Consumer Preferences for Electric Vehicle Charging Infrastructure Based on the Text Mining Method

Abstract

The construction of charging infrastructure has a positive effect on promoting the diffusion of new energy vehicles (NEVs). This study uses natural language processing (NLP) technology to explore consumer preferences for charging infrastructure from consumer comments posted on public social media. The findings show that consumers in first-tier cities pay more attention to charging infrastructure, and the number of comments accounted for 36% of the total. In all comments, consumers are most concerned about charging issues, national policy support, driving range, and installation of private charging piles. Among the charging modes of charging piles, direct current (DC) fast charging is more popular with consumers. The inability to find public charging piles in time to replenish power during travel or high energy consumption caused by air conditioning is the main reason for consumers’ range anxiety. Increasing battery performance, improving charging convenience, and construction of battery swap station are the main ways consumers prefer to increase driving range. Consumers’ preference for charging at home is the main reason for their high attention to the installation of private charging piles. However, the lack of fixed parking spaces and community properties have become the main obstacles to the installation of private charging piles. In addition, consumers in cities with different development levels pay different amounts of attention to each topic of charging infrastructure. Consumers in second-tier and above cities are most concerned about charging issues. Consumers in third-tier and above cities pay significantly more attention to the installation of private charging piles than consumers in fourth-tier and fifth-tier cities. Consumers in each city have almost the same amount of attention to driving range.

Analysis of Current Situation, Problems and Potential Solutions of Electric Vehicle Industry

“…One potential solution to the lack of charging pile issue can be installing more charging piles in the public places usually populated with energy vehicle users. According to Wang, Chi, Xu, and Li, one of the most comments consumer made about new energy car is the related to its charging issue [6]. The consumers prefer to enhance the development of the infrastructure for charging, including the setting up of private charge piles and the setting up of public charging stations.…”

Exploring user satisfaction and improvement opportunities in public charging pile resources

“…In this context, [20] analyze EV users' comments posted on social media to reveal EV users' preferences for charging infrastructure. By conducting sentiment analysis on EV users' comments, [21] identify the key factors that trigger negative attitudes by focusing on the negative comments, while [22] predict the sale quantity of EV by combing the EV sales data.…”

“…This approach may face challenges in adequately processing intricate semantic and contextual information, thereby limiting its ability to classify various comment types accurately. The application of word frequency analysis, word similarity analysis, and topic analysis in [20] may inadvertently neglect crucial dimensions of keywords and limit the scope of relevant factor words identified, which fail to provide a comprehensive reflection of the issues existing within the charging pile service.…”

Initial location selection of electric vehicles charging infrastructure in urban city through clustering algorithm

“…Trinko et al [14] interpret and analyzes the charging price information from EVs charging data platform (the charging level, prices, geographic location, location type, network, and provider based on an ad hoc text mining method. Wang et al [15] utilized the natural language processing (NLP) technique for investigating consumer preferences in EV charger infrastructure based on public social media posts. Almaghrebi et al [16] analyzed user charging behavior to support efficiently managing the electrical grid employing the regression method XGBoost.…”