Multi-objective design optimization of battery thermal management system for electric vehicles

Lithium batteries are commonly used as the primary power storage unit for electric vehicles, and their performance is sensitive to temperature. Thus, the battery thermal management system is crucially needed to allow the EVs to work safely and efficiently. This paper mainly focuses on the performance analysis and design optimization of the battery thermal management system with a U-shaped cooling channel. A Computational fluid dynamics model of a battery thermal management system is built to study the battery temperature distribution and pressure distribution. Through the establishment of the genetic programming model, sensitivity analysis and parameter interaction analysis are carried out to analyze the influence of cooling plate thickness, cooling plate wall thickness, inlet coolant temperature and flow velocity on the comprehensive performance of the battery thermal management system. A new surrogate-assisted multi-objective optimization scheme is proposed by introducing an integrated AI system that includes a surrogate battery model built with genetic programming (GP) and a design optimizer driven by the second-generation non-dominated sorting genetic algorithm (NSGA-II). Results show that the inlet coolant temperature has the most significant influence on the rise of battery temperature (59.87%) but has no influence on the pressure drop. The structural parameters of the cooling plate and the velocity of the inlet coolant have apparent effects on the uniformity of the battery temperature distribution and the pressure drop. The battery thermal management system achieves an ideal comprehensive performance when the thickness of the cooling plate is 4.50 mm, the thickness of the cooling plate wall is 1.49 mm, the inlet coolant temperature is 298.15 K, and the inlet coolant velocity is 0.29 m/s. Under such optimized parameter settings, the max temperature rise of the battery reduces from 7.72 K to 7.69 K, the standard deviation of the temperature distribution 2.54 K (a drop of 0.02 K), and the pressure drop decrease from 1022.1 Pa to 436.43 Pa (decrease by 57.3%). Such results have guiding significance for the design of the battery thermal management system with a U-shaped channel and the application of genetic programming in system performance analysis and optimization.

Automated summary

This paper focuses on the performance analysis and design optimization of a battery thermal management system with a U-shaped cooling channel. By building a computational fluid dynamics model and genetic programming model, the study conducted sensitivity analysis and parameter interaction analysis to optimize the system. The proposed multi-objective optimization scheme with AI integration shows significant improvements in temperature distribution and pressure drop, indicating the effectiveness of the approach.