基于 GBDT 算法的锂电池剩余使用寿命预测

针对现有方法对锂电池剩余使用寿命(RUL)预测精度不高,模型训练时间较长的问题,提出一种基于梯度提升决策树 算法(GBDT)结合网格搜索法(GS)的预测模型。 首先,分析锂电池的充放电循环过程,确定电压、电流、温度为可用健康因子 (HI);其次,处理历史数据中的异常值,并均值化可用健康因子数据为特征输入;最后,通过 GBDT 算法建立锂电池剩余使用寿 命预测模型,并采用 GS 优化模型参数。 基于 NASA 锂电池容量衰减数据,实验结果表明,模型在 RMSE、MAE、MAPE 评价指标 上相对其他方法均提升了约 10 倍,并且可将锂电池剩余使用寿命预测误差率控制在 0. 05 以内,训练时间缩减至 4. 5 s。

Remaining useful lifetime prediction for lithium battery based on GBDT algorithm

To solve the problems of the existing remaining useful lifetime prediction methods for lithium battery with low prediction accuracy and long training time, a prediction model based on GBDT algorithm with grid search method is proposed. Firstly, analyze the charge-discharge cycle of lithium battery and select voltage, current and temperature as useful health index. Secondly, process the outliers of historical data and average useful health index data as feature input. Finally, establish the remaining useful lifetime prediction model for lithium battery by GBDT algorithm and optimize parameters by grid search method. Based on the capacity decay data of NASA lithium battery, the results show that the prediction model is superior to other methods about tenfold in RMSE, MAE, MAPE. The remaining useful lifetime prediction error is within 0. 05 and the training time reduces to 4. 5 s.