# A Literature Review on the Charging Behaviour of Private Electric Vehicles
## 2 Literature Review and Method
### Review Papers  
Defining user charging behavior is a complex task for several reasons. First, as already mentioned, charge behavior is influenced by many factors, which can be psychological, sociological, demographic, or geographical or linked to the maturity of the EV market and thediffusion of charging infrastructures. Secondly, the data from which to extract or infer these behaviors are limited, as in the case of targeted surveys, or suffer from intrinsic limitations, such as mobility data also relating to ICE vehicles or charging data relating only to certain operators or geographical areas. Several review articles have addressed the problem of examining and classifying papers that have dealt with the charging question. [PERSON] et al. [23] examined the approaches and data sources used to model charging behaviors, aiming to address their implementation in the planning of charging infrastructures. [PERSON] et al. [14] presented a review of studies to identify which characteristics of an EV and its system of services, including the infrastructure system and policies for promoting electric mobility, impact consumer choices. [PERSON] et al. [24] reviewed open databases relevant to EV charging demand modeling and gave an overview of the forecasting models, with approaches ranging from statistical characterization, stochastic processes, and machine learning. An examination of studies on the impact of charging demand on the energy distribution grid is presented in [PERSON] et al. [5]. [PERSON] & [PERSON] [25] provided a collection of data sets on sales volumes, driving, EV charging, and automotive battery performance. Their discussion includes an analysis of some EV models and types of EVSE, the impact of EV charging behavior on the local infrastructure, and some smart-charging optimization approaches. [PERSON] et al. [26] analyzed studies on user interactions and preferences for charging infrastructure, using data based on questionnaires, GPS data from vehicles, and EVSE data. Although home charging emerged as the preferred option, the authors stressed that further analysis is needed to determine the best strategy for developing the infrastructure needed to support EV rollouts. In addition, [PERSON] et al. [27] reviewed studies investigating the medium-long-term demand for recharging infrastructure, comparing the framework conditions in different countries to highlight the differences. The authors concluded that public charging infrastructure seems necessary as an alternative to home charging only in some densely populated areas. [PERSON] et al. [19] reviewed the literature related to user experience, including driving and travel behaviors, vehicle interactions, and subjective aspects of the user experience, including symbolic and social aspects such as environmentalism, futurism, and social status. The current work focuses on the charging behavior of private electric vehicle users, disregarding aspects related to the impact on the network that some previous reviews may have covered. Our primary objective was to provide a comprehensive overview of the different factors that can impact user charging demand. More specifically, our objective was to verify whether it is possible to identify, within the literature, the variables that influence charging behavior beyond contextual differences; in addition, we aimed to verify if and how local characteristics affect individual behaviors. Some recommendations on areas that require further investigations are also given in the conclusions.