Data Driven Methods for Finding Coefficients of Aerodynamic Drag and Rolling Resistance of Electric Vehicles
This research investigated an alternate method for establishing the complex coefficients used in an electric vehicle’s mathematical energy consumption model. While other methods for creating electric vehicle energy models exist, it would be beneficial to have a rapid and inexpensive technique that r...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | World Electric Vehicle Journal |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2032-6653/14/6/134 |
| _version_ | 1797592261302681600 |
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| author | Ryan Van Greunen Christiaan Oosthuizen |
| author_facet | Ryan Van Greunen Christiaan Oosthuizen |
| author_sort | Ryan Van Greunen |
| collection | DOAJ |
| description | This research investigated an alternate method for establishing the complex coefficients used in an electric vehicle’s mathematical energy consumption model. While other methods for creating electric vehicle energy models exist, it would be beneficial to have a rapid and inexpensive technique that remains accurate. Producing a mathematical energy model for such a vehicle has the challenge of determining its aerodynamic drag and rolling resistance coefficients. Currently and most often, expensive and tedious (time-consuming) methods are used to find these coefficients. Computational fluid dynamics (CFD), wind tunnel testing, and extensive mathematics make this objective challenging. For this work, a solar-powered electric vehicle provided the source data to derive its coefficients cost-effectively and efficiently. Data were collected during a road test of the solar electric vehicle from South Africa to Namibia stretching over 2000 km, in which all required energy variables were recorded. The collected data were used in an optimisation routine to establish the two coefficients by minimising the actual and modelled energy consumption error and controlling the driving speed. The outcome of the optimisation routine produced accurate coefficients with a final error value of less than 5% when applied to a validation data set not used during optimisation. With minor modifications, this method may be integrated into any electric vehicle computer system to autonomously identify its two hard-to-find coefficients while driving, which can be used to provide an accurate and realistic driving range estimation to the driver. |
| first_indexed | 2024-03-11T01:48:56Z |
| format | Article |
| id | doaj.art-4e399e9e0af9499e8537282559e68255 |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-11T01:48:56Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-4e399e9e0af9499e8537282559e682552023-11-18T13:06:15ZengMDPI AGWorld Electric Vehicle Journal2032-66532023-05-0114613410.3390/wevj14060134Data Driven Methods for Finding Coefficients of Aerodynamic Drag and Rolling Resistance of Electric VehiclesRyan Van Greunen0Christiaan Oosthuizen1Department of Mechanical and Mechatronics Engineering, Tshwane University of Technology, Pretoria 0183, South AfricaDepartment of Mechanical and Mechatronics Engineering, Tshwane University of Technology, Pretoria 0183, South AfricaThis research investigated an alternate method for establishing the complex coefficients used in an electric vehicle’s mathematical energy consumption model. While other methods for creating electric vehicle energy models exist, it would be beneficial to have a rapid and inexpensive technique that remains accurate. Producing a mathematical energy model for such a vehicle has the challenge of determining its aerodynamic drag and rolling resistance coefficients. Currently and most often, expensive and tedious (time-consuming) methods are used to find these coefficients. Computational fluid dynamics (CFD), wind tunnel testing, and extensive mathematics make this objective challenging. For this work, a solar-powered electric vehicle provided the source data to derive its coefficients cost-effectively and efficiently. Data were collected during a road test of the solar electric vehicle from South Africa to Namibia stretching over 2000 km, in which all required energy variables were recorded. The collected data were used in an optimisation routine to establish the two coefficients by minimising the actual and modelled energy consumption error and controlling the driving speed. The outcome of the optimisation routine produced accurate coefficients with a final error value of less than 5% when applied to a validation data set not used during optimisation. With minor modifications, this method may be integrated into any electric vehicle computer system to autonomously identify its two hard-to-find coefficients while driving, which can be used to provide an accurate and realistic driving range estimation to the driver.https://www.mdpi.com/2032-6653/14/6/134solar electric vehiclecoefficientsaerodynamic dragrolling resistanceoptimisationmathematical modelling |
| spellingShingle | Ryan Van Greunen Christiaan Oosthuizen Data Driven Methods for Finding Coefficients of Aerodynamic Drag and Rolling Resistance of Electric Vehicles World Electric Vehicle Journal solar electric vehicle coefficients aerodynamic drag rolling resistance optimisation mathematical modelling |
| title | Data Driven Methods for Finding Coefficients of Aerodynamic Drag and Rolling Resistance of Electric Vehicles |
| title_full | Data Driven Methods for Finding Coefficients of Aerodynamic Drag and Rolling Resistance of Electric Vehicles |
| title_fullStr | Data Driven Methods for Finding Coefficients of Aerodynamic Drag and Rolling Resistance of Electric Vehicles |
| title_full_unstemmed | Data Driven Methods for Finding Coefficients of Aerodynamic Drag and Rolling Resistance of Electric Vehicles |
| title_short | Data Driven Methods for Finding Coefficients of Aerodynamic Drag and Rolling Resistance of Electric Vehicles |
| title_sort | data driven methods for finding coefficients of aerodynamic drag and rolling resistance of electric vehicles |
| topic | solar electric vehicle coefficients aerodynamic drag rolling resistance optimisation mathematical modelling |
| url | https://www.mdpi.com/2032-6653/14/6/134 |
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