Practical Nonlinear Model Predictive Control for Improving Two-Wheel Vehicle Energy Consumption
Increasing the range of electric vehicles (EVs) is possible with the help of eco-driving techniques, which are algorithms that consider internal and external factors, like performance limits and environmental conditions, such as weather. However, these constraints must include critical variables in...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/4/1950 |
| _version_ | 1811153824453230592 |
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| author | Yesid Bello Juan Sebastian Roncancio Toufik Azib Diego Patino Cherif Larouci Moussa Boukhnifer Nassim Rizoug Fredy Ruiz |
| author_facet | Yesid Bello Juan Sebastian Roncancio Toufik Azib Diego Patino Cherif Larouci Moussa Boukhnifer Nassim Rizoug Fredy Ruiz |
| author_sort | Yesid Bello |
| collection | DOAJ |
| description | Increasing the range of electric vehicles (EVs) is possible with the help of eco-driving techniques, which are algorithms that consider internal and external factors, like performance limits and environmental conditions, such as weather. However, these constraints must include critical variables in energy consumption, such as driver preferences and external vehicle conditions. In this article, a reasonable energy-efficient non-linear model predictive control (NMPC) is built for an electric two-wheeler vehicle, considering the Paris-Brussels route with different driving profiles and driver preferences. Here, NMPC is successfully implemented in a test bed, showing how to obtain the different parameters of the optimization problem and the estimation of the energy for the closed-loop system from a practical point of view. The efficiency of the brushless DC motor (BLCD) is also included for this test bed. In addition, this document shows that the proposal increases the chance of traveling the given route with a distance accuracy of approximately 1.5% while simultaneously boosting the vehicle autonomy by almost 20%. The practical result indicates that the strategy based on an NMPC algorithm can significantly boost the driver’s chance of completing the journey. If the vehicle energy is insufficient to succeed in the trip, the algorithm can guide the minimal State of Charge (SOC) required to complete the journey to reduce the driver energy-related uncertainty to a minimum. |
| first_indexed | 2024-03-11T08:53:11Z |
| format | Article |
| id | doaj.art-c9dac652c8224847b009830f4a4dc562 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T08:53:11Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-c9dac652c8224847b009830f4a4dc5622023-11-16T20:20:03ZengMDPI AGEnergies1996-10732023-02-01164195010.3390/en16041950Practical Nonlinear Model Predictive Control for Improving Two-Wheel Vehicle Energy ConsumptionYesid Bello0Juan Sebastian Roncancio1Toufik Azib2Diego Patino3Cherif Larouci4Moussa Boukhnifer5Nassim Rizoug6Fredy Ruiz7Energy and Embedded Systems for Transportation Research Department, ESTACA-LAB, 78066 Montigny-Le-Bretonneux, FranceEnergy and Embedded Systems for Transportation Research Department, ESTACA-LAB, 78066 Montigny-Le-Bretonneux, FranceEnergy and Embedded Systems for Transportation Research Department, ESTACA-LAB, 78066 Montigny-Le-Bretonneux, FranceJaveriana Electronics Department, Pontificia Universidad, Bogotá 110231, ColombiaEnergy and Embedded Systems for Transportation Research Department, ESTACA-LAB, 78066 Montigny-Le-Bretonneux, FranceUniversité de Lorraine, LCOMS, F-57000 Metz, FranceEnergy and Embedded Systems for Transportation Research Department, ESTACA-LAB, 78066 Montigny-Le-Bretonneux, FranceSystems and Control Department Italy, Politecnico de Milano, 20158 Milan, ItalyIncreasing the range of electric vehicles (EVs) is possible with the help of eco-driving techniques, which are algorithms that consider internal and external factors, like performance limits and environmental conditions, such as weather. However, these constraints must include critical variables in energy consumption, such as driver preferences and external vehicle conditions. In this article, a reasonable energy-efficient non-linear model predictive control (NMPC) is built for an electric two-wheeler vehicle, considering the Paris-Brussels route with different driving profiles and driver preferences. Here, NMPC is successfully implemented in a test bed, showing how to obtain the different parameters of the optimization problem and the estimation of the energy for the closed-loop system from a practical point of view. The efficiency of the brushless DC motor (BLCD) is also included for this test bed. In addition, this document shows that the proposal increases the chance of traveling the given route with a distance accuracy of approximately 1.5% while simultaneously boosting the vehicle autonomy by almost 20%. The practical result indicates that the strategy based on an NMPC algorithm can significantly boost the driver’s chance of completing the journey. If the vehicle energy is insufficient to succeed in the trip, the algorithm can guide the minimal State of Charge (SOC) required to complete the journey to reduce the driver energy-related uncertainty to a minimum.https://www.mdpi.com/1996-1073/16/4/1950NMPCtwo wheelelectric vehicleeco-driving profileefficiencyoptimization |
| spellingShingle | Yesid Bello Juan Sebastian Roncancio Toufik Azib Diego Patino Cherif Larouci Moussa Boukhnifer Nassim Rizoug Fredy Ruiz Practical Nonlinear Model Predictive Control for Improving Two-Wheel Vehicle Energy Consumption Energies NMPC two wheel electric vehicle eco-driving profile efficiency optimization |
| title | Practical Nonlinear Model Predictive Control for Improving Two-Wheel Vehicle Energy Consumption |
| title_full | Practical Nonlinear Model Predictive Control for Improving Two-Wheel Vehicle Energy Consumption |
| title_fullStr | Practical Nonlinear Model Predictive Control for Improving Two-Wheel Vehicle Energy Consumption |
| title_full_unstemmed | Practical Nonlinear Model Predictive Control for Improving Two-Wheel Vehicle Energy Consumption |
| title_short | Practical Nonlinear Model Predictive Control for Improving Two-Wheel Vehicle Energy Consumption |
| title_sort | practical nonlinear model predictive control for improving two wheel vehicle energy consumption |
| topic | NMPC two wheel electric vehicle eco-driving profile efficiency optimization |
| url | https://www.mdpi.com/1996-1073/16/4/1950 |
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