Driving energy management of front-and-rear-motor-drive electric vehicle based on hybrid radial basis function
DOI:
https://doi.org/10.5604/01.3001.0013.2775Keywords:
electric vehicle, drive, energy management, optimization, torque distribution, predictive model, hardware testAbstract
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.
References
BHATTI, A. R., SALAM, Z., 2018. A rule-based energy management scheme for uninter-rupted electric vehicles charging at constant price using photovoltaic-grid system Renewable Energy, 125, 384-400.
SUN, B., GAO, S., MA, C., 2016. Mathematical Methods Applied to Economy Optimization of an Electric Vehicle with Distributed Power Train System. Mathematical Problems in Engineering, 2016, 4949561.
SUN, B., ZHANG, T., GAO, S., GE, W., LI, B., 2018. Design of brake force distribution model for front-and-rear-motor-drive electric vehicle based on radial basis function. Archives of Transport, 48(4), 87-98
CHINA AUTOMOTIVE TECHNOLOGY RE-SEARCH CENTER, NISSAN (CHINA) IN-VESTMENT CO., LTD., DONGFENG MOTOR COMPANY, 2015. Report on development of new energy automotive industry. Beijing: Social Sciences Literature Press.
GUO, H., HE, H., XIAO, X., 2014. A Predictive Distribution Model for Cooperative Braking System of an Electric Vehicle. Mathematical Problems in Engineering, 2014, 1-11.
PENG, J., HE, H., XIONG, R., 2017. Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming. Applied Energy, 185(2), 1633-1643.
KUMAR, M. S., REVANKAR, S. T., 2017. Development scheme and key technology of an electric vehicle: An overview. Renewable and Sustainable Energy Reviews, 2017, 1266-1285.
MERKISZ-GURANOWSKA, A., PIELECHA, J., 2014. Passenger cars and heavy duty vehicles exhaust emissions under real driving conditions. Archives of Transport, 31(3), 47-59.
SULAIMAN, N., HANNAN, M. A., MOHAMED, A., KER, P. J., MAJLAN, E. H., & DAUD, W. W., 2018. Optimization of energy management system for fuel-cell hybrid electric vehicles: issues and recommendations. Applied energy, 228, 2061-2079.
MUTOH, N., 2012. Driving and Braking Torque Distribution Methods for Front-and Rear-Wheel-Independent Drive-Type Electric Vehicles on Roads With Low Friction Coefficient. IEEE Transactions on Industrial Electronics, 59(7), 3919-3933.
ADDERLY, S. A., MANUKIAN, D., SULLI-VAN, T. D., & SON, M., 2018. Electric vehicles and natural disaster policy implications. Energy Policy, 2018:437-448.
SUN, B., GAO, S., WANG P., ET AL., 2017. A Research on Torque Distribution Strategy for Dual-Motor Four-Wheel-Drive Electric Vehicle Based on Motor Loss Mechanism. Automotive engineering, 39(4), 386-393.
SUN, D., LAN, F.,, HE, X., 2016. Study on Adaptive Acceleration Slip Regulation for Dual-motor Four-wheel Drive Electric Vehicle. Automotive engineering, 38(5), 600-619.
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.
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.
SHI,Y., 2014. Research on energy management strategy of tandem hybrid hydraulic vehicle based on fuzzy logic, Master of Engineering, Jilin University, China.
XI, Z., 2013. Vehicle energy management: modeling, control and optimization. Beijing: China Machine Press.
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