Reliable modeling of proton exchange membrane fuel cell (PEMFC) degradation is essential for improving system reliability and enabling health-aware control. This paper proposes a two-dimensional multiphysics aging model that captures the dynamic evolution of Pt particles by integrating agglomeration, migration, dissolution, and redeposition mechanisms. Furthermore, a health index (HI) is derived from the particle-scale degradation behavior to real-time quantify the latent aging state of fuel cells. The proposed model and HI are validated under both accelerated stress tests and real driving cycles. The results show that the HI under static voltage is 0.012, and the HI under fluctuating voltage increases to 0.105, reflecting that voltage transients exacerbate the spatial imbalance of reaction intensity and performance loss, providing a model basis for health-aware energy management system.

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Model predictive control (MPC) based energy management strategies (EMS) are promising to achieve high-efficiency power conversion for different hybrid electric vehicles. In this work, we investigate the impact of velocity prediction on the performance of EMS. For this, MPC controllers are designed respectively for fuel cell hybrid electric vehicles (FCHEV) using four prediction settings: Prescient MPC, Frozen time MPC, exponentially decreasing MPC, and MPC with Markov chain model. The comparison of the results using different driving cycles is performed to study the effects of prediction horizon and prediction accuracy on the performance of EMS, in terms of hydrogen consumption and battery charge sustainability. Simulation results show that the performance of MPC-based EMS is highly dependent on the prediction accuracy and the control horizon length. With proper velocity prediction methods and horizon length configurations, low hydrogen consumption and sustainable battery charge can be achieved. Moreover, the necessity of co-designing the prediction model and the horizon length by specifying the driving condition is highlighted.

This article proposes an integrated design approach addressing the state/fault estimation (SFE) and fault-tolerant control (FTC) issues for switched T–S fuzzy systems with sensor and actuator faults and external disturbances. A switched fuzzy observer is developed to simultaneously estimate the system states and the sensor and actuator faults from measurement outputs affected by sensor faults. Based on this observer, a switched fuzzy FTC is designed to stabilize the closed-loop system and compensate for the effects of different considered faults, taking into account the presence of external disturbances. The mutual coupling between the estimation unit (SFE) and the control unit (FTC) supports the integrated design of these two units instead of the separated one. However, the separated design is the most adopted in the literature because the integrated design leads to complex stability conditions in the form of bilinear matrix inequalities. This work focuses on the integrated design considering the different interactions between the observer and the controller and thus ensuring good performance in terms of estimation, control, robustness, and fault compensation. For this purpose, a two-step integrated design strategy and a single-step integrated design strategy are proposed to formalize the integrated SFE and FTC design as linear matrix inequalities instead of bilinear ones. These strategies are based on the mode-dependent average dwell time concept, the piecewise Lyapunov function technique, and the robust$H_{\infty }$approach. The applicability and efficiency of the developed results are illustrated by studying a numerical example and a single-link robotic manipulator.

In the paper, a self-learning energy management strategy is proposed for fuel cell hybrid electric vehicles (FCHEV). The studied energy system for FCHEV is composed of fuel cells and lithium batteries. A reinforcement learning (RL) based energy management strategy (EMS) for FCHEV is studied to achieve the power allocation of the two energy sources. The objective is to learn a satisfactory EMS from scratch and only through the interaction of environments. Specifically, Q-Learning, one of the RL methods, is applied to minimize fuel consumption and ensure battery sustainability. Compare with Dynamic Programming (DP), which can reach the best performance of sequential decision problems theoretically, Q-Learning based EMS can achieve results close to DP based EMS. During the process, different objective functions are optimized to be suitable for Q-Learning. Finally, the simulation results with python verify the effectiveness of the method proposed in this paper.

The prognostic of proton exchange membrane fuel cells (PEMFCs) degradation and the estimation of its remaining useful life (RUL) are effective ways to improve the reliability of the target system and reduce maintenance costs, which is of great significance for the wide commercialization of PEMFCs. Many factors cause the degradation of PEMFCs, and these factors are often difficult to measure accurately. The prognostic method based on long short-term memory networks (LSTMs) has better memory ability for time series and has been demonstrated able to describe the degradation trend of PEMFCs. However, the traditional LSTM prediction algorithm seems to easily fall into the local optimal solution in long-term prediction cases. Overfitting like errors may result in an imprecise or even unstable prognostic. This paper proposes a novel method, named navigation sequence driven LSTMs (NSD-LSTMs), to enhance the accuracy of PEMFCs degradation trend prediction. Two types of PEMFCs aging test data under different load conditions were used to verify the performance of NSD-LSTMs. Experimental results show that, compared with traditional LSTMs, NSD-LSTMs can improve the accuracy of trend prediction. Accurate degradation prognostic can be used to predict RUL and provide guidance for the commercial application of PEMFCs.

In many situations, the access to energy can be compromised or limited as for refugee crisis zones, rural areas and military camps. Therefore, it is crucial to be able to ensure the basic electric needs with an off-grid power solution. In this paper, an optimal sizing of a mobile renewable hybrid power system is presented. The proposed solution is composed of a container equipped with a diesel generator, batteries and a photovoltaic system. The optimization process takes into account the particularity of the consumer needs and the accessibility to the local renewable energy resources. In order to investigate the solution performances, simulation results under Matlab\Simulink® are presented and analyzed.

With the accelerated development of the renewable energy and smart grid technologies, more and more electricity consumers are planning integrating local renewable systems for economical and ecological reasons. To be efficient, the system has to be sized optimally in consideration of both energy generation and consumption. Meanwhile, the demand side management subject to consume energy more flexibly has been drawing more and more attention. In this study, an optimal sizing strategy is proposed for grid-connected PV/WT hybrid system with demand side scheduling. To do this, the energy consumption related to different load types are modeled for scheduling. A bi-level optimization framework is then proposed to realize load scheduling within the optimal sizing. In the framework, down-level is for load scheduling and achieved by genetic algorithm, while the up-level is dedicated to optimal sizing and realized by efficient global optimization algorithm. The proposed framework is verified through a case study for an industrial company, whose objective is to size one PV/WT system to compensate the local energy consumption. The obtained results show the benefits of combining system sizing with load scheduling.

In order to correctly implement an energy management strategy for a multi-source system, the output currents of different sources should be regulated to the desired references precisely and rapidly. In this paper, the underlying control of hybrid energy storage system composed by battery and supercapacitor is presented. Three current tracking controllers, PID, sliding mode, and fuzzy logic, are designed for the proposed system. Then, the control performances of the three controllers are compared according to the control precision, feasible control parameter range, and robustness to input voltage variation. The comparison is realized by carrying out a series of simulations.

In this paper, a contribution to solve the system prognostic problem is proposed. For that, the concept is defined in this work as a problem of predictive diagnosis under temporal constraint. Generally, this problem is treated using mainly approaches that are based on dynamic systems, experts' knowledge or are data-driven. Here, in order to describe the behavior of a process, we consider dynamic models that are composed of differential equations. The goal of this work is twofold. First, we present a new strategy for system prognosis based on observer design. Second, we propose a comparative study of two methodologies, dedicated to observer design, with application to an electromechanical process. To illustrate the performances of the approaches, simulation results are proposed.

In modern industries, there is intense pressure to continuously reduce costly, unscheduled maintenance of complex systems. To obtain high availability with reduced life cycle total ownership costs, classical maintenance policies are not optimal. Indeed, these polices do not allow us to perform maintenance only when it is necessary because they are not able to forecast system damage states in the future. To predict precisely the future system damage state, it is necessary to take into account how and where the system will be used. To build incremental damage models, this paper presents a generic methodology and formalism based on the system decomposition in three levels: environment, mission, and process. Predictions are performed via a sequence of known mission parameters, and environmental conditions. This allows for mission and maintenance planning by taking into account the predicted system damages over time.