Study on Particle Filtering Prediction of Remaining Life of Automotive Lithium-Ion Power Battery
Due to the obvious uncertainty of automotive lithium-ion power battery,this paper designs a residual life(RUL)prediction method of lithium-ion power battery based on standard particle filtering particle filtering.In order to obtain more accurate RUL prediction results,the particle filtering method is selected to establish the importance density function and complete the particle filtering optimisation effect.The research results show that the RUL prediction result obtains 33 cycles of prediction error,which is 8.56％,and the prediction error with particle filtering is a total of 26 cycles,which is 6.81％.Particle filtering has better prediction performance relative to particle swarm,and increasing the sample size of training data helps battery RUL to achieve lower prediction error and higher prediction accuracy.The study is easy to implement and has good value for promotion.
power batteryparticle filtering algorithmremaining lifecapacity decay model
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