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

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.

Prediction of Electric Vehicle Range: A Comprehensive Review of Current Issues and Challenges

“…Data-driven methods have shown good accuracy of prediction and straightforwardness for usage. A data-driven energy consumption prediction method for EVs (used for the energy-efficient routing issue) was developed [59]. A feature of the model is that it makes it possible to distinguish between different energy consumption influencing factors (road characteristics, weather, altitude differences, etc.…”

A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

“…Speed profile depends on the road type, driver behavior, traffic (accidents, recurrent congestions), weather conditions, etc. [115,116]. A large number of parameters make it hard to predict a BEV's energy consumption in different traffic scenarios.…”

“…Results showed that the prediction error of trip energy consumption is within 25%. De Cauwer et al [115] applied a similar MLR model for predicting energy consumption on road segments, suited for BEV routing. The authors used a neural network based on the road-traffic and weather-related features to predict the speed profile of a road segment.…”

A Method for the Optimization of Daily Activity Chains Including Electric Vehicles

“…Nonetheless, the results derived from our Practical Consumption Function are realistic and very close to findings reported in the literature of EV routing. The incorporation of more advanced formulas like ones presented in [53,54] our method can be considered as the next step in our research. In order to provide the necessary background for the use of the Practical Consumption Function, the data for the distance and elevation are derived from the OpenTripPlanner API, which bases its calculations on OpenStreetMap data for the road network and SRTM data for the elevation.…”