In electric vehicles and mobile electronic devices, batteries are one of the most critical components. They work by using electrochemical reactions that have been thoroughly investigated to identify their behavior and characteristics at each operating point. One of the fascinating aspects of batteries is their complicated behavior. The type of power cell reviewed in this study is a Lithium Iron Phosphate LiFePO${}_4$ (LFP). The goal of this study is to develop an intelligent model that can forecast the power cell State of Charge (SOC). The dataset used to create the model comprises all the operating points measured from an actual system during a capacity confirmation test. Regression approaches based on Deep Learning (DL), such as Long Short-Term Memory networks (LSTM), were evaluated under different model configurations and forecasting horizons.

The increasing demand for lithium-ion batteries (LIB) across various industries has accentuated the importance of accurately predicting the Remaining Useful Life (RUL) of these energy storage devices. This article introduces a novel approach to RUL prediction by leveraging a federated learning (FL) and homomorphic encryption (HE) model, called FedHEONN. Traditional RUL prediction models often face challenges not only related to the accuracy and reliability of estimations but also to data privacy and security when dealing with sensitive information in Internet of Things (IoT) environments. In response, our approach employs FL, allowing multiple distributed nodes to collaboratively train a predictive model without sharing private data. This ensures data privacy and security while harnessing the collective knowledge from diverse edge computing devices. Furthermore, to address the issue of secure computation over encrypted data, FedHEONN has the capacity to incorporate HE into the learning process. This enables the model to operate directly on encrypted data, providing an additional layer of protection to that of the federated model itself. Our experimental results test the efficacy of this FL method in accurately predicting the RUL of LIB real data against benchmark models, including linear regression with regularisation techniques such as Lasso, Ridge and Elastic-net, and non-linear models such as multilayer perceptron and support vector machine for regression. The experiment demonstrates the preservation of prediction outcomes, upholding an additional level of data safety through encrypted transmission of model information.

Systems engineering plays a key role in the naval sector, focusing on how to design, integrate, and manage complex systems throughout their life cycle; it is therefore difficult to conceive functional warships without it. To this end, specialized information systems for logistical support and the sustainability of material solutions are essential to ensure proper provisioning and to know the operational status of the frigate. However, based on an architecture composed of a set of logistics applications, this information system may require highly qualified operators with a deep knowledge of the behavior of onboard systems to manage it properly. In this regard, failure detection systems have been postulated as one of the main cutting-edge methods to address the challenge, employing intelligent techniques for observing anomalies in the normal behavior of systems without the need for expert knowledge. In this paper, the study is concerned to the scope of the Spanish navy, where a complex information system structure is responsible for ensuring the correct maintenance and provisioning of the vessels. In such context, we hereby suggest a comparison between different one-class techniques, such as statistical models, geometric boundaries, or dimensional reduction to face anomaly detection in specific subsystems of a warship, with the prospect of applying it to the whole ship.