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Control strategy on regenerative braking force of four wheel drive electric vehicle
Published:2018-03-07 author:XIE Bo zhen1, ZHU Shao peng1, LI Jun jie1, LIU Zhen tao1, NING Xiao bin2 Browse: 2967 Check PDF documents
                                     Control strategy on regenerative braking force of four wheel drive electric vehicle
                                       XIE Bo zhen1, ZHU Shao peng1, LI Jun jie1, LIU Zhen tao1, NING Xiao bin2
(1.Power Machinery and Vehicular Engineering Institute, Zhejiang University, Hangzhou 310027, China;2.College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China)


Abstract: In order to improve the efficiency of four wheel driveelectric vehicle braking energy recovery, in the analysis of the mechanical structure and the restraint condition of the regenerative braking system,a fixed proportion allocation strategy based on the parallel regenerative braking system and an ideal braking force distribution strategy based on the series regenerative braking system was established, the relevant regenerative braking coefficient was defined.Two kinds of braking force distribution strategy and the model of regenerative braking system including four wheel drive vehicle, motor and battery in Simulink/Stateflow were established. The braking effect of the two kinds of braking control strategy through the simulation analysis of different speed and different braking intensity was verified.The simulation results indicate that using the reasonable force distribution strategy and improve the battery charging capacity can improve the energy recovery efficiency of the four wheel drive electric vehicle. These two kinds of brake control strategy can accomplish the task in braking very well, and in terms of braking energy recovery efficiency, the ideal braking force distribution strategy is better than the fixed rate allocation strategy.

Key words: in wheel motor; four wheel drive electric vehicle; regenerative braking system; braking force distribution; modeling and simulation

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