# Planning of High-Power Charging Stations for Electric Vehicles: A Review
## 3 Data Acquisition
### Model- and Data-Driven  
Depending on the availability of the input data discussed in the previous section and the objective of the authors, the AS problem is formulated according to a model- or a data-driven approach [32; 116]. The former, so-called \"theoretical approach\" implements mathematical modelling to capture the charging demand. The authors of [116] describe it as a procedure in which a mathematical methodology is coupled with synthetic data to produce AS of CSs. Studies fall in this category if they make use of real transport networks (distances, elevation profiles), but there are significant transport parameters for describing EV driving patterns (e.g., distance traveled) that are computer simulated or do not belong to the studied region/transport network. The model-driven approach is used in [58; 59], wherein the authors apply random spatial distribution together with the Gaussian probability function to compute EVs arrivals. The authors of [58] scale EV arrivalcurves depending on the population density of each neighbourhood. One of the parameters that is found to be commonly calculated through probabilistic methods is SOC. This is probably due to a lack of specific data regarding EV battery operational routines. In [61], the SOC of EVs arriving at charging stations have a normal distribution, whereas charging times (highly dependent on initial SOC) follow an exponential distribution in [117] and a log-normal distribution in [118; 119]. Study [23] outlines a mobility scenario for building an FCS demand profile, wherein the authors make assumptions on the parameters of the study area (covered distances, number of cars present), the habits of the driver, and the features of the EVs fleet.  
In contrast, \"empirical\" (or data-driven) approaches mainly make use of real-world data as a basis of the resolution process, and models are implemented in real case studies (e.g., the topology of real cities). Real data of probabilistic loads can be extracted from urban circulation datasets (real traffic zones measurements), EV ownership data, or EV user charging preference surveys. For example, in [82] the authors identify the best probabilistic distributions for describing arrival and departure times, plus the distance traveled, from a survey of 300 EV drivers. The data are also divided depending on the time of day (morning, evening, or night) and the type of feeder (residential or commercial) where the charging event takes place. In contrast, deterministic transport loads can make use of pre-existing timetable and routes. This is the case for [42], which evaluates 526 bus routes in the city of Stockholm, as well as for the city of Davis, which is investigated in [43]. The authors of [57] take into account six real express bus routes and proceed with accurate simulations in order to quantify the charging demand and travel time. Recently, the authors of [120] studied real charging patterns from several categories of charging sites (residential, office, car parks, and shopping centres). More than 80,000 real charging sessions were analyzed to assess the potential of vehicle aggregation to participate in ancillary services.  
In conclusion, an approach based on a data-driven methodology allocates and sizes CI through accurate transport data. However, as stated in Section 1, a static approach to long-term predictions can lead to transportation scenarios that are not applicable in the future. In fact, emerging trends such as mobility-as-a-service, autonomous vehicles, and first/last-kilometer-solutions will revolutionize the future of transportation [121; 122]. In contrast, model-driven approaches can provide the flexibility to plan with uncertainty, such as varying transport input parameters, and produce heterogeneous AS scenarios.