How can KumuluzCrowdsensing forecast energy consumption for electric vehicles?

The growing use of electric vehicles (EVs) is putting greater pressure on the electricity grid. Accurate forecasts of charging demand are essential for effective management. The GREEN.DAT.AI project is developing a system to monitor and predict EV energy consumption, helping optimise energy distribution and reduce strain on the grid.

Data collection and challenges

Accurate forecasts rely on data such as distance travelled, driving speeds and battery state of charge (SoC). However, different charging station protocols and limited access to EV manufacturers' data make consistent monitoring of energy consumption difficult.

The solution: a mobile crowdsensing app

Instead of connecting directly to charging stations or vehicles, the project uses the KumuluzCrowdsensing platform. A mobile app collects data using sensors in smart devices such as phones and smartwatches. It records EV movements, driving speeds, road conditions and weather, all of which affect energy consumption.

The collected data is analysed using artificial intelligence (AI), which calculates charging needs, expected charging locations and the required charging power. This approach makes data collection straightforward and efficient while improving forecast accuracy.

Availability and further information

Read the full article here. For more information and a demonstration of the platform, email us at info@kumuluz.com.

You can download the EV Crowdsensing app from Google Play or the Apple App Store and help optimise EV charging.

The GREEN.DAT.AI project is funded by the Horizon Europe R&I programme (GA No. 101070416).