Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (6): 1273-1280.doi: 10.13229/j.cnki.jdxbgxb20210020

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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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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

CLC Number: 

  • TM91

Fig.1

Characteristic curves of chargingvoltage variation"

Fig.2

Process of taking point of constant current charging voltage curvre using D-P algorithm"

Fig.3

Comparison results of D-P algorithms with different thresholds"

Fig.4

Result of the sampling method"

Fig.5

Result of D-P algorithm processingbased on the first feature point"

Fig.6

Comparison results of three different algorithms"

Fig.7

Prediction results of the XGBoost algorithmfor batteries 1， 2 and 3"

Table 1

Comparison of three different algorithms"

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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