This paper investigates the optimal sizing of the Energy Storage System (ESS) for a Plug-in Fuel Cell Electric Vehicle (PFCEV) using a multi-objective approach. The ESS consists of a battery, proton-exchange membrane fuel cell (FC), and supercapacitor (SC). The study sets maximum speed and driving range as driving performance requirements and uses the Urban Dynamometer Driving Schedule (UDDS) for simulations. A severity factor aging model is employed to analyze the impact of component sizing on battery life, CO2 emissions, and cost. The SC is utilized to protect the other sources from high currents and supply power during acceleration. A Quadratic Programming (QP) Energy Management System (EMS) algorithm minimizes driving cost, battery aging, and FC aging. The study employs a Genetic Algorithm (GA) to explore the feasible optimal solution domain that satisfies the constraints. Based on the findings, It can be inferred that the utilization of hydrogen is the primary contributor to emissions and a significant factor in the cost. This highlights the need for producing hydrogen in a more environmentally-friendly and cost-effective manner. This study provides insights into the optimal sizing of the PFCEV ESS, with potential implications for the design of an eco-friendly and cost-efficient electric vehicle (EV).

Autonomous vehicles use a set of perceptual and localization data proceeding from sensor measurements, in order to plan a certain trajectory based on decision-making, and finally to track the generated path. Trajectory following is performed by adjusting the steering angle generated by a lateral controller based on a geometric or non-geometric approach. The objective of the lateral controller is to minimize the lateral error between the vehicle and the path at a target point at a look-ahead distance from the vehicle. This paper investigates the look-ahead distance due to its high impact on performance alteration, and energy consumption. An intuitive analysis will be performed to study the effect of three varying parameters on the look-ahead distance, and the necessity to consider them. Then, a Fuzzy Logic approach will be established to adjust the look-ahead distance in accordance with three parameters: longitudinal velocity, road curvature, and original consideration of road adherence. Finally, a non-geometric model-based lateral controller will be developed based on the Super-Twisting Sliding Mode Control technique to control the steering angle via the Active Front Steering. The membership functions and the rules of the inputs and output of the Fuzzy Logic approach are implemented in a Matlab/Simulink environment and tested on a validated full non-linear vehicle model. Simulation results indicate the effectiveness of the fuzzy decision approach on the performance and energy consumption of the autonomous vehicle.

International audience

International audience

International audience

In this paper, we present a robust nonlinear de-centralized control scheme for an islanded DC microgrid (MG) where the main control goals are to achieve a sustained stability for the DC bus voltage at a certain desired value, to maintain the power balance in the system and to insure robustness against disturbances and perturbations. The proposed control strategy uses the Model Free Super-Twisting Sliding Mode (MFSMC) as it needs no accurate representation of the environment in order to be effective which makes it a suitable choice for system with nonlinear model prone to parameters variation. The studied microgrid is composed of a solar photo-voltaic (PV) unit and a hybrid energy storage system including a battery and a supercapacitor (SC) along with DC loads. To attain the intended objectives, a hierarchical cascaded control strategy is designed with two levels: a high-level control that stabilizes the DC bus voltage at a reference value by generating a current reference to be tracked, and a low-level control composed of an energy management system EMS based on a passive filtration to distribute the reference current between the storage system units according to their dynamic specifications. Simulations on MATLAB/Simulink are carried out to evaluate the effectiveness and robustness of the proposed control scheme under various operating conditions created by random variations of power generation and consumption. Noise sensitivity test is also carried out for this controller and for a model-based one that is the Feedback linearization control technique (FL).

This paper presents a new Energy Management System (EMS) strategy for the Energy Storage System (ESS) of a Microgrid (MG) based on the proper time scale of each ESS component. The theoretical framework allows to achieve more efficient use of the different ESS components’. While almost all existing methods rely on filtering load profile signals to achieve power separation, our proposed approach achieves a better performance on components’ ageing rate with less processing complexity using a simple limiter in algorithm. The proposed method is first evaluated as an EMS only and then extended to the whole MG. Results show an ageing rate reduction of 93% for fast component at the price of 24% of ageing rate increase for slow component.

This paper presents a new Energy Management Strategy (EMS) based on frequency separation and fuzzy logic control for a multi-sources system. The studied system is an electric vehicle with hybrid sources including a fuel cell (FC), a battery and an ultracapacitor. Each source is controlled using a DC/DC converter connected to the DC-bus. To maintain a constant DC-bus voltage, a Lyapunov based controller is developed. The overall energy flow of the system is managed using a filtering-based EMS which allows a flexible use of energy sources by self-adapting to the system state evolution. The proposed approach protects the fuel cell and the battery from strong power dynamics, and coordinates the distribution of the energy demand between the battery and the ultracapacitor according to their state of charge. Simulations on MATLAB/SIMULINK, using a real current profile in an urban driving situation, validate the performances of the proposed method.