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

Author

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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)

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.

Suggested Citation

  • David Jiménez & Sara Hernández & Jesús Fraile-Ardanuy & Javier Serrano & Rubén Fernández & Federico Álvarez, 2018. "Modelling the Effect of Driving Events on Electrical Vehicle Energy Consumption Using Inertial Sensors in Smartphones," Energies, MDPI, vol. 11(2), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:2:p:412-:d:131296
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    References listed on IDEAS

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    1. Fiori, Chiara & Ahn, Kyoungho & Rakha, Hesham A., 2016. "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, Elsevier, vol. 168(C), pages 257-268.
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    Cited by:

    1. Bogdan Ovidiu Varga & Arsen Sagoian & Florin Mariasiu, 2019. "Prediction of Electric Vehicle Range: A Comprehensive Review of Current Issues and Challenges," Energies, MDPI, vol. 12(5), pages 1-19, March.
    2. Nan, Sirui & Tu, Ran & Li, Tiezhu & Sun, Jian & Chen, Haibo, 2022. "From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus," Energy, Elsevier, vol. 261(PA).
    3. Andrea Di Martino & Seyed Mahdi Miraftabzadeh & Michela Longo, 2022. "Strategies for the Modelisation of Electric Vehicle Energy Consumption: A Review," Energies, MDPI, vol. 15(21), pages 1-20, October.
    4. Xing, Yang & Lv, Chen & Cao, Dongpu & Lu, Chao, 2020. "Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling," Applied Energy, Elsevier, vol. 261(C).
    5. Pan, Yingjiu & Fang, Wenpeng & Ge, Zhenzhen & Li, Cheng & Wang, Caifeng & Guo, Baochang, 2024. "A hybrid on-line approach for predicting the energy consumption of electric buses based on vehicle dynamics and system identification," Energy, Elsevier, vol. 290(C).

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