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Mobility CM: Electric Vehicles Charging Needs

In A Glance

This calculation module generates the driven distance and charging needs of an area, focusing mainly on commuter mobility. This calculation is based on the density of electric vehicles (EVs), that can be obtained with @CM - Electric vehicle density and a large scale mobility model: either a model based on the distance to the closest city (isodistances to the so called Eurostat cities, or so-called additional cities) or a gravity model. Input parameters such as the ratio of commuters, average driven distance in the area and the average EV’s electricity consumption can be used to scale the mobility models to the area. Charging scenario can be modeled by a user-defined distribution between charging at home, at work, at points of interest (amenities) or during home office. The results are a map of the charging demand per hectare and an average weekday’s load curve for the studied area.

image Example of results

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Mobility Model in a nutshell

The mobility model estimates the average vehicle driven kilometres from the distance to the workplaces. Two models for commuting distance are available: the 'gravity model' and the 'isodistance' model. The gravity model is based on the attractiveness of the LAU2s relative to the other LAU2s, defined by their ratio of population/distance to each LAU2 within a certain range. Using default parameters, the equation is:

\(VKT\) = \(d_{gravity}\) \(\times\) \(d_{leisure+shopping}\)

\(VKT\): Vehicle Kilometer Travelled

\(d_{gravity}\): distance obtained from the gravity model

\(d_{leisure+shopping}\): distance for leisure and shopping

The isodistance model estimates the distances driven by commuters living in the hectare cells of a studied area, by the distance to the closest large city (here called Eurostat city) and to the closest smaller city are considered and combined with each other (by default 70:30) when both are available. Besides, these two distances are both individually combined with a default work-related distance (here: 34 km by default or a user-defined value) with a default ratio of 80:20 (or user-defined), in order to allow the consideration of available information. Using default parameters, the equation is:

\(VKT\) = 0.7 \(\times\) (0.8 \(\times\) \(d_{EC}\) + 0.2 \(\times\) 34 \(km^2\)) + 0.3 \(\times\) (0.8 \(\times\) \(d_{AC}\)+ 0.2 \(\times\) 34 \(km^2\) * ) + \(d_{leisure+shopping}\)

\(VKT\): Vehicle kilometre travelled

\(d_{EC}\): Eurostat city distance

\(d_{AC}\): Additionall city distance, max. 30km

\(d_{leisure+shopping}\): distance for leisure and shopping

*default distance

image Isodistances around Aalborg, Bern and Palermo

Then, mobility for leisure and shopping is added to the estimation of both models. The default distance is used, for places outside the isodistances or gravity model. After the driving ways are summed up, the charging needs at people's homes, workplaces and POIs are calculated according to the charging scenario definition.

Acronyms and definitions

  • EV: Electric Vehicle
  • VKT: Vehicle Kilometre Travelled
  • POI: Point Of Interest
  • Commuting range: the area around a city to which people are commuting to work.

Introduction

The energy needs for electric vehicle charging depend on the distance driven by the cars, which is different for urban, peri-urban and rural areas. The load curves of EV charging depend on the charging behaviour of the drivers. Charging at home, workplace, or point of interest will result in different timeframes and locations of charging. This calculation module uses a geographical approach to compute the charging needs of the cars, from their distance to cities. Then, scenarios can be chosen to generate load curves and maps of charging needs from open data.

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Inputs And Outputs

Inputs

The inputs for designing the scenario are “work”, “work from home” and “point of interest”. The rest of the charging needs are allocated to “home” to reach 100% of the needs.

  1. Smart charging at home
    • The default input is no.
    • This parameter is used to define the charging scenario. If the input is ‘no’, the cars will charge as soon as they are plugged in at home in the evening. If the input is ‘yes’, the charging demand will be spread over the idle time of the cars.
  2. Share of commuters charging at work
    • The default input is 10%.
    • This parameter is used to define the charging scenario. It gives the share of people charging at work. Thus, the charging demand for the cars is distributed over the working areas (Layer 7) with the same city ID (Layers 5-6). The time evolution of the charging demand at work was obtained from Lawson et al. [2]. Here, the commuters charge 100% of their needs.
  3. Share of commuters charging when they work from home
    • The default input is 10%.
    • This parameter is used to define the charging scenario. It gives the share of people who charge when they work from home. Thus, the charging demand for the cars is allocated at their home. Here, the commuters charge 100% of their needs.
  4. Share of commuters charging at Points Of Interest (POIs)
    • The default input is 10%.
    • This parameter is used to define the charging scenario. This parameter is used to define the charging scenario. It gives the share of people charging at point of interest (amenities such as shops, restaurants, theatres, sports fields, etc). Thus, the charging demand for the cars is distributed over the POIs (Layer 8) with the same city ID (Layers 5-6). The time evolution of the charging demand at POI was obtained from a study of the Swiss charging infrastructure usage [3]. Here the commuters charge 100% of their needs.
  5. Coverage of the solar panels
    • The default input is 30%.
    • This parameter is used to compute the photovoltaic potential. It gives the ratio of roofs covered with solar photovoltaic panels.
  6. Efficiency of the solar panels
    • The default input is 20%.
    • This parameter is used to compute the photovoltaic potential. It gives the efficiency of the panels (amount of electricity produced from the solar radiation received).

  7. Performance ratio of the solar panels

    • The default input is 80%.
    • This parameter is used to compute the photovoltaic potential. It gives the performance ratio of the solar infrastructures.

  8. Model

  9. Choose either the gravity or the isodistance model. Be careful to choose the corresponding layers. For significant results with the isodistance model, please apply it to areas not smaller than NUTS3.

Basic inputs

  1. Ratio of commuters to Eurostat cities (>50k inhab.) for the model based on isodistances
    • The default input is 0.8.
    • This input gives the ratio of cars commuting to the city. The daily VKT of those cars will be computed from their distance to the city. For the rest of the cars, a default value will be applied (basic input 3). This value is used in isodistances around cities from the Eurostat study [1] (with more than 50k inhabitants).
  2. Ratio of commuters to additional cities (20k-50k inhab.) for the model based on isodistances
    • The default input is 0.8.
    • As for input 1, this input gives the ratio of cars commuting to the city. The daily VKT of those cars will be computed from their distance to the city. For the rest of the cars, a default value will be applied (basic input 3.). This value is used in isodistances around the additional cities (with 20k - 50k inhabitants).
  3. Cross-border worker average driven distance (round-trip)
    • The default input is 100 km.
    • As cross-border commuters are expected to travel a longer distance, this parameter affects them with a special VKT.
  4. Electricity consumption of EVs
    • The default input is 0.183 kWh/km.
    • This parameter gives the average energy needed in the battery to drive 1 km. The unit is kWh per km.
  5. Maximum of the commuting range to small cities for the model based on isodistances
    • The default input is 30 km.
    • For cities with more than 50k inhabitants, the polygons of commuting ranges from a Eurostats study where used to cut the isodistances [1]. For additional cities, this parameter is used to trim the commuting range to a certain distance to the city center.
  6. Ratio of commuters going to the cities while living in another smaller city commuting range for the model based on isodistances
    • The default input is 0.7.
    • In the case where cars are covered by two commuting ranges, this parameter is used to split the cars between the two destinations. In the default case, 70% of the cars will go to the bigger city, while the remaining 30% will go to the smaller city.
  7. Default driven distance for work (round trip, on weekdays)
    • The default input is 34.
    • This value is allocated as VKT for the remaining share of cars not included by the input 1 and 2, and the cars outside of the commuting ranges.
  8. Daily driven distance for shopping (round trip, on weekdays)
    • The default input is 3 km.
    • This distance is added to the VKT for commuting for all the cars. It is advised to use an average distance for weekdays only.
  9. Daily driven distance for leisure (round trip, on weekdays)
    • The default input is 7 km.
    • This distance is added to the VKT for commuting for all the cars. It is advised to use an average distance for weekdays only.
  10. Share of the workweek where homeworking commuters are working from home
    • The default input is 40%.
    • This parameter is used to define the charging scenario.

Layers

  1. VKT layer

1.1 For the Gravity model:

  • Choose the layer "VKT from Gravity model" as first layer. The second layer will not be used.

1.2 For the isodistance model:

  • Choose a Eurostat city isodistance layer.

    • The default layer is EUROSTAT CITIES ISODISTANCES.
    • This layer is used to compute the distance from a pixel to the city centre of the cities in the Eurostat dataset. It is trimmed to the commuting range of the Eurostat study [1]. The spatial resolution is 5km.

  • Choose an isodistance layer for additional cities.

    • The default layer is ADDITIONAL CITIES ISODISTANCES.
    • This layer is used to compute the distance from a pixel to the city centre of the cities with 20k -50k inhabitants. The range is from 0 to 45 km. The spatial resolution is 5km.

  • Choose a transborder ratio layer.

    • The default layer is TRANSBORDER COMMUTER RATIO.
    • This layer gives the ratio of transborder commuters, based on the data from Eurostat, of the number of people working in a different country per NUTS2 [4]. The assumption is made that the trans border workers are living within 60km of the border, and thus, the ratio is calculated taking into account the population in this area only.

  • Choose an electric vehicle density layer.

    • The default layer is ELECTRIC VEHICLES DENSITY.
    • The layer is obtained by running the calculation module "EV Density" on the Citiwatts platform with the default parameters :

    vehicles_per_habitant = 0.56; vehicle_urban_factor = 0.2; year = 2050; fleet_renewal_share = 0.06; yearly_factor = 0.06; Share_electric_cars_new_registrations_2020 = 0.1

  • Choose an Eurostat city ID layer.

    • The default layer is EUROSTAT CITIES ID.
    • This layer is used to identify each commuting area around European cities.
  • Choose an additional city ID layer.
    • The default layer is ADDITIONAL CITIES ID.
    • This layer is used to identify each commuting area around European cities.
  • Choose a working area layer.
  • Choose a point of interest (POI) density layer.
    • The default layer is PONTS OF INTEREST.
    • This layer shows densities of a selection of Points of Interest in Europe from OpenStreetMap. The density is used as a weight to distribute the commuters charging at points of interest in the commuting range.
  • Choose a solar potential layer.

Outputs

Indicators:

  1. Charging need at home, kWh/day
  2. Charging need at home (during home office), kWh/day
  3. Charging need at work, kWh/day
  4. Charging need at points of interest, kWh/day
  5. Total of charging needs, kWh/day
  6. Total of cars charging in the selected area, kWh/day
  7. Average km per car in the selected area, kWh/day
  8. Total of charging needs for a weekend day, kWh/day
  9. Total of solar potential on building footprint in the area, kWh/day

Graphics:

The charging needs at different places are stacked together.

  1. Charging needs for commuters at home (during the day and outside of work hours)
    • The time evolution profile is from Ref. [5].
  2. Charging needs for commuters at work
    • The time evolution profile is from Ref. [2].
  3. Charging needs for commuters at points of interest
    • The time evolution profile is from a study of Swiss charging infrastructure occupation [3].
  4. The total charging needs (sum of the three previous components)

Layer:

  1. Total charging needs in kWh/day/hectare.

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Method

Step 0: Context

Electric vehicle density

A default EV density layer is available on the platform. However, a custom EV density with local parameters can be obtained by running the calculation module "EV Density" on the Citiwatts platform.

Commuting range of additional cities.

The isodistance layer for additional cities is modified according to the basic input 5 to limit the commuting range of the cities.

Step 1: Vehicle kilometre travelled

The energy consumption of the vehicles is directly related to their VKT.

A: Gravity model

image Gravity model illustration

The gravity model aims to quantify the commuting flows between two areas ( \(A_{start}^{dest}\) ), from their populations ( \(P_{start}\) , \(P_{dest}\) ), the distance between the two locations ( \(d_{start->dest}\) ) and a parameter (β).

\(A_{start}^{dest}\) = \(P_{dest}\) \(\times\) \(d_{start->dest}^{-\beta}\)

The parameter β is calculated using the method of Lenormand et al. [1], from the average area of the LAU2 in each country (A), as shown in Equation 2.

\(\beta\) = 0.3 \(\times\) \(A^{-0.17}\)

For each LAU2, the attractiveness of all the LAU2 in a range of 45 km is calculated, normalised and used as weights to split the commuters to the surrounding LAU2. The average VKT of a 'start' LAU2 is computed by summing the normalised all the 'dest' attractivity multiplied by the distance between 'start' and 'dest'.

[1] M. Lenormand, A. Bassolas, and J. J. Ramasco, ‘Systematic comparison of trip distribution laws and models’, J. Transp. Geogr., vol. 51, pp. 158–169, Feb. 2016, doi: 10.1016/j.jtrangeo.2015.12.008.

B: Isodistance model

  1. For outside of a commuting range:

The value allocated as VKT for the cars not included in a commuting range is \(d^{default}\), given by the basic input 7.

\(VKT^{commut}_k\) = \(d^{default}\)

  1. For cars in a commuting range:

The value of the isodistances (Layers 1 and 2) determines the distance \(d^{city}_k\) between each pixel \(k\) and the city. The ratio of cars commuting to the city \(r^{city}_k\) is given by the basic input 1 for the cities from the Eurostat dataset and basic input 2 for the additional cities. An additional parameter is given for pixels less than 60 km near a border, taking into account the ratio of transborder commuters \(r^{border}_k\) (Layer 3). The value allocated as VKT for the remaining share of cars not included by the basic input 1 and 2 is \(d^{default}\), given by the basic input 7. The average VKT in each pixel \(k\), is obtained from :

\(VKT^{commut}_k\) = \(r^{city}_k\) \(\times\) \(2d^{city}_k\) + \(r^{border}_k\) \(\times\) \(d^{border}\) + (1 - \(r^{city}_k\) - \(r^{border}_k\)) \(\times\) \(d^{default}\)

  1. For cars in two commuting ranges:

If the pixel is located in two commuting areas, one of a major city \(city_1\) and one of a small city \(city_2\), the term \(r^{city}_k \times 2d^{city}_k\) becomes :

\(\alpha\) \(\times\) \(r^{city1}_k\) \(\times\) \(2d^{city1}_k\) + (1- \(\alpha\) ) \(\times\) \(r^{city2}_k\) \(\times\) \(2d^{city2}_k\)

with \(\alpha\), the ratio of commuters who go to the major city while living in a small city commuting area (basic input 6).

C: For both models

  1. Vehicle kilometre travelled for other purposes

Mobility due to leisure and shopping (basic inputs 8 and 9) is added to the VKT layer.

\(VKT^{avg}_k\) = \(VKT^{commut}_k\) + \(VKT^{shop}\) + \(VKT^{leisure}\)

  1. Total VKT per pixel

Until this step, the VKT layer contains the average distance driven by the cars in each pixel. The total VKT is then multiplied by the EV density \(D^{EV}_k\) (input layer 4, number of EVs inside each pixel \(k\)) to obtain the total distance driven by all EVs in each pixel.

\(VKT^{tot}_k\) = \(VKT^{avg}_k\) \(\times\) \(D^{EV}_k\)

Step 2: Charging scenario

Four options of charging can be used to allocate the charging needs of the car to a location: charging at home, charging at work, charging during home office (at home during work hours) and charging at points of interest.

The charging demand is obtained by multiplying the \(VKT^{tot}_k\) for each pixel by \(c\), the average electricity consumption of EV (basic input 4).

\(E_k^{tot}\) = \(c\) \(\times\) \(VKT^{tot}_k\)

Until this step, the charging needs are in the ‘home’ pixel of the car.

  1. Charging at home

By default, the charging needs are allocated at the address of registration of the cars. All the charging needs that are not included in the parameters for charging at work, home office and POIs (inputs 2, 3 and 4) are considered to be at home, so that the total reaches 100%. The rate of people charging at home is:

\(\beta_{home}\) = 1- \(\beta_{work}\) - \(\beta_{homeoffice}\) - \(\beta_{POI}\)

  1. Charging at work (input 2)

The share \(\beta_{work}\) (input 2) of the \(VKT^{tot}_k\) of each pixel \(k\) is summed by commuting range (or closest city).

\(E_{city}^{work}\) = \(\Sigma_{k{\displaystyle \in }city}\) \(\beta_{work}VKT^{tot}_k\)

The sum is then distributed over the pixels identified as a possible work location (input layer 7). The value of the pixel \(W_k\) of the input layer is used as a weight for the distribution.

\(E_k^{work}\) = \(W_k\) \(\times\) \(E_{city}^{work}\) / \(W_{city}^{tot}\)

In the Corine Land Cover (CLC) layer, all the coefficients \(W_k\) are 1. In the layer from Open Street Map, the \(W_k\) are the number of amenities in the pixel corresponding to a tag identified as work location (see the here for more details on this layer).

  1. Home office (input 3)

The share \(\beta_{home\_office}\) (input 3) is defined to account for commuters who charge at home during the day, while working from home. The \(VKT^{tot}_k\) is applied to their pixel of residence.

\(E_{k}^{homeoffice}\) = \(\beta_{homeoffice}\) \(VKT_k^{tot}\)

The charging needs during the home office can be satisfied separately from the charging needs in the evening after work and thus have an effect on the time frame of charging and load curves.

  1. Charging at POIs (input 4)

The share \(\beta_{POI}\) (input 2) of the \(VKT^{tot}_k\) of each pixel \(k\) is summed by commuting range (or closest city).

\(E_{city}^{POI}\) = \(\Sigma_{k \in city}\) \(\beta_{POI}\) \(VKT^{tot}_k\)

The sum is then distributed over the pixels identified as a possible charging location (input layer 3). The value of the pixel \(P_k\) of the input layer is used as a weight for the distribution.

\(E_k^{POI}\) = \(P_k\) \(\times\) \(E_{city}^{POI}\) / \(W_{city}^{tot}\)

In the layer of POIs collected from Open Street Map, the \(W_k\) are the number of amenities in the pixel corresponding to a tag identified as POI for charging (see the here for more details on this layer).

Uncertainties & Limitations

  • The uncertainty on the population layer has a high impact on the results.

Limitations

  • Only private cars are considered (no motorbikes, buses, taxis, ect.)
  • The model considers only mobility undertaken in cars (not by foot, bike, etc)
  • Holiday trips are not considered
  • CO2 emissions reductions are only accounting for driving (car production is not taken into account for now)

Future improvements

  • CO2 calculation will be improved
  • economic aspects will be included

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GitHub Repository Of This Calculation Module

Here you get the bleeding-edge development for this calculation module.

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References

[1]Eurostat, JRC and European, Commission, Directorate-General, et Regional and Urban Policy, « Cities and commuting zones (LAU 2016) », Eurostst. Consulté le: 27 octobre 2022. [En ligne]. Disponible sur: https://ec.europa.eu/statistical-atlas/viewer/?config=RYB-2022.json&mids=BKGCNT,BKGNT02021,CNTOVL,CITYCOMMZONE2018&o=1,1,0.7,1&ch=C01,TRC,CITYCOMMZONE&center=51.55492,18.58786,3&lcis=CITYCOMMZONE2018&

[2] C. Lawson, O. Asensio, et C. Apablaza, « High-resolution electric vehicle charging data from a workplace setting ». Harvard Dataverse, 2020. doi: [10.7910/DVN/QF1PMO](https://doi.org/10.7910/DVN/QF1PMO).

[3]« jerechargemonauto.ch ». Consulté le: 1 juillet 2022. [En ligne]. Disponible sur: https://map.geo.admin.ch/?lang=fr&topic=energie&bgLayer=ch.swisstopo.pixelkarte-grau&zoom=0&layers=ch.bfe.ladestellen-elektromobilitaet&catalogNodes=2419,2420,2427,2480,2429,2431,2434,2436,2767,2441,3206&E=2660000.00&N=1190000.00

[4]Eurostat, « Employment and commuting by sex, age and NUTS 2 regions ». [En ligne]. Disponible sur: https://ec.europa.eu/eurostat/databrowser/view/lfst_r_lfe2ecomm/default/table?lang=en

[5]M. Schlemminger, « ML_Household_End-use_Load-profiles ». LUIS, p. 183413 kb, 2021. doi: 10.25835/0043305.

[6]« Natural Earth ». Consulté le: 27 octobre 2022. [En ligne]. Disponible sur: https://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-populated-places/

[7]« Open Route Service ». Consulté le: 27 octobre 2022. [En ligne]. Disponible sur: https://openrouteservice.org/

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How To Cite

Noémie Jeannin, Alejandro Pena Bello, Nicolas Wyrsch, in Hotmaps-Wiki, CM- electric vehicles charging needs — (March 2025)

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Authors And Reviewers

This page was written by Noémie Jeannin, Alejanndro Pena Bello, Nicolas Wyrsch (PV-lab, EPFL).

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License

Copyright © 2016-2025: Noémie Jeannin, Alejanndro Pena Bello, Nicolas Wyrsch

Creative Commons Attribution 4.0 International License

This work is licensed under a Creative Commons CC BY 4.0 International License.

SPDX-License-Identifier: CC-BY-4.0

License-Text: https://spdx.org/licenses/CC-BY-4.0.html

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Acknowledgement

We would like to convey our deepest appreciation to the Eranet OPENGIS4ET Project (Grant Agreement number 111786) and, for the Swiss partners, the Swiss Federal Office for Energy, which provided the funding to carry out the present investigation.

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