An innovative approach to multi-response optimization of battery thermal management systems using multi-desirability function approach

Abstract

Lithium-ion batteries are an efficient option for energy storage due to their high energy density, specific power, safety, durability, and reduced emissions. However, they require a specific temperature range and uniform temperature profile for optimal operation, which affects vehicle performance. Selecting proper thermal operating conditions to enhance the thermal performance in battery thermal management systems (BTMS) is an important area of research. The present study investigates the influences of thermal process variables on the performance characteristics of a rectangular battery arrangement (RBA) used for BTMS applications. The Box-Behnken design of response surface methodology (RSM) is used to conduct experiments and responses such as battery average temperature (TAvg) and maximum temperature (TMax) have been measured. The RSM technique is used to develop mathematical models that relate input and output parameters, and the statistical analysis of variance is used to examine factor effects on response measures. Finally, the multi-desirability function approach (MDFA) is employed to optimize both responses simultaneously. MDFA predicts that the most effective thermal processing configuration for the simultaneous anticipation of the TAvg, and TMax of an RBA involves a battery discharge rate of 3C, an air inlet velocity of 3.23 m/s, and an air inlet temperature of 9.44 °C. The corresponding outcomes for TAvg and TMax are 16 °C and 21 °C, respectively, and these optimized thermal parametric setting is confirmed through verifications tests, and they can be helpful to enhance the thermal efficiency of BTMS.