Partially and randomly based charging information on electric vehicles and energy storage systems is essential in the proper selection of charging strategies to guarantee effectiveness and safety, and this depends heavily on the accurate online estimation of battery state of health$(\text{SOH})$. This paper presents a comprehensive end-to-end evaluation protocol that uniformly samples a variety of different aspects of partial charging and a comparison of three kinds of neural network architecture feedforward (FNN), convolutional (CNN), and long short-term memory (LSTM)) under direct and transfer learning conditions. The latency and error rates of inference on each of these models are profiled to determine viability to be deployed on-board. Explainability methods detect key charging intervals that work on the predictions. Observations demonstrate that rather than just lightweight implementation, FNN with moderate accuracy, when compared to CNN, leads to enhanced performance through the extraction of local patterns and the best precision when compared to LSTM which models sequences. All the architectures are always improved with transfer learning, with average errors roughly decreasing by$\mathbf{0. 0 5}-\mathbf{0. 1 \%}$point and error bands getting narrower. As far as model compression is concerned, it is posited that the application of efficient LSTM variants would accommodate embedded hardware constraints. The development of this work will offer usable information on how to pick and tune deep learning models to perform well, maintain real-time SOH monitoring in realistic charging situations.

The growth of the renewable energy target has increased the need for a powerful solar system. However, those caused by solar power production itself from irradiation, temperature, and the state of the solar panels present tough challenges in prediction, beneficial use and optimization. This study aims to accurately model solar power generation forecasting using machine learning models like Linear Regression, Decision Tree Regressor and Random Forest Regressor. The study investigates the influence of the environment on power output, underlining the daily diurnal variability of irradiation and temperature. Here, we describe how time-series modelling and clustering algorithms help identify patterns of solar power behavior distilled from data collected across multiple months and locations. The results show that compared to other models, the Random Forest model boosted forecasting accuracy by capturing non-linear overheads and minimizing the variance. The best-case scenario yields 92.5 W peak DC power generation, and the worstcase scenario shows 12 W minimum peak DC power generation. Moreover, solar power generation is heavily affected by temperature and irradiation changes, as in daily temperature fluctuations from 2 0° C to 8 5° C, in addition to combining anomaly detection using Z-Score analysis and Isolation Forest algorithms to identify problems such as degradation of panels and fault of sensors. These findings emphasize the necessity for a near realtime model and complex forecasting models to mitigate issues associated with renewable energy precursors to increase solar energy productivity and efficiency.

The accurate estimation of the state of charge (SOC) in batteries is a critical component of battery management systems (BMS), especially in electric vehicles (EVs), where it directly impacts the efficiency, longevity, and safety of the system. This paper investigates two widely used SOC estimation techniques: the Coulomb counting method and the Extended Kalman filter (EKF) algorithm. The Coulomb counting (CC) method estimates SOC by integrating the battery current over time, making it simple and computationally efficient. However, it suffers from errors due to inaccuracies in the initial SOC estimate and does not account for battery self-discharge. In contrast, the Kalman filter algorithm is a dynamic estimation technique that uses a probabilistic model to estimate SOC, providing more accurate results even in noisy measurements and initial errors. This study compares both techniques by evaluating their performance in terms of accuracy, adaptability, and computational complexity in various battery usage scenarios. The Coulomb counting method, starting with an initial SOC estimate of 80%, shows a maximum estimation error of 15% over a sixhour charge-discharge cycle. The Kalman filter, initialized with a SOC of$\mathbf{8 0 \%}$, converges to the real SOC value of$\mathbf{5 0 \%}$within$\mathbf{1 0}$minutes, achieving an estimation error of less than 5%. The results show that while Coulomb counting is effective in short-term applications with accurate initial estimates, the Kalman filter excels in long-term SOC estimation, offering superior performance in dynamic and noisy conditions. The paper concludes by discussing the practical applications of both methods and providing recommendations for choosing the appropriate technique based on specific system requirements.

This paper aims at examining how machine-learning can be applied to achieve optimal wind-solar hybrid towers performance in relation to predicting thermal updraft. Wind-solar towers use solar irradiance and wind speed to generate energy but due to the complex nature of interaction between the various environment variables, wind-solar towers are relatively hard to predict the thermal updraft due to the interaction between the solar irradiance, air temperature, and wind velocity. A complete dataset that covers 90 days is used, and various machine-learning models, such as linear regression, support vector machines, and neural networks, are tested to make a prediction of thermal updraft. The findings show that among all the alternative models, linear regression had a much superior predictability and efficiency in computation. The obtained result showed a close-toperfect fit,$\mathrm{R}^{2}$being equal to 0.98 and indicating the extremely high ability of the regression model to make predictions of the updraft velocity under heterogeneous environmental conditions. Sensitivity analysis had demonstrated that solar irradiance and wind speed are the best predictors of thermal updraft thereby offering insights that would be adopted to streamline the hybrid renewable energy system, and energy forecasting with potential to boost power generation especially during days with low solar irradiance.

The paper proposes a combined approach of Solar and wind power fluctuation equalization through Electrochemical Ammonia Synthesis (EAS) and Direct Ammonia Fuel Cells (DAFCs), supplemented by different smoothing filters. When full of renewable energy, the surplus is turned into ammonia and stored and later turned back into electricity when there is a shortage. It uses Moving Average, Moving Median, Moving Regression, Gaussian and Savitzky Golay filters on real wind and solar profiles in MATLAB and uses EAS/DAFC models in Engineering Equation Solver (EES). Findings indicate that the MR filter provides the best compromise between noise attenuation and trend fidelity, and peak capacity demands of ammonia production and fuel cell output are lowered by approximately$\mathbf{1 0 - 2 0 \%}$relative to raw profiles. SG and Gaussian filters have nearly similar advantages, whereas MA and MM are suboptimal because of either lag or inconsistent trend. This is the novelty of the work because a comparative evaluation of advanced smoothing methods in an ammonia-based storage cycle has never been carried out, and results are used to inform actionable recommendations regarding storage cycle sizing and cost minimization. The suggested methodology increases the stability of the grid, reduces the sizing of subsystems, and enables the cost-efficient implementation of ammonia fuel cells to integrate renewable energies.