Model migration neural network for predicting battery aging trajectories

Accurate prediction of batteries’ future degradation is a key solution to relief users’ anxiety on battery lifespan and electric vehicle’s driving range. Technical challenges arise from the highly nonlinear dynamics of battery aging. In this paper, a feed-forward migration neural network 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 dataset. 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 neural network, and easily trained via the gradient correlation algorithm. The proposed migration neural network is experimentally verified with four different commercial batteries. The predicted RMSEs are all lower than 2.5% when using only the first 30% of aging trajectories for neural network training. In addition, illustrative results demonstrate that a small size feed-forward neural network (down to 1-5-5-1) is sufficient for battery aging trajectory prediction.