Design of brake force distribution model for front-and-rear-motor-drive electric vehicle based on radial basis function

Binbin SUN; Tiezhu ZHANG; Song GAO; Wenqing GE; Bo LI

doi:10.5604/01.3001.0012.8368

Authors

Binbin SUN Shandong University of Technology, School of Transportation and Vehicle Engineering, Zibo, Shandong, China Author
Tiezhu ZHANG Shandong University of Technology, School of Transportation and Vehicle Engineering, Zibo, Shandong, China Author
Song GAO Shandong University of Technology, School of Transportation and Vehicle Engineering, Zibo, Shandong, China Author
Wenqing GE Shandong University of Technology, School of Transportation and Vehicle Engineering, Zibo, Shandong, China Author
Bo LI Shandong University of Technology, School of Transportation and Vehicle Engineering, Zibo, Shandong, China Author

DOI:

https://doi.org/10.5604/01.3001.0012.8368

Keywords:

electric vehicle, motor drive, front-and-rear-motor-drive, brakes force, brake force distribution

Abstract

To achieve high-efficiency and stable brake of a front-and-rear-motor-drive electric vehicle (FRMDEV) with parallel cooperative braking system, a multi-objective optimal model for brake force distribution is created based on radial basis function (RBF). First of all, the key factors, which are the coefficient of brake force distribution between the front and rear shafts, the coefficient of brake force distribution at wheels, the coefficient of regenerative brake force distribution between front and rear axles, that influence the brake stability and energy recovery of the FRMDEV are analyzed, the fitness functions of brake stability and energy recovery are established. Secondly, the maximum allowed regenerative brake torque influenced by the state of charge of battery is confirmed, the correction model of the optimal distribution coefficient of regenerative brake force is created according to motor temperatures. Thirdly, based on HALTON sequence method, a two-factor database, vehicle velocity and brake strength, that characterizes vehicle operation is designed. Then an off-line response database of the optimal brake force distribution is established with the use of particle swarm optimization (PSO). Furthermore, based on hybrid RBF, the function model of the factor database and the response database is established, and the accuracy of the model is analyzed. Specially, the correlation coefficient is 0.995 and the predictive error variance is within the range between 0.000155 and 0.00018. The both indicate that the multi-objective distribution model has high accuracy. Finally, a hardware-in-loop test platform is designed to verify the multi-objective optimal brake force distribution model. Test results show that the real-time performance of the model can meet the demand of engineering application. Meanwhile, it can achieve both the brake stability and energy recovery. In comparison with the original brake force distribution model based on the rule algorithm, the optimized one proposed in this paper is able to improve the energy, recovered into battery, by 14.75%.

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