# A Literature Review on the Charging Behaviour of Private Electric Vehicles
## 2 Literature Review and Method
### Analysis of Users' Preferences and Needs
#### 2.2.1 Survey-Based Papers  
Valid tools with which to investigate charging behavior are questionnaires addressed to actual or potential EV users. These questionnaires can explore various aspects related to mobility, such as travel and charging habits, responses to policies, and attitudes toward EVs. At the same time, they are intended to highlight possible influences of different parameters on the results, such as socio-economic, territorial, and infrastructural aspects, which are more difficult to identify or are even undetectable using other approaches based, e.g., on charge events or traffic data. Some questionnaires and surveys collect travel data, from which it is possible to infer charging behavior. In fact, the travel pattern of an EV user is a key factor in simulating and predicting the distribution of charging demand. The validity of household travel surveys in terms of estimating charging load was tested in the Swiss context in [28]. In the cited study, the results of a survey on ICE cars were used, and a complete transition to EVs, both pure battery (BEV) and plug-in hybrids (PHEV), was assumed. The load curves obtained under this hypothesis were compared with measurements made in various field tests for EVs, showing a good agreement. The charging decision scheme modeled depended solely on the SOC. The same charging criterion was adopted by [PERSON] et al. [29] to determine the power demand for residential EVSE. Using traffic survey data on ICEs and assuming a transition to EV, they classified daily usage based on different categories of car owners and provided an estimate of SOC based on distances traveled. The importance of travel choices in charging decisions is illustrated in the study by [PERSON] et al. [11]. They explored the relationship between two models of causal choices: in the first, the charging strategy was determined first and affects the travel chain; in the other, the journey influences the charge decision. The preferences from about 500 questionnaires showed that the model in which the travel choice precedes that of recharging is more suitable for interpreting the experimental charging curves. [PERSON] et al. [30] used mobility questionnaires explicitly devoted to EV owners to determine the spatial and temporal distribution of stops as a function of destinations. The findings show that the charging demand in residential areas and workplaces is the largest, followed by public parking lots and curbside parking spaces. A survey on driving and ownership data serving as the basis of charging demand estimation for public infrastructure in urban areas where domestic charging is not broadly available was conducted in [31]. The results reveal that nearly 78% of energy demand can be supplied by private CPs, of which 11% can be provided by chargers installed in shared residential parking lots, reducing the need for public CPs by up to 58%. For commuters without home-charging equipment, workplace charging could lower the need for public charging by 68%. DC fast charging would amount only to 3% of the total charging demand due to the significantly higher cost and greater inconvenience of the dedicated stop. Charge demand at workplace charging facilities is also evaluated in [32]. The survey outcomes show that slow chargers in the workplace can almost completely meet the intra-city travel demand of private EV users, even if the size of the city greatly influences the mobility patterns and the charging demand curves due to the different travel needs. User Charging Preferences Surveys  
Survey results are often combined with data from other sources to obtain even more reliable charging behavior simulations, especially to account for the impact that the variability of trips and charging behaviors has on the estimation of aggregate charging demand [33]. The study reported in [34] crosses the topographic data of various points of interest with the time users spend near them, the average stops for daily activity, and vehicle fleet data.