1,720,963 research outputs found
MODELING THE THERMAL BEHAVIOR OF INTERNAL COMBUSTION IN HYBRID ELECTRIC VEHICLES WITH AND WITHOUT EXHAUST GAS HEAT RECIRCULATION
No full textA first-order lumped-parameter model for the prediction of thermal behavior of a single-cylinder gasoline engine for Hybrid Electric Vehicles (HEVs) has been implemented. The model is coupled with a zero-dimension in-cylinder model that evaluates the working cycle of the engine according to the actual operating conditions and calculates the temperature of the exhaust gases, the overall efficiency of the engine and the exhaust gases flow rate. The model takes into account the possibility of using exhaust gas heat recirculation in order to enhance engine warm-up during cold start which improves its efficiency. The supervisory strategy takes into account not only predicted speed and ambient and road conditions along a future time window but also actual battery state of the charge and engine temperature to select the optimal power split between the ICE-generator group and the batteries. The proposed model represents an improvement with respect to a previous investigation of the authors where the temperature of the engine were assumed to increase/decrease of on Celsius degree in each seconds according to the state of the engine (ON/OFF)
A method for the prediction of future driving conditions and for the energy management optimisation of a Hybrid Electric Vehicle
No full textVehicular communications are expected to enable the development
of Intelligent Cooperative Systems to be exploited for solving
crucial problems related to mobility: road safety, traffic management etc.
Information and Communication Technologies could also play a very important role
in order to optimize the energy management of conventional, hybrid and electrical vehicles and, thus,
to reduce their environment impact. In particular, vehicular communications could be used
to predict driving conditions with the objective to determinate future load power demand.
An adaptative energy management strategy for series hybrid electric vehicles based on genetic algorithm
optimized maps and the SUMO (Simulation of Urban Mobility) predictor is presenter here.
The control stategy paremeters are optimized over a series of possible mini cycles (duration ) obteined by
a K-means clustering algorithm. These references mini cycles are colled centroids. The centroids are abteined
with respect at time windowed standard driving cycles (UDDS, EUDC, etc) and realistic driving cycles acquired
Development of an Energy Management Strategy for Plug-in Series Hybrid Electric Vehicle Based on the Prediction of the Future Driving Cycles by ICT Technologies and Optimized Maps
No full textAn adaptative energy management strategy for series hybrid electric vehicles based on optimized maps and the SUMO (Simulation of Urban MObility) predictor is presented here. The first step of the investigation is the off line optimization of the control strategy parameters (already developed by the authors) over a series of reference mini driving cycles (duration of 60s) obtained from standard driving cycles (UDDS, EUDC, etc) and realistic driving cycles acquired on the ITAN500 HEV. The optimal variables related to each mini driving cycle are stored in maps that are then implemented on the ITAN500 vehicles. When the vehicle moves, a wireless card is used to exchange information with surrounding vehicle and infrastructure. These information are used by a local instance of the SUMO traffic prediction tool (run on board) to predict the driving conditions of the HEV in the future period of time T=60s. The predicted driving cycle is compared with the reference mini driving cycles and the most similar one is found. The optimal control strategy parameters mapped for that reference cycle are then used to select the power-split in the future time window. This process is repeated every T seconds obtaining an adaptative control strategy which do not requires much computational power on board. The proposed approach has been compared numerically with the “no knowledge” approach and the “full knowledge” approach. In the “no knowledge” case, the energy management was optimized for NEDC and then applied to three realistic driving cycles. In the “full knowledge” approach the energy management was optimized for each realistic driving cycle. The “full knowledge” approach allows the best fuel consumption to be obtained but requires the knowledge of the whole vehicle mission while the “no knowledge” method gives poor results since it cannot exploit the potentiality of a PHEV. The proposed approach allows good results to be obtained in terms of fuel consumption thanks to a better usage of the internal combustion engine
Gestione intelligente del motore termico in veicoli ibridi plug-in
No full textNegli ultimi anni particolare attenzione è stata data, da parte della comunità scientifica, allo sviluppo di soluzioni tecnologiche per veicoli ibridi plug-in e in particolare per l’ottimizzazione della gestione dei flussi energetici al fine di minimizzare consumi ed emissioni. In questa memoria si presenta una strategia auto-adattativa di gestione dell'energia per i veicoli ibridi elettrici in configurazione serie basata su mappe ottimizzate per i diversi componenti e su predizioni di profili di velocità effettuate da SUMO (Simulation of Urban MObility). Il primo passo della ricerca è stato quello di eseguire l'ottimizzazione off-line dei parametri della strategia di controllo basata su una serie di mini cicli di guida, ottenuta da cicli di guida standard (UDDS, NEDC, ecc.) e cicli di guida reale acquisiti dal veicolo ITAN500 (prototipo realizzato dall’Università del Salento). Le variabili ottimizzate ottenute per ogni mini ciclo di guida vengono memorizzate in mappe che saranno implementate sull'ITAN500. Il sistema proposto prevede uno scambio di informazioni con i veicoli circostanti e le infrastrutture. Tali informazioni sono utilizzate da un simulatore di traffico locale eseguito a bordo, per predire le condizioni di guida dell'HEV in un periodo di tempo futuro (T=60s). Il ciclo di guida predetto viene confrontato con i mini cicli di guida di riferimento per trovare quello più simile e per selezionare i relativi parametri ottimi. I risultati presentati dimostrano che la procedura sviluppata è in grado di gestire in modo ottimale il funzionamento del motore termico consentendo di farlo funzionare per gran parte della missione solo quando si trova in condizioni di massimo rendimento
Sviluppo di un veicolo ibrido ad idrogeno in scala ridotta per il test di strategie di controllo innovative
No full textSimulation and Optimization of the Energy Management of ITAN500 in the SUMO Traffic Model Environment
No full textExperimental Test of a GA-Optimized Control Strategy on the H2-VOLKS demonstrator
No full textA hydrogen hybrid powertrain has been developed for the Volksbot RT3 differential drive mobile robot in order to obtain a remote-controlled small scale hydrogen car named H2-VOLKS. The powertrain is able to implement any control strategies by setting the instantaneous power split between the fuel cell and the batteries. The robot provides the University of Salento with a low-cost system to test models and develop control strategies applicable to real scale vehicles. In particular, a control strategy presented by the authors in a previous investigation has been optimized with a multi-objective genetic algorithm and tested on the H2-VOLKS
A Mobile Test Bench for Fuel Cell Control Strategies
No full textAbstract: - A mobile test bench for testing energy management strategies for fuel cell hybrid electric vehicle has been obtained by modifying a Volksbot RT3 differential drive mobile robot. The robot provides the University of Salento with a low-cost system to test models and develop control strategies applicable to real scale vehicles. In fact, the prototype has been developed with the goal of implementing any control strategies by setting the instantaneous power split between the fuel cell and the batteries. H2Volks can be moved in two modes: a free mode that allow the user to simulate and acquire realistic driving cycles and a controlled mode that can be used to test different control strategies over the same driving cycle. In particular, a control strategy presented by the authors in a previous investigation has been implemented on the H2-VOLKS
ON THE USE OF VEHICULAR COMMUNICATIONS FOR EFFICIENT ENERGY MANAGEMENT OF HYBRID ELECTRIC VEHICLES
No full textOn-Board Simulation of the Traffic Scenario for the Sustainable Mobility
No full textAbstract: Information and Communication Technologies could play a very important role in order to optimize the energy management of conventional, hybrid and electrical vehicles and, thus, to reduce their environmental impact. In particular, vehicular communications could be used to predict driving conditions with the objective to determinate future load power demand. To this, we propose a system which allows to estimate future speed profile on board of a vehicle by gathering state messages that surrounding vehicles and/or infrastructure broadcast and by inputting them to a traffic simulator (SUMO) used as a predictor. The system has been validated by a simulation model which considers a number of vehicles moving on the road network of the Ecotekne campus at the University of Salento. The actual speed profile of a target vehicle has been compared with that estimated on board for prediction horizon duration values ranging from 1 s to 60 s. Simulation results have shown that, even if the horizon duration is set to 60 s, the prediction error, in terms of the root mean square, is lower than 4 km/h. Afterwards, the system has been implemented on real vehicles and its functionalities have been tested in the campus road networ
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