲线、降低网络训练数据维度，同时具有较高的预测精度和较快的运行速度，可实现锂离子电池SOH的快速准确估计。

关键词: 车辆工程, 锂离子电池, 健康状态估计, 道格拉斯-普克算法, XGBoost算法

Abstract:

In order to accurately estimate the State of Health （SOH） of the lithium-ion battery， a method based on Douglas-Puck algorithm and Extreme Gradient Boosting （XGBoost） algorithm is proposed. Firstly， each set of voltage data is preprocessed， and the Douglas-Puck algorithm is used to vectorize the constant current charging voltage curve of each cycle. On the basis of this data， the XGBoost algorithm is applied to establish a lithium-ion battery degradation model and estimate the SOH. The results of comparative experiments show that the proposed method can effectively compress the battery voltage curve and reduce the dimension of network training data. At the same time， the developed method also has a higher prediction accuracy and faster running speed， and can realize the fast and accurate estimation of the lithium-ion battery SOH.

Key words: automotive engineering, Lithium-ion battery, State of health estimation, Douglas-Pucker algorithm, extreme gradient boosting algorithm

中图分类号: 

  • TM91

图1

充电电压变化特性曲线"

图2

利用D-P算法对恒流充电电压曲线取点流程"

图3

不同阈值的D-P算法对比结果"

图4

采用抽值法处理的结果"

图5

基于第一特征点的D-P算法处理的结果"

图6

三种算法对比结果"

图7

1、2、3号电池的XGBoost算法的预测结果"

表1

三种不同的算法对比"

算法电池编号最大误差%均方根误差%确定系数%平均运行时间/s
XGBoost1号3.181.1098.600.2180
2号3.881.3099.15
3号3.701.2099.20
GBDT1号5.911.2498.331.6585
2号5.051.3998.98
3号5.231.3798.89
随机森林1号4.311.1298.601.6730
2号5.461.3799.05
3号4.781.2899.10
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