Advances in Battery Management Storage for Electric Vehicles: When Models Meet Data

Dear Colleagues,

Electric vehicles are emerging as the backbone of the sustainable development of transportation electrification. Their performance, safety, and reliability rely heavily on the energy storage system and battery management control strategies. Inappropriate battery operations may cause premature failures and even catastrophic hazards. In recent decades, model-driven battery management strategies have gained considerable attention from various academic and industrial communities due to their closed-loop design and high robustness. Meanwhile, data-driven methods are also at the forefront of applications for battery modeling, estimation, and optimization. With the rapidly increasing uptake of electric vehicles with high degrees of connectivity, there has been growing interest in fusing model-driven and data-driven approaches into hybrid models to improve the system-level performance in terms of long lifetime, safety, and high reliability. However, the fusion of model-driven and data-driven strategies is still a challenge due to the complexity of battery mechanisms and the increasing volume and diversity of battery data.

This Special Issue, therefore, seeks to inspire ideas related to all aspects of recent advances in model-driven and data-driven battery management technologies, and the ideas on how to fuse model-driven and data-driven frameworks into hybrid models that combine the best aspects of both. Prospective authors are invited to submit original works for review and publication in this Special Issue. Both high-quality original research and review articles are welcome. Potential topics include, but are not limited to, the following:

• Modeling, estimation, control, and optimization for lithium-ion batteries;
• Battery health/aging modeling, diagnosis, and prognostics;
• Optimal, fast, health-aware charging, balancing control, etc.;
• Failure detection and fault tolerance control in battery management;
• Applications of machine learning and artificial intelligence in battery management.

Dr. Guangzhong Dong
Dr. Jincheng Yu
Dr. Ji Wu
Guest Editors

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• modeling, estimation, control, and optimization for lithium-ion batteries
• battery health/aging modeling, diagnosis, and prognostics
• optimal, fast, health-aware charging, balancing control
• failure detection and fault tolerance control in battery management
• applications of machine learning and artificial intelligence in battery management