Modeling and optimization of liquid-based battery thermal management system considering battery electrochemical characteristics

In this research, a novel multi-physics model is proposed to facilitate battery thermal management system (BTMS) design and optimization. For parametric simulations and optimization, a new optimization framework is developed for BTMS by utilizing numerical simulations, artificial intelligence and multi-objective genetic algorithm. It is found that BTMS is inadequate in addressing thermal issues that arise in aged battery pack, primarily because of the increased total heat generation rate resulting from battery aging effect. Besides, it has also been observed that BTMS plays a significant role in managing battery electrochemical performance. Meanwhile, optimizing mini-channel geometrical parameters, mini-channel arrangement and nanoparticle volume fraction are found to be an effective method to further control battery thermal issues. However, it is observed that reducing battery temperature invariably incurs a reduction in battery average potential. Therefore, multivariables global optimizations are conducted based on various combinations of weighted coefficients and optimization strategies. It is found that all obtained optimization schemes can achieve the trade-offs among battery thermal behaviors, pressure loss and electrochemical performance, with meeting the desired temperature requirements even during long-term operation. Furthermore, the selection about weighted coefficient and optimization strategy can be tailored to meet the specific demands and prerequisites of various engineering applications.