State of Health Estimation of Lithium-ion Battery Based on Feature Optimization and Random Forest Algorithm

doi:10.3901/JME.2024.12.335

Abstract

Abstract: State of health(SOH) estimation plays a significant role in the battery management system for electric vehicles. Accurate estimation of the SOH is conducive to extending the lifespan of lithium-ion batteries and ensuring vehicles' safe and reliable operation. Aiming at the problem that the previous data-driven methods cannot balance the accuracy of SOH estimation and the cost of model calculation, a solution based on feature optimization is proposed. Firstly, several features are extracted based on the partial charging voltage curve and incremental capacity curve. Moreover, the importance of each feature is calculated by the Gini coefficient in the random forest algorithm. Then, the optimal feature subset is selected by considering the estimation accuracy of the model and the feature number of the selected subset. Finally, the random forest algorithm is employed to establish the battery aging model and estimate the SOH. The results show that the mean absolute error and root mean square error of the proposed SOH estimation method are within 0.4 % and 0.5 %, respectively. Here, the most relevant feature set can be selected by the developed feature optimization strategy. Hence, combined with the random forest algorithm, it can achieve higher SOH estimation accuracy while reducing the calculation cost of the model.

Key words: lithium-ion battery, state of health estimation, feature selection, random forest algorithm

CLC Number:

TM912

WU Ji, FANG Leichao, LIU Xingtao, CHEN Jiajia, LIU Xiaojian, Lü Bang. State of Health Estimation of Lithium-ion Battery Based on Feature Optimization and Random Forest Algorithm[J]. Journal of Mechanical Engineering, 2024, 60(12): 335-343.

References

[1] WU J，ZHANG C，CHEN Z. An online method for lithium-ion battery remaining useful life estimation using importance sampling and networks[J]. Applied Energy，2016，173：134-140.
[2] 刘兴涛，李坤，武骥，等. 基于EKF-SVM算法的动力电池SOC估计[J]. 汽车工程，2020，42(11)：1522-1528，1544. LIU Xingtao，LI Kun，WU Ji，et al. State of charge estimation for traction battery based on EKF-SVM algorithm[J]. Automotive Engineering，2020，42(11)：1522-1528，1544.
[3] 姜久春，高洋，张彩萍，等. 电动汽车锂离子动力电池健康状态在线诊断方法[J]. 机械工程学报，2019，55(20)：60-72. JIANG Jiuchun，GAO Yang，ZHANG Caiping，et al. Online diagnostic method for health status of lithium-ion battery in electric vehicle[J]. Journal of Mechanical Engineering，2019，55(20)：60-72.
[4] 刘轶鑫，张頔，李雪，等. 基于SOC-OCV曲线特征的SOH估计方法研究[J]. 汽车工程，2019，41(10)：1158-1163. LIU Yixin，ZHANG Di，LI Xue，et al. A research on SOH estimation method based on SOC-OCV curve characteristics[J]. Automotive Engineering，2019，41(10)：1158-1163.
[5] 冯能莲，陈龙科，汤杰. 锂离子电池健康状态估计方法[J]. 北京工业大学学报，2016，42(11)：1750-1755. FENG Nenglian，CHEN Longke，TANG Jie. Methods for SOH estimation of Li-ion battery[J]. Journal of Beijing Polytechnic University，2016，42(11)：1750-1755.
[6] CHEN L，LÜ Z，LIN W，et al. A new state-of-health estimation method for lithium-ion batteries through the intrinsic relationship between ohmic internal resistance and capacity[J]. Measurement，2018，116：586-595.
[7] LI J，ADEWUYI K，LOTFI N，et al. A single particle model with chemical/mechanical degradation physics for lithium-ion battery State of Health (SOH) estimation[J]. Applied Energy，2018，212：1178-1190.
[8] HAN X，OUYANG M，LU L，et al. Simplification of physics-based electrochemical model for lithium-ion battery on electric vehicle. Part II：pseudo-two-dimensional model simplification and state of charge estimation[J]. Journal of Power Sources，2015，278：814-825.
[9] JOKAR A，RAJABOO B，DESILETS M，et al. Review of simplified pseudo-two-dimensional models of lithium-ion batteries[J]. Journal of Power Sources，2016，327：44-55.
[10] XU T，PENG Z，WU L. A novel data-method for predicting the circulating capacity of lithium-ion battery under random variable current[J]. Energy，2021，218：119530.
[11] WU J，WANG Y，ZHANG X，et al. A novel state of health estimation method of Li-ion battery using group method of data handling[J]. Journal of Power Sources，2016，327：457-464.
[12] 舒星，刘永刚，申江卫，等. 基于改进最小二乘支持向量机与Box-Cox变换的锂离子电池容量预测[J]. 机械工程学报，2021，57(14)：118-128. SHU Xing，LIU Yonggang，SHEN Jiangwei，et al. Capacity prediction for lithium-ion batteries based on improved least squares support vector machine and box-cox transformation[J]. Journal of Mechanical Engineering，2021，57(14)：118-128.
[13] 刘兴涛，刘晓剑，武骥，等. 基于曲线压缩和XGBoost算法的锂离子电池SOH估计[J]. 吉林大学学报，2022，52(6)：1273-1280. LIU Xingtao，LIU Xiaojian，WU Ji，et al. Curve compression and xgboost based state of health estimation method for lithium-ion battery[J]. Journal of Jilin University，2022，52(6)：1273-1280.
[14] ZHENG X，DENG X. State-of-health prediction for lithium-ion batteries with multiple gaussian process regression model[J]. IEEE Access，2019，7：150383-150394.
[15] LI X，WANG Z，ZHANG L，et al. State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis[J]. Journal of Power Sources，2019，410/411：106-114.
[16] LI X，YUAN G，WANG B. State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression[J]. Energy，2020，203：117852.
[17] HE W，WILLIARD N，OSTERMAN M，et al. Prognostics of lithium-ion batteries based on Dempster–Shafer theory and the Bayesian Monte Carlo method[J]. Journal of Power Sources，2011，196(23)：10314-10321.
[18] BERECIBAR M，GANDIAGA I，VILLARREAL I，et al. Critical review of state health estimation methods of Li-ion batteries for real applications[J]. Renewable and Sustainable Energy Reviews，2016，56：572-87.
[19] PAN W，CHEN Q，ZHU M，et al. A data-driven fuzzy information granulation approach for battery state of health forecasting[J]. Journal of Power Sources，2020，475：228716.
[20] 林甜甜，陈自强，刘健. 基于等幅度充电时间的锂离子电池健康状态估计[J]. 装备环境工程，2018，15(12)：65-69. LIN Tiantian，CHEN Ziqiang，LIU Jian. Health state estimation of lithium-ion battery based on equal time interval charging[J]. Equipment Environment Engineering，2018，15(12)：65-69.
[21] LI Y，ZOU G，BERECIBAR M，et al. Random forest regression for online capacity estimation of lithium-ion batteries[J]. Applied Energy，2018，232：197-210.
[22] 方德宇，楚潇，刘涛，等. 基于数据-模型驱动的锂离子电池健康状态估计[J]. 电气工程学报，2022，17(4)：20-31. FANG Deyu，CHU Xiao，LIU Tao，et al. Research on health assessment method of lithium-ion battery based on data-model hybrid drive[J]. Journal of Electrical Engineering，2022，17(4)：20-31.
[23] 杨淞元，田勇，田劲东. 基于iCEEMDAN和迁移学习的锂离子电池SOH估计[J]. 电气工程学报，2022，17(4)：2-10. YANG Songyuan，TIAN Yong，TIAN Jindong. State of health estimation of lithium-ion batteries based on iCEEMDAN and transfer learning[J]. Journal of Electrical Engineering，2022，17(4)：2-10.
[24] 党月懋，张雪纯，徐楚奕，等. 基于温度变化率曲线的锂离子电池健康状态评估算法[J]. 电气工程学报，2022，17(3)：58-65. DANG Yuemao，ZHANG Xuechun，XU Chuyi，et al. Lithium-ion battery state of health assessment algorithm based on DT curve[J]. Journal of Electrical Engineering，2022，17(3)：58-65.