Modeling and Simulation of Electric City Bus Power System Based on Multi-objective Genetic Algorithm

Pure electric vehicles are a new type of electric vehicle with great development potential, but they still face many challenges in terms of range and dynamic characteristics, such as small battery capacity, slow charging rate, poor safety, and serious secondary pollution. This article used ADVISOR (Advanced Vehicle Simulator) to model the entire vehicle, wheels, reducer, and battery. On this basis, by establishing a complete vehicle model, this paper proposed a new design method for the driving system of electric city buses based on multi-objective genetic algorithm. This article studied the theoretical basis of multi-objective optimization problems to determine the optimization design variables, optimization objective functions, and required constraints. The designed vehicle model was parameterized and optimized, ultimately determining the optimal driving system component parameter combination scheme. Through comparison with before and after optimization, it was found that pure electric vehicles have significantly improved energy efficiency and power performance. The specific energy consumption of electric city buses has decreased from 0.098 kWh/km*t to 0.096 kWh/km*t. This article verified that the various performance indicators of the established vehicle model met the design requirements, thereby proving that the parameter matching design of the designed car is reasonable and correct.