Multi-objective optimization on thermal performance and energy efficiency for battery module using gradient distributed Tesla cold plate

To address energy-efficient cooling of battery module at a high discharge rate, this work presents a novel gradient distributed Tesla cold plate. Multi-objective optimization is performed to achieve optimal thermal performance and energy efficiency using coupled battery-cold plate simulations, kriging surrogate model and second non-dominated sorting genetic algorithm. Both cold plate structure and operating parameters are considered with objectives of maximum temperature, temperature uniformity and pressure drop. The results demonstrate that the mass flow rate poses a most significance on hydro-thermal performance, followed by channel depth and Tesla valve distance. A conflicting demand for the mass flow rate and channel depth is observed to achieve best hydro-thermal performance. In accordance with Pareto frontier solution, the improvement of pressure drop is most significant with a maximum reduction of 75.7% compared to base case. A moderate mass flow rate with increasing channel depth is recommended as the optimal strategy, yielding moderate liquid convection percentage (51.8%), cooling efficiency (233) and the highest Nu (6.7). Moreover, present gradient distribution of fractal inlet and Tesla unit contributes to high heat transfer coefficient zones along flow direction, achieving favorable temperature uniformity with maximum temperature difference of module middle section lower than 4 °C.