Elham Hosseinzadeh

The optimisation of electric vehicle battery boxes while preserving their structural performance presents a formidable challenge. Many studies typically involve fewer than 10 design variables in their optimisation processes, a deviation from the reality of battery box design scenarios. The present study, for the first time, attempts to use sensitivity analysis to screen the design variables and achieve an efficient optimisation design with a large number of original design variables. Specifically, the sensitivity analysis method was proposed to screen a certain number of optimisation variables, reducing the computational complexity while ensuring the efficiency of the optimisation process. A combination of the Generalised Regression Neural Network (GRNN) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was employed to construct surrogate models and solve the optimisation problem. The optimisation model integrates these techniques to balance structural performance and weight reduction. The optimisation results demonstrate a significant reduction in battery box weight while maintaining structural integrity. Therefore, the proposed approach in this study provides important insights for achieving high-efficiency multi-objective optimisation of battery box structures.

As electric vehicles (EVs) gain market dominance, ensuring safety during the battery usage is crucial. This paper presents a new thermal management approach to address the battery heat accumulation challenge through a novel combination of composite phase change material (CPCM) with liquid cooling systems. An optimised hybrid cooling model is developed to evaluate the proposed battery thermal management system (BTMS) under high-temperature and high-power conditions. Benchmark studies are conducted to assess the impact of inlet position, inlet flow rate, and flow channel distribution on the cooling performance to achieve a uniform temperature distribution within the battery. The optimised BTMS, consisting of a five-cell battery pack, demonstrates a maximum temperature of 41.15 °C and a temperature difference of 4.89 °C in a operating condition at 36 °C with a discharge rate of 3 C. The BTMS outperforms the initial model, reducing the maximum temperature by 1.5%, temperature difference by 5%, and liquid fraction by 13%, with a slight (1.3%) increase in weight. The cooling performance is most efficient at a liquid flow rate of 0.1 m/s, minimising energy consumption. The proposed BTMS with CPCM-3 is also sufficient enough to keep the battery pack under a thermal runaway event. Overall, the theoretical simulation highlights the BTMS’s ability to effectively control battery temperatures and temperature differences, ensuring safe operation during high-temperature and high-power conditions in practical EV usage.