Intelligent Energy Management Agent for a Parallel Hybrid Vehicle—Part I: System Architecture and Design of the Driving Situation Identification Process

Langari, Reza; Won, Jong-Seob

doi:10.1109/tvt.2005.844685

Cited by 262 publications

(109 citation statements)

References 26 publications

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“…The charge patterns obtained through this optimization were substantially different from those optimized for energy cost or battery health alone. As can be seen from the recent research studies, DP also requires a large amount of memory and computational power to perform the optimization [10]; -heuristic and knowledge-based online optimization such as rule based algorithms [11][12][13], fuzzy logic [14][15][16] and equivalent consumption minimization strategy [17] can on the other hand be implemented on board of a vehicle. The heuristic and knowledge-based algorithms do require some information to perform optimization but it is typically less information than that required by DP.…”

Section: Energy Management Of Phevsmentioning

confidence: 99%

Intelligent Energy Management for Plug-in Hybrid Electric Vehicles: The Role of ITS Infrastructure in Vehicle Electrification

Marano

Rizzoni

Tulpule

et al. 2012

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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Résumé -Gestion énergétique intelligente pour véhicules électriques hybrides rechargeables : rôle de l'infrastructure de systèmes de transport intelligents (STI) dans l'électrification des véhicules -Le désir de réduire les émissions de carbone issues des sources de transport a conduit durant la dernière décennie au développement de nouvelles technologies de propulsion, axées sur l'électrification des véhicules (comprenant les véhicules électriques hybrides, hybrides rechargeables et sur batteries). Ces technologies de propulsion, en même temps que les avancées en matière de télécommunication et de puissance de calcul, présentent le potentiel de rendre les véhicules particuliers et commerciaux plus efficaces sur le plan énergétique et plus écologiques. En particulier, les algorithmes de gestion énergétique sont partie intégrante des véhicules rechargeables et sont très importants pour atteindre les bénéfices de performances. Les performances optimales des algorithmes de gestion énergétique dépendent fortement de la capacité à prévoir la demande énergétique du véhicule. Les informations disponibles concernant l'environnement (température, humidité, vent, qualité de route, etc.) et le trafic (densité du trafic, feux de circulation, etc.) sont très importantes en termes de fonctionnement d'un véhicule à efficacité optimale. Le présent article passe brièvement en revue certaines technologies actuelles susceptibles d'aider à atteindre cet objectif d'efficacité optimale. En plus des informations disponibles issues des systèmes d'informations télématiques et géographiques, la connaissance de la demande de chargement projetée des véhicules sur le réseau électrique est nécessaire pour construire un dispositif de commande de gestion énergétique intelligent pour les futurs véhicules hybrides rechargeables et électriques. L'incidence du chargement de millions de véhicules à partir du réseau électrique pourrait être significative, sous forme d'une charge accrue des centrales électriques, des lignes de transmission et de distribution, des émissions et de l'aspect économique (des informations sont données et discutées dans le cas des USA). En conséquence, ces effets doivent être pris en considération d'une manière intelligente en commandant/programmant le chargement par l'intermédiaire d'un dispositif de commande répartie basé sur la communication. Abstract

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Section: Energy Management Of Phevsmentioning

confidence: 99%

Intelligent Energy Management for Plug-in Hybrid Electric Vehicles: The Role of ITS Infrastructure in Vehicle Electrification

Marano

Rizzoni

Tulpule

et al. 2012

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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“…Knowledge of the current driving conditions can have several benefits: user interface adaptation, engine power management, and driver monitoring (Huang et al, 2011;Langari and Won, 2005;; all of which strive to improve driver safety and vehicle efficiency. In this paper we present a data mining methodology, based on the general data mining process, for driving condition monitoring via Controller Area Network (CAN)-bus data.…”

Section: Introductionmentioning

confidence: 99%

Data Mining for Vehicle Telemetry

Taylor

Griffiths

Bhalerao

et al. 2016

Applied Artificial Intelligence

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This paper presents a data mining methodology for driving condition monitoring via CAN-bus data that is based on the general data mining process. The approach is applicable to many driving condition problems and the example of which is demonstrated to successfully ascertain road type from CAN-bus data, and it is shown that the classification correlates well with input signals such as vehicle speed, steering wheel angle, and suspension height.

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“…Numerous scientific papers were published about the drive system topology and types of an electrical motor used in EV [1,2].…”

Section: Introductionmentioning

confidence: 99%

Model of the double-rotor induction motor in terms of electromagnetic differential

Adamczyk¹,

Wilk²,

Michna³

2016

Archives of Electrical Engineering

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Abstract:The paper presents a concept, a construction, a circuit model and experimental results of the double-rotor induction motor. This type of a motor is to be implemented in the concept of the electromagnetic differential. At the same time it should fulfill the function of differential mechanism and the vehicle drive. One of the motor shafts is coupled to the direction changing mechanical transmission. The windings of the external rotor are powered by slip rings and brushes. The inner rotor has the squirrel-cage windings. The circuit model parameters were calculated based on the 7.5 kW real single-rotor induction motor (2p = 4). Experimental verification of the model was based on comparison between the mentioned single-rotor motor and double-rotor model with the outer rotor blocked. The presented results showed relatively good compliance between the model and real motor.

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Intelligent Energy Management Agent for a Parallel Hybrid Vehicle—Part I: System Architecture and Design of the Driving Situation Identification Process

Cited by 262 publications

References 26 publications

Intelligent Energy Management for Plug-in Hybrid Electric Vehicles: The Role of ITS Infrastructure in Vehicle Electrification

Intelligent Energy Management for Plug-in Hybrid Electric Vehicles: The Role of ITS Infrastructure in Vehicle Electrification

Data Mining for Vehicle Telemetry

Model of the double-rotor induction motor in terms of electromagnetic differential

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