Modelling the Effect of Driving Events on Electrical Vehicle Energy Consumption Using Inertial Sensors in Smartphones
Open accessAbstract
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.
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Jiménez et al. use smartphone sensor data to identify driving behavior that leads to higher energy consumption in EVs.
“For example, Jiménez et al. [392] use data from smart phone sensors to identify driving behavior that leads to higher energy consumption in EVs.”
From Tackling climate change with machine learning (2023) · cites this paper as [392]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Jiménez, D., Hernández, S., Fraile-Ardanuy, J., Serrano, J., Fernández, R., & Álvarez, F.
- Affiliations
- Universidad Politécnica de Madrid (ES)
- Information Processing and Telecommunications Center (ES)
- Grupo de Aplicaciones de Procesado de Señales (GAPS), IPTC, Universidad Politécnica de Madrid, 28040 Madrid, Spain
- Corresponding author
- Jiménez, David
- ORCID
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2018-02-10
- Volume
- 11
- Issue
- 2
- Pages
- 412
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W2787723628
- Cited by
- 46
- References
- 20
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 6 · 2023: 10 · 2022: 6 · 2021: 5 · 2020: 4 · 2019: 5 · 2018: 2
- FWCI
- 1.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energies (publishedVersion, cc-by)
- Hispana (acceptedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UPM Digital Archive (Technical University of Madrid) (submittedVersion, cc-by)
- Funders
- Spanish Ministry of Economy and Competitiveness (TEC2015-66126-R)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Physical Sciences › Engineering › Automotive Engineering › Vehicle emissions and performance
- Keywords
- electric vehicle energy consumption, smartphone inertial sensors, driver behavior, vehicle dynamics, LSTM, electric vehicle range prediction, non-intrusive measurement
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