EV Charging Station Network Planning Optimization in Europe

One of the biggest groups of automotive companies, resulting in the fusion of very importan groups of brand companies had a deal with Locatium. They are a leading multi-brand car manufacturer, and they wanted to deploy EV charging stations across Europe at a scale. Locatium had a conversation with them our solution based on Omnichannel retail Network Optimization for makind thrm current target happened. Here you can know how we did it.

CHALLENGE

This one of the largest groups of automotive companies had as a priority to invest in charging stations for electric vehicles, both fast and slow charging stations, and ask Locatium the following questions:

  • What are the optimal locations for fast EV chargers and for slow EV chargers?
  • What retail partners we should have to deploy charging stations across several European countries.
  • What is the capacity of each market, specifically, how many charging stations could implement in each city?
  • How many charges should install in each charging station?

Tell us the particularities and needs your project needs to be able to implement a plan specially designed for you.

Project development and the final score

In the first place, we took into count our huge repository of data and based our machine learning model algorithm on:

  • Tracking of billions of mobile devices (which we use to do dwelling analysis, home location identification, visitation analytics, etc.)
  • Tracking of billions of vehicles (which we use to do traffic on the road network, vehicle home location identification, etc.)
    Consumer Data (purchasing power, consumer profiles by category, etc.)
  • Advanced Demographics (gender, income, education, nationality, etc.)
  • Real Estate insights.
  • Credit Card insights.
  • Social Network Insights.
  • Points of interest, including the location of competitors.

Then, we match our data sets with our client’s sales data and put them together on our model algorithm. At the end of the process, we were able to tell them:

  • What were the optimal locations for charging stations, both slow and fast.
  • What type of retail partners should have to deploy charging stations across several European countries.
  • What was the capacity of each market, specifically, how many charging stations could be implemented in each city.
  • Finally, we could tell them how many charges they should install in each charging station.

Finally, our client obtained an exact retail predictive model to optimize their business decision-making, saving investment, and knowing exactly where to be oriented to their customers. 

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