Accurate estimation of the state of charge (SoC) of lithium-ion batteries is crucial for battery management systems, particularly in electric vehicle (EV) applications where real-time monitoring ensures safe and robust operation. This study introduces three advanced algorithms to estimate the SoC: deep neural network (DNN), gated recurrent unit (GRU), and long short-term memory (LSTM). The DNN, GRU, and LSTM models are trained and validated using laboratory data from a lithium-ion 18650 battery and simulation data from Matlab/Simulink for a LiCoO₂ battery cell. These models are designed to account for varying temperatures during charge/discharge cycles and the effects of battery aging due to cycling. This paper is the first to estimate the SoC by a deep neural network using a variable current profile that provides the SoC curve during both the charge and discharge phases. The DNN model is implemented in Matlab/Simulink, featuring customizable activation functions, multiple hidden layers, and a variable number of neurons per layer, thus providing flexibility and robustness in the SoC estimation. This approach uniquely integrates temperature and aging effects into the input features, setting it apart from existing methodologies that typically focus only on voltage, current, and temperature. The performance of the DNN model is benchmarked against the GRU and LSTM models, demonstrating superior accuracy with a maximum error of less than 2.5%. This study highlights the effectiveness of the DNN algorithm in providing a reliable SoC estimation under diverse operating conditions, showcasing its potential for enhancing battery management in EV applications.

Due to the rapid development of information and emerging communication technologies, developing and implementing solutions in the Internet of Medical Things (IoMTs) field have become relevant. This work developed a novel data security algorithm for deployment in emerging wireless biomedical sensor network (WBSN) and IoMTs applications while exchanging electronic patient folders (EPFs) over unsecured communication channels. These EPF data are collected using wireless biomedical sensors implemented in WBSN and IoMTs applications. Our algorithm is designed to ensure a high level of security for confidential patient information and verify the copyrights of bio-signal records included in the EPFs. The proposed scheme involves the use of Hahn’s discrete orthogonal moments for bio-signal feature vector extraction. Next, confidential patient information with the extracted feature vectors is converted into a QR code. The latter is then encrypted based on a proposed two-dimensional version of the modified chaotic logistic map. To demonstrate the feasibility of our scheme in IoMTs, it was implemented on a low-cost hardware board, namely Raspberry Pi, where the quad-core processors of this board are exploited using parallel computing. The conducted numerical experiments showed, on the one hand, that our scheme is highly secure and provides excellent robustness against common signal-processing attacks (noise, filtering, geometric transformations, compression, etc.). On the other hand, the obtained results demonstrated the fast running of our scheme when it is implemented on the Raspberry Pi board based on parallel computing. Furthermore, the results of the conducted comparisons reflect the superiority of our algorithm in terms of robustness when compared to recent bio-signal copyright protection schemes.

In the study of population dynamics, understanding the interactions between different species is crucial. Traditional models often rely on integer-order derivatives, which lack memory effects and non-local interactions. This research extends eco-epidemiological models by incorporating the Caputo-Fabrizio fractional derivative, providing a more accurate representation of biological processes with memory. The existence and uniqueness of positive solutions, as well as the local stability of equilibrium points in the Caputo-Fabrizio sense, are proven, ensuring the model's reliability for studying eco-epidemiological dynamics. Furthermore, the study establishes the generalized Hyers-Ulam stability for the model, ensuring robustness to small perturbations. Additionally, we introduce and analyze a novel numerical approach by constructing multistep methods of Adams-Bashforth type with step sizes ranging from 1 to 6 to solve the proposed fractional-order differential equations, enhancing the accuracy and stability of the solutions. We applied these methods to analyze three examples of fractional differential equations with known exact solutions, focusing on the dynamics for different values of the fractional order. Numerical simulations are provided to illustrate the dynamics of susceptible, infected, and predator populations, validating the theoretical findings. This comprehensive approach offers significant insights and improvements in the modeling of complex biological and epidemiological processes.