Structural optimization of serpentine channel water-cooled plate for lithium-ion battery modules based on multi-objective Bayesian optimization algorithm

Abstract

Maintaining the battery within its optimal operating temperature range while preventing thermal runaway is crucial. Serpentine channel water-cooled plate (SCWCP) has been widely employed in battery pack cooling. The challenge lies in enhancing the cooling efficiency of SCWCP while minimizing energy consumption. Due to the high efficiency and robustness of the multi-objective Bayesian optimization (MOBO), it is employed to systematically optimize the SCWCP for lithium batteries. The width, depth, and turning radius of the serpentine flow channels are optimized to minimize both the maximum battery module temperature (T_max) and the pumping power (PP) of the SCWCP. The MOBO process integrates structural parameter adjustments into Computational Fluid Dynamics (CFD) simulations through an automated iterative approach. Subsequently, a Pareto front is generated based on T_max and PP, and the K-means clustering algorithm identifies four design solutions with different performance orientations. Compared with the initial design, T_max of the optimal design decreases slightly, but with a reduction in PP of 71 %. Compared to other evolutionary algorithms, the MOBO algorithm exhibits superior computational efficiency in providing an optimal design solution set at a lower computational cost.