# A Literature Review on the Charging Behaviour of Private Electric Vehicles
## 2 Literature Review and Method
### Analysis of Users' Preferences and Needs
#### 2.2.2 Mobility and Charging Behavior Data  
Mobility data constitute a valid source for extracting travel and transport habits in each area visited. The quantity and quality of information can vary greatly depending on the methods used to collect and record data. Mobility data can be combined with recordings of the charging events and information from EVs' on-board instrumentation to provide a more comprehensive picture of the charging behaviors [25]. Mobility data can be used to identify the points of greatest attraction and the spatiotemporal distribution of trips. In some cases, they can refer to ICE vehicles and translate to electric mobility data, with the hypothesis that journeys, especially in urban areas, do not change radically with the transition from one type of powertrain to another [75]. EV charging preferences are the subject of various studies based on mobility and EV data. They usually concentrate on initial and final SOC during charges, the frequency of charging with respect to distance travelled, or the number and nature of stops. Some work referred to the initial phase of EV diffusion. The prevalent use of charging at home and work emerged in two studies [76; 77]. Both studies show that the start-of-charging SOC is, on average, above 50%. Using static and dynamic data regarding EVsand CPs from over 10 European countries, the authors of [78] found four main patterns of behavior, characterized by different temporal distributions of trips and charges, depending on the type of EV and the location of the CP. Mobile telephone data integrated with census data, a survey on PEV drivers, and measurements made at CPs made it possible to build a high-definition space-time mobility model [79], which detects how charging behaviors follow traffic trends, suggesting the absence of a charging strategy. Furthermore, considering the SOC, the consumption, the charge time, and the distance between successive charges, it emerges that EV users charge more frequently than necessary [80]. This result was confirmed by [PERSON] et al. [81]: analyzing driving and recharging behaviors, they found that the distances between consecutive recharges are generally shorter than the average daily distances, indicating a tendency to charge whenever there is a convenient opportunity, regardless of the remaining range. This behavior is comparable with that identified in [82], as well as with the results in [83], highlighting a high daily number of opportunity charges. A risk analysis of the interval times between recharging events showed that both vehicle attributes, such as state of charge, distance traveled, and average driving speed, and individual characteristics (range anxiety, age, and purpose of travel) significantly influence the instantaneous rate of occurrence of charging events [84]. Vehicle usage also influences charging behavior, with commercial EVs needing to be charged after a trip more often than private ones, with a tendency for private BEVs to synchronize charging with the cheapest electricity rates [85]. Regarding fast charging, users generally prefer stations that require shorter detours and are greatly influenced in their choice by the residual SOC [86]. Different approaches have been used to classify private charge behaviors, applying statistical analysis techniques [87; 88], clustering [89; 90], or data mining [91], substantially confirming the prevalence of slow night charging on weekdays. Using aggregate analysis of charging demand, one can gain insights into the charging behaviors of different types of users [92]. For example, the authors of [93] found that the probability of using public charging in a given area is proportional to the average number of cars per household and inversely proportional to the percentage of private homes in the residential area considered. [PERSON] et al. [94; 95] provided a model for estimating the aggregated charging profiles of different driver groups whose charging behaviors were clusters derived from a large data set on workplace, public, and residential charging.