Separating Multiscale Battery Dynamics and Predicting Multi-Step Ahead Voltage Simultaneously Through a Data-Driven Approach

Abstract

Accurate prediction of battery performance under various ageing conditions is necessary for reliable and stable battery operations. Due to complex battery degradation mecha-nisms, estimating the accurate ageing level and ageing-dependent battery dynamics is difficult. This work presents a health-aware battery model that is capable of separating fast dynamics from slowly varying states of degradation and state of charge (SOC). The method is based on a sequence to sequence learning-based encoder-decoder model, where the encoder infers the slowly varying states as the latent space variables in an unsupervised way, and the decoder provides health-aware multi-step ahead prediction conditioned on slowly varying states from the encoder. The proposed approach is verified on a Lithium-ion battery ageing dataset based on real driving profiles of electric vehicles.