| Title |
Charging Pattern of Electric Vehicle User and Affecting Factors : Latent Class Analysis Approach |
| Authors |
박지영(Jiyoung Park) ; 김찬성(Chansung Kim) |
| DOI |
https://doi.org/10.5370/KIEE.2022.71.11.1639 |
| Keywords |
Electric Vehicle; Charging infrastructure; Charging pattern; Latent class analysis; Affecting factors |
| Abstract |
Reliable charging infrastructure is an essential element to transform the current fossil fuel-centered automobile market into electric vehicles. In Korea, the supply level of public charging infrastructure is better than that of other countries, but the residential charging infrastructure is hard to expand due to the domestic characteristics. Therefore, in order to meet the electric vehicle era in the future, charging infrastructure supply strategies suitable for the domestic situation should be prepared. This study analyzed the charging patterns of electric vehicle drivers as essential data necessary for future charging infrastructure plans and decision-making on the supply of charging facilities. This study utilized the data of one-week charging events survey of 297 electric car drivers conducted in 2021, and the Latent Class Analysis was applied to identify the charging pattern of individual driver. As a result, the charging patterns of electric car drivers were classified into four types: Mixed & Slow 69.3%, Home & Slow 16.5%, Public-centric 8.2%, and Work & Slow 6.1%. As a result of analyzing the predictive variables of the charging pattern through multi-logit analysis, accessibility by charging infrastructure type and preference by type of charging infrastructure were found to be statistically significant affecting factors for all charging patterns. For some classes of charging pattern, annual driving mileage and parking conditions at home were also found to have a significant effect. |