A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

Cedric De Cauwer, Wouter Verbeke, Thierry Coosemans, Saphir Faid and Joeri Van Mierlo
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Cedric De Cauwer: Mobility, Logistics and Automotive Technology Research Centre (MOBI), Electrotechnical Engineering and Energy Technology (ETEC) Department, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Wouter Verbeke: Mobility, Logistics and Automotive Technology Research Centre (MOBI), Electrotechnical Engineering and Energy Technology (ETEC) Department, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Thierry Coosemans: Mobility, Logistics and Automotive Technology Research Centre (MOBI), Electrotechnical Engineering and Energy Technology (ETEC) Department, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Saphir Faid: Punch Powertrain, Industriezone Schurhovenveld 4125, 3800 Sint-Truiden, Belgium
Joeri Van Mierlo: Mobility, Logistics and Automotive Technology Research Centre (MOBI), Electrotechnical Engineering and Energy Technology (ETEC) Department, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium

Energies, 2017, vol. 10, issue 5, 1-18

Abstract: Limited driving range remains one of the barriers for widespread adoption of electric vehicles (EVs). To address the problem of range anxiety, this paper presents an energy consumption prediction method for EVs, designed for energy-efficient routing. This data-driven methodology combines real-world measured driving data with geographical and weather data to predict the consumption over any given road in a road network. The driving data are linked to the road network using geographic information system software that allows to separate trips into segments with similar road characteristics. The energy consumption over road segments is estimated using a multiple linear regression (MLR) model that links the energy consumption with microscopic driving parameters (such as speed and acceleration) and external parameters (such as temperature). A neural network (NN) is used to predict the unknown microscopic driving parameters over a segment prior to departure, given the road segment characteristics and weather conditions. The complete proposed model predicts the energy consumption with a mean absolute error ( MAE ) of 12–14% of the average trip consumption, of which 7–9% is caused by the energy consumption estimation of the MLR model. This method allows for prediction of energy consumption over any route in the road network prior to departure, and enables cost-optimization algorithms to calculate energy efficient routes. The data-driven approach has the advantage that the model can easily be updated over time with changing conditions.

Keywords: electric vehicle (EV); energy consumption; prediction; routing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (23)

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