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Optimal Operating Point Determination Method Design for Range-Extended Electric Vehicles Based on Real Driving Tests

Author

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  • Gye-Seong Lee

    (Industrial Technology (Robotics), Korea University of Science and Technology, 217 Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea
    EV Components & Materials R&D Group, Korea Institute of Industrial Technology, 6 Cheomdan-gwagiro 208 Beon-gil, Buk-gu, Gwangju 61012, Korea)

  • Dong-Hyun Kim

    (EV Components & Materials R&D Group, Korea Institute of Industrial Technology, 6 Cheomdan-gwagiro 208 Beon-gil, Buk-gu, Gwangju 61012, Korea)

  • Jong-Ho Han

    (EV Components & Materials R&D Group, Korea Institute of Industrial Technology, 6 Cheomdan-gwagiro 208 Beon-gil, Buk-gu, Gwangju 61012, Korea)

  • Myeong-Hwan Hwang

    (EV Components & Materials R&D Group, Korea Institute of Industrial Technology, 6 Cheomdan-gwagiro 208 Beon-gil, Buk-gu, Gwangju 61012, Korea)

  • Hyun-Rok Cha

    (Industrial Technology (Robotics), Korea University of Science and Technology, 217 Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea
    EV Components & Materials R&D Group, Korea Institute of Industrial Technology, 6 Cheomdan-gwagiro 208 Beon-gil, Buk-gu, Gwangju 61012, Korea)

Abstract

In this study, a method to determine the optimal generator operating point is proposed to enhance the utilization of power resources in a range-extended electric vehicle (Re-EV). Currently, the Re-EV is being developed as one of the solutions to the short driving range and charge problem of electric vehicles (EVs). In particular, we present a method for flexibly determining the operating point of the generators mounted on Re-EVs based on the power consumption trends of the users. Our proposed method can address the problem in existing algorithms wherein all the available resources are not utilized, even though there is fuel remaining in the EV because the battery is not completely discharged. The proposed algorithm was developed based on data acquired through actual driving tests using an agricultural utility vehicle; these data can be applied to various power consumption patterns, including nonlinear consumption patterns. In addition, this algorithm can be applied to other types of Re-EV with different battery and generator specifications. We perform simulations and experiments to verify the proposed algorithm and the results demonstrate the effectiveness of the proposed approach compared with other existing methods.

Suggested Citation

  • Gye-Seong Lee & Dong-Hyun Kim & Jong-Ho Han & Myeong-Hwan Hwang & Hyun-Rok Cha, 2019. "Optimal Operating Point Determination Method Design for Range-Extended Electric Vehicles Based on Real Driving Tests," Energies, MDPI, vol. 12(5), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:845-:d:210873
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    References listed on IDEAS

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    1. Li, Junqiu & Wang, Yihe & Chen, Jianwen & Zhang, Xiaopeng, 2017. "Study on energy management strategy and dynamic modeling for auxiliary power units in range-extended electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 363-375.
    2. Du, Jiuyu & Chen, Jingfu & Song, Ziyou & Gao, Mingming & Ouyang, Minggao, 2017. "Design method of a power management strategy for variable battery capacities range-extended electric vehicles to improve energy efficiency and cost-effectiveness," Energy, Elsevier, vol. 121(C), pages 32-42.
    3. Hongwei Liu & Chantong Wang & Xin Zhao & Chong Guo, 2018. "An Adaptive-Equivalent Consumption Minimum Strategy for an Extended-Range Electric Bus Based on Target Driving Cycle Generation," Energies, MDPI, vol. 11(7), pages 1-26, July.
    4. Lihe Xi & Xin Zhang & Chuanyang Sun & Zexing Wang & Xiaosen Hou & Jibao Zhang, 2017. "Intelligent Energy Management Control for Extended Range Electric Vehicles Based on Dynamic Programming and Neural Network," Energies, MDPI, vol. 10(11), pages 1-18, November.
    5. Li, Junqiu & Jin, Xin & Xiong, Rui, 2017. "Multi-objective optimization study of energy management strategy and economic analysis for a range-extended electric bus," Applied Energy, Elsevier, vol. 194(C), pages 798-807.
    6. Cordiner, Stefano & Galeotti, Matteo & Mulone, Vincenzo & Nobile, Matteo & Rocco, Vittorio, 2016. "Trip-based SOC management for a plugin hybrid electric vehicle," Applied Energy, Elsevier, vol. 164(C), pages 891-905.
    7. Chen, Bo-Chiuan & Wu, Yuh-Yih & Tsai, Hsien-Chi, 2014. "Design and analysis of power management strategy for range extended electric vehicle using dynamic programming," Applied Energy, Elsevier, vol. 113(C), pages 1764-1774.
    8. Shabbir, Wassif & Evangelou, Simos A., 2014. "Real-time control strategy to maximize hybrid electric vehicle powertrain efficiency," Applied Energy, Elsevier, vol. 135(C), pages 512-522.
    9. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    10. Chambon, Paul & Curran, Scott & Huff, Shean & Love, Lonnie & Post, Brian & Wagner, Robert & Jackson, Roderick & Green, Johney, 2017. "Development of a range-extended electric vehicle powertrain for an integrated energy systems research printed utility vehicle," Applied Energy, Elsevier, vol. 191(C), pages 99-110.
    11. Redelbach, Martin & Özdemir, Enver Doruk & Friedrich, Horst E., 2014. "Optimizing battery sizes of plug-in hybrid and extended range electric vehicles for different user types," Energy Policy, Elsevier, vol. 73(C), pages 158-168.
    12. Yong Tian & Bizhong Xia & Mingwang Wang & Wei Sun & Zhihui Xu, 2014. "Comparison Study on Two Model-Based Adaptive Algorithms for SOC Estimation of Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 7(12), pages 1-19, December.
    13. Xiang, Changle & Ding, Feng & Wang, Weida & He, Wei, 2017. "Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control," Applied Energy, Elsevier, vol. 189(C), pages 640-653.
    14. Wang, Bin & Xu, Jun & Cao, Binggang & Ning, Bo, 2017. "Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 596-608.
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    Cited by:

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    2. Aleš Hace, 2019. "The Advanced Control Approach based on SMC Design for the High-Fidelity Haptic Power Lever of a Small Hybrid Electric Aircraft," Energies, MDPI, vol. 12(15), pages 1-31, August.
    3. Jakub Lasocki & Artur Kopczyński & Paweł Krawczyk & Paweł Roszczyk, 2019. "Empirical Study on the Efficiency of an LPG-Supplied Range Extender for Electric Vehicles," Energies, MDPI, vol. 12(18), pages 1-23, September.
    4. Paweł Krawczyk & Artur Kopczyński & Jakub Lasocki, 2022. "Modeling and Simulation of Extended-Range Electric Vehicle with Control Strategy to Assess Fuel Consumption and CO 2 Emission for the Expected Driving Range," Energies, MDPI, vol. 15(12), pages 1-41, June.

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