Travel Activity Based Stochastic Modelling of Load and Charging State of Electric Vehicles

Muhammad Naveed Iqbal, Lauri Kütt, Matti Lehtonen, Robert John Millar, Verner Püvi, Anton Rassõlkin and Galina L. Demidova
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Muhammad Naveed Iqbal: Department of Power Engineering and Mechatronics, Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia
Lauri Kütt: Department of Power Engineering and Mechatronics, Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia
Matti Lehtonen: Department of Electrical Engineering and Automation, Aalto University, Maarintie 8, 02150 Espoo, Finland
Robert John Millar: Department of Electrical Engineering and Automation, Aalto University, Maarintie 8, 02150 Espoo, Finland
Verner Püvi: Department of Electrical Engineering and Automation, Aalto University, Maarintie 8, 02150 Espoo, Finland
Anton Rassõlkin: Department of Power Engineering and Mechatronics, Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia
Galina L. Demidova: Faculty of Control Systems and Robotics, ITMO University, 197101 Saint Petersburg, Russia

Sustainability, 2021, vol. 13, issue 3, 1-14

Abstract: The uptake of electric vehicles (EV) is increasing every year and will eventually replace the traditional transport system in the near future. This imminent increase is urging stakeholders to plan up-gradation in the electric power system infrastructure. However, for efficient planning to support an additional load, an accurate assessment of the electric vehicle load and power quality indices is required. Although several EV models to estimate the charging profile and additional electrical load are available, but they are not capable of providing a high-resolution evaluation of charging current, especially at a higher frequency. This paper presents a probabilistic approach capable of estimating the time-dependent charging and harmonic currents for the future EV load. The model is based on the detailed travel activities of the existing car owners reported in the travel survey. The probability distribution functions of departure time, distance, arrival time, and time span are calculated. The charging profiles are based on the measurements of several EVs.

Keywords: activity based modelling; EV charging current; EV load model; managed charging; SOC; unmanaged charging (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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