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For an accurate evaluation of the driving range of an Electric Vehicle (EV), many conditions must be considered (road profile, traffic influence, etc.). However cabin heating system is not often considered despite its significant impact. In this paper, the impact of the cabin heating system is studied on the driving range of an EV. A real EV is used as a reference. A multi-domain model is developed and validated by experimental results on the vehicle. From this validated model, the impact of the heating system on the range is evaluated up to 30% in cold climatic conditions. In a classical approach, an eco-driving mode enables an increase in the range by reducing the vehicle acceleration and velocity. When considering the heating system, the energy balance is more complex: the eco-driving mode can lead to an over-consumption of energy. A better compromise is required as a function of the climatic condition

In the automotive sector, Hardware-In-the-Loop (HIL) simulation is a useful step to develop new controls and energy management methods. We propose to develop a comprehensive and efficient full- scale power HIL simulation of a commercial electric vehicle using the graphical formalism of the Energetic Macroscopic Representation. This platform allows taking into account the limitations and component constraints at real power scale. After a validation step, we propose to modify the vehicle energy management to demonstrate the flexibility of the approach. Vehicle modifications may be validated in real time at the full-scale power.

In this paper, a multi Fuel Cell (FC) system is added to the Tazzari Zero Electric Vehicle. With a fixed amount of hydrogen, the influence of the number of FCs is studied by simulation according to a strategy that maximizes the use of the batteries. The vehicle range has been extended from 90 to 109.4 km, depending on the number of FCs. However, the distance drive with the H2 power is much more expensive than the equivalent distance with full electric power. A compromise between the full electric, the travel range and the system cost should define the final FC system’s architecture. The study has been done with a simulation tool by using the Energetic Macroscopic Representation.

In this paper, the traction system modeling of a commercial electric car is studied. Experimental data acquired during an on-road test drive are used to determine an efficiency map of the traction system. Using the deduced model, simulation results are compared to experimental results. The simulation tool using the proposed efficiency map method yields less than 5 % error on energy consumption compared to experimental test drive results.

In this paper the drive control of a commercial Electric Vehicle (EV) equipped with a low power Range Extender (RE) Fuel Cell (FC) system is studied. By using backstepping control, the drive control of the RE-EV is deduced and compared to the Inversion Based-Control deduced from its Energetic Macroscopic Representation. From the modelling, the backstepping defines the control structure and ensures the stability of a system regarding to Lyapunov-LaSalle theorems.

The study of an electric vehicle is an attractive topic for students. At the University of Lille1 (France), an electric car is used to teach and develop drive control skills. From software simulation and Hardware-inthe- loop simulation, the students in electrical engineering learn drive control steps with a real electric vehicle. In this paper, the Energetic Macroscopic Representation is used to describe the electric car. This graphical tool allows the decomposition of the studied vehicle in accordance with physical laws. The control scheme is then deduced from the description using the inversion-based control rules.

In this paper the driving range of a commercial Electric Vehicle is extended using a low power fuel cell system. By using two driving cycles (Urban Driving Cycle (UDC) and a real cycle), both vehicles are compared in simulation using Energetic Macroscopic Representation. By adding a 1.2 kW fuel cell system and 2700 sl, 19.5 kg hydrogen tanks, the driving range is extended from 105.6 km to 128.2 km for an UDC, and from 68.3 km to 73.2 km for a real cycle.

The paper presents a control design oriented simulation model of a multi-stack Fuel Cell System (FCS). The aim of such a system is to improve performance, reliability and system integration versus a common single stack FCS. This paper is based on Energetic Macroscopic Representation principles and on preliminary works on FCS modeling and simulation. This paper shows the development of a complete multi-stack system simulation model from elementary parts. The used stack model is experimentally validated and simulation results of the multi-stack system are provided.

Le développement de la commande d’un véhicule à pile à combustible et supercondensateurs doit prendre en compte les contraintes liées à l’association de ses composants. La commande par inversion apporte une solution. Elle utilise une approche systémique et cognitive afin d’identifier la cause qui produit l’effet désiré et inverse les modèles des éléments associés pour obtenir une structure de commande. Ainsi, les commandes déduites de la Représentation Energétique Macroscopique (REM) et du Backstepping font partie de la catégorie des commandes par inversion. Le Backstepping déduit une commande stable de la plupart des systèmes non-linéaires. Cependant, il ne permet pas de gérer de façon claire et efficace les couplages énergétiques. Il est alors possible de décomposer physiquement le système suivant les règles de la REM. La REM permet également de séparer explicitement la commande et la stratégie de gestion de l’énergie. Dans cette thèse en cotutelle franco-québécoise, les caractéristiques des deux commandes ont été soulignées. Les deux méthodes apparaissent alors complémentaires. L’intégration du Backstepping apporte à la REM l’assurance d’une stabilité intrinsèque. Les principes de la REM, quant à eux, permettent au Backstepping de gérer les couplages énergétiques. Le développement d’une commande par inversion stable d’un véhicule à pile à combustible et supercondensateurs est donc proposé par la combinaison de ces deux méthodes de commande, suivant une procédure d’inversion combinée. La répartition des puissances du véhicule est ainsi réalisée en temps réel, sur dispositif expérimental, suivant une stratégie de filtrage et la commande stable par inversion combinée développée.