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).

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.

In this paper, the Adaptive Frequency Droop Virtual Power (AFDPV) method is implemented in a distributed control-based consensus to ensure the State of Charge (SoC) balancing of BESSs (Battery Energy Storage Systems), in an AC Microgrid (MG). An average consensus algorithm is used to acquire the average SoC value of the BESSs and the AFDVP strategy is designed in this proposal with a distributed architecture, to balance the SoC of BESSs while maintaining the MG stability, the frequency and the voltage variations between the permissible range the European norm EN 50160. In this distributed control architecture, the SoC balancing, the frequency and voltage restoration are realized, only thanks to local information of the Distributed Generator (DG) and information from their neighbors. A Matlab/Simulink simulation is performed in order to verify the effectiveness of the proposed method in the case of communication failure. In order to compare their performances, a comparison between the proposed distributed control and a centralized control is also provided.

This paper proposes a control strategy to reduce speed ripples at low speed working conditions of Permanent Magnet Synchronous Machines (PMSMs) for Electric Vehicle Applications. The treated issue is related to the periodic torque ripples which induce speed oscillation that deteriorate the drive performance. To ensure a high-performance control regarding this issue, the reported work proposes an original control method based on Grey Wolf (GW) algorithm. The key idea of the proposed approach is to incorporate the benefit of fast optimization process of the GW optimizer in order to find input controls which satisfy the speed tracking and minimize speed ripples. The proposed method is described and the speed ripple issue is analyzed. Experimental results show that the proposed control method can be implemented in real-time on embedded hardware, offering high performance in both steady and transient states of the PMSM drives even at low speed range.

Comparing with classical three phase generators, multiphase ones attract more and more attention for industrial applications due to their capability of fault tolerance, high quality of output power and so on. In the event of a fault occurrence, before reconfiguring the control algorithms, it is necessary to detect the fault. A second order filter-based fault detection method is thus proposed in this paper and applied to a five-phase permanent magnet synchronous generator. The method is based on the analysis of the phase current in the natural basis when it varies suddenly. Thus, a second order filter is studied with a view to detecting faults and defining a fast indicator extracted from the derivative of the phase current. This approach is then applied to tidal energy conversion chain using a five-phase PMSG with non-sinusoidal EMF. Two faults are investigated successfully: open phase fault of the generator and open switch fault in its serial converter legs.

As the growth penetration of photovoltaic (PV) renewable power generation systems connected into grid increases, high power quality and good efficiency are needed. Hence, in photovoltaic grid-connected systems, parallel two-level inverters (VSIs) connected with common AC and DC buses are largely used to ensure high power quality and efficiency. Besides, such a solution rises a number of concerned issues, especially, zero sequence circulating current (ZSCC) which will distort the performance of the system. This study proposes a finite set control model predictive method for suppressing the ZSCC, and controlling currents with rapid transient response. The causes of the zero-sequence circulating current are presented in detail. The proposed current controller is realized by minimizing the cost function and pick the optimal vector to generate the control requirement. Finally, Simulation results show that the proposed control scheme achieves to ensure power quality, suppression of circulating currents, and also maintains the DC-link voltage constant.

This paper proposes a robust control approach for improving the power quality in three-phase standalone based micro-grids. The key developed idea is to increase the disturbance rejection ability of an Interconnection and Damping Assignment Passivity-Based Controller (IDA-PBC)by adding an integral action. Indeed, the classical IDA-PBC techniques suffer from their parametric dependency. Hence, the use of the proposed technique allows enhancing the load voltage quality either in nominal operating conditions or in presence of system uncertainties. The power system circuit is modeled as a Port-Controlled Hamiltonian (PCH)system. Then, the control design is detailed. The validity of the proposed control algorithm is verified using simulation studies, and its effectiveness is illustrated through a comparison analysis with a Proportional-Integral (PI)under various operating conditions.

This paper presents an energy management algorithm to be embedded for a residential application disconnected from the power grid. The multisource system studied includes solar panels as a renewable energy source, a fuel cell as a secondary source, three batteries and a bank of supercapacitors as storage systems. The proposed algorithm uses as input data, future estimates obtained from power consumption and meteorological forecasting data, and historical of the load power and the renewable power obtained from measurements. It is assumed that these two input powers are imposed and uncontrollable. In the case where the future estimates have errors compared to what has actually been measured, a mathematical approach shows that the algorithm is able to compensate these forecasting errors by sharing them between the different sources of the system while respecting their different characteristics. In addition to the optimal energy distribution, the algorithm gives the optimal size of each source and storage devices. In this work, the total cost of the system is chosen as the criteria to be optimized. The simulations are carried out over a year, with a time step of 1 second and in the presence of significant forecasting errors. The results obtained are particularly convincing and make it possible to validate this energy management strategy.

Reducing greenhouse gas emissions becomes a top priority in the world with the emergence of global warming and environmental problems. Thus, a variety of renewable energy appears during the last decades. The Ocean, which covers two-thirds of the world, captures and stores huge amounts of energy which could satisfy 5 times of world energy demand. During the last 10 years, various Marine Current Energy Conversion Systems (MCECSs) have been developed around the world. In this paper, the marine current energy and its extraction forms are briefly presented. Then, various projects are classified into two categories, geared drive train system and direct drive train system, according to the different generators. The mainly characteristics of the different generators are also discussed. The relative converters are future presented. According to this paper, the researchers will easily find that the Permanent Magnet Synchrous Generator (PMSG) and Induction Generator (IG) are preferred in MCECS.

This paper deals with fault tolerant control of a sinusoidal EMF five-phase permanent-magnet synchronous generator (PMSG) connected to an AC-DC converter without neutral connection. Under one open phase condition, the proposed global method is based on an original approach optimization problem minimizing copper losses and cancelling torque ripples. The used optimization algorithm is based to a Particle Swarm Optimization (PSO) method. The main idea is to generate automatically the best waveform of the phase currents. Two strategies are investigated; both are minimizing the copper losses. In the first approach, the four phases are unbalanced. In the second case, the first approach is improved by adding criteria leading to balanced currents. The obtained currents phase shift and magnitude are analyzed and the performances of the system are discussed.

The objective of this paper is to estimate the photovoltaic energy on inclined panels for a given site. Its characterization is based on measurements carried out on horizontal plane. A comparison of different models of the diffused radiation released with respect to the monthly and annual energy balance is presented. An optimization of the inclination angle (tilt angle) by maximizing the annual photovoltaic energy is proposed. At the end of this study, energy efficiency of different solar tracking systems is also analyzed.

In this paper, a control strategy is proposed to enhance the capability in terms of active filtering, of a Wind Energy Conversion System (WECS) based on a Doubly Fed Induction Generator (DFIG). The stator of the DFIG is directly connected to the grid and the rotor is connected to the grid through a back to back AC-DC-AC PWM converter. The proposed algorithm is applied to the Rotor Side Converter (RSC) to achieve simultaneously the Maximum Power Point Tracking (MPPT) and the power quality improvement at the Point of Common Coupling (PCC). This enables the grid to supply only sinusoidal currents at Unity Power Factor (UPF). Depending on the nominal RSC power, reactive power of linear loads and harmonic currents due to the nonlinear loads can be compensated. The proposed RSC control strategy manages the WECS function's priorities between active power production, reactive power compensation and active filtering without any system over-rating. To enhance the capability of the RSC, in terms of active filtering, it is proposed to eliminate instantaneously the whole of the 5thand the 7thpredominant harmonic components if possible, otherwise, the filtering operation is omitted. The Grid Side Converter (GSC) is controlled in such a way to guarantee a smooth DC voltage. The presented simulation results demonstrate the performance and the effectiveness of the proposed control strategy.