Many-objective optimization of BEV design parameters based on gradient boosting decision tree models and the NSGA-III algorithm considering the ambient temperature

In this study, extensive efforts have been devoted to optimizing the energy distribution and performance of a battery electric vehicle (BEV). An integrated simulation model based on energy flow test data is built and validated, and a parallel framework to implement automatic batch simulations is developed. On this basis, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) models of the BEV are established and compared, and many-objective optimization is carried out based on the Non-dominated Sorting Genetic Algorithm-III (NSGA-III) algorithm. The results indicate that the developed parallel framework can efficiently perform automatic batch computations of the integrated simulation model for the BEV, and the CatBoost model demonstrates superior prediction performance on both the train and test sets. A uniformly distributed non-dominated set approximating the Pareto Front (PF) is obtained by the many-objective optimization, with the fourth non-dominated solution exhibiting a favorable optimization effect. The electricity consumption per 100 km, half-axis effective work, electricity recovered by battery, energy utilization and recovery efficiency are improved by 9.4 %, 12.2 %, 6.4 %, 96.3 % and 16.0 %, respectively. These findings can provide the theoretical basis, directional guidance and data support for the accurate modeling, batch simulation and many-objective optimization of BEVs.