Practical Nonlinear Model Predictive Control for Improving Two-Wheel Vehicle Energy Consumption - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Energies Année : 2023

Practical Nonlinear Model Predictive Control for Improving Two-Wheel Vehicle Energy Consumption

Yesid Bello
Juan Sebastian Roncancio
Toufik Azib
Connectez-vous pour contacter l'auteur
Diego Patino
Cherif Larouci
Moussa Boukhnifer
Nassim Rizoug
Fredy Ruiz

Résumé

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.
Fichier principal
Vignette du fichier
energies-16-01950_VF.pdf (7.92 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-04021668 , version 1 (09-03-2023)

Identifiants

Citer

Yesid Bello, Juan Sebastian Roncancio, Toufik Azib, Diego Patino, Cherif Larouci, et al.. Practical Nonlinear Model Predictive Control for Improving Two-Wheel Vehicle Energy Consumption. Energies, 2023, 16, ⟨10.3390/en16041950⟩. ⟨hal-04021668⟩

Collections

UNAM ESTACA
14 Consultations
5 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More