An adaptive regenerative braking strategy design based on naturalistic regeneration performance for intelligent vehicles

Ziadia, M., Kelouwani, S., Amamou, A. et Agbossou, K. (2023). An adaptive regenerative braking strategy design based on naturalistic regeneration performance for intelligent vehicles. IEEE Access, 11 . pp. 99573-99588. ISSN 2169-3536 DOI 10.1109/ACCESS.2023.3313553

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Résumé

The effectiveness of regenerative braking strategies plays an important role in extending the driving range of electric vehicles. Since the driver is still an essential factor in levels 3 and 4 of intelligent electric vehicles, improving user acceptance and adoption of the braking control strategy is crucial. This paper puts forward a new regenerative braking strategy to find a compromise between optimal braking control performance and naturalistic regeneration performance while satisfying the maximum speed preference when driving between two-stop events. Unlike other similar works that only maximize regenerative braking energy while satisfying the physical limits of an electrified powertrain, this paper considers naturalistic regeneration performance. To achieve this, firstly, the power regenerated by three drivers is predicted with a long-horizon (30 seconds), using long-short-term memory networks (LSTM) and non-linear autoregressive exogenous model (NARX). Subsequently, an estimation of the energy recovery maximization rate is performed to give a perception of the naturalistic regeneration performance. As this performance varies, the deceleration planning employs three horizon scales of long, medium, and short, determined by the energy recovery maximization rate. Finally, dynamic programming (DP) is utilized to optimize a deceleration profile. The study utilizes real data of inverter efficiency, transmission efficiency, and motor-to-battery efficiency map. The outcome of this study shows that the proposed regeneration braking strategy is adaptive, improving regeneration efficiency by 39,6% for driver 1, 16% for driver 2, and 26% for driver 3, and forecasting the optimality of some deceleration behaviors.

Type de document:	Article
Mots-clés libres:	Eco-driving Acceptance Driving behaviors Regenerative braking Intelligent vehicles Machine learning Optimal control
Date de dépôt:	22 mai 2024 19:02
Dernière modification:	22 mai 2024 19:02
Version du document déposé:	Version officielle de l'éditeur
URI:	https://depot-e.uqtr.ca/id/eprint/11324

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