Weather-Adaptive Regenerative Braking Strategy Based on Driving Style Recognition for Intelligent Electric Vehicles

Ziadia, M., Kelouwani, S., Amamou, A. et Agbossou, K. (2025). Weather-Adaptive Regenerative Braking Strategy Based on Driving Style Recognition for Intelligent Electric Vehicles. Sensors, 25 (4). p. 1175. ISSN 1424-8220 DOI 10.3390/s25041175

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Résumé

This paper examines the energy efficiency of smart electric vehicles equipped with regenerative braking systems under challenging weather conditions. While Advanced Driver Assistance Systems (ADAS) are primarily designed to enhance driving safety, they often overlook energy efficiency. This study proposes a Weather-Adaptive Regenerative Braking Strategy (WARBS) system, which leverages onboard sensors and data processing capabilities to enhance the energy efficiency of regenerative braking across diverse weather conditions while minimizing unnecessary alerts. To achieve this, we develop driving style recognition models that integrate road conditions, such as weather and road friction, with different driving styles. Next, we propose an adaptive deceleration plan that aims to maximize the conversion of kinetic energy into electrical energy for the vehicle’s battery under varying weather conditions, considering vehicle dynamics and speed constraints. Given that the potential for energy recovery through regenerative braking is diminished on icy and snowy roads compared to dry ones, our approach introduces a driving context recognition system to facilitate effective speed planning. Both simulation and experimental validation indicate that this approach can significantly enhance overall energy efficiency.

Type de document:	Article
Mots-clés libres:	Intelligent electric vehicle Adaptive regenerative braking Weather conditions Driving style recognition Machine learning Energy recovery optimization
Date de dépôt:	20 août 2025 15:16
Dernière modification:	20 août 2025 15:16
Version du document déposé:	Version officielle de l'éditeur
URI:	https://depot-e.uqtr.ca/id/eprint/12207

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