Enhancing Battery Thermal Management in Electric Vehicles through Reduced Order Modeling and Predictive Control for Quick Charging

In the realm of electric vehicles (EVs), effective battery thermal management is critical to avert thermal runaway, overheating, and extend the operational lifespan of batteries. The process of designing thermal management systems can be substantially expedited through the utilization of modeling and simulation techniques. However, the high-fidelity 3D computational fluid dynamics (CFD) simulations often demand significant computational resources to provide comprehensive results under varying conditions. In this paper, we develop a reduced order model (ROM) to capture the battery thermal dynamics employing a sub-space method. To construct this ROM, we use high-fidelity CFD simulations to generate step responses of battery temperature with respect to the heat generation and cooling power. These step responses are subsequently used as training data for the ROM. To minimize computational expenses while preserving accuracy, we determine the minimal dimensionality of the ROM through the analysis of the singular values of the oblique projection matrix. To assess the accuracy and reliability of the developed ROM, a comprehensive comparison is conducted between ROM results and both CFD solutions and experimental data, specifically in a quick charge scenario. The ROM exhibits good agreement with both CFD and experimental results. Furthermore, a novel predictive control strategy is developed to enhance battery thermal management by leveraging the ROM-derived predictive information for real-time adjustments to the cooling setpoint. The predictive control approach leads to a reduction in total charging time, achieving an improvement of up to 16.2% compared to a baseline case with a constant cooling setpoint. Furthermore, the developed predictive control strategy outperforms traditional feedback control systems that rely solely on current state information.