Abstract
In this paper the factors that influence the energy consumption of electric vehicles are examined. The main factors affecting the driving resistance such as load, grades, vehicle speed, and additional factors are considered. For example the climate control system and the influence of ambient temperature on the electric vehicle range. The impact of the electric drive efficiency map is also taken into account. The impact of each of the factors was evaluated through a numerical study. Recommendations are given for the strategy of an adaptive predictive model for the energy management of an electric vehicle. To be planned the point of next recharge for a long distance trip, the travel conditions must be taken into account. This is done by measuring some parameters before and during the trip. The information from GPS navigation for the intended trip must also be taken into account. It could give information for road inclines and the location of the charging stations.
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Acknowledgements
This work is supported by the ECEPE project. The ECQA Certified Electric Powertrain Engineer project (ECEPE) is co-funded by the Erasmus+ Call 2019 Round 1 KA203 Programme of the European Union under the agreement 2019-1-CZ01-KA203-061430.
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Pavlov, N., Gigov, B., Stefanova-Pavlova, M., Dimitrova, Z. (2020). Adaptive Predictive Energy Management Strategy Example for Electric Vehicle Long Distance Trip. In: Yilmaz, M., Niemann, J., Clarke, P., Messnarz, R. (eds) Systems, Software and Services Process Improvement. EuroSPI 2020. Communications in Computer and Information Science, vol 1251. Springer, Cham. https://doi.org/10.1007/978-3-030-56441-4_6
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