A Neural Network Control Strategy for Composite Braking System of Electric Vehicle

Hui Jia, Chen Wang, Hongwen He


The composite braking system is significant to improve the total energy efficiency of electric vehicles by recycling braking energy. A neural network controller for the CBS is proposed to improve the energy regeneration rate and braking stability of EVs in this paper. The regenerative and hydraulic braking torques were optimized offline by using Downhill-Simplex method, and the NN model related to the driving state parameters were trained. Compared with a parallel braking strategy, the energy economy of NN is improved by 4.64% than the parallel controller’s in 3 NEDC cycles, and performs more closely to the I curve in a specific braking condition with a decreasing braking severity.


Electric vehicle, composite braking system, neural network, energy regeneration rate, braking stability


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