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Strategies for the Modelisation of Electric Vehicle Energy Consumption: A Review

Author

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  • Andrea Di Martino

    (Department of Energy, Politecnico di Milano, Via La Masa, 34, 20156 Milan, Italy)

  • Seyed Mahdi Miraftabzadeh

    (Department of Energy, Politecnico di Milano, Via La Masa, 34, 20156 Milan, Italy)

  • Michela Longo

    (Department of Energy, Politecnico di Milano, Via La Masa, 34, 20156 Milan, Italy)

Abstract

The continuous technical improvements involving electric motors, battery packs, and general powertrain equipment make it strictly necessary to predict or evaluate the energy consumption of electric vehicles (EVs) with reasonable accuracy. The significant improvements in computing power in the last decades have allowed the implementation of various simulation scenarios and the development of strategies for vehicle modelling, thus estimating energy consumption with higher accuracy. This paper gives a general overview of the strategies adopted to model EVs for evaluating or predicting energy consumption. The need to develop such solutions is due to the basis of each analysis, as well as the type of results that must be produced and delivered. This last point strongly influences the whole set-up process of the analysis, from the available and collected dataset to the choice of the algorithm itself.

Suggested Citation

  • Andrea Di Martino & Seyed Mahdi Miraftabzadeh & Michela Longo, 2022. "Strategies for the Modelisation of Electric Vehicle Energy Consumption: A Review," Energies, MDPI, vol. 15(21), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8115-:d:959222
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    References listed on IDEAS

    as
    1. Pedram Asef & Ramon Bargallo & Andrew Lapthorn & Davide Tavernini & Lingyun Shao & Aldo Sorniotti, 2021. "Assessment of the Energy Consumption and Drivability Performance of an IPMSM-Driven Electric Vehicle Using Different Buried Magnet Arrangements," Energies, MDPI, vol. 14(5), pages 1-22, March.
    2. Yang, S.C. & Li, M. & Lin, Y. & Tang, T.Q., 2014. "Electric vehicle’s electricity consumption on a road with different slope," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 41-48.
    3. Wang, Hua & Zhao, De & Meng, Qiang & Ong, Ghim Ping & Lee, Der-Horng, 2020. "Network-level energy consumption estimation for electric vehicles considering vehicle and user heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 30-46.
    4. Liu, Kai & Wang, Jiangbo & Yamamoto, Toshiyuki & Morikawa, Takayuki, 2018. "Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption," Applied Energy, Elsevier, vol. 227(C), pages 324-331.
    5. Al-Wreikat, Yazan & Serrano, Clara & Sodré, José Ricardo, 2022. "Effects of ambient temperature and trip characteristics on the energy consumption of an electric vehicle," Energy, Elsevier, vol. 238(PC).
    6. Kyoungho Ahn & Sangjun Park & Hesham A. Rakha, 2020. "Impact of Intersection Control on Battery Electric Vehicle Energy Consumption," Energies, MDPI, vol. 13(12), pages 1-11, June.
    7. Jin Li & Feng Wang & Yu He, 2020. "Electric Vehicle Routing Problem with Battery Swapping Considering Energy Consumption and Carbon Emissions," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    8. Anatole Desreveaux & Alain Bouscayrol & Elodie Castex & Rochdi Trigui & Eric Hittinger & Gabriel-Mihai Sirbu, 2020. "Annual Variation in Energy Consumption of an Electric Vehicle Used for Commuting," Energies, MDPI, vol. 13(18), pages 1-15, September.
    9. Fiori, Chiara & Ahn, Kyoungho & Rakha, Hesham A., 2016. "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, Elsevier, vol. 168(C), pages 257-268.
    10. Al-Wreikat, Yazan & Serrano, Clara & Sodré, José Ricardo, 2021. "Driving behaviour and trip condition effects on the energy consumption of an electric vehicle under real-world driving," Applied Energy, Elsevier, vol. 297(C).
    11. David Jiménez & Sara Hernández & Jesús Fraile-Ardanuy & Javier Serrano & Rubén Fernández & Federico Álvarez, 2018. "Modelling the Effect of Driving Events on Electrical Vehicle Energy Consumption Using Inertial Sensors in Smartphones," Energies, MDPI, vol. 11(2), pages 1-23, February.
    12. Chung, Yu-Wei & Khaki, Behnam & Li, Tianyi & Chu, Chicheng & Gadh, Rajit, 2019. "Ensemble machine learning-based algorithm for electric vehicle user behavior prediction," Applied Energy, Elsevier, vol. 254(C).
    13. Li, Yongfu & Zhong, Zhenyu & Zhang, Kaibi & Zheng, Taixiong, 2019. "A car-following model for electric vehicle traffic flow based on optimal energy consumption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
    14. Zhang, Jin & Wang, Zhenpo & Liu, Peng & Zhang, Zhaosheng, 2020. "Energy consumption analysis and prediction of electric vehicles based on real-world driving data," Applied Energy, Elsevier, vol. 275(C).
    15. Seyed Mahdi Miraftabzadeh & Michela Longo & Federica Foiadelli & Marco Pasetti & Raul Igual, 2021. "Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey," Energies, MDPI, vol. 14(16), pages 1-24, August.
    16. Wang, Yachao & Wen, Yi & Zhu, Qinggong & Luo, Jiaxin & Yang, Zhengjun & Su, Sheng & Wang, Xin & Hao, Lijun & Tan, Jianwei & Yin, Hang & Ge, Yunshan, 2022. "Real driving energy consumption and CO2 & pollutant emission characteristics of a parallel plug-in hybrid electric vehicle under different propulsion modes," Energy, Elsevier, vol. 244(PB).
    17. Pelletier, Samuel & Jabali, Ola & Laporte, Gilbert, 2019. "The electric vehicle routing problem with energy consumption uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 225-255.
    18. Krause, Jette & Thiel, Christian & Tsokolis, Dimitrios & Samaras, Zissis & Rota, Christian & Ward, Andy & Prenninger, Peter & Coosemans, Thierry & Neugebauer, Stephan & Verhoeve, Wim, 2020. "EU road vehicle energy consumption and CO2 emissions by 2050 – Expert-based scenarios," Energy Policy, Elsevier, vol. 138(C).
    19. Wieczorek, Maciej & Lewandowski, Mirosław, 2017. "A mathematical representation of an energy management strategy for hybrid energy storage system in electric vehicle and real time optimization using a genetic algorithm," Applied Energy, Elsevier, vol. 192(C), pages 222-233.
    20. Polychronis Spanoudakis & Nikolaos C. Tsourveloudis & Lefteris Doitsidis & Emmanuel S. Karapidakis, 2019. "Experimental Research of Transmissions on Electric Vehicles’ Energy Consumption," Energies, MDPI, vol. 12(3), pages 1-15, January.
    21. Wang, Hewu & Zhang, Xiaobin & Ouyang, Minggao, 2015. "Energy consumption of electric vehicles based on real-world driving patterns: A case study of Beijing," Applied Energy, Elsevier, vol. 157(C), pages 710-719.
    22. Lin, Xinyou & Li, Yalong & Zhang, Guangji, 2022. "Bi-objective optimization strategy of energy consumption and shift shock based driving cycle-aware bias coefficients for a novel dual-motor electric vehicle," Energy, Elsevier, vol. 249(C).
    23. Lei, Fei & Bai, Yingchun & Zhu, Wenhao & Liu, Jinhong, 2019. "A novel approach for electric powertrain optimization considering vehicle power performance, energy consumption and ride comfort," Energy, Elsevier, vol. 167(C), pages 1040-1050.
    24. Ruan, Jiageng & Walker, Paul & Zhang, Nong, 2016. "A comparative study energy consumption and costs of battery electric vehicle transmissions," Applied Energy, Elsevier, vol. 165(C), pages 119-134.
    25. Hamed Jafari Kaleybar & Morris Brenna & Huan Li & Dario Zaninelli, 2022. "Fuel Cell Hybrid Locomotive with Modified Fuzzy Logic Based Energy Management System," Sustainability, MDPI, vol. 14(14), pages 1-22, July.
    26. Zhang, Shuai & Gajpal, Yuvraj & Appadoo, S.S. & Abdulkader, M.M.S., 2018. "Electric vehicle routing problem with recharging stations for minimizing energy consumption," International Journal of Production Economics, Elsevier, vol. 203(C), pages 404-413.
    27. Luin, Blaž & Petelin, Stojan & Al-Mansour, Fouad, 2019. "Microsimulation of electric vehicle energy consumption," Energy, Elsevier, vol. 174(C), pages 24-32.
    28. Seyed Mahdi Miraftabzadeh & Michela Longo & Federica Foiadelli, 2021. "Estimation Model of Total Energy Consumptions of Electrical Vehicles under Different Driving Conditions," Energies, MDPI, vol. 14(4), pages 1-15, February.
    29. Gian Luca Patrone & Elena Paffumi & Marcos Otura & Mario Centurelli & Christian Ferrarese & Steffen Jahn & Andreas Brenner & Bernd Thieringer & Daniel Braun & Thomas Hoffmann, 2022. "Assessing the Energy Consumption and Driving Range of the QUIET Project Demonstrator Vehicle," Energies, MDPI, vol. 15(4), pages 1-21, February.
    30. Andrés Montero Romero & Andrea Di Martino & Michela Longo & Linda Barelli & Dario Zaninelli, 2022. "Full Implementation of Electric Mobility in a Countryside Region of Spain," Energies, MDPI, vol. 15(17), pages 1-19, August.
    31. Xu, Bin & Shi, Junzhe & Li, Sixu & Li, Huayi & Wang, Zhe, 2021. "Energy consumption and battery aging minimization using a Q-learning strategy for a battery/ultracapacitor electric vehicle," Energy, Elsevier, vol. 229(C).
    32. Tu, Wei & Santi, Paolo & Zhao, Tianhong & He, Xiaoyi & Li, Qingquan & Dong, Lei & Wallington, Timothy J. & Ratti, Carlo, 2019. "Acceptability, energy consumption, and costs of electric vehicle for ride-hailing drivers in Beijing," Applied Energy, Elsevier, vol. 250(C), pages 147-160.
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    2. Ma, Yifan & Sun, Wei & Zhao, Zhoulun & Gu, Leqi & Zhang, Hui & Jin, Yucheng & Yuan, Xinmei, 2024. "Physically rational data augmentation for energy consumption estimation of electric vehicles," Applied Energy, Elsevier, vol. 373(C).

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