engleski

Estimating energy consumption of electric vehicles based on the GNSS data

Due to the new laws and policies related to greenhouse gas emissions, and the rise of social and ecological awareness of transport sustainability, the electric vehicle market share is continually growing. Electric vehicles (EVs) have a shorter driving range compared to the conventional vehicles with an internal combustion engine. To efficiently manage a route of an EV and increase it's maximal range, it is necessary to give a high-quality estimate of EV's energy consumption on the route. In this paper, we developed a model to estimate energy consumption on the road-link level based on the historical GNSS data. First, the real energy consumption data was used to develop energy consumption models that take as input (i) the GNSS trip data features: speed, acceleration, distance and travel time, and (ii) EV's characteristics: mass, rolling friction, frontal surface area, etc. In total, four models were developed and compared: basic longitudinal dynamics model, multiple linear regression model, polynomial multiple linear regression model, and decision trees model. Further on, the proposed models were used to estimate energy consumption on the road-link level based on the aggregated historical GNSS data. As a case study, the road network in Croatia was selected with GNSS data collected in a five year period from 2009 to 2014. The results support the feasibility of the approach for estimating energy consumption on the road network with the application in the routing of EVs.

electric vehicle ; energy estimation ; GNSS

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