This paper provides a comprehensive review of hybrid energy systems (HESs), focusing on their challenges, optimization techniques, and control strategies to enhance performance, reliability, and sustainability across various applications, such as microgrids (MGs), commercial buildings, healthcare facilities, and cruise ships. The integration of renewable energy sources (RESs), including solar photovoltaics (PVs), with enabling technologies such as fuel cells (FCs), batteries (BTs), and energy storage systems (ESSs) plays a critical role in improving energy management, reducing emissions, and increasing economic viability. This review highlights advancements in multi-objective optimization techniques, real-time energy management, and sophisticated control strategies that have significantly contributed to reducing fuel consumption, operational costs, and environmental impact. However, key challenges remain, including the scalability of optimization techniques, sensitivity to system parameter variations, and limited incorporation of user behavior, grid dynamics, and life cycle carbon emissions. The review underlines the need for robust, adaptable control strategies capable of accommodating rapidly changing energy environments, as well as the importance of life cycle assessments to ensure the long-term sustainability of RES technologies. Future research directions emphasize the integration of variable RESs, advanced scheduling, and the application of emerging technologies such as artificial intelligence and blockchain to improve system resilience and efficiency. This paper introduces a novel classification framework, distinct from existing taxonomies, addressing gaps in prior reviews by incorporating emerging technologies and focusing on the dynamic nature of energy management in hybrid systems. It also advocates for bridging the gap between theoretical advancements and real-world implementation to promote the development of more sustainable and reliable HESs.

Lower-limb exoskeletons have been proven to be beneficial for motor function disability patients, in both clinical rehabilitation settings and daily activities. However, exoskeletons for the pediatric field are still very limited. In this paper, a novel lower-limb exoskeleton design for children is presented. Based on the anthropometric data of the target group, the size of the exoskeleton is designed adjustable to suit children from 8 to 12 years old. It employs six active joints on the knee and hip, actuated by Brushless DC motors and Harmonic Drive gears. The controller is based on a finite state machine model and the weight shift between two feet to generate the gait trajectory. An innovative automatic step-triggering mechanism is also proposed, based on the feedback from the ground reaction force sensor on the foot. Two user interfaces on the exoskeleton and the host PC are designed for easily operating the exoskeleton by clinicians and engineers. Experiments have been conducted with three healthy volunteers following the predesigned rehabilitation session protocol. Results show that the exoskeleton can well follow the gait trajectory generated by the control algorithm. All volunteers can fulfill all tasks in the test protocol with reduced efforts and the steps are automatically triggered by the presented controller.

La pile à combustible (PàC) est actuellement la solution alternative aux énergies fossiles la plus prometteuse. Il convient cependant d’améliorer sa fiabilité. Cela nécessite la mise en place d’algorithmes capables d’estimer en temps réel l´état de santé de son fonctionnement et de prédire sa durée de vie résiduelle. Les méthodes de pronostic basées sur un modèle physique offrent des résultats précis car ne nécessitent ni apprentissage de modes de fonctionnement ni expertise de l’opérateur. Toutefois, la problématique pour un système PàC réside dans le couplage de plusieurs phénomènes physique, l’incertitude des paramètres du modèle et la faible instrumentation du cœur de pile. Ainsi, nous utilisons des modèles incertains basés sur l’outil Bond Graph bien adapté pour la PàC. Concrètement, les incertitudes sont intégrées au modèle d´évolution des puissances qui est utilisé pour la détection du vieillissement et l’estimation de la dégradation de la PàC. Un modèle de dégradation est utilisé par un filtre de Kalman étendu pour l’estimation de l’état de santé, de la dynamique du vieillissement et permet de quantifier l’incertitude pour toute condition opératoire. Un algorithme Inverse First Order Reliability Method permet ensuite la prédiction de la durée de vie résiduelle et de l’incertitude de prédiction inhérente. La méthode globale a été validée sur différents jeux de données expérimentales. Grâce à l’ensemble de ces outils, un contrôle par inversion de modèle REM (Représentation Energétique Macroscopique) à paramètres variant, robuste au vieillissement a été développé en se basant sur l’estimation de l’état de santé.