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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.

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The use of cold storage devices to store energy at low peaks of power usage and release it at peaks is an economic measure. It is also one of the main means to solve the imbalance between electricity shortages during the day and abundance at night. Adding cold storage balls equipped with phase change materials (PCMs) into water cold storage devices can utilize the sensible heat of water and the latent heat of PCMs for cold storage, which has a high energy storage density and can provide cold water with a suitable temperature. PCMs can be divided into organic phase change materials (OPCMs) and inorganic phase change materials (IPCMs), but both have defects. Adding additives or other materials to them to form composite phase change materials (CPCMs) can obtain suitable thermal properties. In the process of material application, some PCMs have problems of poor thermal conductivity (TC) and liquid leakage. The use of additives such as expanded graphite (EG), metal foam, and nanoparticles is a better solution to the above problems. In recent years, many scholars have researched PCMs and additives for increasing thermal conductivity (AITC). However, there is still a lack of detailed comparison among EG, metal foam, and nanoparticles. This article will introduce the PCMs used in the temperature zone (TZ) of cold storage air-conditioning systems (CSASs) and analyze their advantages, disadvantages, and improvement methods. This paper will focus on the latest research on the effect of additives such as nanoparticles, EG, and metal foam on the TC of PCMs. It will also compare the special effects of AITC in preventing supercooling, preventing phase separation, preventing leakage of liquid PCMs, and flame retarding. The future research directions of AITC will be discussed.