电器与能效管理技术Issue(3)：31-37,75,8.DOI:10.16628/j.cnki.2095-8188.2025.03.005
结合CatBoost算法与ARIMA模型的电池健康状态预测与优化
Battery State of Health Prediction and Optimization by Combining CatBoost Algorithm and ARIMA Model
马玲琦 1邹海荣 1李兴家2
作者信息
- 1. 上海电机学院电气学院,上海 201306
- 2. 上海良信电器有限公司,上海 200137
- 折叠
摘要
Abstract
For the prediction of the battery state of health(SOH),an SOH estimation method that integrates the categorical boosting(CatBoost)algorithm and the autoregressive integrated moving average(ARIMA)models is proposed.The features are extracted through time series analysis,and the residuals of the ARIMA model are obtained.These residuals are used as additional features and processed by the CatBoost algorithm to construct an enhanced dataset for prediction.The experimental results demonstrate that the prediction performance is significantly enhanced by the proposed method.The optimal root mean square error(RMSE)reaches 0.004 6,the mean absolute error(MAE)reaches 0.003 4,and the coefficient of determination(R2)reaches 0.999 4.Compared with the model using only the initial data,it has higher accuracy and stability.关键词
电池健康状态/CatBoost算法/ARIMA模型/残差/增强数据集Key words
battery state of health/CatBoost algorithm/ARIMA model/residuals/enhanced dataset分类
动力与电气工程引用本文复制引用
马玲琦,邹海荣,李兴家..结合CatBoost算法与ARIMA模型的电池健康状态预测与优化[J].电器与能效管理技术,2025,(3):31-37,75,8.
