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

作　　者：马玲琦 邹海荣[1] 李兴家 MA Lingqi;ZOU Hairong;LI Xingjia(School of Electrical Engineering,Shanghai Dianji University,Shanghai 201306,China;Shanghai Liangxin Electric Co.,Ltd.,Shanghai 200137,China)

机构地区：[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

出　　处：《电器与能效管理技术》2025年第3期31-37,75,共8页Electrical & Energy Management Technology

摘　　要：针对电池健康状况(SOH)的预测,提出一种集成分类提升(CatBoost)算法和自回归差动平均(ARIMA)模型的SOH估算方法。通过时间序列分析提取特征并获取ARIMA模型残差,将其作为额外特征经CatBoost算法处理,构建增强数据集进行预测。实验结果表明,所提方法显著提升了预测性能,最佳均方根误差(RMSE)达到0.0046,平均绝对误差(MAE)达到0.0034,拟合度(R^(2))达到0.9994,相比仅使用初始数据的模型具有更高的准确性和稳定性。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 0.0046,the mean absolute error(MAE)reaches 0.0034,and the coefficient of determination(R^(2))reaches 0.9994.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 

分 类 号：TM910[电气工程—电力电子与电力传动]