摘要
Abstract
Given the challenges in applying the extreme learning machine(ELM)to predict the remaining useful life(RUL)of lithium-ion batteries,such as unstable predictions and low accuracy,this paper proposes a Cubic sine-cosine firefly-enhanced sparrow search algorithm-extreme learning machine(CSFSSA-ELM)prediction algorithm.This method is based on the ELM optimized by an improved sparrow search algorithm(CSFSSA),incorporating three enhancement strategies:initializing the sparrow population with Cubic mapping,updating discoverer positions with the sine-cosine algorithm,and refining sparrow positions with a firefly disturbance strategy.Firstly,five indirect health indicators(HI)were extracted from the lithium-ion battery datasets for health status evaluation:the constant voltage charging time(HI1),the constant voltage discharging time(HI2),the lowest discharge voltage time(HI3),the highest discharge temperature time(HI4),and the average discharge voltage(HI5).Then,the CSFSSA-ELM model was tested using publicly available datasets from NASA's research center and CALCE,demonstrating the effectiveness of the proposed algorithm.Finally,prediction results on these datasets were compared among the original ELM,sparrow search algorithm-ELM(SSA-ELM),and Cubic-enhanced sparrow search algorithm-ELM(CSSA-ELM)models.Experimental results showed that the proposed CSFSSA-ELM model improved prediction accuracy compared to the ELM,SSA-ELM,and CSSA-ELM models,while reducing prediction errors with RMSE below 0.03,MAE below 0.02,and R2 above 0.98 for all predictions,confirming the effectiveness of the proposed algorithm.关键词
锂离子电池/极限学习机/麻雀算法/Cubic映射/剩余使用寿命Key words
lithium-ion battery/extreme learning machine/sparrow algorithm/Cubic mapping/remaining useful life(RUL)分类
动力与电气工程
