Experimental Study of the Interaction Bewteen a Turbulent Flow and a River Biofilm Growing on Macrorugosities

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Commande d’un Système Multi-Sources Dédié au Véhicule Électrique

Au cours des dernières décennies, les stratégies de gestion de l’énergie (SGE) dans les véhicules électriques (VEs) et les véhicules électriques à pile à combustible (PàC) ont fait l’objet d’un grand intérêt en raison de son augmentation importante de l’utilisation afin d’améliorer les performances du véhicule ainsi que d’avoir une répartition optimale de la puissance entre les sources impliquées. La plupart des travaux actuels sont principalement basés sur la répartition de la puissance en fonction de la dynamique de la source, sans tenir compte de la régulation de l’état de charge des sources auxiliaires ainsi que de la dégradation des sources d’énergie embarquées. Pour combler cette lacune, cette thèse présente deux SGE pour un système de stockage hybride composé par des batteries et des super-condensateurs (SCs) dans un VE comme une première partie. Dans la deuxième partie, un autre système hybride a été étudié, il est constitué d’une PàC et des batteries lithium-ion utilisés pour la motorisation d’un véhicule réel. Deux stratégies de gestion de l’énergie ont été développées pour le système hybride multisources, la première est une méthode basé sur l’intelligence artificielle qui est la méthode logique floue, tandis que l’autre est une technique d’optimisation qui prend en compte les variations paramétriques linéaires (LPV). Une comparaison a été effectuée pour évaluer les performances des deux techniques. Pour le véhicule électrique à PàC, une SGE soucieuse à la santé de la batterie est basé sur la commande prédictive (MPC) a été développé, ce nouveau travail proposé cherche à maintenir non seulement l’état de charge de la batterie mais aussi à minimiser le vieillissement des batteries et à prolonger leur durée de vie. L’ensemble des systèmes est modélisé par des équations d’état, ainsi l’évaluation des techniques de contrôle proposées est simulée à l’aide du logiciel Matlab-Simulink, puis validée expérimentalement

Neural network power management for hybrid electric elevator application

International audienceThe present paper addresses the control and the power management of a hybrid system dedicated to an elevator application. In fact, the multi-source includes a photovoltaic generator as a main source supported by a battery-bank and a stack of super capacitors (SC). On the traction part, a permanent magnet synchronous motor (PMSM) is used to carry the elevator box. The power supervising mission is performed via a neural network (NN) routine trained with a frequency based strategy (FBS). The main objective of the applied control routines is to manage effectively the splits of the load demand. Therefore, they can provide the required power amounts in both steady-state and transient state, respecting the dynamic behavior of each source. Obviously, a fuzzy logic MPPT method has been applied to the PV side to permanently track the maximum power point through an adequate tuning of a boost converter regardless of the solar irradiance variations. Whereas, the controller of the DC–DC bidirectional converters of the battery and SC stack is based on the direct Lyapunov theory. To test the effectiveness of the proposed techniques, intensive numerical tests are done using MATLAB/Simulink Package. The obtained results prove the feasibility of the proposed approach, where the system switches smoothly between the operating modes

A Novel Energy Management Strategy in Electric Vehicle Based on H∞ Self-Gain Scheduled for Linear Parameter Varying Systems

International audienceThe present paper exhibits a real time assessment of a robustEnergy Management Strategy (EMS) for battery-super capacitor (SC)Hybrid Energy Storage System (HESS). The proposed algorithm,dedicated to an electric vehicular application, is based on aself-gain scheduled controller, which guarantees the H∞ performancefor a class of linear parameter varying (LPV) systems. Assumingthat the duty cycle of the involved DC-DC converters are consideredas the variable parameters, that can be captured in real time, andforwarded to the controller to ensure both; the performance androbustness of the closed-loop system. The subsequent controller istherefore time-varying and it is automatically scheduled accordingto each parameter variation. This algorithm has been validatedthrough experimental results provided by a tailor-made test benchincluding both the HESS and the vehicle traction emulation system.The experimental results demonstrate the overall stability of thesystem, where the proposed LPV supervisor successfully accomplishesa power frequency splitting in an adequate way, respecting thedynamic of the sources. The proposed solution provides significantperformances for different speed levels

A Novel Energy Management Strategy in Electric Vehicle Based on H∞ Self-Gain Scheduled for Linear Parameter Varying Systems

International audienceThe present paper exhibits a real time assessment of a robustEnergy Management Strategy (EMS) for battery-super capacitor (SC)Hybrid Energy Storage System (HESS). The proposed algorithm,dedicated to an electric vehicular application, is based on aself-gain scheduled controller, which guarantees the H∞ performancefor a class of linear parameter varying (LPV) systems. Assumingthat the duty cycle of the involved DC-DC converters are consideredas the variable parameters, that can be captured in real time, andforwarded to the controller to ensure both; the performance androbustness of the closed-loop system. The subsequent controller istherefore time-varying and it is automatically scheduled accordingto each parameter variation. This algorithm has been validatedthrough experimental results provided by a tailor-made test benchincluding both the HESS and the vehicle traction emulation system.The experimental results demonstrate the overall stability of thesystem, where the proposed LPV supervisor successfully accomplishesa power frequency splitting in an adequate way, respecting thedynamic of the sources. The proposed solution provides significantperformances for different speed levels

Multi-Objective Optimization-Based Health-Conscious Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles

International audienceThe Energy Management Strategy (EMS) in Fuel Cell Hybrid Electric Vehicles (FCHEVs) isthe key part to enhance optimal power distribution. Indeed, the most recent works are focusing onoptimizing hydrogen consumption, without taking into consideration the degradation of embeddedenergy sources. In order to overcome this lack of knowledge, this paper describes a new health-conscious EMS algorithm based on Model Predictive Control (MPC), which aims to minimize thebattery degradation to extend its lifetime. In this proposed algorithm, the health-conscious EMSis normalized in order to address its multi-objective optimization. Then, weighting factors areassigned in the objective function to minimize the selected criteria. Compared to most EMSs basedon optimization techniques, this proposed approach does not require any information about thespeed profile, which allows it to be used for real-time control of FCHEV. The achieved simulationresults show that the proposed approach reduces the economic cost up to 50% for some speed profile,keeping the battery pack in a safe range and significantly reducing energy sources degradation. Theproposed health-conscious EMS has been validated experimentally and its online operation abilityclearly highlighted on a PEMFC delivery postal vehicl