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Large-Scale Electric Vehicle Energy Demand Considering Weather Conditions and Onboard Technology

  • Simin Luo
  • Yan Tian
  • Wei Zheng
  • Xiaoheng Zhang
  • Jingxia Zhang
  • Bowen ZhouEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 925)

Abstract

Accurate knowledge of the state-of-charge (SOC) parameter estimated for electric vehicle (EV) batteries is of particular importance when calculating the EV energy demand. Many factors and methods have been proposed for SOC estimation. However, often methods focus on the battery itself rather than customer usage and related factors. This paper proposes a EV energy demand estimation method based on SOC, where onboard power electronics and weather conditions are considered. A practical mathematical model is proposed for large-scale EV provision in which a correction factor is included for calculation of actual maximum range. Typical values from real EVs and statistics are used to exemplify the model in a case study. The results presented in the paper indicate that with inclusion of weather factors and use of onboard technology, electricity consumption is greater and consequently the actual range of an EV is shortened and the EV energy demand is quantitatively increased.

Keywords

Electric Vehicle (EV) State-of-Charge (SOC) Energy demand Weather condition Onboard technology 

Notes

Acknowledgments

This paper was supported by the National Natural Science Foundation of China (61703081), and Natural Science Foundation of Liaoning Province (20170520113).

References

  1. 1.
    Pop, V., Bergveld, H.J., Notten, P.H.L., et al.: State-of-the-art of battery state-of-charge determination. Meas. Sci. Technol. 16(4), R93–R110 (2005)CrossRefGoogle Scholar
  2. 2.
    Williamson, S.S.: Future charging infrastructures and energy management strategies for electric and plug-in hybrid electric vehicles. In: 2012 25th IEEE Canadian Conference on Electrical & Computer Engineering (CCECE), pp. 1–3. IEEE Press, New York (2013)Google Scholar
  3. 3.
    Erjavec, J.: Hybrid, Electric & Fuel-Cell Vehicles, 2nd edn. Delmar, New York (2013)Google Scholar
  4. 4.
    Stević, Z.: New Generation of Electric Vehicles. InTech, Rijeka (2012)CrossRefGoogle Scholar
  5. 5.
    Liu, W., Xuan, Z., Jian, M.: A new neural network model for the state-of-charge estimation in the battery degradation process. Appl. Energy 121, 20–27 (2014)CrossRefGoogle Scholar
  6. 6.
    Xu, J., Mi, C.C., Cao, B., et al.: The state of charge estimation of lithium-ion batteries based on a proportional-integral observer. IEEE Trans. Veh. Technol. 63, 1614–1621 (2014)CrossRefGoogle Scholar
  7. 7.
    Zhang, C., Li, K., Pei, L., et al.: An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries. J. Power Sources 283, 24–36 (2015)CrossRefGoogle Scholar
  8. 8.
    Zhang, P., Qian, K., Zhou, C., et al.: A methodology for optimization of power systems demand due to electric vehicle charging load. IEEE Trans. Power Syst. 27, 1628–1636 (2012)CrossRefGoogle Scholar
  9. 9.
    Cai, H., Du, W., Yu, X., et al.: Day-ahead optimal charging/discharging scheduling for electric vehicles in micro-grids. In: 2nd IET Renewable Power Generation Conference (RPG 2013), pp. 1–4. IET, London (2013)Google Scholar
  10. 10.
    Qi, Z.: Advances on air conditioning and heat pump system in electric vehicles – a review. Renew. Sustain. Energy Rev. 38, 754–764 (2014)CrossRefGoogle Scholar
  11. 11.
    Chukwu, U.C., Mahajan, S.M.: V2G parking lot with PV rooftop for capacity enhancement of a distribution system. IEEE Trans. Sustain. Energy 5, 119–127 (2014)CrossRefGoogle Scholar
  12. 12.
    Farghal, S.A., Tantawy, M.A., El-Alfy, A.E.: Optimum design of stand alone solar thermal power system with reliability constraint. IEEE Trans. Energy Convers. EC-2, 215–221 (2009)CrossRefGoogle Scholar
  13. 13.
    Chien, J., Tseng, K., Yan, B.: Design of a hybrid battery charger system fed by a wind-turbine and photovoltaic power generators. Rev. Sci. Instrum. 82, 095106 (2011)CrossRefGoogle Scholar
  14. 14.
    Leahy, P.G., Foley, A.M.: Impact of weather conditions on electric vehicle performance. In: Proceedings of the Irish Transport Research Network Conference 2011, ITRN2011, Cork, pp. 1–4 (2011)Google Scholar
  15. 15.
    Jayaweera, D., Islam, S.: Risk of supply insecurity with weather condition-based operation of plug in hybrid electric vehicles. IET Gener. Transm. Distrib. 8, 2153–2162 (2014)CrossRefGoogle Scholar
  16. 16.
    Wua, X., Freeseb, D., Cabrerab, A., et al.: Electric vehicles’ energy consumption measurement and estimation. Transp. Res. Part D Transp. Environ. 34, 52–67 (2015)CrossRefGoogle Scholar
  17. 17.
    Engvall, L., Cook, A., Khaligh, A.: A predictive trip-based method for state of charge maintenance in series PHEVs to boost cold weather efficiency. In: 2012 IEEE Transportation Electrification Conference and Expo (ITEC), pp. 1–6. IEEE Press, New York (2012)Google Scholar
  18. 18.
    Smith, R., Morison, M., Capelle, D., et al.: GPS-based optimization of plug-in hybrid electric vehicles’ power demands in a cold weather city. Transp. Res. Part D Transp. Environ. 16, 614–618 (2011)CrossRefGoogle Scholar
  19. 19.
    Shams-Zahraei, M., Kouzani, A.Z., Kutter, S., et al.: Integrated thermal and energy management of plug-in hybrid electric vehicles. J. Power Sources 216, 237–248 (2012)CrossRefGoogle Scholar
  20. 20.
    Department for Transport: National Travel Survey 2012. https://www.gov.uk/government/publications/national-travel-survey-2012
  21. 21.
  22. 22.
    Tsapakis, I., Cheng, T., Bolbol, A.: Impact of weather conditions on macroscopic urban travel times. J. Transp. Geogr. 28, 204–211 (2013)CrossRefGoogle Scholar
  23. 23.
    Mahmassani, H.S., Dong, J., Kim, J., et al.: Incorporating weather impacts in traffic estimation and prediction systems. US Department of Transportation. FHWA-JPO-09-065, EDL# 14497 (2009)Google Scholar
  24. 24.
    Zhou, B., Littler, T., Foley, A.: Electric vehicle capacity forecasting model with application to load levelling. In: IEEE PES General Meeting 2015, pp. 1–5. IEEE Press, New York (2015)Google Scholar
  25. 25.
    European commission: Energy Roadmap 2050 Impact assessment and scenario analysis. SEC(2011) 1565 final (2011)Google Scholar
  26. 26.
    Zhou, B., Littler, T., Wang, H.: The impact of vehicle-to-grid on electric power systems: a review. In: IET Renewable Power Generation Conference 2013, pp. 1–4. IET, London (2013)Google Scholar
  27. 27.
    Cui, M., Ke, D., Sun, Y., et al.: Wind power ramp event forecasting using a stochastic scenario generation method. IEEE Trans. Sustain. Energy 6(2), 422–433 (2015)CrossRefGoogle Scholar
  28. 28.
    Cui, M., Zhang, J., Wu, H., et al.: Wind-friendly flexible ramping product design in multi-timescale power system operations. IEEE Trans. Sustain. Energy 8(3), 1064–1075 (2017)CrossRefGoogle Scholar
  29. 29.
    Cui, M., Zhang, J., Florita, A., et al.: An optimized swinging door algorithm for identifying wind ramping events. IEEE Trans. Sustain. Energy 7(1), 150–162 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Simin Luo
    • 1
  • Yan Tian
    • 1
  • Wei Zheng
    • 2
  • Xiaoheng Zhang
    • 3
  • Jingxia Zhang
    • 3
  • Bowen Zhou
    • 3
    Email author
  1. 1.Guangzhou Power Supply BureauGuangzhouChina
  2. 2.State Grid Huludao Electric Power Supply CompanyHuludaoChina
  3. 3.College of Information Science and EngineeringNortheastern UniversityShengyangChina

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