Electric vehicles (EVs) hold promise for attaining sustainable development objectives and mitigating the effects of global climate change due to their substantial benefits, such as high energy efficiency and low carbon emissions. Research on purchase intentions and behaviors may accelerate the adoption of EVs. Considering that the number of studies on EVs increases in tandem with the size of the market and that mutual interaction supports this two-way growth, conducting studies on consumer behavior in this area in countries where the electric vehicle market is developing, such as Turkey, will provide valuable insights for both the industry and the government. In this study, published articles on the consumer behavior of current and potential purchasers of electric vehicles were analyzed on the axis of Turkey, and the trend of academic studies in the literature was systematized from a holistic standpoint. Co-citation, co-keyword, geographical, and thematic analysis were applied to articles about EV consumer behaviors published between 2004 and 2022 in journals indexed by WoS, Scopus, TR Index, and DergiPark. The results of this study can inform numerous inter-disciplinary studies and researchers on the consumer behavior of electric vehicles. The bibliometric analysis of academic studies on electric vehicle consumers not only closes the market’s knowledge gap and accelerates the adoption process by increasing consumer awareness, but also provides industry representatives and policymakers with insights for the expansion of the EV market.

The charging load forecasting of residential Electric Vehicles help grid operators make informed decisions in terms of scheduling and managing demand response. The residence can include integrated residential appliances with multi‐state and high‐frequency features. For this reason, it is difficult to estimate the total load of residence accurately. To overcome this problem, this paper proposes a hybrid forecasting model using the empirical mode decomposition and Bayesian optimised Long Short‐Term Memory for load balancing based on residential electricity meter data. The residential electricity meter data includes three datasets as Electric Vehicle, heat pump and photovoltaic system. To decompose of the data characteristics, the empirical mode decomposition method performs to the original data. Then, the Bayesian optimised Long Short‐Term Memory is applied to forecast for each sub‐component of the data sequentially. The main features of the proposed model include a significant improvement in prediction accuracy and capture the local maximums. The advantage of the proposed method over existing methods are also verified over with experiments of data‐driven on the IEEE 33 busbar test system. The result of simulation forecasting model indicates that predict closely the busbar outflow power, voltage drop, transformer loading states and power losses to compare with actual load model.

A successful distribution network can continue to operate despite the uncertainties at the charging station, with appropriate equipment retrofits and upgrades. However, these new investments in the grid can become complex in terms of time and space. In this paper, we propose a dynamic charge coordination (DCC) method based on the battery state of charge (SOC) of electric vehicles (EVs) in line with this purpose. The collective uncoordinated charging profiles of EVs charged at maximum power were investigated based on statistical data for distances of EVs and a real dataset for charging characteristics in the existing grid infrastructure. The proposed strategy was investigated using the modified Roy Billinton Test System (RBTS) performed by DIgSILENT Powerfactory simulation software for a total 50 EVs in 30 different models. Then, the load balancing situations were analyzed with the integration of the photovoltaic (PV) generation and battery energy storage system (BESS) into the bus bars where the EVs were fed into the grid. According to the simulation results, the proposed method dramatically reduces the effects on the grid compared to the uncoordinated charging method. Furthermore, the integration of PV and BESS system, load balancing for EVs was successfully achieved with the proposed approach.

Despite the acceptance of electric vehicles (EVs) by consumers in developed countries, consumers' intentions towards these smart devices (SD) and the steps that can be taken to expand in this market continue to be investigated in developing countries such as Turkey. In this study, policies and incentives for the purchase of Electric Vehicles in different countries were examined, consumer concerns before the adoption of SDs were evaluated, and then consumer intentions in adopting EVs with models such as reasoned action theory, planned behavior theory, and technology acceptance model were evaluated with bibliometric analysis through conducted studies. Data from 63 publications accessed from Scopus, Web of Science, and DergiPark databases were used in the field mapping process. The results provide insights into increasing the market share of electric vehicles, which are critical in reducing the carbon footprint, by recommending the issues that need to be highlighted to the industry and researchers.