Abstract
The planetary gear train is often used as the main device for decelerating and increasing the torque of the drive motor of electric vehicles. Considering the lightweight requirement and existing uncertainty in structural design, a multi-objective uncertainty optimization design (MUOD) framework is developed for the planetary gear train of the electric vehicle in this study. The volume and transmission efficiency of the planetary gear train are taken into consideration as optimization objectives. The manufacturing size, material, and load input of the planetary gear train are considered as uncertainties. An approximate direct decoupling model, based on subinterval Taylor expansion, is applied to evaluate the propagation of uncertainties. To improve the convergence ability of the multi-objective evolutionary algorithm, the improved non-dominated sorting genetic algorithm II (NSGA-II) is designed by using chaotic and adaptive strategies. The improved NSGA-II has better convergence efficiency than classical NSGA-II and multi-objective particle swarm optimization (MOPSO). In addition, the multi-criteria decision making (MCDM) method is applied to choose the most satisfactory solution in Pareto sets from the multi-objective evolutionary algorithm. Compared with the multi-objective deterministic optimization design (MDOD), the proposed MUOD framework has better reliability than MDOD under different uncertainty cases. This MUOD method enables further guidance pertaining to the uncertainty optimization design of transportation equipment, containing gear reduction mechanisms, in order to reduce the failure risk.
Highlights
In recent years, electric vehicle technology has developed rapidly [1,2]
In order to design a reasonable planetary reduction gear system, matching the electric vehicle motor, this study proposes a multi-objective uncertainty optimization design (MUOD) framework for the planetary gear train of an electric vehicle
The optimization results of MUOD show that the Pareto front gradually moves to the upper right corner with the uncertainty increases
Summary
The planetary gear reducer is used in electric vehicles due to its high transmission efficiency and compact structure. Parmar et al [3] proposed a novel multi-objective optimization method, for planetary gear trains, using NSGA-II. Miler et al [4] chose transmission volume and power loss as design objectives, and they optimized the parameters of the planetary gear train with multi-objective optimization. Sedak et al [5] proposed a constrained multi-objective nonlinear optimization problem for planetary gearboxes, based on a hybrid element heuristic algorithm, considering gear volume, center distance, contact ratio, and power loss as optimization objectives. To obtain a reliable structural design, the uncertainties of the planetary gear train of electric vehicles need to be considered
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