The paper presents an optimal fast-charging control strategy at the cell level for reconfigurable series-parallel (S/P) Lithium-ion battery packs, ensuring electro-thermal safety, adaptability to the state of health (SoH), and optimization with respect to the time-of-use (ToU) electricity tariff.The core contribution lies in a multi-objective optimal control framework that integrates voltage, temperature, and aging constraints to predefined charging time by users while maintaining safety and longevity.The system employs an electro-thermal coupled model together with real-time state feedback (including SoC, SoH, SoT, polarization voltage, and internal resistance) to adaptively regulate charging current/voltage and determine the transition point between the constant-current (CC) and constant-voltage (CV) phases.The actively reconfigurable S/P structure enables dynamic current allocation to individual cells, eliminating the limitation of sharing a common charging current determined by the weakest cell in traditional series configurations.From a modeling perspective, the paper utilizes a coupled electro-thermal model combined with switching variables and a projection-type topological constraint to decouple "cell dynamics" (invariant across configurations) from "cell interconnection constraints" (dependent on S/P mode), thereby facilitating real-time optimization.The experimental setup is seen as a cell-level emulation platform for series-parallel reconfigurable charging instead of a complete hardware realization.Comprehensive simulation and experimental scenarios are designed to reflect practical operating conditions with variations in SoC, SoH, temperature, maximum allowable current, electricity pricing, and cost-function weighting factors.These scenarios validate the controller's adaptability, electro-thermal safety, and economic efficiency.The results demonstrate that the proposed strategy not only ensures safety under all operating conditions but also achieves high charging speed, uniform terminal SoC across cells, and significantly reduced energy losses-representing an important step toward real-world implementation in nextgeneration battery management systems (BMS).
Phuong et al. (Mon,) studied this question.