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Battery System Modeling and Management in Electric Vehicles
This special issue belongs to the section “Control and Systems Engineering“.
Special Issue Information
Dear Colleagues,
The rapid electrification of the transportation sector has placed lithium-ion batteries and emerging chemistries at the forefront of global energy research. Recent progress in this field has been defined by a shift from simple empirical monitoring to sophisticated, intelligent management systems capable of predicting battery behavior under dynamic and extreme conditions. However, a significant gap remains between high-fidelity laboratory models and real-time on-board applications. From my perspective, the next generation of Battery Management Systems (BMS) must integrate multiphysics modeling with machine learning and optimization to accurately estimate states and ensure safety. Addressing these complexities is not only a theoretical challenge but a critical requirement for enhancing the reliability, safety, and longevity of electric vehicles.
This Special Issue aims to collate cutting-edge research that addresses the challenges of scaling battery technology from cell-level physics to pack-level system integration. The scope of this Special Issue encompasses the full spectrum of battery modeling, including electrochemical and thermal models, as well as control strategies. The primary objective is to highlight novel methodologies that improve the accuracy of state estimation (SOC, SOH, SOP) and thermal management. By bridging the gap between theoretical modeling and practical implementation, this collection seeks to provide significant contributions to the safety and efficiency of next-generation electric powertrains.
Dr. Eunji Kwak
Guest Editor
Manuscript Submission Information
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Keywords
- advanced physics-based modeling for EV batteries
- AI and machine learning applications in Battery Management Systems (BMS)
- thermal management systems and thermal runaway prediction/prevention
- state estimation methodologies (State of Charge, State of Health, State of Power)
- diagnosis, prognosis, and safety monitoring in battery packs
- optimization of charging protocols and fast-charging strategies
- modeling and management of emerging battery chemistries (e.g., sodium-ion, solid-state) for EV applications
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