Fédération de Recherche FCLAB

The global energy transition towards a carbon neutral society requires a profound transformation of electricity generation and consumption, as well as of electric power systems. Hydrogen has an important potential to accelerate the process of scaling up clean and renewable energy, however its integration in power systems remains little studied. This paper reviews the current progress and outlook of hydrogen technologies and their application in power systems for hydrogen production, re-electrification and storage. The characteristics of electrolysers and fuel cells are demonstrated with experimental data and the deployments of hydrogen for energy storage, power-to-gas, co- and tri-generation and transportation are investigated using examples from worldwide projects. The current techno-economic status of these technologies and applications is presented, in which cost, efficiency and durability are identified as the main critical aspects. This is also confirmed by the results of a statistical analysis of the literature. Finally, conclusions show that continuous efforts on performance improvements, scale ramp-up, technical prospects and political support are required to enable a cost-competitive hydrogen economy.

Despite that the Proton Exchange Membrane Fuel Cell (PEMFC) is considered to be an efficient power device; around half of the energy produced from the electrochemical reaction is dissipated as heat due to irreversibility of the cathodic reaction, Ohmic resistance, and mass transport overpotentials. Effective heat removal from the PEMFC, via cooling, is very important to maintain the cell/stack at a uniform operating temperature ensuring the durability of the device as excessive operating temperature may dry out the membrane and reduces the surface area of the catalyst hence lowering the performance of the cell. In addition to cooling, capturing the produced heat and repurposing it using one of the Waste Heat Recovery (WHR) technologies is an effective approach to add a great economic value to the PEMFC power system. Global warming, climate change, and the high cost of energy production are the main drivers to improve the energy efficiency of PEMFC using WHR. This paper presents an overview of the recent progress concerning the cooling strategies and WHR opportunities for PEMFC. The main cooling techniques of PEMFCs are described and evaluated with respect to their advantages and disadvantages. Additionally, the potential pathways for PEMFC-WHR including heating, cooling, and power generation are explored and assessed. Furthermore, the main challenges and the research prospects for the cooling strategies and WHR of PEMFCs are discussed.

To cope with the new transportation challenges and to ensure the safety and durability of electric vehicles and hybrid electric vehicles, high performance and reliable battery health management systems are required. The Battery State of Health (SOH) provides critical information about its performances, its lifetime and allows a better energy management in hybrid systems. Several research studies have provided different methods that estimate the battery SOH. Yet, not all these methods meet the requirement of automotive real-time applications. The real time estimation of battery SOH is important regarding battery fault diagnosis. Moreover, being able to estimate the SOH in real time ensure an accurate State of Charge and State of Power estimation for the battery, which are critical states in hybrid applications. This study provides a review of the main battery SOH estimation methods, enlightening their main advantages and pointing out their limitations in terms of real time automotive compatibility and especially hybrid electric applications. Experimental validation of an online and on-board suited SOH estimation method using model-based adaptive filtering is conducted to demonstrate its real-time feasibility and accuracy.

The fast depletion of fossil fuels and the growing awareness of the need for environmental protection have led us to the energy crisis. Positive development has been achieved since the last decade by the collective effort of scientists. In this regard, renewable energy sources (RES) are being deployed in the power system to meet the energy demand. The microgrid concept (AC, DC) is introduced, in which distributed energy resources (DERs), the energy storage system (ESS) and loads are interconnected. DC microgrids are appreciated due to their high efficiency and reliability performance. Despite its significant growth, the DC microgrid is still relatively novel in terms of grid architecture and control systems. In this context, an energy management system (EMS) is essential for the optimal use of DERs in secure, reliable, and intelligent ways. Therefore, this paper strives to shed light on DC microgrid architecture, control structure, and EMS. With an extensive literature survey on EMSs’ role, different methods and strategies related to microgrid energy management are covered in this article. More attention is centered on the EMS for DC microgrids in terms of size and cost optimization. A very concise analysis of multiple optimization methods and techniques has been presented exclusively for residential applications.

In this paper, an online energy management control strategy is proposed based on a novel fractional-order extremum seeking (ES) method. The proposed method is an online adaptive optimization algorithm, which can be effectively used in the applications of fuel cell hybrid electric vehicles. Compared with the traditional integer-order ES method, the presented method uses Oustaloup approximation based fractional-order calculus in order to achieve faster convergence speed and higher robustness. A detailed mathematical analysis of the proposed method is presented to give a stability proof and shows how the fractional-order calculus improves the integer-order ES method. In order to support the stability analysis results and demonstrate the effectiveness and robustness of the proposed method, a hardware-in-the-loop test bench is developed to provide two experimental case studies. Experimental results show that, by using the presented fractional-order ES approach, the operation points of a proton exchange membrane fuel cell stack system can be effectively controlled in its maximum efficiency area. In addition, the fuel cell system durability can be improved.

Ground heat exchangers are surrounded by grout material, making it one of the most important components in geothermal energy applications since it significantly affects the system's thermal performance. The current study reviews the different types of grout materials and compares their thermophysical properties. The most critical parameter is the grout's thermal conductivity in which it always presents a proportional relation with the system's efficiency. Numerous factors are involved in this review to ascertain theier impact on the grouts’ performance such as flowability, shrinkage, moisture content, freezing, heat capacity, strength, permeability, solubility and thermal imbalance. The different grouts compared are bentonite, cement, sand, graphite, controlled low-strength material, dolomite, and phase change materials. The literature shows that phase change materials are the best choices of grouting since they can provide high storage capacity, stability and temperature uniformity. The major problem of such materials is their low thermal conductivity. Thus, it is recommended to use composite phase change materials to enhance their thermal conductivity and increase the storage/retrieval rate.

Energy management strategy (EMS) is the key to the performance of fuel cell / battery hybrid system. At present, reinforcement learning (RL) has been introduced into this field and has gradually become the focus of research. However, traditional EMSs only take the energy consumption into consideration when optimizing the operation economy, and ignore the cost caused by power source degradations. It would cause the problem of poor operation economy regarding Total Cost of Ownership (TCO). On the other hand, most studied RL algorithms have the disadvantages of overestimation and improper way of restricting battery SOC, which would lead to relatively poor control performance as well. To solve these problems, this paper establishes a TCO model including energy consumption, equivalent energy consumption and degradation of power sources at first, then adopt the Double Q-learning RL algorithm with state constraint and variable action space to determine the optimal EMS. Finally, using hardware-in-the-loop platform, the feasibility, superiority and generalization of proposed EMS is proved by comparing with the optimal dynamic programming and traditional RL EMS and equivalent consumption minimum strategy (ECMS) under both training and unknown operating conditions. Results prove that the proposed strategy has high global optimality and excellent SOC control ability regardless of training or unknown conditions.

Researchers are continually striving to improve the efficiency of photovoltaic panels which contain solar cells that convert light energy to electrical energy. The objective is to improve photovoltaic (PV) efficiency by maintaining it below maximum allowable temperature. Accordingly, the excess in heat must be dissipated efficiently in order to avoid excessive high temperatures, which have an adverse effect on the electrical performance of the cell. Therefore, in this paper an experimental study is presented to enhance the performance of PV panels using Aluminum finned plate, and cooling under natural convection. The use of heat sinks has been conducted to address this problem by using an optimum design of Aluminum finned plate. The results show that the use of an Aluminum finned plate has increased the solar to electrical conversion efficiency by 1.75%, and the output power by 1.86 Watt.

Last years, the fuel cell has become well-known as an efficient and clean energy converter being a potential alternative to internal combustion engines. However, despite being very promising, the durability of those systems is still a bottleneck. Most of the time, a fuel cell is integrated in a hybrid system which considers the fuel cell stack, the battery, and the balance of plant. To keep improving the durability of such a system, diagnostic and prognostic tools are of particular importance and to implement such tools, modeling the system is a mandatory step. The purpose of this paper is to propose a critical review of the existing methods to model all elements of a hybrid fuel cell system according to operating conditions and degradation. In this review, interactions and major degradation mechanisms occurring at all components will be presented and the physics-based models, data-driven and hybrid models of these components reviewed. Finally, methods will be discussed, and advantages and drawbacks will be summarized.

A new thermal model, which allows temperature distribution determination inside a supercapacitor cell, is developed. The model is tested for a supercapacitor based on the activated carbon and organic electrolyte technology. In hybrid vehicle applications, supercapacitors are used as energy-storage devices, offering the possibility of providing the peak-power requirement. They are charged and discharged at a high current rate. The problem with this operating mode is the large amount of heat produced in the device which can lead to its destruction. An accurate thermal modeling of the internal temperature is required to design a cooling system for supercapacitor module, meeting the safety and reliability of the power systems. The purpose of this paper is to study the supercapacitor temperature distribution in steady and transient states. A thermal model is developed; it is based on the finite-differential method which allows for the supercapacitor thermal resistance determination. The originality of this paper is in the fact that a thermocouple (type K) was placed inside the supercapacitor from Maxwell Technologies. A test bench is realized. The cases of supercapacitor thermal distribution using natural and forced convection are studied. Simulations and experimental results are reported to validate the proposed model. The results obtained with this model may be used to determine the cooling system required for actual supercapacitor applications.

Environmental concerns of greenhouse gases (GHG) effect from fossil commodities and the fast increase in global energy demand have created awareness on the need to replace fossil fuels with other sources of clean energy. PEM fuel cell (PEMFC) is a promising source of energy to replace fossil fuels. The commercialization of the cell depends on its price, weight and mechanical strength. Bipolar plates are among the main components of PEMFC which perform some significant functions in the fuel cell stack. Metal bipolar plate is considered by the research community as the future material for fuel cells. However, surface coating is required for metals to enhance its corrosion resistance, hydrophilicity and interfacial contact resistance (ICR) in PEM fuel cells. Open pore cellular metal foam (OPCMF) materials have been used to replace the conventional flow field channel in recent times due to its low electrical resistance, high specific area and high porosity; however, it endures the same corrosion problem as the metallic bipolar plate. This investigation offers an overview on different types of bipolar plates and techniques in coating metallic bipolar platse and open pore metal foam as flow field channel materials to improve the corrosion resistance which will eventually increase the efficiency of the fuel cell appreciably.

In this paper, a novel degradation prediction model for proton-exchange-membrane fuel cell (PEMFC) performance is proposed based on a multiphysical aging model with particle filter (PF) and extrapolation approach. The proposed multiphysical aging model considers major internal physical aging phenomena of fuel cells, including fuel cell ohmic losses, reaction activity losses, and reactants mass transfer losses. Furthermore, in order to obtain accurate values of electrochemical activation losses under a variable load profile, a bisection solver is presented to solve the implicit Butler-Volmer equation. The proposed aging model is initialized at first by fitting the PEMFC polarization curve at the beginning of lifetime. During the prediction process, the aging dataset is then divided into two parts, learning and prediction phases. The PF framework is used to study the degradation characteristics and update the aging parameters during the learning phase. The suitable fitting curve functions are then selected to satisfy the degradation trends of trained aging parameters, and used to further extrapolate the future values of aging parameters in the prediction phase. By using these extrapolated aging parameters, the prediction results are thus obtained from the proposed aging model. Three experimental validations with different aging testing profiles have been performed. The results demonstrate the robustness and advantages of the proposed prediction method.

With the rapid development of electric vehicles (EVs), the dramatic rise in the demand for electricity is creating heavy pressure on local grids. The combination of renewable energy and EV charging stations (EVCSs) provides a promising solution for alleviating the scarcity of electricity. In this paper, a finite-horizon Markov decision process (MDP) model is proposed for the optimal control of a photovoltaic (PV)-assisted EVCS in a university campus. The proposed model employs the vehicle-to-grid (V2G) technology to provide ancillary services and takes dynamic electricity price and the uncertainty of the EV owners’ parking behaviors into consideration. To guarantee computational efficiency, an adapted bounded real-time dynamic programming (BRTDP) algorithm is developed as the solution technique of the MDP model. Numerical simulations based on the dynamic electricity price of France, real PV data, and the vehicle parking patterns in a university campus are conducted to demonstrate the effectiveness of the proposed energy management system (EMS). Simulation results show that the EMS can reduce the total costs by more than 55% and 29% in summer and winter compared to the conventional charging policy, while guaranteeing the satisfaction rate of the demand for EV-charging without knowing the departure times of EVs a priori.

Proton exchange membrane fuel cell (PEMFC) degradation prediction is essential especially in transportation applications, since one of the major issues that hinder its worldwide commercialization is represented by its durability. However, due to the complex physical phenomena inside the fuel cell, which are usually strongly inter-coupled, the conventional semi-empirical model-based prognostics approach may fail to predict the aging phenomena under various fuel cell operating conditions. In order to improve prognostics accuracy, this paper proposed a data-fusion approach to forecast the fuel cell performance based on long short-term memory (LSTM) recurrent neuron network (RNN) and auto-regressive integrated moving average (ARIMA) method. LSTM can efficiently make a prediction regarding long-term physical degradation, whereas the fusion with ARIMA can effectively track the degradation tendency. In order to validate the performance of the proposed data-fusion approach, two different PEMFCs are tested for recording the aging experimental datasets. The forecasting results indicate that the proposed LSTM-ARIMA approach can accurately predict PEMFC degradation, which can be then used directly to optimize fuel cell performance implemented in transportation applications.

This paper proposes a novel error correction grey prediction model for degradation prediction of renewable energy storages. The proposed approach uses an error correction factor ψ to eliminate the inherent error of the original grey model (GM), and at the same time retain the original simplicity and fast prototyping. In addition, due to the uncertainty and complexity of failure mechanisms, a trigonometric residual modification is considered in order to well-describe the influence of operating conditions or cyclic fluctuation on the renewable energy storages. Two experimental case studies, including lithium-ion battery and fuel cell aging tests, are performed to validate the performance of the proposed method. In particular, the accuracy of the proposed method is investigated for different prediction horizon lengths, in order to further demonstrate its effectiveness and robustness. It is worth mentioning that the proposed method can ensure the accuracy of the remaining useful life estimation in the case of long-term forecasting, and thus, the maintenance management and corrective action of renewable energy storages can be scheduled earlier, leading to more effective cost minimization and risk mitigation.

In the more electric aircraft context, dc distribution systems have a time-varying structure due to the flexible distributed loads and complex operation conditions. This feature poses challenges for system stability and increases the difficulty of the stability analysis. Besides, the risk of instability may be increased under constant power load condition due to the negative incremental impedance characteristic. To this end, this article proposes an improved interconnection and damping assignment passivity-based control scheme. Particularly, an adaptive interconnection matrix is developed to establish the internal links in port-controlled Hamiltonian models and to generate the unique control law. The damping assignment technique is addressed to tune the dynamic characteristic. In order to meet the load requirements of different voltage levels, the design procedures were given for determining the control law in both boost converter and buck converter cases. The simulation and experimental results are performed to demonstrate the validity of the proposed control approach.

Lithium-ion battery State of Health (SOH) estimation is an essential issue in battery management systems. In order to better estimate battery SOH, Extreme Learning Machine (ELM) is used to establish a model to estimate lithium-ion battery SOH. The Swarm Optimization algorithm (PSO) is used to automatically adjust and optimize the parameters of ELM to improve estimation accuracy. Firstly, collect cyclic aging data of the battery and extract five characteristic quantities related to battery capacity from the battery charging curve and increment capacity curve. Use Grey Relation Analysis (GRA) method to analyze the correlation between battery capacity and five characteristic quantities. Then, an ELM is used to build the capacity estimation model of the lithium-ion battery based on five characteristics, and a PSO is introduced to optimize the parameters of the capacity estimation model. The proposed method is validated by the degradation experiment of the lithium-ion battery under different conditions. The results show that the battery capacity estimation model based on ELM and PSO has better accuracy and stability in capacity estimation, and the average absolute percentage error is less than 1%.

International audience

In this paper, a data-driven strategy is proposed for polymer electrolyte membrane fuel cell system diagnosis. In the strategy, features are first extracted from the individual cell voltages using Fisher discriminant analysis . Then, a classification method named spherical-shaped multiple-class support vector machine is used to classify the extracted features into various classes related to health states. Using the diagnostic decision rules, the potential novel failure mode can be also detected. Moreover, an online adaptation method is proposed for the diagnosis approach to maintain the diagnostic performance. Finally, the experimental data from a 40-cell stack are proposed to verify the approach relevance.

Heat exchangers are widely utilized in different thermal systems for diverse industrial aspects. The selection of HEx depends on the thermal efficiency, operating load, size, flexibility in operation, compatibility with working fluids, better temperature and flow controls, and comparatively low capital and maintenance costs. Heat transfer intensification of heat exchangers can be fulfilled using passive, active, or combined approaches. Utilizing nanofluids as working fluids for heat exchangers have evolved recently. The performance of heat exchangers employed different nanofluids depends mainly on the characteristics and improvement of thermophysical properties. Regarding the unique behavior of different nanofluids, researchers have attended noteworthy progress. The current study reviews and summarizes the recent implementations carried out on utilizing nanofluids in different types of heat exchangers, including plate heat exchangers, double-pipe heat exchangers, shell and tube heat exchangers, and cross-flow heat exchangers. The results showed that nanofluids with enhanced thermal conductivity, although accompanied by a considerable decrease in the heat capacity and raising viscosity, has resulted in performance enhancement of different heat exchangers types. So, the performance evaluation criterion that combines the thermal enhancement and increases the pumping power for any type of heat exchangers is requisite to evaluate the overall performance properly. The challenges and opportunities for future work of heat transfer and fluid flow for different types of heat exchangers utilizing nanofluids are discussed and presented.