Model Predictive Temperature Balancing Control Based on a Generalized Thermal Network Model for Lithium-Ion Battery Modules

Temperature variations within a battery module result from factors such as cell layout, uneven aging, and mismatched line resistances, leading to current imbalance and accelerated degradation. This paper presents a two-dimensional thermal network model (TNM) that extends single-cell thermal modeling to multi-cell configurations. The model accurately captures thermal coupling effects, enables real-time temperature prediction for each cell, and significantly reduces simulation time compared to finite element methods. Bayesian Optimization (BO) is employed to identify key model parameters, while model predictive temperature balancing control (MPTBC) is used to minimize steady-state temperature disparities. Case studies on a four-cell battery module validate the proposed model and demonstrate that reducing temperature differences can effectively mitigate capacity degradation.