Gender Aspects in Driving Style and Its Impact on Battery Ageing

Evelina Wikner (), Raik Orbay, Sara Fogelström and Torbjörn Thiringer
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Evelina Wikner: Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden
Raik Orbay: Volvo Car Corporation, 405 31 Gothenburg, Sweden
Sara Fogelström: Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden
Torbjörn Thiringer: Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden

Energies, 2022, vol. 15, issue 18, 1-15

Abstract: The long and tiring discussion of who are the best drivers, men or women, is not answered in this article. This article, though, sheds some light on the actual differences that can be seen in how men and women drive. In this study, GPS-recorded driving dynamics data from 123 drivers, 48 women and 75 men, are analysed and drivers are categorised as aggressive, normal or gentle. A total of 10% of the drivers was categorised as aggressive, with an even distribution between the genders. For the gentle drivers, 11% of the drivers, the men dominated. The driving style investigation was extended to utilise machine learning, confirming the results from statistical tools. As driving style highly impacts a vehicle’s fuel consumption, while switching over to battery electric vehicles it is important to investigate how the different driving styles impact battery utilisation. Two Li-ion battery cell types were tested utilising the same load cycle with three levels of current amplitude, to represent accelerations for the three drive categories. While one cell type was insensitive to the current amplitude, the highly energy-optimised cell proved to be sensitive to higher current amplitudes, corresponding to a more aggressive driving style. Thus, the amplitude of the dynamic current can for some cells be a factor that needs to be considered for lifetime predictions, while it can be neglected for other cells.

Keywords: lithium-ion battery; test; electric vehicle; gender; driving style; machine learning; support vector machine (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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