A New Regenerative Anti-Idling System for Service Vehicles: Load Identification, Optimal Power Management
Notice bibliographique
Résumé
Service vehicles, such as refrigerator trucks and tour buses, are equipped with auxiliary devices, including refrigeration systems and cabin air conditioning systems, which consume significant amount of energy. The engine of these vehicles should idle to supply power for auxiliary devices when they stop for a long time, e.g. for loading and unloading goods. This study proposes a new anti-idling system for service vehicles that powers auxiliary devices by a battery pack and an engine-driven generator (or alternator). In addition to idle elimination which is the main objective of all current anti-idling systems, the proposed system called Regenerative Auxiliary Power System (RAPS) attempts to reduce fuel consumption by enabling regenerative braking and utilizing an optimal power management system. The objectives of this study are to identify drive and service loads of a service vehicle for component sizing of the RAPS and to develop an optimal power management system for more fuel saving. \nIn order to determine the size of required components (a battery pack and a generator) for the RAPS, drive and service loads of a given service vehicle should be identified. The drive load is the amount of power that is required for moving the vehicle, and the service load is the power consumption of the auxiliary devices. To identify drive and service loads, all the parameters in power balance equation of the engine should be either measured or estimated. As two inputs with unknown variations in this equation, vehicle mass and torque of auxiliary devices are required to be estimated. This study proposes a model-based algorithm that utilizes available signals in the CAN bus of the vehicle as well as a signal from a GPS receiver (road grade information) for simultaneous estimation of the vehicle mass and torque of auxiliary devices. \nThe power management system of the RAPS should determine the split ratio of auxiliary power demand between the generator and battery in order to minimize fuel consumption. It should also guarantee that the battery has enough energy for powering auxiliary devices at all the engine-OFF stops. To meet these objectives, a two-level control system is proposed in this study. In the high-level control system, a fast dynamic programming (DP) technique which utilizes extracted features of the predicted drive and service loads obtains an SOC trajectory. In the low-level control system, a refined Adaptive Equivalent Fuel Consumption Minimization (A-ECMS) technique is employed to track the SOC trajectory obtained by the high-level control scheme. \nMany numerical simulations are carried out to test the functionality of the proposed identification algorithm and power management system. Moreover, the numerical simulations are validated by Hardware-In-The-Loop (HIL) simulations. The results show the idling is completely eliminated and a significant amount of fuel is saved by implementing the RAPS on a service vehicle. Therefore, the cost of energy can be noticeably reduced and consequently the cost of RAPS is recouped in a short period of time.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».