基于多任务集成学习的储能电池剩余使用寿命预测OA北大核心CSTPCD

Driven by the goal of achieving carbon peak and neutrality,electric vehicles are crucial in the transformation of transportation energy.Thus,the accurate prediction of the remaining useful life(RUL)can be useful in periodic maintenance and reduce the risk of accidents.Therefore,this paper proposes a multi-task ensemble learning-based model for accurately predicting the RUL of lithium-ion batteries under driving conditions.First,an incremental capacity-differential voltage curve is used to quantify the loss of conductivity,active material,and lithium ions.Electrochemical impedance spectroscopy is used to calculate the ohmic,charge transfer,solid electrolyte,and Warburg impedances.Second,based on multi-task learning,the inter-feature correlation is analyzed to ensure full utilization of the features and reduce the experimental cost.Subsequently,based on the light gradient boosting machine improved by adaptive robust loss,an RUL prediction model is constructed,and it improves the prediction accuracy.Experimental data under driving conditions(vibration conditions:reference,X-axis,Y-axis,and Z-axis)were used to verify the effectiveness of the proposed model.The results show that the proposed prediction model can achieve a mean absolute error of less than 1.4%,a mean absolute percentage error of less than 0.06%,and a root mean square error of less than 1.20%.The proposed prediction model can improve RUL prediction accuracy and ensure stable and safe operation of the battery.