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Most energy models rely on assumptions rather than real-world data, leading to inaccurate predictions about EV charging demand. Our latest study analyzes real charging data from Denmark, covering home, public, and fast-charging stations to reveal peak demand trends, seasonal variations, and the impact of temperature on charging efficiency.
The findings, based on research by Andersen, Jacobsen, & Gunkel (2021), show that EV charging behavior is highly variable, with home chargers displaying steady demand while fast chargers experience unpredictable spikes. This variability challenges conventional energy modeling, emphasizing the need for data-driven infrastructure planning to integrate renewable energy and improve grid resilience.
This study looks at real charging data collected from Denmark in 2018, including hourly readings from 14 homes with EVs, 32 public charging stations, and one fast charger. The data covers weekdays and weekends, seasonal changes, and even how outside temperatures impact charging demand.
By offering monthly average charging profiles, this provides energy modelers, policymakers, and infrastructure planners with the precise insights they need to forecast energy demand.
Electric vehicle (EV) charging stations are increasingly becoming an integral part of modern infrastructure. As the adoption of EVs rises, so does the need for more accurate data to predict their energy demands. However, there remains a gap in reliable data regarding charging profiles for electric charging stations.
There are lots of factors that influence EV charging behaviour—things like the type of car, the power output of the charging station, and individual user habits. All of this makes it tough to predict exactly how much energy EV charging stations will pull from the grid. Many current energy models are based on assumptions or survey data, which only offer a rough idea of how EV chargers affect energy consumption.
The goal of this analysis is to provide real-world data from different types of EV charging stations, so energy modelers can get a clearer picture of the energy demand from EV chargers. With this data, we can make more accurate predictions about energy use, which will help in planning future infrastructure and integrating more renewable energy sources.
Several key factors influence EV charging profiles:
Battery Charge Capacity: The size and capacity of EV batteries vary significantly across different makes and models.
Output Power of Charging Stations: Higher kilowatt (kW) output results in faster charging times.
Battery State of Charge (SoC): Charging speed is faster when the battery’s SoC is low and slows down as the SoC increases.
Battery Temperature: Extreme battery temperatures can reduce charging efficiency or cause battery damage.
Battery Health: Over time, battery health declines, resulting in reduced capacity and efficiency.
This analysis covers eight distinct categories of charging patterns:
(1) Home Charging Station – Weekdays.
(2) Home Charging Station – Weekends.
(3) Small Public Charging Station – Weekdays.
(4) Small Public Charging Station – Weekends.
(5) Large Public Charging Station – Weekdays.
(6) Large Public Charging Station – Weekends.
(7) Fast Public Charging Station – Weekdays.
(8) Fast Public Charging Station – Weekends.
Read on to discover the full detailed study.
Comprehensive Analysis of Charging Patterns Across Different Stations:
Heatmap, Hourly Trends, and Monthly Peaks
HEAT MAP REPRESENTATION
LINE CHART REPRESENTATION
MAXIMUM CHARGE AND CORRESPONDING TIME INTERVAL PER MONTH
These charts indicate that peak charging hours typically occur from 17:00 to 24:00, with the highest charging activity taking place in February and March. In contrast, charging levels are considerably lower between 07:00 and 17:00. Generally, demand is notably higher in the evening, particularly during the winter months.
HEAT MAP REPRESENTATION
LINE CHART REPRESENTATION
MAXIMUM CHARGE AND CORRESPONDING TIME INTERVAL PER MONTH
These charts indicate that peak charging hours typically occur from 20:00 to 24:00 and 1:00 and 5:00, with the highest charging activity taking place in February and March. In contrast, charging levels are considerably lower between 05:00 and 14:00. Generally, demand is notably higher in the evening, particularly during the winter months.
HEAT MAP REPRESENTATION
LINE CHART REPRESENTATION
MAXIMUM CHARGE AND CORRESPONDING TIME INTERVAL PER MONTH
These charts indicate that peak charging hours typically occur from 10:00 to 14:00, with the highest charging activity taking place in December and March. In contrast, charging levels are considerably lower between 01:00 to 8:00 and 20:00 to 24:00 . Generally, demand is notably higher in the middle of the day and evening chagrining is minimal.
HEAT MAP REPRESENTATION
LINE CHART REPRESENTATION
MAXIMUM CHARGE AND CORRESPONDING TIME INTERVAL PER MONTH
These charts indicate that peak charging hours typically occur from 11:00 to 16:00, with the highest charging activity taking place in December and October. In contrast, charging levels are considerably lower between 01:00 to 9:00. Generally, demand is high in the afternoon with reduced evening activity.
HEAT MAP REPRESENTATION
LINE CHART REPRESENTATION
MAXIMUM CHARGE AND CORRESPONDING TIME INTERVAL PER MONTH
These charts indicate that peak charging hours typically occur from 13:00 to 18:00, with the highest charging activity taking place in December and October. In contrast, charging levels are considerably lower between 01:00 to 8:00 and 19:00 to 24:00. Generally, demand is high in the middle of the day and evening charging is moderate.
HEAT MAP REPRESENTATION
LINE CHART REPRESENTATION
MAXIMUM CHARGE AND CORRESPONDING TIME INTERVAL PER MONTH
These charts indicate that peak charging hours typically occur from 11:00 to 18:00, with the highest charging activity taking place in July and November. In contrast, charging levels are considerably lower between 01:00 to 8:00. Generally, Afternoon demand is high in the afternoon with moderate evening charging.
HEAT MAP REPRESENTATION
LINE CHART REPRESENTATION
MAXIMUM CHARGE AND CORRESPONDING TIME INTERVAL PER MONTH
These charts indicate that peak charging hours typically occur from 12:00 to 20:00, with the highest charging activity taking place in July and March. In contrast, charging levels are considerably lower between 01:00 to 9:00 and 21:00 to 24:00. Generally, demand is high in the middle of the day with moderate evening charging.
HEAT MAP REPRESENTATION
LINE CHART REPRESENTATION
MAXIMUM CHARGE AND CORRESPONDING TIME INTERVAL PER MONTH
These charts indicate that peak charging hours typically occur from 11:00 to 18:00, with the highest charging activity taking place in July and August. In contrast, charging levels are considerably lower between 01:00 to 9:00 and 19:00 to 24:00. Generally, demand is high in the middle of the day especially in summer periods.
The following table presents a detailed overview of the charging profiles for various types of EV charging stations. It outlines the key metrics observed during the analysis, such as peak charging hours, peak months for high energy demand, and hours of low charging activity. Additionally, the table includes important observations related to each profile.
Moreover, the two graphs below presented provide insights into the energy demand of electric vehicle (EV) home charging stations and how this demand varies with seasons and is dependant of the outdoor temperature.
The first graph shows the relationship between Heating Degree Days (HDD), Cooling Degree Days (CDD), and average monthly temperatures in Denmark.
The second graph compares the average home charging station loads between winter and summer across a 24-hour period. This visualization demonstrates how energy consumption differs between the seasons.
The table below summarizes the relationship between outdoor temperature ranges and battery efficiency for electric vehicles (EVs). It highlights how temperature extremes can significantly impact EV battery performance, affecting both the charging process and overall vehicle operation.
The table categorizes the efficiency of EV batteries under different temperature conditions and outlines the key effects observed at each temperature range.
In conclusion, the charging profiles presented in this study are highly applicable for countries with temperature conditions similar to those of Denmark. However, for regions with warmer climates, these profiles will need to be carefully adjusted and offset to account for the higher ambient temperatures, which can significantly impact EV charging behaviour and battery efficiency.
By making these modifications, energy modelers can ensure that the data remains relevant and provides accurate predictions for EV charging demand across different geographic locations.
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