Abstract
With the rapid development of electric vehicle, the energy utilization efficiency of battery electric vehicle (BEV) becomes particularly important in real applications. This paper presents a fuzzy energy management strategy that uses a BP neural network to identify the type of driving conditions, and designs a specific fuzzy controller to calculate the torque percentage values of the motor controller output under different driving styles. Finally, the results show that the proposed method is verified and has higher energy efficiency than the one that does not consider driving style and fuzzy control method.
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References
Mo, T., Li, Y., Lau, K., et al.: Trends and emerging technologies for the development of electric vehicles. Energies 15(17), 6271 (2022)
Dong, P., Zhao, J., Liu, X., et al.: Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: development stages, challenges, and future trends. Renew. Sustain. Energy Rev. 170, 112947 (2022)
Bingham, C., Walsh, C., Carroll, S.: Impact of driving characteristics on electric vehicle energy consumption and range. IET Intel. Transport Syst. 6(1), 29–35 (2012)
Sun, B., Deng, W., Wu, J., et al.: Research on the classification and identification of driver’s driving style. In: 2017 10th International Symposium on Computational Intelligence and Design (ISCID), vol. 1, pp. 28–32. IEEE (2017)
Murphey, Y.L., Milton, R., Kiliaris, L.: Driver’s style classification using jerk analysis. In: 2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems, pp. 23–28. IEEE (2009)
Langari, R., Won, J.S.: Intelligent energy management agent for a parallel hybrid vehicle-part I: system architecture and design of the driving situation identification process. IEEE Trans. Veh. Technol. 54(3), 925–934 (2005)
Won, J.S., Langari, R.: Intelligent energy management agent for a parallel hybrid vehicle-part II: torque distribution, charge sustenance strategies, and performance results. IEEE Trans. Veh. Technol. 54(3), 935–953 (2005)
Liu, T., Tan, W., Tang, X., et al.: Driving conditions-driven energy management strategies for hybrid electric vehicles: a review. Renew. Sustain. Energy Rev. 151, 111521 (2021)
Wang, J.: Research on Key Technologies of Energy Management System and High Voltage Safety Strategy of Pure Electric Vehicles. Beijing Institute of Technology (2014) (in Chinese)
Kantor, S., Stárek, T.: Design of algorithms for payment telematics systems evaluating driver’s driving style. Trans. Transp. Sci. 7(1), 9 (2014)
Wang, R., Lukic, S.M.: Review of driving conditions prediction and driving style recognition based control algorithms for hybrid electric vehicles. In: 2011 IEEE Vehicle Power and Propulsion Conference, pp. 1–7. IEEE (2011)
Huang, X., Tan, Y., He, X.: An intelligent multi-feature statistical approach for the discrimination of driving conditions of a hybrid electric vehicle. IEEE Trans. Intell. Transp. Syst. 12(2), 453–465 (2010)
Tang, X., Chen, J., Pu, H., et al.: Double deep reinforcement learning-based energy management for a parallel hybrid electric vehicle with engine start–stop strategy. IEEE Trans. Transp. Electrification 8(1), 1376–1388 (2021)
Acknowledgments
This work was funded by National Natural Science Foundation of China (61703068) and Chongqing Municipal Education Commission Science and Technology Research Project (KJ1704097) funded project.
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Ma, Y., Huang, B., Piao, C., Luo, G., Ma, W. (2024). Fuzzy Energy Management Strategy for Battery Electric Vehicles Considering Driving Style Recognition. In: Yang, Q., Li, Z., Luo, A. (eds) The Proceedings of the 18th Annual Conference of China Electrotechnical Society. ACCES 2023. Lecture Notes in Electrical Engineering, vol 1168. Springer, Singapore. https://doi.org/10.1007/978-981-97-1068-3_36
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DOI: https://doi.org/10.1007/978-981-97-1068-3_36
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