The rapid development and implementation of distributed control algorithms for dc microgrids has increased the vulnerability of this type of system to false data injection attacks, being one of the most prominent types of cyber attacks. This fact has motivated the development of different false data detection and impact mitigation strategies. A common approach for the detection is based on implementing an observer that can achieve a reliable estimation of the system states. However, approaches available in the literature assume that the underlying microgrid model is linear, which is generally not the case, specially when the dc microgrid supplies nonlinear constant power loads (CPLs). Consequently, this article proposes a distributed nonlinear observer approach that can robustly detect and reconstruct the applied false data attack in the dc microgrid's current sensors and cyber links, even in the presence of local unknown CPLs. First, the system is transformed into an observable form. Second, a high-order sliding-mode observer is implemented to estimate the system states and CPL, even in the presence of false data. Finally, the estimation is used to reconstruct the attack signal. The robustness of the proposed strategy is validated through numerical simulations and in an experimental prototype under measurement noise, uncertainty, and communication delays.

This paper addresses the energy management of a standalone renewable energy system. The system is configured as a microgrid, including photovoltaic generation, a lead-acid battery as a short term energy storage system, hydrogen production, and several loads. In this microgrid, an energy management strategy has been incorporated that pursues several objectives. On the one hand, it aims to minimize the amount of energy cycled in the battery, in order to reduce the associated losses and battery size. On the other hand, it seeks to take advantage of the long-term surplus energy, producing hydrogen and extracting it from the system, to be used in a fuel cell hybrid electric vehicle. A crucial factor in this approach is to accommodate the energy consumption to the energy demand and to achieve this, a model predictive control (MPC) scheme is proposed. In this context, proper models for solar estimation, hydrogen production, and battery energy storage will be presented. Moreover, the controller is capable of advancing or delaying the deferrable loads from its prescheduled time. As a result, a stable and efficient supply with a relatively small battery is obtained. Finally, the proposed control scheme has been validated on a real case scenario.

This article proposes an observer-based methodology to detect and mitigate false data injection attacks in collaborative dc microgrids. The ability of observers to effectively detect such attacks is complicated by the presence of unknown nonlinear constant power loads. This article determines that, in the presence of unknown constant power loads, the considered attack detection and mitigation problem involves nonlinearities, locally unobservable states, unknown parameters, uncertainty, and noise. Taking into account these limitations, a distributed nonlinear adaptive observer is proposed to overcome these limitations and solve the concerned observation problem. The necessary conditions for the stability of the distributed scheme are found out. Moreover, numerical simulations are performed and then validated in a real experimental prototype, where communication delay, uncertainty, and noise are considered.

Lithium-ion batteries are widely recognised as the leading technology for electrochemical energy storage. Their applications in the automotive industry and integration with renewable energy grids highlight their current significance and anticipate their substantial future impact. However, battery management systems, which are in charge of the monitoring and control of batteries, need to consider several states, like the state of charge and the state of health, which cannot be directly measured. To estimate these indicators, algorithms utilising mathematical models of the battery and basic measurements like voltage, current or temperature are employed. This review focuses on a comprehensive examination of various models, from complex but close to the physicochemical phenomena to computationally simpler but ignorant of the physics; the estimation problem and a formal basis for the development of algorithms; and algorithms used in Li-ion battery monitoring. The objective is to provide a practical guide that elucidates the different models and helps to navigate the different existing estimation techniques, simplifying the process for the development of new Li-ion battery applications.

En el campo de las pilas de combustible PEM, la gestión de agua líquida es una de las problemáticas más importantes que afectan a la eficiencia y vida útil del sistema. Las técnicas de control activo y supervisión del agua se ven limitadas por la ausencia de sensores que puedan medir la saturación de agua líquida en línea. Por eso, en este trabajo se presenta el diseño de un observador de estado para la estimación de la saturación de agua líquida en la capa catalizadora del cátodo de una pila de combustible PEM de cátodo abierto. El observador propuesto se basa en técnicas de alta ganancia. Además, se modifica con una función de zona muerta autoajustable con el fin de reducir su sensibilidad al ruido en la medida. Los resultados se han validado mediante simulación numérica y experimentación. Estos muestran que, en ausencia de ruido, el observador propuesto presenta unas prestaciones similares a las de su equivalente sin zona muerta. Además, en presencia de ruido, la zona muerta disminuye significativamente el error de estimación inducido por este.