Solar water heating systems have been widely used around the world. However, exposure to sunlight can overheat the device, affecting the efficiency and durability of the system. This article proposes an adaptive deck controller that protects the system from overheating without compromising the availability of domestic hot water. Solar water heaters are considered one of the most effective ways to reduce a home’s carbon footprint. They are a renewable energy source that reduces reliance on fossil fuels and saves money. Thus, this paper aims to develop a dynamic cover for solar water heaters that prevent overheating using an artificial neural network to optimize the design of control systems. Based on a self-organizing map network, the controller automatically adjusts the temperature of the solar collector through a fabric screen covering the main subsystems, depending on many parameters such as weather conditions, collector temperature and domestic hot water depending on demand. A suggested technique of four different shade percentages (0%, 20%, 25% or 32%) can avoid overheating and maintain the amount of hot water the home needs. Although renewable energy is free, proper controls are required to ensure maximum efficiency or proper use. In addition, the control of renewable energy leads to longer service life.

This paper aims to investigate the cubical fins and the thermal-hydraulic characteristics in a Printed Circuit Heat Exchanger (PCHE). The working fluid is considered a supercritical LNG. The outcomes show that the thermal-hydraulic performance is enhanced using the cubical fins comparing it to the straight channel. The maximum and minimum difference of N_u/E_u between the cubical fins channel and the straight channel were 71.7 % and 64.8 % respectively. It is noticed that the pressure drop and Heat Transfer coefficient are increasedsimultaneously with increasing the mass flux.Using Ansys Fluent 15.0, numerical optimization isperformed to examine and analyzethe influence of the sparser staggered arrangement. The computed results show that this type of arrangement improves the thermal-hydraulic efficiency of the cubical fins in a PCHE. A comparison between the straight fins arrangement and the staggered fins arrangement is reported.It has been noticed that the velocity of the LNG flow in the cubical fins channel is increased along the length of the PCHE,and decreased byincreasing the vertical separationL_V. The side effect of the vertical separation L_V on the PCHE’s efficiency was more evident than that of the staggered arrangementL_S. Finally, afeasibility study is performed to inspect the power consumption of the new design.

The energy management system (EMS) is becoming a focal point of research in the renewable energy sector, especially when integrating PV solar systems, BESS, and a standby diesel generator of a microgrid (MG). The EMS controls, monitors, and manages the power dispatch of different integrated sources based on the strategy that balances the demand with the supply, with the available energy sources related to solar and BESS. The EMS application strategy directly affects the BESS SOH and, thus, increases its operational remaining useful lifetime (RUL). Therefore, this study develops an intelligent EMS (iEMS) implemented within the MG based on predictive artificial neural network (ANN) control power dispatch strategy. The proposed iEMS has proved to be effective and accurate (MAE = 6.43%) which improved and optimized the BESS SOH through the prosecution of a 6‐h prediction on blackout occurrence and noncritical load shedding. Consequently the iEMS preserved the SOH to 33% (an increase of 45% compared to classical EMS) and decreased the blackout occurrence by 56% (−5203 h) in contrast with the classical EMS where the SOH reached 20% and blackout occurrences totaled 11,899 h. This proves that the model is effective, and the control logic avoids high loads being dispatched from the BESS at critical time intervals where the AI model predicts a blackout occurrence.

For efficient battery management that ensures lifetime and dependability in applications like electric vehicles, an accurate real‐time assessment of the State of Charge (SOC) and State of Health (SOH) of lithium‐ion (Li‐ion) batteries is essential. To overcome the difficulties presented by aging, unmodeled dynamics, and temperature fluctuations, this study attempts to create a reliable estimation method that improves the precision and robustness of SoC and SoH assessments. To maximize transient responsiveness and guarantee estimator convergence to the actual battery state, the suggested system combines a H _∞ /H ₂ controller with pole placement, which is built using Linear Matrix Inequality (LMI) techniques. Furthermore, this controller is complemented by a sliding mode estimator to assess SoH, which is a novel combination in battery state estimating techniques. By optimizing the disturbance matrix structure and taking into account changes in internal resistances, capacitances, and actual capacity, the H _∞ /H ₂ controller is designed to reduce disturbances caused by things like age and temperature fluctuations. To evaluate SoH, the sliding mode estimator makes use of state variables from the H _∞ /H ₂ controller. The approach is validated under real‐world circumstances, including driving schedules like UDDS, US06, and HWFET, using numerical simulations that consider variations in battery internal properties. The accuracy and dependability of SOC and SOH assessments are significantly improved by the combined estimation technique. By lowering estimating errors, the controller improves resilience to disruptions. The resilience of the approach is shown by simulations conducted under a range of driving circumstances, suggesting that battery management systems might use it in practice.

Because of their great energy density and efficiency, lithium-ion batteries (LIBs) are essential to renewable energy systems and electric vehicles. Effective battery management requires precise estimation of the state of health (SoH) and state of charge (SoC). In order to overcome the difficulties caused by parameter fluctuations and real-world disturbances, this work presents a novel μ-analysis-based methodology designed to improve the resilience and accuracy of online SoC and SoH estimations in LIBs. In contrast to conventional techniques, the suggested strategy successfully manages both structured and unstructured uncertainties in battery systems by combining μ-analysis with model-based estimation. The framework creates an estimator that is resistant to parameter drift and outside perturbations by combining model-based estimation approaches with μ-analysis tools. Simulations using UDDS, US06, and HWFET driving cycles are used to verify its performance. When evaluating battery health and condition in dynamic and uncertain operating scenarios, the μ-analysis-based estimator demonstrates superior accuracy compared to conventional H∞-pole placement filter methods. The proposed approach enhances system robustness, achieving an 8 dB improvement in disturbance attenuation, as verified through MATLAB/Simulink. Stability analysis reveals the μ-analysis controller maintains robust performance up to ‖∆‖∞ = 3.5 at 10 Hz, compared to only ‖∆‖∞ = 1.5 for the H∞-pole placement controller—demonstrating significantly greater tolerance to parameter variations and unmodeled dynamics. These capabilities make the μ-analysis approach particularly suitable for electric vehicle applications requiring next-generation battery management systems.