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Understanding Electric Vehicle Charging Demand: An Activity-Based Approach

Author: Martijn Hollestelle

With the continuous growth of electric vehicle (EV) adoption, ensuring that charging stations are accessible and efficiently distributed is essential to support this growth. Our comprehensive study offers detailed insights into charging demand today and in the future through data analysis and simulations.

The Challenge 

Finland's ambitious EV adoption targets are creating an urgent need for strategic charging infrastructure planning. With electric vehicles taking up an increasingly large portion of the vehicle stock, charging infrastructure must keep pace – but where should it be placed? 

Key Takeaways 

• Activity-based modelling provides unique insights into where and when EV charging is needed
• Charging behaviour varies significantly based on location type and driver demographics 
• Most charging demand occurs at destinations rather than on-route 
• Advanced spatial analysis can help optimize charging infrastructure placement 

A New Approach to Charging Infrastructure Planning 

Traditional methods of planning charging infrastructure often rely on simple metrics like population density or traffic volumes. Our approach brings together multiple data streams to create a more nuanced understanding: 

• Travel patterns: Using BRUTUS by Ramboll, our comprehensive disaggregated travel demand models covering all of Finland, provide details insights into daily movement patterns of millions of synthetic persons in Finland.
• Customer Usage and Transaction Data: Analysis of millions of transportation-related transactions across Finland, revealing detailed patterns of EV ownership and charging behaviour across different population segments.
• Socio-demographic Factors: Integration of Finnish Statistics data and regional forecasts from the Ministry of Transport and Communications.

This data-driven simulation-based approach allows us to: 

• Trace electric vehicles in detailed trip chains and understand the complete daily driving needs
• Analyse actual charging probabilities in space and time
• Include socio-economic factors to EV adoption
• Project future demand based on ministry forecasts

With this approach, Ramboll can predict EV charging demand on every main road in Finland and at every destination with an accuracy between 1 kilometre to 250 meters, depending on the location. 

Our approach distinguishes between on-route charging (vehicles stopping by a roadside charging facility) and at destination charging (vehicles charging while passengers undertake an activity). 

BRUTUS by Ramboll 

Unlike traditional transport models that look at aggregated flows between areas, activity-based models simulate how people chain their daily activities together, from morning commutes to evening shopping trips. BRUTUS, Ramboll’s activity-based model creates synthetic populations and traces their movement patterns throughout the day, considering factors like time constraints and vehicle availability. For EV charging analysis, this is crucial: by understanding complete daily travel patterns, we can identify exactly where and when drivers have sufficient dwell time to make charging practical and convenient.

Demographics matter 

Our analysis revealed clear patterns in EV ownership and usage: 

• Peak adoption among 35–54 year-olds
• Higher rates among employed, educated, and higher-income groups
• Significant variation based on housing type and location 

Model validation: Theory meets reality 

Our model validation revealed valuable insights into how predicted charging demand correlates with actual usage. 

On-route charging demand from longer distance trips is easier to predict than at destination charging: 

• Strong correlation (87% explanatory power) between modelled and actual charging volumes
• Optimal catchment area of approximately 35 kilometres around charging sites

Local – at destination – charging is more complex: 

Correlation of >50 % between modelled and actual charging volumes can be considered successful considering the complexity 

Multiple factors influence actual usage:  

• Competition from other charging options
• Availability of home and workplace charging
• Site-specific amenities and services

The key to successful charging facilities 

The study revealed several important factors that influence charging site success: 

1. Mixed-Use Sites: Some locations serve both long-distance and local needs, creating higher-than-predicted usage
2. Site Amenities: Additional services (restaurants, shops) can significantly boost charging volumes
3. Regional Variations: Natural catchment areas vary between northern and southern regions

Looking ahead

• Sustained Need for On-Route Charging: Despite increasing range of new EVs, the necessity for on-route charging remains critical due to physical constraints on vehicle weight and battery size. As the EV fleet grows and current models stay on the road for many years, on-route charging infrastructure will continue to be vital.
• Enhanced Data Availability: The availability and integration of EV-related data are becoming more central to mobility analysis. Client data was essential in our study, and moving forward, incorporating comprehensive EV datasets will allow for even more precise forecasting and planning of charging infrastructure, benefiting also public sector clients.
• Infrastructure Expansion: With the continued rise in EV adoption, expanding both on-route and destination charging networks will be crucial. Strategic placement and the adoption of ultra-fast chargers will enhance user convenience and support the growing number of EV users.
• Electricity grid capacity planning: Electrification of the car fleet increase pressure on the electricity grid. The approach can help grid operators strategically plan the development of their network to accommodate for the increasing electricity demand from vehicle charging.