Performance optimization and scheme evaluation of liquid cooling battery thermal management systems based on the entropy weight method

Abstract

Liquid cooling battery thermal management system (BTMS) is widely used in electric vehicles (EVs). A suitable liquid cooling BTMS scheme needs to be selected based on the magnitude of the weights for system performance. In this paper, thirty-six liquid cooling BTMS schemes involving the variables of cooling plate material types, flow channel layouts and inlet flow velocities are evaluated by using the entropy weight method (EWM) based on information theory. The weights and information entropies of the cooling performance, energy consumption performance and material cost of the system have been calculated and analyzed. The optimal scheme is selected considering global optimization based on the evaluation indicators. In addition, the cooling performance of the optimal scheme is examined under cyclic testing conditions (WLTC and US06). It is found that the flow channel layouts of the liquid cooling plate have a greater influence on the temperature control capability and temperature uniformity capability of the system. The liquid cooling system with a serpentine flow channel at an inlet flow velocity of 0.5 m·s−1, and aluminum as the cooling plate material exhibits the best cooling performance, energy consumption performance, and lowest material cost. The weights of material cost are 0.44, 0.32, and 0.34 under 1C discharge rate and cycle tests (WLTC and US06), respectively, which are the largest weight among the evaluation indicators. Material cost has the greatest degree of information dispersion because of the minimum information entropy, which plays a significant role in multi-attribute decision-making process. The temperature rise variation of the maximum temperature of the battery module under cyclic testing conditions is more reliable and dynamic compared with constant discharge rate conditions.