基于NLPQL算法的电动轮汽车差速助力转向参数优化

徐晓宏¹，赵万忠^{1, 2}，王春燕¹，陈伟¹

(1. 南京航空航天大学 能源与动力学院，江苏 南京，210016；
2. 重庆大学 机械传动国家重点实验室，重庆，400044)

摘 要：建立力与位移耦合控制的电动轮汽车差速助力转向系统模型，给出转向路感、转向灵敏度、转向稳定性以及转向经济性的量化公式；根据多目标多约束优化问题的特点，以转向路感和转向经济性为优化目标，以转向稳定性和转向灵敏度为约束条件，设计非线性二次规划算法(NLPQL)，对系统参数进行优化设计。仿真结果表明：基于NLPQL算法的电动轮汽车差速助力转向多目标优化，可在保证系统具有较好的转向稳定性和较高的转向灵敏度基础上，有效提高系统的转向路感，并降低系统的转向能耗，为电动轮系统的设计和优化提供理论基础。

关键词：车辆工程；电动轮汽车；差速助力转向；NLPQL算法；多目标优化

Parameters optimization of differential assisted steering for electric vehicle with motorized wheels based on NLPQL algorithm

XU Xiao-hong¹, ZHAO Wan-zhong^{1, 2}, WANG Chun-yan¹, CHEN Wei¹

(1. College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
2. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China)

Abstract:The model of the differential assisted steering system with force and displacement coupled control for electric vehicle with motorized wheels was built. Based on these models of system, the quantitative expressions of the road feeling, sensitivity, operation stability and economy of the steering were proposed. According to the features of multi-constrained optimization of multi-objective function, NLPQL algorithm was designed. Taking the road feeling and energy consumption of the steering as optimization objectives, and operation stability and sensitivity of the steering as constraint, the system parameters were optimized. The results show that optimization based on NLPQL algorithm can improve the steering road feeling, reduce the steering energy consumption more effectively and can also guarantee the operation stability and steering sensibility, which provides theoretical basis for the design and optimization of the electric vehicle with motorized wheels system.

Key words:vehicle engineering; electric vehicle with motorized wheels; differential assisted steering; NLPQL algorithm; multi-objective optimization