Multi-Objective Optimization Design and Experimental Investigation for a Prismatic Lithium-Ion Battery Integrated with a Multi-Stage Tesla Valve-Based Cold Plate

High current rate charging causes inevitable severe heat generation, thermal inconsistency, and even thermal runaway of lithium-ion batteries. Concerning this, a liquid cooling plate comprising a multi-stage Tesla valve (MSTV) configuration with high recognition in microfluidic applications was proposed to provide a safer temperature range for a prismatic-type lithium-ion battery. Meanwhile, a surrogate model with the objectives of the cooling performance and energy cost was constructed, and the impact of some influential design parameters was explored through the robustness analysis of the model. On this basis, the multi-objective optimization design of the neighborhood cultivation genetic algorithm (NCGA) was carried out. The obtained results demonstrated that if the MSTV channel was four channels, the valve-to-valve distance was 14.79 mm, and the thickness was 0.94 mm, the cold plate had the most effective cooling performance and a lower pumping power consumption. Finally, the optimization results were verified by a numerical simulation and an experiment, and the performance evaluation was compared with the traditional serpentine channel. The results reported that the optimized design reduced the maximum temperature and standard surface standard deviation of the cold plate by 26% and 35%, respectively. The additional pump power consumption was 17.3%. This research guides the design of battery thermal management systems to improve efficiency and energy costs, especially under the high current rate charging conditions of lithium-ion batteries.

Automated summary

A liquid cooling plate with a multi-stage Tesla valve (MSTV) configuration was proposed to provide a safer temperature range for a prismatic-type lithium-ion battery. A surrogate model was constructed to analyze the impact of design parameters, and the optimization design was carried out using the neighborhood cultivation genetic algorithm (NCGA). The optimized design reduced the maximum temperature and standard surface standard deviation of the cold plate by 26% and 35%, respectively, with an additional pump power consumption of 17.3%.