Electric Vehicle Charging Load Prediction for Private Cars and Taxis Based on Vehicle Usage Data

Abstract

Electric Vehicles (EVs) are growing attention for their higher efficiency and less-polluting specifications. However, a massive introduction of EVs could lead to several issues in power systems. Several authors have proposed various smart charging approaches. But, these approaches could not be appropriately implemented without knowing the charging behavior of the EV customers. Thus, this paper proposes an EV charging load prediction for the particular case of Quito, Ecuador. This forecasting is performed based on data obtained from a GPS system and on statistical methods.

Optimal siting and sizing of electric taxi charging stations considering transportation and power system requirements

“…With so few ETs in Quito, charging behavior must be analyzed to provide a charging load curve for optimal charging station sizing. To that end, GPS data from driving behavior were gathered from taxi drivers in Quito [46]. A gasoline-to-electricity conversion rate is used to divide the required electricity energy by the number of taxis, resulting in an average required energy of 30.35 kWh per taxi.…”

Coordinated Siting and Sizing of Electric Taxi Charging Stations Considering Traffic and Power Systems Conditions

“…There are very few ETs in Quito, so the charging behavior must be modeled to obtain a charging load curve for the optimal sizing of each charging station. To this end, real information from driving behavior based on GPS was obtained from taxi drivers in Quito [23]. A conversion rate from gasoline to electricity was used.…”

Urban Traffic Flow Mapping of an Andean Capital: Quito, Ecuador

“…Based on a previous work that considers the GPS travel patterns of various taxi drivers, it was found that the vehicle is stopped during lunchtime, dinner time, and most of them during all night [46]. Typically, taxi vehicle owners park their cars at home when it is not in use.…”