基于ADAMS的电动汽车后悬架性能分析与优化

Performance Analysis and Optimization of Electric Vehicle Rear Suspension System Based on ADAMS

为了满足后轮定位参数的优化设计要求,基于虚拟样机技术,建立电动汽车后悬架系统模型,并与ADAMS/Car中提供的悬架系统试验台相结合,完成±50 mm双轮同向跳动试验,分析后悬架系统在随车轮跳动过程中后轮前束角和外倾角随车轮跳动的变化;以此为优化目标,应用ADAMS/Insight模块,进行多目标遗传算法优化,使后轮前束角的变化范围由-0.631~0.536°变为-0.214~0.168°,后轮外倾角的变化范围由-0.957~1.284°变为-0.760~1.148°,达到理想的设计变化范围。优化结果表明,结合ADAMS/Insight进行电动汽车后悬架遗传算法优化确保了后悬架系统的良好性能。

In this paper, taking the virtual prototype model as the basis, the double wheels traveling in the same direction ±50 mm tests were carried out respectively by the combination of the electric vehicle suspension system simulation model and suspension system test platform in ADAMS/Car, to find out unreasonable wheel alignment parameters of the electric vehicle rear suspension systems changing along with wheel run out. The unreasonable parameters as a test object, multi-objective genetic algorithm was adopted to optimize the parameters with ADAMS/Insight module, the range of rear wheel toe-in angle is changed from-0.631- 0.536°to -0.214-0.168°, the range of rear wheel camber angle is changed from -0.957-1.284°to -0.760-1.148°, making them achieve variation range as ideal as possible. The results show that combined with ADAMS/Insight to canT out the multi-objective genetic algorithm optimization for rear suspension can ensure the performance of rear suspension system.