Numerical Analysis and Multi-Objective Optimization of Tesla Valve Cold Plates for Lithium-Ion Battery Thermal Management

Lithium-ion batteries are increasingly widely used in electric vehicles. However, excessively high operating temperatures can impair their performance and even trigger thermal runaway. This study conducts a numerical simulation investigation into the thermal dissipation performance of Tesla valve cold plate. Through multi-parameter collaborative optimization design, the heat exchange efficiency of the battery thermal management system is enhanced. First, based on orthogonal experiments, the influence of shunt angle (30° to 50°), number of unit pairs (3 to 7 pairs), channel asymmetry ratio (0 to 0.5), and branch channel width (2 to 4 mm) on the maximum temperature difference and pressure drop of the cold plate was analyzed. It was revealed that the number of unit pairs and the asymmetry ratio are the key influencing factors. Subsequently, optimal Latin hypercube sampling and Kriging surrogate models were employed to establish response relationships between the parameters and performance. Combined with the NSGA-II genetic algorithm, multi-objective optimization was performed with the objectives of minimizing temperature difference and pressure drop, leading to the identification of the Pareto frontiers. The optimized structural parameters are a shunt angle of 30°, 7 unit pairs, an asymmetry ratio of 0, and a branch channel width of 4 mm. Simulation results demonstrate that for this optimized configuration, the maximum temperature difference is reduced by 9.13% and the average temperature difference is reduced by 15.03%, while the pressure drop experiences only a marginal increase of 0.36%. This study confirms that the Tesla valve cold plate, through multi-parameter collaborative optimization, can significantly enhance thermal uniformity, providing a theoretical foundation and practical methodology for the design of electric vehicle battery thermal management systems.