End-to-end capacity estimation of Lithium-ion batteries with an enhanced long short-term memory network considering domain adaptation

Abstract

Real-time capacity estimation of lithium-ion batteries is crucial but challenging in battery management systems (BMSs). Due to the complexity of battery degradation mechanism, data-driven methods are prevalent recently. Despite achieved promising results, most of developed approaches still assume that the degradation trajectories of batteries are same between the training and testing domains. However, the inconsistency of batteries and the randomness during degradation process lead to the distribution discrepancy, which further affects the estimation precision of trained model. To overcome this challenge, a novel deep learning framework assisted with domain adaptation is proposed in this paper. First, a deep long short-term memory (LSTM) network is designed to capture the nonlinear mapping from monitored data, specially, terminal voltage and current, to battery capacity. Then, a domain adaptation layer is integrated to the LSTM with the purpose of degradation feature alignment between the source and target batteries. The proposed method is capable of establishing the general capacity estimation model for the discrepant batteries by only using a few cycling data of target batteries. Extensive experiments on two battery datasets from NASA Ames Prognostics Data Repository demonstrate that the proposed method outperforms the state-of-the-art data-driven methods in terms of estimation precision and robustness. • A battery capacity estimation framework integrating domain adaptation is presented. • The LSTM is enhanced by MMD minimization for battery capacity estimation. • Battery capacity is accurately and quickly predicted with fewer training data. • The proposed method is superior to other popular data-driven methods.