Driving energy management of front-and-rear-motor-drive electric vehicle based on hybrid radial basis function

• Autorzy: Sun Binbin , Zhang Tiezhu , Ge Wenqing , Tan Cao , Gao Song

Identyfikatory

DOI

10.5604/01.3001.0013.2775

• Języki publikacji: EN

Abstrakty

EN

This paper presents mathematical methods to develop a high-efficiency and real-time driving energy management for a front-and-rear-motor-drive electric vehicle (FRMDEV), which is equipped with an induction motor (IM) and a permanent magnet synchronous motor (PMSM). First of all, in order to develop motor-loss models for energy optimization, database of with three factors, which are speed, torque and temperature, was created to characterize motor operation based on HALTON sequence method. The response surface model of motor loss, as the function of the motor-operation database, was developed with the use of Gauss radial basis function (RBF). The accuracy of the motor-loss model was verified according to statistical analysis. Then, in order to create a two-factor energy management strategy, the modification models of the torque required by driver (Td) and the torque distribution coefficient (β) were constructed based on the state of charge (SOC) of battery and the motor temperature, respectively. According to the motor-loss models, the fitness function for optimization was designed, where the influence of the non-work on system consumption was analyzed and calculated. The optimal β was confirmed with the use of the off-line particle swarm optimization (PSO). Moreover, to achieve both high accuracy and real-time performance under random vehicle operation, the predictive model of the optimal β was developed based on the hybrid RBF. The modeling and predictive accuracies of the predictive model were analyzed and verified. Finally, a hardware-in-loop (HIL) test platform was developed and the predictive model was tested. Test results show that, the developed predictive model of β based on hybrid RBF can achieve both real-time and economic performances, which is applicable to engineering application. More importantly, in comparison with the original torque distribution based on rule algorithm, the torque distribution based on hybrid RBF is able to reduce driving energy consumption by 9.51% under urban cycle.

Słowa kluczowe

EN

electric vehicle; drive; energy management; optimization; torque distribution; predictive model; hardware test

PL

pojazd elektryczny; napęd; zarządzanie energią; optymalizacja; moment obrotowy; model predykcyjny

• Wydawca: Warsaw University of Technology, Faculty of Transport
• Czasopismo: Archives of Transport
• Rocznik: 2019
• Tom: Vol. 49, iss. 1
• Strony: 47--58
• Opis fizyczny: Bibliogr. 17 poz., fot., rys., tab., wykr.

Twórcy

autor

Sun Binbin

• Shandong University of Technology, School of Transportation and Vehicle Engineering, Zibo, Shandong, China

autor

Zhang Tiezhu

• Shandong University of Technology, School of Transportation and Vehicle Engineering, Zibo, Shandong, China

autor

Ge Wenqing

• Shandong University of Technology, School of Transportation and Vehicle Engineering, Zibo, Shandong, China

autor

Tan Cao

• Shandong University of Technology, School of Transportation and Vehicle Engineering, Zibo, Shandong, China

autor

Gao Song

• Shandong University of Technology, School of Transportation and Vehicle Engineering, Zibo, Shandong, China

Bibliografia

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• [14] SUN, B., GAO, S., WU Z., ET AL., 2017. Parameters Design and Economy Study of an Electric Vehicle with Powertrain Systems in Front and Rear Axle. International Journal of Engineering Transactions A: Basics, 29(4), 454-463.
• [15] CHEN, S. Y., WU, C. H., HUNG, Y. H., & CHUNG, C. T., 2018. Optimal strategies of energy management integrated with transmission control for a hybrid electric vehicle using dynamic particle swarm optimization. Energy, 160, 154-170.
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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).