AI for Smart Battery State Estimation: A Perspective

In battery management systems (BMSs), state estimation stands as a pivotal element yet encounters significant challenges. These include the poor observability inherent in fixed configuration battery packs, limited generalizability of pre-trained machine learning models, and the deficiency of higher-level management strategies. To address these obstacles, we propose a forward-looking perspective on the future BMS state estimation, introducing the concept of a "Smart Battery". Battery digital twin enables synthetic data generation and physics-informed AI development. This approach integrates battery digital twin to generate synthetic data, which is then used for data augmentation and physics-informed AI development. Additionally, it incorporates advanced data cleaning and selection techniques to preserve essential information and augment data management efficiency. Leveraging cutting-edge AI algorithms, such as transfer learning and meta-learning, aims to mitigate issues of model generalization and feature invalidation under various operating conditions. Furthermore, this paper emphasizes the importance of multi-task learning for batteries, enabling comprehensive health assessments. By fully utilizing both short-term estimations and long-term predictions, the proposed framework contributes to the advancement of higher-level health and thermal management designs. We aim to furnish pioneering insights for state estimation in future intelligent BMSs.