# A Literature Review on the Charging Behaviour of Private Electric Vehicles
## 2 Literature Review and Method  
The analysis of charging behavior is of fundamental importance for the correct planning of infrastructures, the selection of optimal charge management strategies, and the application of policies centered on improving the penetration of electric mobility and demand integration with the electricity distribution network. There are different possible approaches to such an investigation, which may depend on the type of data used, the variables taken into consideration, and the specific purposes of the studies being conducted. Indeed, charge demand analysis can rely on different data and information sources. Some studies reviewed in this work are devoted to behavioral investigation, while others use data to obtain load estimates for further purposes. The studies were generally conducted on limited geographical areas and for well-defined periods. The datasets used to obtain information on charging choices and load curves include charging point (CP) information, historical charging session series, traffic monitors, travel surveys, user questionnaires, and EV information. The relevant data analysis approaches include statistical characterization, stochastic processes, and machine learning. Aggregated or disaggregated analysis can be used for the assessment of demand. Aggregated analyses generally do not consider the characteristics of a vehicle and/or its user. They rely on data in which the charge requests are evaluated according to various recharge parameters, such as time, location, power, and, when available, the state of charge (SOC) of a battery. Disaggregated analysis, on the contrary, starts from the \"mobility profile\" of individuals, often correlating these data with socio-economic information. While in principle it is possible to determine disaggregated demand starting from aggregated load curves [21], using disaggregated data allows for more precise analysis and calculation of the aggregate demand [22]. The works included in this review are presented according to the following scheme: first, we will illustrate some recent review papers on EV charging; then, we review the literature based on their approaches to charging behavior analysis, conducted from the point of view of users' preferences or the exploitation of the existing charging infrastructure. A search was carried out using the keywords 'charging behavior'; 'EV charging'; 'charge infrastructures usage'; 'EV user behavior'; 'EV charge'; and combinations of the previous ones using the main search tools of scientific publications (Google Scholar, Internet Archive Scholar (IAS), and CORE). We limited the search to papers published after 2015, except for some particularly relevant works. In the searches conducted on Google Scholar and CORE, we used the \"OR\" operator between keywords. For IAS, the string used was \"charge* behav*\" for the search in the 'Description' field. Google Scholar returned around 24.400 results; 2.374 research outputs were found using CORE. For IAS, we obtained 5.721 results. Out of these, 1.098 were texts. When we filtered for Subject (Subject and number of texts: Computing Research Repository, 26; Mathematical Physics, 18; Chemical Physics, 17; Systems and Control, 10; Computational Physics, 9; Disordered Systems and Neural Networks, 9; Information Theory, 7; Qualitative Research, 2; \"CHEMICAL REACTIONS, 2. ), only 84 texts were returned. We selected articles that specifically mentioned the charging behavior of EV users in their titles or abstracts. From this selection, we further refined our search to include only those articles that relied on survey data related to mobility and charging habits, charging infrastructures, or floating car data. We excluded papers that only evaluated aggregated charging behaviors without analyzing individual charging behavior patterns. Additionally, we disregarded studies that solely relied on synthetic models to assess charging behavior and demand.