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An Innovative Methodology to Take into Account Traffic Information on WLTP Cycle for Hybrid Vehicles

Author

Listed:
  • Antonio Galvagno

    (Engineering Department, University of Messina, C. da Di Dio, 98166 Mesina, Italy)

  • Umberto Previti

    (Engineering Department, University of Messina, C. da Di Dio, 98166 Mesina, Italy)

  • Fabio Famoso

    (Engineering Department, University of Messina, C. da Di Dio, 98166 Mesina, Italy)

  • Sebastian Brusca

    (Engineering Department, University of Messina, C. da Di Dio, 98166 Mesina, Italy)

Abstract

The most efficient energy management strategies for hybrid vehicles are the “Optimization-Based Strategies”. These strategies require a preliminary knowledge of the driving cycle, which is not easy to predict. This paper aims to combine Worldwide Harmonized Light-Duty Vehicles Test Cycle (WLTC) low section short trips with real traffic levels for vehicle energy and fuel consumption prediction. Future research can focus on implementing a new strategy for Hybrid Electric Vehicle (HEV) energy optimization, taking into account WLTC and Google Maps traffic levels. First of all, eight characteristic parameters are extracted from real speed profiles, driven in urban road sections in the city of Messina at different traffic conditions, and WLTC short trips as well. The minimum distance algorithm is used to compare the parameters and assign the three traffic levels (heavy, average, and low traffic level) to the WLTC short trips. In this way, for each route assigned from Google maps, vehicle’s energy and fuel consumption are estimated using WLTC short trips remodulated with distances and traffic levels. Moreover, a vehicle numerical model was implemented and used to test the accuracy of fuel consumption and energy prediction for the proposed methodology. The results are promising since the average of the percentage errors’ absolute value between the experimental driving cycles and forecast ones is 3.89% for fuel consumption, increasing to 6.80% for energy.

Suggested Citation

  • Antonio Galvagno & Umberto Previti & Fabio Famoso & Sebastian Brusca, 2021. "An Innovative Methodology to Take into Account Traffic Information on WLTP Cycle for Hybrid Vehicles," Energies, MDPI, vol. 14(6), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1548-:d:514994
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    References listed on IDEAS

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    1. Lu Han & Xiaohong Jiao & Zhao Zhang, 2020. "Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging," Energies, MDPI, vol. 13(1), pages 1-22, January.
    2. 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.
    3. Lincun Fang & Shiyin Qin & Gang Xu & Tianli Li & Kemin Zhu, 2011. "Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on Multi-Objective Genetic Algorithms," Energies, MDPI, vol. 4(3), pages 1-13, March.
    4. Hongwen He & Jinquan Guo & Nana Zhou & Chao Sun & Jiankun Peng, 2017. "Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles," Energies, MDPI, vol. 10(11), pages 1-19, November.
    5. Yavasoglu, H.A. & Tetik, Y.E. & Gokce, K., 2019. "Implementation of machine learning based real time range estimation method without destination knowledge for BEVs," Energy, Elsevier, vol. 172(C), pages 1179-1186.
    6. Shen, Peihong & Zhao, Zhiguo & Zhan, Xiaowen & Li, Jingwei & Guo, Qiuyi, 2018. "Optimal energy management strategy for a plug-in hybrid electric commercial vehicle based on velocity prediction," Energy, Elsevier, vol. 155(C), pages 838-852.
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    1. Umberto Previti & Sebastian Brusca & Antonio Galvagno & Fabio Famoso, 2022. "Influence of Energy Management System Control Strategies on the Battery State of Health in Hybrid Electric Vehicles," Sustainability, MDPI, vol. 14(19), pages 1-20, September.

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