Modelling the Effect of Driving Events on Electrical Vehicle Energy Consumption Using Inertial Sensors in Smartphones

David Jiménez, Sara Hernández, Jesús Fraile-Ardanuy, Javier Serrano, Rubén Fernández and Federico Álvarez
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David Jiménez: Grupo de Aplicación de Telecomunicaciones Visuales (GATV), IPTC, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Sara Hernández: Grupo de Aplicaciones de Procesado de Señales (GAPS), IPTC, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Jesús Fraile-Ardanuy: Grupo de Sistemas Dinámicos, Aprendizaje y Control (SISDAC), IPTC, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Javier Serrano: Grupo de Aplicación de Telecomunicaciones Visuales (GATV), IPTC, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Rubén Fernández: Grupo de Aplicaciones de Procesado de Señales (GAPS), IPTC, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Federico Álvarez: Grupo de Aplicación de Telecomunicaciones Visuales (GATV), IPTC, Universidad Politécnica de Madrid, 28040 Madrid, Spain

Energies, 2018, vol. 11, issue 2, 1-23

Abstract: Air pollution and climate change are some of the main problems that humankind is currently facing. The electrification of the transport sector will help to reduce these problems, but one of the major barriers for the massive adoption of electric vehicles is their limited range. The energy consumption in these vehicles is affected, among other variables, by the driving behavior, making range a value that must be personalized to each driver and each type of electric vehicle. In this paper we offer a way to estimate a personalized energy consumption model by the use of the vehicle dynamics and the driving events detected by the use of the smartphone inertial sensors, allowing an easy and non-intrusive manner to predict the correct range for each user. This paper proposes, for the classification of events, a deep neural network (Long-Short Time Memory) which has been trained with more than 22,000 car trips, and the application to improve the consumption model taking into account the driver behavior captured across different trips, allowing a personalized prediction. Results and validation in real cases show that errors in the predicted consumption values are halved when abrupt events are considered in the model.

Keywords: energy consumption; electric vehicle; smartphone; sensors; driving behavior (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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