The client, a Private Equity firm, asked the TresVista team to build a macro model which would forecast the demand for electric vehicle charging stations by country till 2050. The countries targeted were part of Europe. The team was asked to use various research forecasts from BNEF and government bodies on electric vehicles and then incorporate those forecasts into the model to predict the number of charging stations required. The client also wanted a toggle to select various sources with various projections as per government sources.
To build a comprehensive model that can project the demand for electric chargers by country.
The TresVista team followed the following process:
• Approach Formation: In this stage, the team built the base macro model with the flow of the assumptions, which would act as a template for projecting the EV charging demand for all the regions. This included the EV forecasts, the % private charging compared to % public charging split, the average mileage driven, the number of charging stations in the country and the number of charging stations required
• Research about Assumptions and Available Forecasts: In this stage, the team identified the challenges specific to the country the team was forecasting the demand for. Research about the assumptions specific to the target country were also covered in this section
• Information Integration: The model would ultimately output total number of charges demanded by country for each year from 2022 – 2050
The major hurdles faced by the TresVista team were:
• Technical Understanding: While the EV charging industry is growing rapidly, the industry is riddled with technical jargons. The team had to understand them to manage client calls and queries effectively
• Research Hurdles: Clients’ requirement was to look for EV projections from reliable sources to incorporate into the charging demand model. However, as the model was country-specific, each country presented its unique set of challenges for EV demand
• Charger Utilization: Researching the 2022 charger utilization by country was a challenge as data was not readily available. The team leveraged the existing research space and used formulae to calculate the charger utilization by country
The team overcame these hurdles by doing extensive research to find the most accurate electric vehicle projections and other datapoints. In case of projections not being readily available, the team used the most comparable projections to project EV charging demand. For Norway, the most developed nation in the EV space, the team built a basic regression model regressing population to fleet growth. As for the existing charging infrastructure, approaches ranged from using government sources, private competitive analyses, and charger maps to track available chargers.
The TresVista team created an output sheet where the client would be able to toggle between the various countries, and the various EV projections of those countries at the click of a button. The team also enabled the graphs to dynamically change with the same toggle. This enabled the client to have a one stop shop for comparison between the charger demand by country, by vehicle type and by the source of research.
