Astudillo Heras, GaloGaloAstudillo HerasBeiranvand, HamzehHamzehBeiranvandCecati, FedericoFedericoCecatiWürsig, AndreasAndreasWürsigLiserre, MarcoMarcoLiserre2025-07-242025-07-242025https://publica.fraunhofer.de/handle/publica/48992510.1109/JESTPE.2025.35814352-s2.0-105009415167The lifetime of a battery pack consisting of many cells in series is determined by the weakest cell. Heterogeneous degradation of the battery pack, as a result of inherent discrepancy between cells, accelerates the aging of the weakest cell, which may not be overcome by conventional passive and active balancing techniques. Therefore, this paper presents a methodology for charging series-reconfigurable Lithium-ion battery packs. To mitigate the negative effects of unregulated temperature increases, thermal gradients, state-of-charge imbalances, and other cell-to-cell variations, we formulate and evaluate a charging strategy that addresses temperature and charge homogenization and temperature tracking. Model Predictive Control (MPC) was used to generate an optimal current trajectory to reach the desired temperature and voltage goals; while two complementary algorithms were developed to homogenize the charge and temperature in the reconfigurable battery. Comparative simulations were performed on the proposed reconfigurable battery, showing its superior performance when dealing with battery cells with different aging conditions. Moreover, the solver time does not increase with the number of cells, ensuring the desired scalability for larger battery systems. Finally, experiments were conducted for three different case studies to verify the proposed MPC-based charge and temperature homogenization and control.enfalseelectro-thermal battery modelnonlinear Model Predictive Controlreconfigurable battery packjournal article