Representation learning accelerates the development of models for Li-ion battery health diagnostics and prognostics

In recent years, the evolution of Li-ion battery material components, cell architectures, and application scenarios has posed significant challenges for the rapid adaptation of battery management systems (BMS). Accurate health diagnostics and prognostics are fundamental to reliable battery operation. However, traditional approaches based on empirical equations, physical models, or handcrafted features often suffer from limited generalization, heavy data demands, and time-consuming development. Representation learning, a major advancement in deep learning, is emerging as a powerful tool to accelerate battery health modeling. Under novel chemistries and unseen operating conditions, it mitigates data scarcity through generative learning and enables rapid model adaptation via transfer learning, which was overlooked in earlier reviews. We systematically summarize representation learning architectures tailored for battery data, highlight their applications in data augmentation and cross-domain transfer, and further identify key challenges and future opportunities in data privacy, multimodal information integration, and model interpretability. Overall, representation learning establishes a solid foundation for the efficient development of next-generation intelligent BMS.