Model Migration Neural Network for Predicting Battery Aging Trajectories

An accurate prediction of batteries' future degradation is a key solution to relief the users' anxiety on battery lifespan and electric vehicles' driving range. Technical challenges arise from the highly nonlinear dynamics of battery aging. In this article, a feed-forward migration neural network (NN) is proposed to predict the batteries' aging trajectories. Specifically, a base model that describes the capacity decay over time is first established from the existed battery aging data set. This base model is then transformed by an input-output slope and bias correction (SBC) method structure to capture the degradation of target cell. To enhance the model's nonlinear transfer capability, the SBC model is further integrated into a four-layer NN and easily trained via the gradient correlation algorithm. The proposed migration NN is experimentally verified with four different commercial batteries. The predicted root-mean-square errors (RMSEs) are all lower than 2.5% when using only the first 30% of aging trajectories for NN training. In addition, the illustrative results demonstrate that a small-sized feed-forward NN (down to 1-5-5-1) is sufficient for battery aging trajectory prediction.