Research on RUL Predictive Optimization Method Based on ELM Lithium-Ion Battery
In view of the inaccurate prediction effect of the traditional Extreme Learning Machine(ELM)algorithm on the Remaining Useful Life(RUL)of lithium-ion battery,this paper proposes to investigate the influence of the basic data import value of the number of cycles on the prediction result.Through integration adjustment,the frequency of RUL estimation is reduced in the early stage,and the frequency of algorithm integration and RUL estimation is increased in the later stage,so as to further improve the accuracy of RUL prediction of lithium-ion batteries.The results show that this method has the advantages of short test time and small error,and can provide a more rapid and low-cost testing scheme for the remaining service life or cycle life of lithium-ion battery.
extreme learning machine ELMremaining useful life RULintegration adjustmentlithium-ion battery
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