Predicting battery aging trajectory via a migrated aging model and Bayesian Monte Carlo method
Paper i proceeding, 2019
Thanks to the fast development in battery technologies, the lifespan of the lithium-ion batteries increases to more than 3000 cycles. This brings new challenges to reliability related researches because the experimental time becomes overly long. In response, a migrated battery aging model is proposed to predict the battery aging trajectory. The normal-speed aging model is established based on the accelerate aging model through a migration process, whose migration factors are determined through the Bayesian Monte Carlo method and the stratified resampling technique. Experimental results show that the root-mean-square-error of the predicted aging trajectory is limited within 1% when using only 25% of the cyclic aging data for training. The proposed method is suitable for both offline prediction of battery lifespan and online prediction of the remaining useful life.
Aging trajectory prediction
Bayesian Monte Carlo