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Energies 2017, 10(5), 608;

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

Mobility, Logistics and Automotive Technology Research Centre (MOBI), Electrotechnical Engineering and Energy Technology (ETEC) Department, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
Punch Powertrain, Industriezone Schurhovenveld 4125, 3800 Sint-Truiden, Belgium
Author to whom correspondence should be addressed.
Academic Editor: Michael Gerard Pecht
Received: 11 March 2017 / Revised: 19 April 2017 / Accepted: 21 April 2017 / Published: 1 May 2017
(This article belongs to the Special Issue Advances in Electric Vehicles and Plug-in Hybrid Vehicles 2017)
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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. View Full-Text
Keywords: electric vehicle (EV); energy consumption; prediction; routing electric vehicle (EV); energy consumption; prediction; routing

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De Cauwer, C.; Verbeke, W.; Coosemans, T.; Faid, S.; Van Mierlo, J. A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions. Energies 2017, 10, 608.

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