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The distribution of the magnetic field generated by an electromagnetic device can provide insights into their operational condition. This paper focuses on diagnosing electrolyzers through magneto-tomography, a method which involves mapping the magnetic field generated by electrical devices. Since current density and magnetic field are directly linked, this study can characterize the normal and abnormal operation of an electrolyzer. To achieve this, a two-dimensional (2-D) Biot–Savart model in polar coordinates is developed. To validate this model for electrolyzer diagnostics, an experimental setup was established to replicate the electrolyzer operation. The purely analytical model was successfully validated, with the average difference between the analytical results and experimental measurements established. The model validation is performed by comparing the experimental data to the theoretical results, and it is observed that the difference does not exceed 5%. [ABSTRACT FROM AUTHOR]

For grid stability, operation, and planning, solar irradiance forecasting is crucial. In this paper, we provide a method for predicting the Global Horizontal Irradiance (GHI) mean values one hour in advance. Sky images are utilized for training the various forecasting models along with measured meteorological data in order to account for the short-term variability of solar irradiance, which is mostly caused by the presence of clouds in the sky. Additionally, deep learning models like the multilayer perceptron (MLP), convolutional neural networks (CNN), long short-term memory (LSTM), or their hybridized forms are widely used for deterministic solar irradiance forecasting. The implementation of probabilistic solar irradiance forecasting, which is gaining prominence in grid management since it offers information on the likelihood of different outcomes, is another task we carry out using quantile regression. The novelty of this paper lies in the combination of a hybrid deep learning model (CNN-LSTM) with quantile regression for the computation of prediction intervals at different confidence levels. The training of the different machine learning algorithms is performed over a year's worth of sky images and meteorological data from the years 2019 to 2020. The data were measured at the University of French Polynesia (17.5770° S, 149.6092° W), on the island of Tahiti, which has a tropical climate. Overall, the hybrid model (CNN-LSTM) is the best performing and most accurate in terms of deterministic and probabilistic metrics. In addition, it was found that the CNN, LSTM, and ANN show good results against persistence. [ABSTRACT FROM AUTHOR]

An original non-invasive methodology of the fuel cell diagnosis is proposed to identify different positions of the faults in Proton Exchange Membrane Fuel Cell (PEMFC) stacks from external magnetic field measurements. The approach is based on computing the external magnetic field difference between normal and faulty PEMFC operating conditions. To evaluate the external magnetic field distribution, in this paper, we propose an improved design of the magnetic field analyzer. This analyzer amplifies the magnetic field around the cell to perform an accurate detection of the fault position. Moreover, the main contribution of this work is represented by conceiving and implementing a 3D multi-physical current distribution emulator of a proton exchange membrane fuel cell. The new concept of a proton exchange membrane fuel cell emulator has been specially designed to emulate the magnetic field of a real fuel cell stack. This emulator concept is also beneficial for a new model of the fuel cell, which implies a multi-physical coupling between electrochemical electric conduction and the generated magnetic field. Finally, finally, the numerical model and the emulator have been involved in the realization of numerical simulations and experimental analysis to prove the ability of the system to detect and localize 3D faults. [ABSTRACT FROM AUTHOR]

Solar-power-generation forecasting tools are essential for microgrid stability, operation, and planning. The prediction of solar irradiance (SI) usually relies on the time series of SI and other meteorological data. In this study, the considered microgrid was a combined cold- and power-generation system, located in Tahiti. Point forecasts were obtained using a particle swarm optimization (PSO) algorithm combined with three stand-alone models: XGboost (PSO-XGboost), the long short-term memory neural network (PSO-LSTM), and the gradient boosting regression algorithm (PSO-GBRT). The implemented daily SI forecasts relied on an hourly time-step. The input data were composed of outputs from the numerical forecasting model AROME (Météo France) combined with historical meteorological data. Our three hybrid models were compared with other stand-alone models, namely, artificial neural network (ANN), convolutional neural network (CNN), random forest (RF), LSTM, GBRT, and XGboost. The probabilistic forecasts were obtained by mapping the quantiles of the hourly residuals, which enabled the computation of 38%, 68%, 95%, and 99% prediction intervals (PIs). The experimental results showed that PSO-LSTM had the best accuracy for day-ahead solar irradiance forecasting compared with the other benchmark models, through overall deterministic and probabilistic metrics. [ABSTRACT FROM AUTHOR]

This article is on the subject of the multiple-fuel cell (MFC) power module system in heavy-duty (HD) electric transportation. The MFC system is promising for increasing the system power level, redundancy, and lifetime and to reduce costs. In particular, the number of FC modules can affect system fluidic and electrical architectures significantly. Furthermore, system complexity and performance can also be influenced. Different MFC system electric architectures can lead to system global efficiency varying a lot, which is closely related to fuel economy. Meanwhile, an optimization strategy can also be used to help assign power demand among FC modules more efficiently, then help reduce power loss and hydrogen consumption. Hence, this article aims to make an investigation of MFC system architecture development in HD electric transportation and find the most suitable architecture from the view of efficiency and fuel economy based on quantitative analysis. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic programming-based optimization analysis is performed to quantitatively assess the energy-saving potential associated to the battery hybridization of railcars, which is nowadays regarded as a high-potential solution to reduce the greenhouse-gas impact of regional trains. A computationally effective longitudinal model is first developed, starting from an available energetic macroscopic representation modeling tool. The resulting simplified railcar model was first integrated with a battery state of charge simulator and then embedded within a fast constrained optimization algorithm. The optimization outcomes, reachable within reasonable timeframe and considering cost-effective hybridization scenarios, are highly informative for train manufacturers and operators interested in substantial hybridization of railcars. Particularly, fuel savings as high as 18% can be achieved with respect to the current diesel-powered trains. As a consequence, useful design and control guidelines are made available, considering both retrofitting or replacing the existing railway systems (particularly focusing on regional trains), while coping with capital and operating costs, safety, and traffic constraints. [ABSTRACT FROM AUTHOR]

This study presents a review of prognostic methods applied to automotive proton exchange membrane fuel cell (PEMFC). PEMFC durability is strongly affected when it is subjected to automotive load cycling (ALC). ALC is normally composed of four operation modes such as start‐up, idle, transient high‐current demand and shutdown. All of these operation modes drastically change the internal variables of the system like temperature, pressure, relative humidity etc. causing degradation of the fuel cell components in a short time. Prognostic methods could be a possible solution to tackle the PEMFC's low durability issue because they allow predicting the remaining useful life of the system in order to apply preventive maintenance plans. Therefore, the objective of this study is to review the prognostic techniques applied to PEMFC under ALC. In the first part of this study, a summary of PEMFC degradation mechanisms caused by ALC is realised based on literature review. In the second part, the prognostic methods review for automotive PEMFCs is carried out and a general synthesis and future challenges are given in the third part of the study. [ABSTRACT FROM AUTHOR]

As one of the most promising types of fuel cells, the proton exchange membrane fuel cells (PEMFCs) can be utilized in many applications. However, it still faces two main challenges before large-scale industrial applications, namely short lifetime and high costs. The aim of this paper is to establish an accurate online short-term prognostics method to help users extend the lifetime and reduce the cost of PEMFCs. First, we compare the short-term prognostics accuracy and computational efficiency of several different methods including the Elman neural network, the group method of data handling, the adaptive neuro-fuzzy inference system (ANFIS) with different fuzzy inference system creation strategies, and the wavelet decomposition approach. Test results show that the ANFIS with fuzzy c-means (ANFIS-FCM) strategy has the best short-term prognostics performance. Then, we propose an automatic parameter adjustment method for ANFIS-FCM by using the particle swarm optimization (PSO) algorithm. Test results show that the PSO algorithm can effectively adjust the parameters and achieve improved prognostics results. Finally, the proposed prognostics methods are verified on a PEMFC experimental platform. Experimental results show that the proposed methods have great potential for practical applications. [ABSTRACT FROM AUTHOR]

In this paper, a data-driven fault diagnostic strategy is designed for proton exchange membrane fuel cell systems. In order to take the system dynamics and spatial inhomogeneity into account, the individual cell voltages measured in a sliding diagnosis window are considered integrally as a diagnostic observation. In the proposed diagnostic approach, a time-series analysis tool, named shapelet transform, is used to extract the discriminative features from the diagnostic observations. The classification tool, named sphere shaped multiclass support vector machine, is then carried out in the feature space in order to realize both fault detection and fault isolation. The approach is validated on the experimental testbench. The performance of the approach is evaluated and the necessity of considering both dynamic behaviors and the spatial inhomogeneity is highlighted. [ABSTRACT FROM AUTHOR]

This article studies the energy management of a hybrid electric vehicle (HEV) equipped with a battery, an ultracapacitor system (UCS), and a fuel cell system (FCS). The energy management strategy (EMS) is designed to enable degraded operation should an energy source fail. In the considered architecture, the vehicle is not operational without a battery because it imposes the dc bus voltage; therefore, degraded operations are only considered for failures in the FCS or UCS. The objective is not to develop a new EMS for each degraded operation mode but, rather, to slightly modify the global EMS developed for normal operation. The article presents simulation and experimental validation results. [ABSTRACT FROM PUBLISHER]

This paper presents a detailed modeling along with an energetic macroscopic graphical representation of the marine current turbine (MCT) system. The MCT system under study consists of a 1.5-MW fixed pitch turbine, a permanent magnet synchronous generator, an ac–dc fully controlled machine side converter, a dc link, a dc–ac fully controlled grid-side converter, connection cables, a transformer, and the main grid. The control of each converter is designed by considering the tuning chain as a part of the system's energetic macroscopic representation. The machine side converter's control makes use of the maximum power point tracking torque control and the generator loss minimization (output maximization) techniques to enhance the overall efficiency when the system runs under the rated marine current speed. When the system runs above the base speed, the control mode is switched to the rated power limiting mode. The grid-side converter is controlled to stabilize the dc-link voltage within a certain range besides controlling the reactive power. The proposed control strategies are evaluated based on the model results by using the real data of The Alderney Race (Raz Blanchard in French) marine site. [ABSTRACT FROM PUBLISHER]

Prognostics and health management (PHM) techniques for proton exchange membrane fuel cell (PEMFC) systems are of great importance for increasing their reliability and sustainability. PEMFC systems suffer from relatively poor long-term performance and durability, and prediction and prognosis can give early indications about when components should be fixed or replaced. Prognostics modeling needs to take account of a number of phenomena, including degradation mechanisms that are not easily measured. A number of works are currently investigating PHM in fuel cell systems, as well as the problem of estimating remaining useful lifetime. Any reduction in the volume of data required for making predictions is clearly advantageous. In this paper, a univariate prognostic approach based on signal processing, namely discrete wavelet transform (DWT), is proposed. The proposed approach aims at achieving an online prognostic for PEMFC systems. DWT is first introduced, and then, the predictions are built using the power signals of two different PEMFC stacks in two different scenarios, namely static and dynamic operating conditions. Results show that the method is reliable for online prediction of power, with prediction errors less than 3%. [ABSTRACT FROM PUBLISHER]

Although, the proton exchange membrane fuel cell is a promising clean and efficient energy converter that can be used to power an entire building in electricity and heat in a combined manner, it suffers from a limited lifespan due to degradation mechanisms. As a consequence, in the past years, researches have been conducted to estimate the state of health and now the remaining useful life (RUL) in order to extend the life of such devices. However, the developed methods are unable to perform prognostics with an online uncertainty quantification due to the computational cost. This paper aims at tackling this issue by proposing an observer-based prognostic algorithm. An extended Kalman filter estimates the actual state of health and the dynamic of the degradation with the associated uncertainty. An inverse first-order reliability method is used to extrapolate the state of health until a threshold is reached, for which the RUL is given with a 90% confidence interval. The global method is validated using a simulation model built from degradation data. Finally, the algorithm is tested on a dataset coming from a long-term experimental test on an eight-cell fuel cell stack subjected to a variable power profile. [ABSTRACT FROM AUTHOR]

Multi‐stack fuel cell (MFC) systems provide higher performances and reliability than single‐stack fuel cell systems through higher flexibility. The aim of this study is to investigate the degraded mode operation of MFC systems upon the loss of one of its stack. The different power allocation strategies impact the behaviour of the MFC system. Fuel consumption results obtained through simulation using a driving cycle acquired on a test vehicle are used to compare the chosen allocation strategies. The simulations have been conducted in a Matlab–Simulink environment. The results show that with a simple reconfiguration of the system the multi‐stack system can still complete the driving cycle. The fuel consumption results during normal and degraded mode operation are compared and show acceptable efficiency loss during degraded mode operation. [ABSTRACT FROM AUTHOR]

Proton Exchange Membrane Fuel Cells (PEMFC) are promising energy converters, but still suffer from a short life duration. Applying Prognostics and Health Management seems to be a great solution to overcome that issue. But developing prognostics to anticipate and try to avoid failures is a critical challenge. To tackle this problem, a hybrid prognostics approach is proposed. It aims at predicting the power aging of a PEMFC stack working at a constant operating condition and a constant current solicitation. The main difficulties to overcome are the lack of adapted modeling of the aging for prognostics, and the occurrence of disturbances creating recovery phenomena through aging. Consequently, this work proposes a new empirical model for power aging that takes into account these recoveries based on different features extracted from the data. These models are used in a joint particle filter framework directly initialized by an automatic parameter estimate process. When sufficient data are available, the prognostics can give accurate behavior predictions compared to experimentation. Remaining useful life estimates can be given with an error smaller than 5% for a horizon of 500 hours on a life duration of 1750 hours, which is clearly long enough for decision making. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]