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With the growing demand for battery-powered devices and electric vehicles, the need for improved battery performance and safety is paramount. A key determinant of battery health is the accurate monitoring of surface temperature (ST). Conventional ST estimation often depends on direct sensor measurements, which may not be cost-effective and can impact system reliability. This paper presents DeepTimeNet, a novel approach leveraging deep learning (DL) architectures for sensorless ST prediction in lithium-ion batteries. DeepTimeNet combines Convolutional Neural Networks (CNN), ResNet blocks, Inception modules, Bidirectional LSTM, and GRU layers to precisely model the time-dependent behaviour of batteries. A comprehensive evaluation against traditional models, across temperatures ranging from -20 °C to 25 °C and under various driving profiles, including US06 and Urban Dynamometer Driving Schedule (UDDS), is conducted. DeepTimeNet’s performance is quantified by metrics such as mean absolute error (MAE), surpassing that of models like Gated Recurrent Unit-Recurrent Neural Network (GRU-RNN), Convolutional Neural Network-Long Short Term Memory Network (CNN-LSTM), and Long Short Term Memory Network (LSTM). The results demonstrate DeepTimeNet’s superior performance, with an RMSE of 0.0971, MSE of 0.0099, MAE of 0.0912, and MAXE of 0.3963, validating it as an advanced tool for enhancing the efficacy of battery management systems and underscoring its potential as a benchmark for future innovations.

The battery's performance heavily influences the safety, dependability, and operational efficiency of electric vehicles (EVs). This paper introduces an innovative hybrid deep learning architecture that dramatically enhances the estimation of the state of charge (SoC) of lithium-ion (Li-ion) batteries, crucial for efficient EV operation. Our model uniquely integrates a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM), optimized through evolutionary intelligence, enabling an advanced level of precision in SoC estimation. A novel aspect of this work is the application of the Group Learning Algorithm (GLA) to tune the hyperparameters of the CNN–Bi-LSTM network meticulously. This approach not only refines the model's accuracy but also significantly enhances its efficiency by optimizing each parameter to best capture and integrate both spatial and temporal information from the battery data. This is in stark contrast to conventional models that typically focus on either spatial or temporal data, but not both effectively. The model's robustness is further demonstrated through its training across six diverse datasets that represent a range of EV discharge profiles, including the Highway Fuel Economy Test (HWFET), the US06 test, the Beijing Dynamic Stress Test (BJDST), the dynamic stress test (DST), the federal urban driving schedule (FUDS), and the urban development driving schedule (UDDS). These tests are crucial for ensuring that the model can perform under various real-world conditions. Experimentally, our hybrid model not only surpasses the performance of existing LSTM and CNN frameworks in tracking SoC estimation but also achieves an impressively quick convergence to true SoC values, maintaining an average root mean square error (RMSE) of less than 1 %. Furthermore, the experimental outcomes suggest that this new deep learning methodology outstrips conventional approaches in both convergence speed and estimation accuracy, thus promising to significantly enhance battery life and overall EV efficiency.

Typically, parasitic capacitances exist between the ground and the solar panel terminals in grid-connected PV systems. These parasitic capacitances provide a path for a leakage current, which leads to significant safety concerns, observable and seriously hazardous harmonic orders aligned with the injected grid current, and significant safety difficulties. In this research, a robust PWM controlling method that used competently in reducing the level of the leakage current and improving the power quality of a switched-capacitor Multilevel Inverter. This technique creates developed reference signals from the main signal to generate the switching scheme for the converter circuit. Additionally, the suggested control strategy only works with a small number of carrier signals, resulting in a quick system response and a simpler controller algorithm. Likewise, this controlling approach offers a stable way to maintain a constant output voltage in the suggested converter by adjusting the switching capacitors' voltages, which is not possible with traditional control techniques. MATLAB/Simulink is used to simulate the outcomes for both the suggested control approach and the traditional Phase Disposition (PD) PWM control method whereas the leakage current component reduces to 25 % compared to the captured component with the PDPWM. The simulation and the practical results based on the dSPACE-1103 hardware are quite similar.

The integration of Smart Grid technology and conceptual Industry 5.0 has paved the way for advanced energy management systems that enhance efficiency and revolutionized the parallel integration of power sources in a sustainable manner. However, this digitization has opened a new stream of the threat and opportunities of electricity theft posing a significant challenge to the security and reliability of Smart Grid networks. In this paper, we propose a secure and reliable theft detection technique using deep federated learning (FL) mechanism. The technique leverages the collaborative power of FL to train a Convolutional Gated Recurrent Unit (ConvGRU) model on distributed data sources without compromising data privacy. The training deep learning model backbone consists of a ConvGRU model that combines convolutional and gated recurrent units to capture spatial and temporal patterns in electricity consumption data. An improvised preprocessing mechanism and hyperparameter tuning is done to facilitate FL mechanism. The halving randomized search algorithm is used for hyperparameters tuning of the ConvGRU model. The impact of hyperparameters involved in the ConvGRU model such as number of layers, filters, kernel size, activation function, pooling, GRU layers, hidden state dimension, learning rate, and the dropout rate is elaborated. The proposed technique achieves promising results, with high accuracy, precision, recall, and F1 score, demonstrating its efficacy in detecting electricity theft in Smart Grid networks. Comparative analysis with existing techniques reveal the superior performance of the deep FL-based ConvGRU model. The findings highlight the potential of this approach in enhancing the security and efficiency of Smart Grid systems while preserving data privacy.