Towards a smarter battery management system: A critical review on deep learning-based state of charge estimation of lithium-ion batteries

An accurate state of charge (SOC) estimation of lithium-ion batteries underpins a safe and optimized operation of the system. In recent years, deep learning-based SOC estimation has made significant progress. In order to provide researchers in this rapidly advancing field a comprehensive overview of the state of the art, this paper carries out a structured review on deep learning-based SOC estimation of lithium-ion batteries. A detailed taxonomy of SOC estimation approaches and popularly used public datasets is provided as an introduction to the technical background. A systematic walk-through of the existing deep learning-based SOC estimation approaches, together with the frequently applied optimization strategies, is presented, where we also appeal for a standardized evaluation protocol in this field. As highlight, the current trends and emerging perspectives are pointed out and discussed in detail, including physics-informed neural networks (PINNs), multi-task learning (MTL), few-shot learning, and continual learning. We believe this work could not only provide the researchers and practitioners new to this topic with a clear and detailed manual to start with, but also point out the emerging perspectives for further cutting-edge studies towards a smarter battery management system.