The Optimal Design of the Power Train Suspension System of Hybrid Electric Vehicles Based on Genetic Algorithm

In order to solve the challenges of the optimal design of the power train suspension system of hybrid electric vehicles, in view of the shortcomings of the existing herd algorithm, an innovative research method for the optimal design of the power train suspension system based on genetic algorithm is proposed. The new approach uses the principles of simulated biological behavior theory to accurately identify and locate key influencing factors, and accordingly enables intelligent classification of indicators to reduce possible interference. At the same time, using the unique mechanism of genetic algorithm, the design strategy of the suspension system is cleverly constructed. The empirical results show that the proposed scheme shows a significant improvement compared with the traditional horse herd algorithm in terms of the accuracy of the optimal design research of the power train suspension system, the processing efficiency of key factors, and other key performance indicators, showing its obvious strong advantages. In hybrid vehicles, the optimal design research of power train suspension system plays a crucial role, which can accurately predict and optimize the growth trend and output results of the optimal design research of hybrid vehicle power train suspension system. However, in the face of complex simulation tasks, traditional horse herd algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this problem, this study introduces a new idea of the optimal design of the power train suspension system optimized by genetic algorithm, and accurately controls the influencing parameters through the theory of simulated biological behavior, and uses this as the road map for index allocation, and then uses genetic algorithm to innovate and construct a system scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme has been significantly optimized in terms of accuracy and processing speed for a variety of challenges, showing stronger performance superiority. Therefore, in the research process of optimal design of the power train suspension system of hybrid electric vehicles, the simulation scheme based on genetic algorithm successfully overcomes the shortcomings of the traditional horse herd algorithm, and significantly improves the accuracy and operation efficiency of the simulation.