Practical Grid-Based Spatial Estimation of Number of Electric Vehicles and Public Chargers for Country-Level Planning with Utilization of GIS Data

Abstract

This research proposes an approach to estimate the number of different types of electric vehicles for a vast area or an entire country, which can be divided into a large number of small areas such as a subdistrict scale. The estimation methodology extensively utilizes the vehicle registration data in conjunction with Thailand’s so-called EV30@30 campaign and GIS-based road infrastructure data. To facilitate the analysis, square grids are built to form cells representing the number of electric vehicles in any specific area of interest. This estimated number of electric vehicles is further analyzed to determine the energy consumption, calculate the recommended number of public chargers, and visualize an increase in the substation loads from those charging stations. The effectiveness of the proposed methods is demonstrated using the whole area of Thailand, consisting of five regions with a total area of 513,120 km2. The results show that the trucks contribute the most energy consumption while taxis rely on a lot of public chargers. The total energy consumption is about 79.4 GWh per day. A total of 12,565 public fast chargers are needed across the country to properly support daily travel, around half of them being located in the metropolitan area.

Related Work and Motivation for Electric Vehicle Solar/Wind Charging Stations: A Review

“…They also stressed the necessity for more research to investigate the impact of charging and discharging cycles on battery longevity and energy expenses. Prakobkaew and Sirisumrannukul [37] described a grid-based spatial estimate technique for public fast chargers in Thailand. The authors estimate the total daily energy use to be around 79.4 GWh.…”

Multi-Stage Multi-Criteria Decision Analysis for Siting Electric Vehicle Charging Stations within and across Border Regions

“…In [21], the total number of EVs across a country area was used in conjunction with GIS-based road infrastructure data to determine locational vehicular energy demands and estimate the number of public EV charging stations that would be required within specific areas of the country. A mathematical model [22] uses travel route, characteristics of customer demands, and charge and discharge characteristics of EV batteries to optimally site recharging stations using a clustering process to divide customers within a larger geographical area into smaller areas.…”