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Mobility CM: Electric Vehicle Density

In A Glance

This calculation module generates the density of electric vehicles for a year between 2020 and 2050 in a raster file. The calculation is based on the population and the motorization rate, and can be adapted for rural and urban areas. The assumption is made that the new cars will be completely electric from 2035.

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Introduction

The geographical distribution of electric vehicles is the basis for planning the charging infrastructure and forecast the impact of the charging events on the electric grid. This calculation module output a electric vehicle density map from the population density map and the motorization rate. The motorization rate is implemented in Citiwatts through a layer at NUTS 2 level. It can be adapted to the population density locally to take into account its rural and urban variations. The fleet is assumed to become electric toward its usual renewal share with a growing amount of electric vehicles in the new registrations. From 2035, the new cars are assumed to be 100% electric accordingly to the decision of the European Union to ban the sale of combustion engine cars from this date. Only private passenger car are considered.

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Inputs And Outputs

Inputs

  1. Do you want to use a custom value for the motorization rate ?
    • The default input is ‘no’.
    • If ‘no’, the motorization rate is given by the vector layer ‘Motorization Rates NUTS3’.
    • If ‘yes’, the motorization rate is taken from the following input.
  2. Custom motorization rate :
  3. Modification of the motorization rate using the population density to reflect difference between urban and rural areas:
    • The default value is 0%. If the selected NUTS2 contains rural areas and a metropolitan area, we advice to choose 20%. This value was calculated from the data of average vehicle per household in urban, peri-urban and rural areas in Switzerland: [Swiss BFS](https://www.bfs.admin.ch/bfs/en/home/statistics/catalogues-databases.assetdetail.24025430.html).
    • Factor to reduce the motorization rate in higher population density areas (urban areas) and increase the motorization rate in low population density areas (rural areas).
    • Example with 20%: 20% more vehicles per inhabitant are allowed in the tertile (3-quantile) with lower population density and 20% less vehicle per inhabitant are allowed in the tertile with the higher population density compared to the value provided as motorization rate.
  4. Projection year
    • The default value is 2050.
    • Use this parameter to define in which year you want to obtain the population density.
  5. Fleet yearly renewal share
    • The default value is 6%.
    • This input gives the share of car in the fleet that is leaving the fleet and replaced by a new car.
    • For countries where this data is not available, its value is obtained by computing the following equation: ‘number of new registrations’/’number of cars in the fleet’. Data can be found on the ACEA website [1],[2].
  6. Fleet electric vehicle share 2020
    • The default value is 6%.
    • This input is used as starting point for the evolution of the number of electric vehicles in the fleet.
  7. Share of electric cars new registrations 2020
    • The default value is 10%.
    • This input is used as starting point for the evolution of the share of electric vehicles in the new registrations.
  8. Choose a population density layer.
    • Input a layer of population density. This must be a raster of type ‘populaton’. The population 2020 layer is based on the latest available data on population density. This layer is more precise if you want to run a simulation up to 2029. The population 2030 layer is a projection of the population in 2030. This layer is useful if you want to run a simulation after 2030.
    • The default layer is ‘Population Total’ [3]
  9. Choose a motorization rate layer.
    • Input a layer of motorization rate. This must be a vector of type ‘motorization rate’.
    • The default layer is ‘Motorization Rate’ with a resolution of NUTS 3. [4]

Outputs

  • Indicator: Electric vehicles in total in [year]
    • Total of electric vehicles in the selected area for the given year.
  • Layer: LAYERS OF EV DENSITY IN [year]
    • The output layer gives the number of electric vehicle per pixel depending on the resolution of the population layer. With the default layer, the result is given in vehicle per hectare.

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Method

As written previously, the motorization rate is either given by a layer (Motorization Rate by default), either by a custom input value (2).

If the parameter ‘Effect of the population density on the motorization rate’ (3) is null, the vehicle density is obtained by applying the motorization rate to the population density layer.

If not, the population layer is then divided in three quantiles, one corresponding to the lower population density, one to the higher population density and one in the middle.

V1: The three quantiles should contain about the same number of inhabitants. The motorization rate is applied to the pixels belonging to the middle quantile. On the pixels from the high population quantile, the motorization rate is lowered by the value from the parameter (3). On the pixels from the low population quantile, the motorization rate is increased by the value from the parameter (3). Note that this step conserves the average motorization rates from the input but affect differently the number of vehicles in a pixel depending on the area selected.

V2: The quantile is now fixed at 95% (cities are considered to be on the 5% with the more population).

The share of electric vehicles in the fleet for a given year (4) is based on the data from 2020 (6). The share of electric vehicles in the new registration is assumed to be linear between 2020 (7) and 2035 (when it reaches 100%), and then fixed at 100% until 2050. Hence, the CM will build up on the number of electric vehicles in 2020 with a increasing share of electric vehicles in the new registrations, that are accounting for a share of the fleet equal to parameter (5).

Uncertainties

At the hectare scale, the uncertainties propagated from the input parameters are negligible in front of the uncertainties given by the population density layer.

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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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Sample Run

Here, the calculation module is run for the case study of Neuchâtel. First, use the "Go To Place" bar to navigate to Neuchâtel and select the NUTS3 of Neuchâtel. Click on the "Calculation Modules" button to open the "Calculation Modules" window. In the list of calculation modules, select "CM - ELECTRIC VEHICLE DENSITY".

Test Run: default input values

Now, input the parameters in the window of the calculation module. For this test run, the default values are used. Then, clic on the ‘RUN CM’ button to run the calculation.

The results are displayed in the window ‘RESULTS’ on the right.

A new layer was added on the bottom of the ‘Layers’ window.

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References

[1] New passenger car registrations in EU [link](https://www.acea.auto/figure/new-passenger-car-registrations-in-eu/)

[2] Vehicles in use Europe 2022 [link](https://www.acea.auto/publication/report-vehicles-in-use-europe-2022/)

[3] Population total [GitLab](https://gitlab.com/hotmaps/pop_tot_curr_density/blob/master/README.md)

[4] Motorization rate by NUTS 3

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How To Cite

Noémie Jeannin, Alejanndro Pena Bello, Nicolas Wyrsch, in Hotmaps-Wiki, CM- EV density (December 2023)

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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 © 2016-2023: 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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