Electric Vehicle Power System in Intelligent Manufacturing Based on Soft Computing Optimization

Abstract

Soft computing technology has attracted extensive attention in the fields of computer engineering and automatic control because it can deal with uncertainties, fuzziness and complex practical problems. In order to realize the cooperative optimization of electric vehicle's dynamic performance and economic performance, this paper adopts Genetic Algorithm (GA) in soft computing technology. The advantage of soft computing technology lies in its adaptability to uncertainty, fuzziness and complex practical problems, which makes GA an effective tool to solve complex optimization problems. Firstly, the power system structure and energy management strategy of electric vehicles are investigated and analyzed. Then, the improved non-dominated sorting genetic algorithm II (NSGA-II) is selected to optimize the parameters of electric vehicles because of its simple operation and high optimization accuracy. Then, NSGA-II is used to construct the power and energy configuration of electric vehicles, with power performance and economic performance as the main optimization objectives. Finally, in order to achieve the optimization goal, the relevant variables are selected, and the optimization objective function and constraint conditions are established, and the model is simulated and evaluated. The results show that the acceleration time of the optimized electric vehicle is significantly reduced, the dynamic performance is improved by more than 7%, and the power loss is reduced by 5%. In addition, compared with the current multi-objective optimization model, this model enables electric vehicles to travel longer distances under the same power. These findings provide valuable reference for the performance improvement of electric vehicles in intelligent manufacturing.