Research on virtual calibration technology for multi objective operating parameters of thermal management system based on thermodynamic indicators

Abstract

With the rapid development of battery electric vehicle, technical problems still exist, and an efficient and reliable thermal management system is a core challenge to improve vehicle performance.This study proposes a multi-mode directly-cooling thermal management system to meet temperature control requirements while optimizing energy efficiency, cost, and environmental impact. An experimental and simulation platform for the system was established as the data source, and a thermodynamic analysis architecture including three indicators was developed to evaluate the impact of different operating parameters on system performance using experimental data. The results show that a 5 °C increase in the system evaporation temperature reduced total energy loss by approximately 12.6 % and environmental impact by 4.65 %, while increasing costs by about 12 % in both modes, system has a set of optimal operating parameters under different operating modes. Under the guidance of thermodynamic analysis, three thermodynamic indicators and key operating parameters were optimized as objective functions and decision variables. The Non-dominated sorting genetic algorithm was applied to develop a multi-objective virtual calibration technology for system parameters, leading to an 18.2 % increase in exergy efficiency across both modes, along with reductions of 11.1 % in total cost rate and 30.9 % in environmental impact rate. Based on the AMESim platform, virtual calibration of optimized parameters demonstrates that the proposed scheme ensures temperature control performance while significantly improves energy efficiency, and reduces economic and environmental impacts, showing strong application potential.