Real-Time Energy Management of Parallel ...
Document type :
Article dans une revue scientifique: Article original
DOI :
Permalink :
Title :
Real-Time Energy Management of Parallel Hybrid Electric Vehicles Using Linear Quadratic Regulation
Author(s) :
Nguyễn, Bảo-Huy [Auteur]
Trovão, João Pedro F. [Auteur]
German, Ronan [Auteur]
Laboratoire d'Électrotechnique et d'Électronique de Puissance (L2EP) - ULR 2697
Bouscayrol, Alain [Auteur]
Laboratoire d'Électrotechnique et d'Électronique de Puissance (L2EP) - ULR 2697
Trovão, João Pedro F. [Auteur]
German, Ronan [Auteur]

Laboratoire d'Électrotechnique et d'Électronique de Puissance (L2EP) - ULR 2697
Bouscayrol, Alain [Auteur]

Laboratoire d'Électrotechnique et d'Électronique de Puissance (L2EP) - ULR 2697
Journal title :
Energies
Abbreviated title :
Energies
Volume number :
13
Pages :
5538
Publisher :
MDPI AG
Publication date :
2020-10-22
ISSN :
1996-1073
English keyword(s) :
energy management strategy (EMS)
parallel hybrid electric vehicle (HEV)
energetic macroscopic representation (EMR)
hardware-in-the-loop (HIL)
parallel hybrid electric vehicle (HEV)
energetic macroscopic representation (EMR)
hardware-in-the-loop (HIL)
HAL domain(s) :
Sciences de l'ingénieur [physics]
English abstract : [en]
Optimization-based methods are of interest for developing energy management strategies due to their high performance for hybrid electric vehicles. However, these methods are often complicated and may require strong ...
Show more >Optimization-based methods are of interest for developing energy management strategies due to their high performance for hybrid electric vehicles. However, these methods are often complicated and may require strong computational efforts, which can prevent them from real-world applications. This paper proposes a novel real-time optimization-based torque distribution strategy for a parallel hybrid truck. The strategy aims to minimize the engine fuel consumption while ensuring battery charge-sustaining by using linear quadratic regulation in a closed-loop control scheme. Furthermore, by reformulating the problem, the obtained strategy does not require the information of the engine efficiency map like the previous works in literature. The obtained strategy is simple, straightforward, and therefore easy to be implemented in real-time platforms. The proposed method is evaluated via simulation by comparison to dynamic programming as a benchmark. Furthermore, the real-time ability of the proposed strategy is experimentally validated by using power hardware-in-the-loop simulation.Show less >
Show more >Optimization-based methods are of interest for developing energy management strategies due to their high performance for hybrid electric vehicles. However, these methods are often complicated and may require strong computational efforts, which can prevent them from real-world applications. This paper proposes a novel real-time optimization-based torque distribution strategy for a parallel hybrid truck. The strategy aims to minimize the engine fuel consumption while ensuring battery charge-sustaining by using linear quadratic regulation in a closed-loop control scheme. Furthermore, by reformulating the problem, the obtained strategy does not require the information of the engine efficiency map like the previous works in literature. The obtained strategy is simple, straightforward, and therefore easy to be implemented in real-time platforms. The proposed method is evaluated via simulation by comparison to dynamic programming as a benchmark. Furthermore, the real-time ability of the proposed strategy is experimentally validated by using power hardware-in-the-loop simulation.Show less >
Language :
Anglais
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
Université de Lille
Centrale Lille
Arts et Métiers Sciences et Technologies
Junia HEI
Centrale Lille
Arts et Métiers Sciences et Technologies
Junia HEI
Research team(s) :
Équipe Commande
Submission date :
2023-02-02T14:28:08Z
2023-02-07T15:20:36Z
2024-01-04T19:38:51Z
2023-02-07T15:20:36Z
2024-01-04T19:38:51Z
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