Impact of emotional states on the effective range of electric vehicles
Over the last decade, a large interest in reducing transportation dependence on fossil fuels as well as the cost reduction in battery technologies, have driven the electric cars market uptake. However, information is scarce about factors that affect the driving range. Besides the battery’s capacity,...
- Autores:
-
Dominguez, Juan
Campillo, Javier
Campo-Landines, Kiara
Contreras-Ortiz, Sonia H.
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12211
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12211
- Palabra clave:
- Automobile;
Alternative Fuel Vehicles;
Electric Car
LEMB
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
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Impact of emotional states on the effective range of electric vehicles |
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Impact of emotional states on the effective range of electric vehicles |
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Impact of emotional states on the effective range of electric vehicles Automobile; Alternative Fuel Vehicles; Electric Car LEMB |
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Impact of emotional states on the effective range of electric vehicles |
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Impact of emotional states on the effective range of electric vehicles |
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Impact of emotional states on the effective range of electric vehicles |
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Impact of emotional states on the effective range of electric vehicles |
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Impact of emotional states on the effective range of electric vehicles |
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Dominguez, Juan Campillo, Javier Campo-Landines, Kiara Contreras-Ortiz, Sonia H. |
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Dominguez, Juan Campillo, Javier Campo-Landines, Kiara Contreras-Ortiz, Sonia H. |
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Automobile; Alternative Fuel Vehicles; Electric Car |
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Automobile; Alternative Fuel Vehicles; Electric Car LEMB |
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LEMB |
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Over the last decade, a large interest in reducing transportation dependence on fossil fuels as well as the cost reduction in battery technologies, have driven the electric cars market uptake. However, information is scarce about factors that affect the driving range. Besides the battery’s capacity, other factors may affect the overall vehicle’s range, for instance: driving behavior, fluctuations in temperature, number of battery cycles, etc. Accordingly, this paper proposes an approach to evaluate the impact of emotions and driving behavior on the range of electric cars using physiological signals and vehicle performance features. This work was developed in three stages. During the first stage, the heart rate and galvanic skin response of 20 volunteers were recorded from biosensors. The vehicle’s data was obtained from a driving simulator. Afterward, the driving profile was used as an input source to simulate an object-oriented electric vehicle model to estimate the driving range. Finally, during the third stage, feature selection techniques and subject-dependent classifiers were evaluated using metrics such as the accuracy and the area under the curve. Support-vector machines with radial kernel and tree-bagged models provided the best global performance with the bio-signals and driving performance subsets to discriminate between calm and aggressive driving. Results showed that driving behavior could be evaluated from physiological and vehicle features. Furthermore, the subjects’ statements showed that users’ beliefs, thoughts, and prior social contexts influence the way they perceive driving behavior. Reductions in the range of up to 68% when driving aggressively compared to a calm manner were found. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. |
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2023 |
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2023-07-19T21:21:23Z |
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2023-07-19T21:21:23Z |
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2023 |
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2023 |
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Dominguez, J., Campillo, J., Campo-Landines, K., & Contreras-Ortiz, S. H. (2023). Impact of emotional states on the effective range of electric vehicles. Journal of Ambient Intelligence and Humanized Computing, 14(7), 9049-9058. |
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https://hdl.handle.net/20.500.12585/12211 |
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10.1007/s12652-022-04410-x |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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Dominguez, J., Campillo, J., Campo-Landines, K., & Contreras-Ortiz, S. H. (2023). Impact of emotional states on the effective range of electric vehicles. Journal of Ambient Intelligence and Humanized Computing, 14(7), 9049-9058. 10.1007/s12652-022-04410-x Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12211 |
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eng |
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Dominguez, Juanbd8466a9-a81f-42a1-a377-0400f508ab41Campillo, Javierf3ff0112-bc56-4d8f-9a9e-55b707704a07Campo-Landines, Kiarae038ce57-4014-4c34-baf5-a9ab628cf2fdContreras-Ortiz, Sonia H.1d56d7f5-97c9-4429-b47d-48ebe97de2a82023-07-19T21:21:23Z2023-07-19T21:21:23Z20232023Dominguez, J., Campillo, J., Campo-Landines, K., & Contreras-Ortiz, S. H. (2023). Impact of emotional states on the effective range of electric vehicles. Journal of Ambient Intelligence and Humanized Computing, 14(7), 9049-9058.https://hdl.handle.net/20.500.12585/1221110.1007/s12652-022-04410-xUniversidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarOver the last decade, a large interest in reducing transportation dependence on fossil fuels as well as the cost reduction in battery technologies, have driven the electric cars market uptake. However, information is scarce about factors that affect the driving range. Besides the battery’s capacity, other factors may affect the overall vehicle’s range, for instance: driving behavior, fluctuations in temperature, number of battery cycles, etc. Accordingly, this paper proposes an approach to evaluate the impact of emotions and driving behavior on the range of electric cars using physiological signals and vehicle performance features. This work was developed in three stages. During the first stage, the heart rate and galvanic skin response of 20 volunteers were recorded from biosensors. The vehicle’s data was obtained from a driving simulator. Afterward, the driving profile was used as an input source to simulate an object-oriented electric vehicle model to estimate the driving range. Finally, during the third stage, feature selection techniques and subject-dependent classifiers were evaluated using metrics such as the accuracy and the area under the curve. Support-vector machines with radial kernel and tree-bagged models provided the best global performance with the bio-signals and driving performance subsets to discriminate between calm and aggressive driving. Results showed that driving behavior could be evaluated from physiological and vehicle features. Furthermore, the subjects’ statements showed that users’ beliefs, thoughts, and prior social contexts influence the way they perceive driving behavior. Reductions in the range of up to 68% when driving aggressively compared to a calm manner were found. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.application/pdfengJournal of Ambient Intelligence and Humanized ComputingImpact of emotional states on the effective range of electric vehiclesinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Automobile;Alternative Fuel Vehicles;Electric CarLEMBinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Cartagena de IndiasAlvarez, R., López, A., De La Torre, N. Evaluating the effect of a driver's behaviour on the range of a battery electric vehicle (2015) Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 229 (10), pp. 1379-1391. 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Detecting driving stress in physiological signals based on multimodal feature analysis and kernel classifiers (2017) Expert Systems with Applications, 85, pp. 279-291. Cited 167 times. doi: 10.1016/j.eswa.2017.01.040Cooper, C.L., Dewe, P. Stress: A Brief History (2008) Stress: A Brief History, pp. 1-144. Cited 30 times. http://onlinelibrary.wiley.com/book/10.1002/9780470774755 ISBN: 978-047077475-5; 1405107448; 978-140510744-0 doi: 10.1002/9780470774755Dominguez-Jimenez, J.A., Campillo, J. Object-oriented mathematical modeling for estimating electric vehicle’s range using modelica (2018) Communications in Computer and Information Science, 885, pp. 444-458. Cited 4 times. http://www.springer.com/series/7899 ISBN: 978-331998997-6 doi: 10.1007/978-3-319-98998-3_34Dominguez-Jimenez, J.A., Campo-Landines, K.C., Contreras-Ortiz, S.H. A Methodology for Driving Behavior Recognition in Simulated Scenarios Using Biosignals (Open Access) (2019) Communications in Computer and Information Science, 1052, pp. 357-367. http://www.springer.com/series/7899 ISBN: 978-303031018-9 doi: 10.1007/978-3-030-31019-6_31Domínguez-Jiménez, J.A., Campo-Landines, K.C., Martínez-Santos, J.C., Delahoz, E.J., Contreras-Ortiz, S.H. A machine learning model for emotion recognition from physiological signals (2020) Biomedical Signal Processing and Control, 55, art. no. 101646. Cited 102 times. http://www.elsevier.com/wps/find/journalbibliographicinfo.cws_home/706718/description#bibliographicinfo doi: 10.1016/j.bspc.2019.101646Felipe, J., Amarillo, J.C., Naranjo, J.E., Serradilla, F., Diaz, A. Energy Consumption Estimation in Electric Vehicles Considering Driving Style (2015) IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2015-October, art. no. 7313117, pp. 101-106. Cited 34 times. 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