电力建设2024，Vol.45Issue(11)：25-33,9.DOI:10.12204/j.issn.1000-7229.2024.11.003
基于多任务集成学习的储能电池剩余使用寿命预测
Multi-Task Ensemble Learning-Based Prediction of Remaining Useful Life of Energy-Storage Batteries
摘要
Abstract
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.关键词
锂离子电池/剩余使用寿命/行驶工况/多任务学习/集成学习Key words
lithium-ion battery/remaining useful life/driving conditions/multi-task learning/ensemble learning分类
信息技术与安全科学引用本文复制引用
王伟亮,刘会巧,张天宇,阮鹏,徐劲,肖迁..基于多任务集成学习的储能电池剩余使用寿命预测[J].电力建设,2024,45(11):25-33,9.基金项目
国家自然科学基金项目(52107121) （52107121）
天津市自然科学基金多元投入重点项目(22JCZDJC00710) （22JCZDJC00710）
天津市企业科技特派员项目(23YDTPJC00090) （23YDTPJC00090）
天津大学自主创新基金项目(2024XHX-0028) This work is supported by the National Natural Science Foundation of China(No.52107121),Tianjin Natural Science Foundation Diversified Investment Key Program(No.22JCZDJC00710),Tianjin Enterprise Science and Technology Commissioner Project(No.23YDTPJC00090)and Seed Foundation of Tianjin University(No.2024XHX-0028). （2024XHX-0028）
