This work presents a battery management system for lead–acid batteries that integrates a battery-block (12 V) sensor that allows the online monitoring of a cell’s temperature, voltage, and impedance spectra. The monitoring and diagnostic capabilities enable the implementation of improved battery management algorithms in order to increase the life expectancy of lead–acid batteries and report the battery health conditions. The novelty is based on the online monitoring of the evolution of electrochemical impedance spectroscopy (EIS) over a battery’s life as a way to monitor the battery’s performance. Active cell balancing is also proposed as an alternative to traditional charge equalization to minimize excessive electrolyte consumption. A battery-block sensor (VTZ) was validated by using the correlation between experimental data collected from electrochemical impedance spectroscopy lab-testing equipment and sensors that were implemented in a series of 12 V lead–acid battery blocks. The modular design and small size allow easy and direct integration into different commercial cell formats, and the proposed methodology can be used for applications ranging from automotive to stationary energy storage.

Ongoing research on energy storage systems, driven by the energy transition, has led to the development of alternative systems beyond conventional batteries, such as supercapacitors (SCs). These devices offer high‐power density, rapid charge/discharge, and long cycle life, making them suitable for applications requiring quick energy bursts. Moreover, they bear lesser dependency on critical raw materials, enhancing their sustainability. Despite their technological maturity, little is known about their environmental and economic implications from a life cycle perspective. This review offers an insight into life cycle assessment and life cycle costing studies evaluating the environmental impacts and economic viability of SCs. The analysis synthesizes existing research, identifies trends, and highlights key knowledge gaps. By providing a systematic overview of the life cycle sustainability metrics for SC technologies, this study contributes to a deeper understanding of their role as a viable and sustainable energy storage solution. Due to heterogeneous system boundaries and product systems found in literature, a clear estimation of average environmental impacts and cost performance remains challenging. Additionally, the sustainability implications of next‐generation SCs are not fully understood. Further research is needed to establish comprehensive sustainability assessments, improve methodological consistency, and guide future development.

Physical models are suitable for the development and optimization of materials and cell designs, whereas models based on experimental data and electrical equivalent circuits (EECs) are suitable for the development of operation estimators, both for cells and batteries. This research work develops an innovative unsupervised artificial neural network (ANN) training cost function for identifying equivalent circuit parameters using electrochemical impedance spectroscopy (EIS) to identify and monitor parameter variations associated with different physicochemical processes that can be related to the states or failure modes in batteries. Many techniques and algorithms are used to fit a predefined EEC parameter, many requiring high-human-expertise support work. However, once the appropriate EEC model is selected to model the different physicochemical processes associated with a given battery technology, the challenge is to implement algorithms that can automatically calculate parameter variations in real time to allow the implementation of estimators of capacity, health, safety, and other degradation modes. Based on previous studies using data augmentation techniques, the new ANN deep learning method introduced in this study yields better results than classical training algorithms. The data used in this work are based on an aging and characterization dataset for 80 Ah and 12 V lead–acid batteries.

Wind energy has recently become one of the most prominent technologies among electrical energy generation systems. As a result, wind-based renewable energy generation systems are incessantly growing, and wind turbines of different characteristics are being installed in many locations around the world. One drawback associated with different characteristics of the wind turbines is that controllers have to be designed individually for each of them. Additionally, stable performance of the wind turbines needs to be ensured in the whole range of their operating conditions. Nowadays, there are many causes for uncertainties in the actual performance of a horizontal axis wind turbine, such as variations in the characteristics of the wind turbine, fabrication tolerances of its elements or non-linearities related to different operating-points. Hence, in order to respond to these uncertainties and ensure the stability of the wind turbine, robust control and stability theories have been gaining importance during recent years. Nevertheless, the use of robust stability analyses with complex wind turbine models still needs to be faced and remarkably improved. In this paper, a stability analysis of the pitch system control of a horizontal axis wind turbine based on the Kharitonov robust stability method is proposed. The objective was to assess the robust stability of a pitch controller in response to uncertainties arising from varying operating conditions of the National Renewable Energies Laboratory (NREL) 5 MW class IIA wind turbine. According to the results, the proposed method could satisfactorily respond to limited variations in the characteristics of the model, but could lack accuracy in cases of bigger variations or employment of high order complex mathematical models.

The degradation and safety study of lithium-ion batteries is becoming increasingly important given that these batteries are widely used not only in electronic devices but also in automotive vehicles. Consequently, the detection of degradation modes that could lead to safety alerts is essential. Existing methodologies are diverse, experimental based, model based, and the new trends of artificial intelligence. This review aims to analyze the existing methodologies and compare them, opening the spectrum to those based on artificial intelligence (AI). AI-based studies are increasing in number and have a wide variety of applications, but no classification, in-depth analysis, or comparison with existing methodologies is yet available.

Obtaining tools to analyze and predict the performance of batteries is a non-trivial challenge because it involves non-destructive evaluation procedures. At the research level, the development of sensors to allow cell-level monitoring is an innovative path, and electrochemical impedance spectrometry (EIS) has been identified as one of the most promising tools, as is the generation of advanced multivariable models that integrate environmental and internal-battery information. In this article, we describe an algorithm that automatically identifies a battery-equivalent electrochemical model based on electroscopic impedance data. This algorithm allows in operando monitoring of variations in the equivalent circuit parameters that will be used to further estimate variations in the state of health (SoH) and state of charge (SoC) of the battery based on a correlation with experimental aging data corresponding to states of failure or degradation. In the current work, the authors propose a two-step parameter identification algorithm. The first consists of a rough differential evolution algorithm-based identification. The second is based on the Nelder–Mead Simplex search method, which gives a fine parameter estimation. These algorithm results were compared with those of the commercially available Z-view, an equivalent circuit tool estimation that requires expert human input.