电力工程技术2026，Vol.45Issue(2)：51-59,9.DOI:10.12158/j.2096-3203.2026.02.006
基于在线顺序极限学习机模型的锂离子电池健康状况预测
Lithium-ion battery health prediction based on online sequential extreme learning machine model
摘要
Abstract
Aiming at the problems that the prediction accuracy of lithium battery health status is not high and the model cannot be updated online,a lithium-ion battery health prediction method based on the online sequential extreme learning machine(OSELM)model is proposed.The health factors with high correlation with battery capacity are obtained from the historical charge and discharge data of lithiumion batteries,and the OSELM model is optimized by goose algorithm(GOOSE-OSELM)to improve the prediction accuracy of the model.At the same time,the Cauchy inverse cumulative distribution operator and tangent flight operator are introduced to improve the goose algorithm to improve the global optimization ability and convergence speed of the model,and form an algorithm model with fast calculation speed and online update.The prediction results of the improved goose algorithm-optimized OSELM model(IGOOSE-OSELM)are compared with those of GOOSE-OSELM,OSELM,back propagation(BP)neural networks,and whale optimization algorithm-least squares support vector machine(WOA-LSSVM).The results show that the goodness of fit values of IGOOSE-OSELM in the three battery datasets are above 0.997,and the root mean square error is less than 0.004 5.Finally,the generalization ability of the model is verified by using the Oxford battery dataset and the NASA battery dataset.The results show that the IGOOSE-OSELM model can accurately predict the health status of the battery,and the model has high robustness and adaptability.关键词
电池健康状态/在线顺序极限学习机(OSELM)/鹅优化算法/收敛速度/泛化能力/鲁棒性Key words
battery state of health/online sequential extreme learning machine(OSELM)/goose optimization algorithm/convergence rate/generalization capability/robustness分类
信息技术与安全科学引用本文复制引用
郑启达,赵谡,汪彪,赵孝磊,王亚林,尹毅..基于在线顺序极限学习机模型的锂离子电池健康状况预测[J].电力工程技术,2026,45(2):51-59,9.基金项目
国家自然科学基金资助项目(52107019) （52107019）
