Proton Exchange Membrane (PEM) water electrolysis system is one of the promising technologies to produce green hydrogen from renewable energy sources (wind and solar). However, performance and dynamic analysis of PEM water electrolysis systems are challenging due to the intermittent nature of such sources and involved multi-physical behaviour of the components and subsystems. This study proposes a generic dynamical model of the PEM electrolysis system represented in a modular fashion using Bond Graph (BG) as a unified modelling approach. Causal and functional properties of the BG facilitate the formal PEM electrolyser model to adapt and to fit the different configurations of the electrolyser ranging from laboratory scale to industrial scale. The system-specific key parameter values are identified optimally for a laboratory-scale electrolyser system running on a multi-source energy platform using experimental data. The mean absolute percentage error between simulation and experimental data is found to be less than 5%. The performance characteristic curves of the electrolyser are predicted at different operating temperatures using the identified key parameters. The predicted performance is in good agreement with the expected behaviour of the electrolyser found in the literature. The model also estimates the different energy losses and the real-time efficiency of the system under dynamic inputs. With these capabilities, the developed model provides an economical mean for design, control, and diagnosis development of such systems.

The graphical approaches often have different backgrounds and view a system or an algebraic model from different perspectives in order to facilitate the communication and the understanding. These graphical approaches satisfy the modeling needs and give a clear and easily understandable overview of the behavioral and functional models and make easier to see what the process is, which vulnerabilities and asset that are involved and how the system works. The main goal of this paper is to develop and implement a methodology which combines the functional analysis and the bond graph (BG) tool for intelligent and autonomous systems. As a result, a supervisory interface is obtained, given under a finite automaton, displaying to the operators the possibilities the system has to achieve or not, its objectives. Each operating mode, corresponding to a vertex of the automaton, is associated with a set of services from a functional point-of-view and is defined accurately by a behavioral BG model. Furthermore, the service availability (associated to the BG elements) and the conditions for switching from one mode to another one are analyzed by fault detection and isolation algorithms generated on the basis of the structural and causal properties of the BG tool. Moreover, when a fault is not completely isolable some results can nevertheless be expressed in terms of available or unavailable services.

In this paper, we propose an approach based on generic component model to realize the model aggregation for reconfigurable control. This is a component-oriented method. Two elementary notions, the service and the operating mode, are introduced to construct a hierarchical system and to assure the coherences between specification and realization at each level and between levels. The consideration of aggregated, complex components leads to extend this description to the possibilities of reconfiguration

This paper continues to present the framework of multi-faults diagnosis from the preceding paper. There are two main contributions in this part. One is aimed at developing a new diagnostic process for continuous and dynamic system and its corresponding consistency-checking module. This diagnostic process and its consistency-checking module are all based on the models defined by STRIPS (STandford Research Institute Problem Solver) actions. The other is that the fault models are introduced into the consistency-checking module for preventing the impossible diagnoses. Moreover, the STRIPS can qualitatively define the fault models without requiring detail and precise knowledge about faulty components.

Accurate detection of faults in a dynamic system is very beneficial as this information can be used in a wide variety of ways by the machine operators or designers. This advantage becomes many folds when regarding the future condition i.e. time to failure, named remaining useful life, is available in addition to that of the present condition. Thus, prognosis is one of the most useful tools to improve the working of a machine as many critical decisions can be made. Prognosis can be critical for applications that risk loss of life and property. In this paper, a hybrid method, utilizing bond graph and artificial intelligence, is proposed for system health estimation (SHE) and prognosis. The Bond Graph model is used to calculate Energy Activity, which is used as a common metric for both SHE and prognosis. The proposed method is checked by simulation on a spring mass damper system undergoing a fault.