A novel NSGA-based co-optimization design framework of dual-motor parameters and energy management for electric cargo vehicles

By virtue of the high overall efficiency and enhanced power performance, the dual-motor system is widely used in electric vehicles. However, it remains a significant challenge to determine the optimal dual-motor power level before energy management, and it is difficult to simultaneously equilibrate the power performance and economy. Therefore, a novel co-optimization design framework that integrates the instantaneous optimal energy consumption strategy into the non-dominated sorting genetic algorithm-II is proposed for a dual-motor electric cargo vehicle. First, based on the vehicle power performance index, an explicit power performance objective function is designed by calculating the reserve power. Second, an improved energy management strategy is developed to optimize the power distribution coefficient between dual motors, and a dual-motor average efficiency for one driving cycle is obtained simultaneously, which is defined as the outcome of economic objective function. Then, the appropriate dual-motor power level and corresponding parameters are determined based on Pareto-optimality and vehicle usage scenarios. Finally, in order to verify the superiority of the co-optimization design framework, a model predictive control-based energy management strategy is implemented for comparison. The results indicate that, the proposed methodology decreases the 50 km/h acceleration time by 1.8s, and reduces energy consumption by 10.93% over one typical driving cycle. Furthermore, by analyzing the motor operating points, it can be observed that the proposed method increases the dual-motor operating points in the efficiency region above 90% by approximately 2%–3%, while reducing the points in the efficiency region below 80% by about 0.2%.