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A new algorithm for eco-friendly path guidance focused on electric vehicles

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  • Ku, Donggyun
  • Choi, Minje
  • Yoo, Nakyoung
  • Shin, Seungheon
  • Lee, Seungjae

Abstract

The automobile industry is showing increasing interest in eco-friendly electric vehicles (EVs) with the aim of replacing vehicles that rely on traditional fossil fuels for energy. This study investigates the routing of an EV with maximum efficiency relative to the terrain. As EVs have poor climbing ability due to limitations associated with battery efficiency, this study determines the optimal route using 3D spatial information data and the slope of each link in the route. In the transportation field, the Bureau of Public Roads function classifies the shortest paths and optimizes paths. By adding slope-related variables, a new functional expression that includes weighting as a result of slope-related speed reduction is constructed, and a new functional expression system and network are built. As a result of assigning a route to optimize battery efficiency, the energy efficiency was improved by 7.84 km/kWh at an average speed of 70 km/h. The effectiveness was verified by comparing the total travel time and energy efficiency differences between the two methods. The proposed approach enables efficient transport and ultimately achieves the goal of green transportation with maximum energy efficiency.

Suggested Citation

  • Ku, Donggyun & Choi, Minje & Yoo, Nakyoung & Shin, Seungheon & Lee, Seungjae, 2021. "A new algorithm for eco-friendly path guidance focused on electric vehicles," Energy, Elsevier, vol. 233(C).
    • Handle: RePEc:eee:energy:v:233:y:2021:i:c:s0360544221014468
    DOI: 10.1016/j.energy.2021.121198
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Hemmatpour, Mohammad Hasan & Rezaeian Koochi, Mohammad Hossein & Dehghanian, Pooria & Dehghanian, Payman, 2022. "Voltage and energy control in distribution systems in the presence of flexible loads considering coordinated charging of electric vehicles," Energy, Elsevier, vol. 239(PA).
    2. Zoltán Pusztai & Péter Kőrös & Ferenc Szauter & Ferenc Friedler, 2023. "Implementation of Optimized Regenerative Braking in Energy Efficient Driving Strategies," Energies, MDPI, vol. 16(6), pages 1-20, March.
    3. Choi, Minje & Ku, DongGyun & Kim, Sion & Kwak, Juhyeon & Jang, Yoonjung & Lee, Doyun & Lee, Seungjae, 2023. "Action plans on the reduction of mobility energy consumption based on personal mobility activation," Energy, Elsevier, vol. 263(PD).
    4. Liu, Yonggang & Chen, Qianyou & Li, Jie & Zhang, Yuanjian & Chen, Zheng & Lei, Zhenzhen, 2023. "Collaborated eco-routing optimization for continuous traffic flow based on energy consumption difference of multiple vehicles," Energy, Elsevier, vol. 274(C).
    5. Li, Xinyu & Cao, Yue & Yan, Fei & Li, Yuzhe & Zhao, Wanlin & Wang, Yue, 2022. "Towards user-friendly energy supplement service considering battery degradation cost," Energy, Elsevier, vol. 249(C).

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