Test of regenerative braking system with speed tracking control based on RBF neural network
1.School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; 2.Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, Jiangsu 212013, China
Abstract：To evaluate the regenerative braking system of electric vehicle, a testing method for regenerative braking system was proposed based on automatic speed tracking. According to the offline data from vehicle, the dynamic mathematical model for openloop control of speed tracking was established. The driver model of speed tracking closedloop control was built by RBF neural network, and the parameters in RBF neutral network were trained by PSO algorithm. The test was conducted on vehicle inertia simulation bench. The experimental results show that compared with traditional fuzzy PID control method, the speed tracking error is reduced by the RBF neural network algorithm, and the accuracy of regenerative braking system test is also improved.The feasibility of the proposed method is verified in actual regenerative braking test.
陈燎, 谢明维, 盘朝奉, 周孔亢. 基于RBF神经网络车速控制的再生制动系统测试[J]. 江苏大学学报（自然科学版）, 2016, 37(3): 256-263.
CHEN Liao, XIE Ming-Wei, PAN Chao-Feng, ZHOU Kong-Kang. Test of regenerative braking system with speed tracking control based on RBF neural network[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2016, 37(3): 256-263.
2016, Vol. 37 
