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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

This research work aims to study the impact of the heating system on the fuel consumption of a hybrid electric vehicle (HEV). The thermal engine is less used in an HEV than in a thermal vehicle, thus the cabin heating is partly ensured by electrical resistances. However, because the battery is partly charged by the thermal engine, this electrical heating has an impact on the fuel consumption. In the present work, a multi-domain model is proposed to analyze the impact of the heating system on the fuel consumption of a HEV. The models of the different physical subsystems are organized and unified by energetic macroscopic representation (EMR). Experimental validations, with an accuracy of 95%, are provided for each subsystem model. The validated simulation models are used to study the impact of the heating system for a specific driving cycle and climatic condition. For a simple energy management strategy (EMS), there is an over-consumption of 19% that is due to the heating system. When a more efficient EMS is used, the over-consumption is reduced to 12%. This study shows the interest in developing advanced energy management strategies that couple the traction and the heating functions of the vehicle.

In the simulation of new vehicles, the Internal Combustion Engine (ICE) is generally modeled by a static map. This model yields the mechanical power and the fuel consumption. But some studies require the heat energy from the ICE to be considered (i.e. waste heat recovery, thermal regulation of the cabin). A dynamical multi-physical model of a diesel engine is developed to consider its heat energy. This model is organized using Energetic Macroscopic Representation (EMR) in order to be interconnected to other various models of vehicle subsystems. An experimental validation is provided. Moreover a multi-physical quasi-static model is also derived. According to different modeling aims, a comparison of the dynamical and the quasi-static model is discussed in the case of the simulation of a thermal vehicle. These multi-physical models with different simulation time consumption provide good basis for studying the effects of the thermal energy on the vehicle behaviors, including the possibilities of waste heat recovery.

This paper aims to compare different models and simulation approaches for an energetic simulation of an automatic subway. For this purpose, several models are carried out from a dynamic model which is validated by comparison with experimental measurements. Furthermore, two different simulation approaches are compared: backward and forward approaches. A simplified model is obtained and allows the reduction of the simulation time by 96 compared to the dynamic model by keeping an accuracy of more than 99 %.

Energy management strategy (EMS) is the key for efficient hybrid vehicles. Two EMS kinds can be generally used: optimal-based algorithms to reach optimum and rule-based algorithms for its easy implementation. The paper objective is to propose a hybrid optimal EMS using optimal-based and rule-based approaches to use benefits of each approach. This hybrid optimal approach enables to reduce the computation time keeping the optimal behavior. This mix is realized thanks to a multi-level structuration of the EMS. This hybrid optimal EMS is a transient step to structure the EMS design. This hybrid optimal EMS, applied on a complex hybrid vehicle enables to divide the computation time by 16 keeping the optimal behavior.

The energy management strategy is one of the key technologies for the design and development of hybrid electric vehicles (HEVs). In order to investigate the maximum potentials of various powertrain architectures and components in terms of fuel and emissions savings, an optimization for the energy management strategy is necessary. This paper will introduce one of the optimization-based energy management strategies, Dynamic Programming (DP). It is well known as it guarantees a global optimization of the power flow distribution between the combustion engine and the electric machines while taking into account the constraints of subsystems. In this paper, the principle of DP will be presented, and an application example in Peugeot 3008 Hybrid4 is given. The optimized strategy was simulated and analysis has been done based on the results with and without optimization. Even though DP is an off-line and non-causal optimization, it serves as a benchmark for comparison with the existing strategy. Thanks to DP, a rule-based strategy extracted from the DP optimization results is implemented and compared. This could be referred in the future strategy design.

Electric vehicle (EV) are one solution for the next decades to face the greenhouse gases emission issue. The most important drawbacks about the EV concern the limited range depending of the battery performances and the charging time. The vehicle range is estimated with standard drive cycles and ideal conditions. The objective of this paper is to propose a simulation tool to calculate the energy distribution in the EV including the thermal exchange between the heating system and the cabin. Indeed, the heating system (using thermal resistance) is an important energy consumer in the vehicle. All models are organized using Energetic Macroscopic Representation (EMR)

Because it does not participate in the vehicle traction, the thermal energy is not highlighted in the electric and hybrid electric vehicle (EV and HEV) study, especially for electrical components like electric machines and batteries. Nevertheless, the thermal states of these components can have a great impact on the vehicle behaviour, such as safety, performances and autonomy, etc. This impact is more important if the driver turns on the heating system. In order to consider the thermal dimension of the energetic study for EV/HEV applications, a multiphysical modeling and description (for organizing this model with the energetic focus) of each component is required. This paper is focused on the multiphysical (electrical, mechanical and thermal) modeling and description of the electrical machine using EMR.

The aim of this paper is to compare different models of an automatic subway in order to determine the most appropriate of them for the evaluation of the energy consumption of different studied cases (normal mode and fault-modes). Several simplified models are then deduced from a reference dynamical model and are compared in term of accuracy and computation time. A quasi-static model with an equivalent single wheel is obtained and allows the reduction of the computation cycle by 708 compared to the dynamical model by keeping accuracy on the energy consumption of more than 99.3 % for a normal operating mode. By contrast, this model is not able to simulate correctly the system in fault-modes. This case requires a more complex model taking into account all the drives. Energetic Macroscopic Representation (EMR) is used to organize the different models.

The objective of this paper is to develop an energetic dynamical description of a model of an Internal Combustion Engine. This model is based on a simple power balance between input and output power of engine. The Energetic Macroscopic Representation (EMR) is used to organize the IC engine model. This energetic description will be coupled with the whole vehicle previously developed with EMR. The fuel consumption and the detailed power flow will be analysis for a complete drive cycle

This paper aims to compare a forward and backward simulation of the power propulsion system of an automatic subway. In the forward approach the control of the system is required. In the backward approach, no control is required but a derivative relationship has to be computed. Both simulations are compared in terms of accuracy of dynamical performances, maximal values of different variables and energy consumption and specifically when limitations occur.

This article deals with the consideration of thermal model for the evaluation of the energy consumption and losses of an ICE vehicle. The Energetic Macroscopic Representation (EMR) is used to organize this complex model of an ICE vehicle. This thermal representation has been validated by comparisons with other similar models.

This article deals with several models for the estimation of electric consumption of an automatic subway. Backward and forward models are simulated. The forward simulation model is deduced from Energetic Macroscopic Representation (EMR). Different models are then compared in terms of accuracy and computation.

La réduction de la consommation énergétique dans tous les domaines (automobile, industrie, habitation…) est une nécessité écologique et économique. Les pertes énergétiques, issues de différents phénomènes (résistance, frottement…) se manifestent dans la quasi-totalité des cas par un dégagement de chaleur. La valorisation de cette chaleur, si elle est en quantité suffisante, peut être une solution pour réduire la consommation d’énergie globale du système. Le générateur thermoélectrique est un convertisseur utilisant l’effet Seebeck pour convertir l’énergie thermique en énergie électrique. L’objectif de cet article est de modéliser et simuler ces modules. Une validation utilisant les données constructeurs sera également présentée. Les modèles seront organisés en fonction du formalisme R.E.M. (Représentation Energétique Macroscopique) afin de déduire, ultérieurement, une structure de commande de ces modules et de les coupler aux systèmes déjà organisés avec cet outil de représentation.

Le véhicule hybride combine plusieurs sources d’énergie pour se déplacer, réduisant ainsi sa consommation de carburant. La stratégie de gestion d’énergie est l’organe intelligent du véhicule qui permet de décider comment et quand utiliser ces sources. Différentes stratégies de gestions d’énergie sont possibles pour un même véhicule. Plus elle prend de contraintes en considération, plus elle est efficace, mais plus elle est difficile à réaliser en temps réel. Les stratégies de gestion d’énergie optimales permettent de déterminer la meilleure répartition de puissance pour obtenir la consommation minimale. Si cette stratégie peut être déterminée d’un point de vue théorique en simulation, elle ne peut pas être reproduite sur un véhicule réel. En revanche, il est possible de déduire une gestion d’énergie temps réel à partir des stratégies optimales. Néanmoins, cette réalisation est complexe. Si les gestions d’énergie tendent à intègrer de plus en plus de contraintes, il est à noter que la gestion du chauffage habitacle (impactant notablement la consommation d’un véhicule hybride) est cependant considérée de manière indépendante de la gestion de la chaîne de traction. L’objectif de la thèse est de proposer une gestion d’énergie globale d’un véhicule hybride incluant à la fois le système de traction et de chauffage. Pour cela, les échanges thermiques sont modélisés et organisés suivant le formalisme REM (Représentation Energétique Macroscopique) permettant d’unifier tous les modèles. Des validations expérimentales permettent de s’assurer de la précision des modèles. Enfin, une démarche progressive est proposée afin de construire une gestion d’énergie utilisable en temps réel et dont les résultats sont proches de l’optimum malgrès la complexité de cette gestion globale.