• Energy Research

Optimisation-based Energy Management Strategies for hybrid and electric vehicles have to face some issues for real-time applications. In this study,an online adaptive optimisation-based strategy is used for an electric vehicle with a Hybrid Energy Storage System including supercapacitors and batteries. The SCs voltage limitations are taken into account by using a feedback control. Indeed the voltage limitation is a key point for safe operations in the vehicle. A comparison with a more classical optimisation-based method is performed by simulations on a normalised driving cycle. The results point out a more effective behaviour of the adaptive strategy.

This paper presents a model of a whole polymer electrolyte fuel cell system including the stack, an air compressor, a cooling system, and a power converter. This model allows its integration in a complete hybrid electric vehicle simulation. The level of detail of the model is chosen to enable control rules design, ancillaries sizing, and study of the interaction between the components of the vehicle. This model is formalized with energetic macroscopic representation, thus organized in a unified multidomain graphical description. Experimental results are compared to simulations for validation of the model accuracy.

With the advent of more stringent regulations related to emissions, fuel economy, and global warming, as well as energy resource constraints, electric, hybrid, and fuel-cell vehicles have attracted increasing attention from vehicle constructors, governments, and consumers. Research and development efforts have focused on developing advanced powertrains and efficient energy systems. This paper reviews the state of the art for electric, hybrid, and fuel-cell vehicles, with a focus on architectures and modeling for energy management. Although classic modeling approaches have often been used, new systemic approaches that allow better understanding of the interaction between the numerous subsystems have recently been introduced.

A key issue of real-time applications is ensuring the operation by taking into account the stability constraints. For multisource vehicles, stability is impacted by the multisource interactions. Backstepping control ensures stable control for most classes of nonlinear systems. Nevertheless, no backstepping control in real time has been yet proposed for multisource vehicles. The objective of this paper is to apply the backstepping control to a multisource vehicle with fuel cell and supercapacitors for real-time implementation. A distribution criterion is used to allocate energy between sources. Experimental results demonstrate that the developed backstepping control can be implemented in real-time conditions. The supercapacitors can thus help the fuel cell to meet the requirements of the load with a guarantee of system stability.

The driving range of electric vehicles is a complex issue. In simulation, this range is determined by coupling a battery model and a traction model of the vehicle. But most of the battery models used for such studies do not take into account the interaction between the temperature of the battery (impacting its electrical parameters) and the traction model of a studied EV. In this paper, a battery electro-thermal model (with all electrical parameters dependent upon the temperature) is dynamically coupled with the traction model of an electric vehicle. Simulation results are provided to show the impact of the temperature on the driving range. The initial battery model (without temperature dependence) leads to an overestimation of the driving range of 3.3% at −5 °C and an underestimation of 2.5% at 40 °C.

This paper deals with inversion-based control of EVs using battery and supercapacitors. Using inversion-based rules of Energetic Macroscopic Representation (EMR), a systematic control structure can be deduced. However, it points out the possibility of an algebraic loop issue. Furthermore, it may result in computation issues. This paper aims to exhibit some solutions to avoid this problem in an efficient way.

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.

Hybrid Electric Vehicles (HEVs) are developed for reduction of consumption and pollutant emissions of road vehicles. Different topologies and hybridization ratios have been studied and different vehicles are now proposed by manufacturers . Because of the complexity of their power trains, the energy management is a key issue in order to ensure dynamic performances and low-consumption operations. In order to perform an advanced control of the HEV including high performance energy management, a systemic approach using simulation is a necessary step. A lot of simulation packages have been developed for the design and the energy management of HEVs. Among the scientific communities working on these aspects, the French MEGEVH network on Hybrid Electric Vehicles (HEVs) is developing generic methodologies for the development of efficient energy management of different kinds of vehicles . This presentation aims to share the experience get from the MEGEVH network concerning energetic modelling and energy management of HEVs. First, a state of the art will be given about existing methodologies of representation used as a support in vehicle software development. Examples of worldwide software will be also showed and commented. A special focus will be made on Energetic Macroscopic Representation developed at Lille University and on VEHLIB software performed by IFSTTAR -LTE . In a second part, an overview of the energy management strategies used in the HEVs control is presented. Basically, the energy management of a HEV could be performed using different methods. The simplest and most known methods use rule based techniques that require a deep expertise for each topology and each components sizing of the HEV . Optimization based methods could be more appropriate when a minimum of consumption and/or emission is needed . In the future, HEVs are going to be more and more complex, most likely toward two directions: the complexity of the storage system and the drive train integration. The use of high performance representation and simulation tools as well as advanced control methods becomes more vital. Two examples, one concerning a hybrid storage system and the other dealing with an integrated Electric Variable Transmission will be given to substantiate the usefulness of the MEGEVH's proposed generic methods.