Optimization Investigation on Air Phase Change Material Based Battery Thermal Management System

Abstract

To ensure the normal operation of a battery pack, a battery thermal management system (BTMS) is required to control the temperature of batteries. Herein, the method using an artificial neural network (ANN) combined with a genetic algorithm (GA) is proposed to optimize the thermal performance of air phase change material (PCM) cooling based BTMS. The ANN is applied to describe the relationship between BTMS parameters (inlet air velocity, inlet air temperature, PCM thickness, battery unit spacing, and discharge rate) and battery pack thermal characteristics. The results show that the PCM thickness and battery unit spacing have little effect on the battery temperature. Then, the optimal parameter combinations of BTMS are solved by GA with the goal of minimizing the maximum temperature. The maximum relative error between simulation and prediction is 0.484 °C, which is only 1.3835% of the simulated value. The optimal parameter combinations help to slow down the temperature rise of the battery pack and delay the phase transition of PCM. The results indicate that the developed model can accurately describe the relationship between the BTMS parameters and battery temperature, which provides a time‐saving and efficient method for the optimal design of BTMS.

Review on the Lithium-Ion Battery Thermal Management System Based on Composite Phase Change Materials: Progress and Outlook

“…Effective thermal management can be achieved without increasing power consumption by implementing structural modifications. 177 Peng et al 178 investigated the effect of the number and location of inlets and outlets on the thermal management of battery packs. The results showed that the shortening of the air flow distance can promote the improvement of the BTMS effect.…”

Recent Developments of Thermal Management Strategies for Lithium‐Ion Batteries: A State‐of‐The‐Art Review

“…Similar studies on PCM units-based hybrid BTMS were also presented in other literature. [124][125][126][127] Furthermore, inspired by the microstructure of shark skin, Yang et al [128] applied a bionic heat sink in battery cooling, which combined with PCM in cavities and axial air cooling. The results suggested that compared with normal cases, the assemble of bionic structure could modify temperature consistency as well as energy consumption and also exhibited good reliability under various dynamic conditions.…”

Investigation and Optimization of a Thermal Management System for Lithium-Ion Batteries Combining Closed Air-Cooling With Phase Change Material

“…In the optimized model, the temperature difference of the battery was reduced by 30%. LIN et al (2021) used an artificial neural network (ANN) and genetic algorithm (GA) to optimize the BTMS based on air and PCM. The optimized system slowed down the temperature rise of the battery pack and delay the phase change.…”