Accurate thermal characterization of lithium-ion batteries is crucial for safety and performance optimization. This study presents a direct method to measure heat generation rate in cylindrical Li-ion cells using surface-mounted heat flux sensors and isoperibolic calorimetry. A calibration procedure using a custom-machined cylindrical resistor was implemented to determine a calibration factor, referred to as the Measured Heat Flow Ratio (MHFR), which represents the fraction of heat conducted through the lateral surface. The influence of sensor size was examined, showing that small sensors significantly underestimate heat generation due to non-uniform surface dissipation. The relationship between sensor size and measured heat flow is not strictly linear, making extrapolation from small sensors unreliable. Optimal measurement conditions were identified, including clamp fixation, terminal screws tightened to 0.7 N & sdot; m, natural convection and thermal paste application. Cell-level experiments showed that about 71% of the heat flows through the lateral surface, with 5% through each terminal and 19% via connectors. The methodology was validated by comparing corrected heat flow measurements with theoretically estimated irreversible heat during square-wave cycling, showing agreement across different C-rates and temperatures. These results confirm the robustness of the MHFR calibration factor and establish this approach as a scalable, cost-effective alternative to traditional calorimetry, readily applicable to other cell formats and chemistries for improved thermal diagnostics and battery management.

Reliable quantification of heat generation in lithium-ion batteries remains challenging, especially under realistic cycling conditions where calorimeters are impractical and thermal models depend on uncertain boundary conditions. This study presents a time-resolved method to quantify the heat released by a 5 Ah commercial NMC811/G-Si cylindrical cell using a calibrated heat-flux sensor covering the lateral surface. A dedicated calibration campaign using a custom cylindrical resistor enabled determination of the Measured Heat-Flow Ratio (MHFR), allowing conversion of local heat-flux measurements into total heat output. To assess the robustness of this approach, two additional heat-generation estimates were obtained independently: one from an inverse lumped thermal model using an experimentally identified overall heat capacity of 61 JK-1 and an external thermal resistance that decreased from 12.47 K W-1 at 1C to 10.97 K W-1 at 2C under natural convection, and another from an electrochemical model incorporating a pseudo-OCV curve measured at C/50 and an entropy-coefficient profile. All three methods were applied to the same cell across C-rates of 1C, 1.5C, and 2C. Quantitative error metrics validate the excellent agreement between direct heat-flux measurements and the inverse thermal model (max RMSE: 1.7% at 2C). Conversely, the electrochemical model deviated significantly at high C-rates (RMSE: 15.4% at 2C), reflecting the sensitivity of reversible heat and overpotentials to internal gradients and thermodynamic hysteresis. These results establish calibrated heat-flux sensing as an experimentally robust alternative for characterizing battery thermal behavior and provide benchmark data for improving electrochemical-thermal models.

Sustainability in agriculture involves protected cultivation systems to create a controlled environment for crop growth, particularly in the face of climate change. Year-round production in Lebanon requires an effective energy management system to sustain essential operations. This study examines combinations of electric generation methods to meet the analytically modeled heating load to maintain an 18 °C internal temperature, during winter, for 20 standard Quonset greenhouses on a 10,000 m2 area of land. Operating strategies include a single diesel generator, with and without heat recovery, dual diesel generators, photovoltaic panels with different layouts (South and East/West), and battery storage. Each strategy is modeled and examined under various operating conditions in 2023. The multi-objective Pareto optimization, with a weighted average approach, is based on the normalized factors: the Levelized Cost of Energy, Renewable Fraction, and payback period, providing an adequate choice given the available land area. Each strategy offers specific economic, environmental, or spatial benefits. The results indicate that fully removing the diesel generator is currently unfeasible for heating applications. The strategy with the highest rank, with equal weights, is the dual diesel generators, followed by the hybrid with south-facing panels, which is the highest with 60% and more importance to the environment. This study provides an optimization tool for an energy management system for a greenhouse farm according to desired economic, environmental, or spatial parameters.

This paper presents AHMT-2D, a two-dimensional axisymmetric enthalpy-based finite-difference model for paraffin-based latent heat thermal energy storage in a multi-tube cylindrical tank. Direction-dependent logistic hysteresis functions distinguish melting and solidification and capture asymmetric phase-change behavior. The model resolves coupled heat transfer among the heat-transfer fluid, tube wall, and PCM while accounting for geometry-dependent PCM mass scaling and volume-weighted energy evaluation. Results show that the multi-tube configuration enhances thermal response, achieving complete melting in about 10.2 h and solidification in about 10.5 h, with an approximately 7 °C shift between melting and solidification midpoints. Reduced-order correlations are proposed for tank-averaged liquid fraction, enthalpy, effective heat capacity, and melting-solidification time asymmetry. The framework provides a compact basis for system-level modeling of PCM storage in solar-assisted absorption cooling and related renewable thermal applications.

A comparison on the performance and economic analysis of a solar tracking system and a fixed-axis photovoltaic (PV) system is done. The increased need of clean energy in the world today makes solar power a significant alternative source of fossil fuels. Specifically, this paper focuses on the techno-economic feasibility of these two system configurations of a solar power plant in Babylon, Iraq. The nature of previous research is usually constrained by the generic comparisons that fail to capture the circumstances of site and lifecycle cost evaluation. The main contribution of this study is the in depth and location specific model, which is a combination of the performance simulation and the financial analysis. The project-specific data used in the methodology is solar resource and load profiles to derive the main measures and indicators of energy efficiency, annual energy production (AEP), initial capital cost and the levelized cost of energy (LCOE). It is discovered that the tracking-axis system has a better efficiency range of 18% - 20% percent than 15% - 18 % in the case of the fixed-axis system. Even though the initial cost of the tracking system is higher 1200 - 1500 S/kWp compared to 1 000 - 1200 S/kWp of the fixed one), the AEP of the tracking system is much greater (1800 – 2100 kWh/kWp per year) as compared to that of the fixed one (1500 – 1800 kWh/kWp per year). The tracking system therefore shows a possibility of a better LCOE throughout the project period.

Although fossil fuels are a highly concentrated source of energy, their reserve is finite. Being exhaustible, once depleted, they cannot be restored. On the other hand, fossil fuels have many disadvantages on the environment because the exhaust gases from their combustion contribute to polluting the air. Moreover, it increases global warming. Accordingly, green energy should expand and evolve. One of the most distinguished and already-implemented renewable energy sources, worldwide, is solar energy. The integration of photovoltaic (PV) systems, of different scales, into the power system is increasing continuously. The main components of the PV system are the PV panels and the inverter. The generation of electrical power from PV systems is dependent on the availability of the sun with other influential factors like ambient temperature and humidity. However, other technical facts may affect the amount of energy harvested, like the inverter’s rated input voltage interval. The inverter requires a minimum DC voltage to initiate its startup sequence, after which it operates within the MPPT voltage range to extract maximum power and feed it to the grid.Each MPPT operates within a defined voltage window that allows power to flow; it requires a minimum ‘start-up’ input voltage and a maximum ‘shut-down’ input voltage beyond which it will not be able to connect to the grid. During the hours of low solar irradiation, early in the morning or late in the afternoon, the low terminal voltage of the PVs prevents the inverter to actively feed power into the grid.In this project, we are going to increase the harvest of electric power from PV systems by changing the connection of PV panels during low solar irradiation to match their terminal voltage to the required MPPT’s voltage interval. This will be done using controllable switches that change state with the aim of changing the connection of the panels.Our target is the Mega Scale Solar Power Plant, where maximizing power generation and effectively harnessing the total available solar energy is essential. The input of the system will be the predicted temperature and global irradiance. The suggested method will be applied and evaluated on an already existing solar PV plant at Al Dhafra, United Arab Emirates (UAE).

The Lebanese agricultural sector relies primarily on crops grown under protected conditions, such as greenhouses, to mitigate the effects of climate change and ensure sustainability. Protected crops require optimal temperature ranges, necessitating adequately sized winter heating systems. This study generally modelled the heating load and the sizing of an air-source heat pump (HP) in Akkar, Lebanon. The developed dynamic model accounted for heat transfer phenomena, describing the real-life interaction between the inside air and its surroundings with instantaneous analysis for the coefficient of performance (COP) with a mixed-air technology. The study is performed in a Quonset greenhouse, 332 m2 and 3 m high, covered with thermal polyethylene. Statistical models for temperature and solar radiation were developed and validated using measured data from the 2021-2024 winter seasons. The heating load was computed for the coldest statistical day to maintain a temperature of 18 degrees C, and then tested to determine the demand for the coldest measured day in 2024 to maintain 12 degrees C. The leaf temperature model's validity was demonstrated by comparing its temperature profiles with those from a study conducted in West Bengal, India, with a maximum error of 5%, and by an experimental validation conducted in Berkayel, Lebanon, with a maximum deviation of 5.5%. The results indicated that the optimal size for the studied case is 16 kW to maintain a temperature of 18 degrees C with a COP of 4.35. Incorporating probabilistic climate uncertainty over 10 years increases the required HP capacity by 10.6% to 17.7 kW. Sensitivity studies show the impact of core parameters on the required heating power, in decreasing order: internal temperature setpoint (12-18 degrees C), greenhouse elevation (2-6 m), geometric structure (Quonset and tunnel), and plant growth stage.

Greenhouses are considered the best practice for protected cultivation. Crops require a certain amount of heating to thrive and withstand the effects of climate change. This study develops a comprehensive, general numerical model to determine heating loads for three Lebanese climate zones: the Coastal area (Akkar), the Bekaa Valley (Zahle), and Mount Lebanon (Aley), capturing sophisticated, realistic physical phenomena. The uncontrolled inside temperature profile model is iteratively developed to represent the possible diseases crops may face without the recommended control. The low-cost experimental setup is explained to validate the uncontrolled temperature variation inside an eggplant and green pepper greenhouse in the 'Berkayel/Akkar' zone. Results identify the representative days with the highest probability over the 10 years (2014-2024) for each region. The uncontrolled temperature analysis successfully predicted frost damage in the 'Zahle' zone. Results show that the heating load for tomatoes is the highest in 'Zahle', around 95 kW (60 kW), and the lowest in 'Akkar', up to 60 kW (28 kW), to maintain 18 degrees C (12 degrees C) inside the greenhouse. The air-source heat pump is sized accordingly and the annual heating demands are computed for different balance point (10-16 degrees C) and set point (12 and 18 degrees C) temperatures. The developed model for uncontrolled temperature is validated using a cost-effective experimental setup, with error ranges of 1-11%. This developed approach will help companies/farmers in any country estimate the required control system, tailored to their goals, for any crop or climate zone, to ensure food safety and sustainable agriculture.

Greenhouses are considered the best practice for protected cultivation. Crops require a certain amount of heating to thrive and withstand the effects of climate change. This study develops a comprehensive, general numerical model to determine heating loads for three Lebanese climate zones: the Coastal area (Akkar), the Bekaa Valley (Zahle), and Mount Lebanon (Aley), capturing sophisticated, realistic physical phenomena. The uncontrolled inside temperature profile model is iteratively developed to represent the possible diseases crops may face without the recommended control. The low-cost experimental setup is explained to validate the uncontrolled temperature variation inside an eggplant and green pepper greenhouse in the ‘Berkayel/Akkar’ zone. Results identify the representative days with the highest probability over the 10 years (2014–2024) for each region. The uncontrolled temperature analysis successfully predicted frost damage in the ‘Zahle’ zone. Results show that the heating load for tomatoes is the highest in ‘Zahle’, around 95 kW (60 kW), and the lowest in ‘Akkar’, up to 60 kW (28 kW), to maintain 18 °C (12 °C) inside the greenhouse. The air-source heat pump is sized accordingly and the annual heating demands are computed for different balance point (10–16 °C) and set point (12 and 18 °C) temperatures. The developed model for uncontrolled temperature is validated using a cost-effective experimental setup, with error ranges of 1–11%. This developed approach will help companies/farmers in any country estimate the required control system, tailored to their goals, for any crop or climate zone, to ensure food safety and sustainable agriculture.

The energy footprint of controlled environment agriculture is related to the power demands of microclimate control systems, particularly heat pumps, which are often oversized and inefficiently deployed due to a lack of precise diagnostic tools. This paper discusses the critical challenge of reducing electrical and fuel consumption by introducing a simple, low-computational-demand inverse method for optimizing the placement of system components. The methodology evaluates available multi-point experimental data on temperature and relative humidity stratification in a 332 m2, 3m high, thermal-polyethylene-covered Quonset greenhouse in Lebanon. The simple gradient inverse method is employed to compute the inferred heat flux, the moisture flux, and the density gradient, qualitatively describing the air movement. The results show the accumulation of heat and moisture at the top-far end of the studied structure, and the inferred air movement from low (bottom inlet) to high (top outlet) density areas. This convergence locates the dominant source of thermal stratification and buoyant flow, which is the primary driver of microclimate non-uniformity and energy waste. The derived technical decision is to strategically place the heat pump and vent openings to actively break the buoyant flow, rather than simply treating the average air volume. Also, the use of a variable perforation duct system for homogeneity at the ground (heating) and plant (cooling) levels for optimum uniformity. This work provides a practical, data-driven framework for power system engineers to optimize the deployment of thermal management equipment in agricultural and industrial settings.

Sustainable greenhouse production under Mediterranean climatic conditions requires reliable heating systems to maintain crop productivity during winter. This study presents a dynamic energy management framework for a greenhouse farm in Lebanon composed of 20 Quonset-type greenhouses installed on a 10,000 m2 site, heated and maintained at 18 °C using an air-source heat pump. Five operating strategies are investigated under limited photovoltaic (PV) installation area constraints, including a single diesel generator (550 kW) with and without heat recovery, dual diesel generators (550 and 120 kW), and hybrid photovoltaic–battery–diesel systems using either south-facing PV panels (426 panels, 92 lithium-ion batteries) or east–west-oriented PV arrays (720 panels, 155 lithium-ion batteries). Unlike conventional annual-average approaches, the proposed methodology employs an hourly dynamic simulation combined with a Multi-Source Dispatch Heuristic Algorithm (MSDHA) to coordinate diesel generation, photovoltaic production, battery storage, and thermal demand. A multi-objective Pareto optimization based on the importance of cost of energy (COE), renewable fraction, and payback period is applied to rank the investigated strategies. In addition, sensitivity analyses are conducted across multiple climate zones, years, and fluctuating diesel fuel prices to evaluate the robustness of the proposed framework under varying environmental and economic conditions. The results demonstrate that fully eliminating diesel generation remains impractical for greenhouse heating under the investigated conditions. Under balanced criteria, the dual-generator configuration achieved the best overall performance with a COE of 0.3455 $/kWh due to improved part-load efficiency. When environmental performance was prioritized (>60%), the hybrid south-facing PV configuration became the optimal solution with a COE of 0.3621 $/kWh for a fuel price of 0.85$/L. Overall, the developed framework provides a scalable optimization tool for greenhouse microgrid design and advanced agricultural energy management.

Recently, fault detection has played a crucial role in ensuring the safety and reliability of inverter operation. Switch failures are primarily classified into Open-Circuit (OC) and short-circuit faults. While OC failures have limited negative impacts, prolonged system operation under such conditions may lead to further malfunctions. This paper demonstrates the effectiveness of employing Artificial Intelligence (AI) approaches for detecting single OC faults in a Packed E-Cell (PEC) inverter. Two promising strategies are considered: Random Forest Decision Tree (RFDT) and Feed-Forward Neural Network (FFNN). A comprehensive literature review of various fault detection approaches is first conducted. The PEC inverter’s modulation scheme and the significance of OC fault detection are highlighted. Next, the proposed methodology is introduced, followed by an evaluation based on five performance metrics, including an in-depth comparative analysis. This paper focuses on improving the robustness of fault detection strategies in PEC inverters using MATLAB/Simulink software. Simulation results show that the RFDT classifier achieved the highest accuracy of 93%, the lowest log loss value of 0.56, the highest number of correctly predicted estimations among the total samples, and nearly perfect ROC and PR curves, demonstrating exceptionally high discriminative ability across all fault categories.

Electric vehicle (EV) charging infrastructure has led to the advancement of grid-tied photovoltaic (PV) battery energy systems (BES) that support bidirectional energy flow. This research presents a detailed analysis of a PV-battery-based EV charging system incorporating both Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) functionalities using bidirectional converters to enhance energy efficiency. In Scenario-1 (PV to SB & EV mode), under full irradiance (1000 W/m2), the PV system supplies 20.62 kW, efficiently charging both the EV battery (-13 kW) and storage battery (-6.59 kW), while the Perturb and Observe (P&O) MPPT ensures optimal power extraction. In Scenario-2 (V2G mode), when PV power is unavailable, the EV battery discharges 17.85 kW to the grid, with an additional 2.64 kW from the storage battery, supporting grid stability. In Scenario-3 (G2V mode), the grid supplies 19.83 kW to charge the EV battery (-20.6 kW) and storage battery (-0.88 kW), ensuring uninterrupted charging availability. The proposed scheme maintains a stable DC link voltage of 400V across all scenarios, demonstrating its robustness. Simulation outcomes validate the advantage of the proposed scheme in confirming efficient energy flow, optimizing renewable energy utilization, and enabling bidirectional power exchange.

Photovoltaic (PV) systems are key renewable energy sources due to their ease of implementation, scalability, and global solar availability. Enhancing their lifespan and performance is vital for wider adoption. Identifying degradation root causes is essential for improving PV design and maintenance, thus extending lifespan. This paper proposes a hybrid fault diagnosis method combining a bond graph-based PV cell model with empirical degradation models to simulate faults, and a deep learning approach for root-cause detection. The experimentally validated model simulates degradation effects on measurable variables (voltage, current, ambient, and cell temperatures). The resulting dataset trains an Optimized Feed-Forward Neural Network (OFFNN), achieving 75.43% accuracy in multi-class classification, which effectively identifies degradation processes.

The growing integration of photovoltaic (PV) energy systems and electric vehicles (EVs) introduces new challenges in managing energy flow within smart grid environments. The intermittent nature of solar energy and the variable charging demands of EVs complicate reliable and efficient power management. Existing strategies for grid-connected PV–battery systems often fail to effectively handle bidirectional power flow between EVs and the grid, particularly in scenarios requiring seamless transitions between vehicle-to-grid (V2G) and grid-to-vehicle (G2V) operations. This paper presents a novel neural network-based model predictive control (NN-MPC) approach for optimizing energy management in a grid-connected PV–battery–EV system. The proposed method combines neural networks for forecasting PV generation, EV load demand, and grid conditions with a model predictive control framework that optimizes real-time power flow under various constraints. This integration enables intelligent, adaptive, and dynamic decision making across multiple objectives, including maximizing renewable energy usage, minimizing grid dependency, reducing transient responses, and extending battery life. Unlike conventional methods that treat V2G and G2V separately, the NN-MPC framework supports seamless mode switching based on real-time system status and user requirements. Simulation results demonstrate a 12.9% improvement in V2G power delivery, an 8% increase in renewable energy utilization, and a 50% reduction in total harmonic distortion (THD) compared to PI control. The results highlight the practical effectiveness and robustness of NN-MPC, making it an effective solution for future smart grids that require bidirectional energy management between distributed energy resources and electric vehicles.

Modern HVAC decarbonization demands working fluids that can operate efficiently with low-grade thermal energy, reducing reliance on compressor-driven, high-GWP vapor compression systems. This work investigates a single-effect absorption chiller using a hybrid refrigerant-absorbent system, 1-ethyl-3-methylimidazolium ethyl sulfate (LiBr + [EMIM][ESO4]/ethanol), benchmarked against conventional LiBr/H2O absorption and an R410A vapor-compression baseline, each designed for the same cooling capacity. Unlike the traditional LiBr/H2O pair, which typically requires generator temperatures (Tg) of 80 degrees C-90 degrees C, the proposed ternary mixture achieves stable operation at substantially lower driving temperatures (50 degrees C-65 degrees C) while sustaining superior performance. At Tg = 50 degrees C, the system attains a COP about 15% higher than LiBr/H2O. Over a 30-year lifetime, the elimination of compressor electricity demand relative to R410A translates into nearly half the total climate impact, with the Life Cycle Climate Performance (LCCP) cut by 47%. Sensitivity analysis indicates an optimal operating window of Tg 45 degrees C-55 degrees C with weak-solution concentrations of 52.7%-55%. These results highlight the novelty of the LiBr + [EMIM][ESO4]/ethanol pair: enabling absorption cooling to transition from high-grade thermal input to low-grade sources such as solar thermal or industrial waste heat, while achieving higher COPs than conventional refrigerants. Although simulation-based, the findings motivate experimental studies to verify vapor-liquid equilibrium, viscosity, and stability of the mixture, as well as safety protocols addressing ethanol flammability. The techno-economic analysis indicates that competitive payback is achievable only when both abundant low-grade heat is available and electricity tariffs are elevated; outside these conditions, the economic viability remains limited. Collectively, this positions the proposed ternary pair as a promising pathway for low-temperature, low-carbon cooling, contingent on experimental validation.

The power grid's expanding incorporation of photovoltaic (PV) and electric vehicle (EV) systems introduces new challenges in controlling peak demand, maintaining system stability, and balancing energy flows. This study proposes a Neural Network-Based Model Predictive Control (NN-MPC) approach to enable dynamic and intelligent energy management in a grid-connected PV-battery system capable of bidirectional communication with the grid and vehicles. To predict short-term energy consumption, PV irradiance, battery State-of-Charge (SOC), and EV charging behavior, the proposed controller leverages neural networks. Based on these predictions, the system optimally manages power exchanges between the grid, PV array, stationary battery (SB), and EV batteries. Simulation results show smooth transitions between three operating modes: PV-only, Grid-to-Vehicle (G2V), and Vehicle-to-Grid (V2G). The SB and EV batteries receive up to 22 kW efficiently from the PV system when available. When PV is unavailable, the system shifts to G2V mode, drawing approximately 20.56 kW from the grid. In V2G mode, the EV battery discharges power back into the grid. Maximum power extraction from the PV is ensured by the Perturb and Observe (P&O) MPPT algorithm, and the DC link voltage remains stable at approximately 400 V throughout all modes. The NN-MPC technique enhances grid reliability, improves energy usage, reduces operating costs, and flattens the load profile. These findings support the feasibility of real-time implementation of the proposed control system in future smart grid scenarios with high penetration of EVs and renewable energy.

The rise of hybrid electric vehicles (HEVs) marks a shift away from traditional engines driven by environmental and economic concerns. With the rapid growth of HEVs worldwide, their reliability becomes of utmost concern; thus, guaranteeing the proper operation of HEVs is a crucial quest. Condition-based monitoring (CBM), which intends to observe different kinds of parameters in the system to detect defects and reduce any unwanted breakdowns and equipment failure, plays an efficient role in enhancing HEVs’ reliability and ensuring their healthy operation. The permanent magnet machine (PMM) is the most used electric machine in the electric propulsion system of HEVs, as well as the most expensive. Hence, the condition monitoring of this machine is of great importance. The magnet crack is one of the most severe faults that may arise in this machine. Artificial intelligence (AI) is showing high capability in the field of CBM, fault detection, and fault identification and prevention. Hence, the aim of this paper is to present two data-based fault detection approaches, which are the support vector machine (SVM) and the Hidden Markov Model (HMM). Their capability to detect primitive faults like tiny cracks in the machine’s magnet will be shown. Applying and evaluating various CBM methods is essential to identifying the most effective approach to maximizing reliability, minimizing downtime, and optimizing maintenance strategies. A strategy to specify the remaining useful life (RUL) of the defected element is proposed.

Abstract In Lebanon, where economic challenges and Mediterranean climate dominate, protected cultivation methods, such as greenhouses, are widely used for sustainable agriculture. Tomatoes, with an annual production of around 250,000 tons in Lebanon, are the focus of this study. Tomato leaves require a temperature range of (18-25ºC) for optimal growth. The present study explores the potential of air-source heat pumps as a sustainable heating method. It includes a comprehensive literature review and discusses alternative heating methods. A dynamic energy model was developed after using statistical models to plot the temperature and solar radiation profiles for the worst-case scenario for heating. This energy model sizes the heat pump to maintain 18 ºC in a standard 332 m2 Quonset greenhouse with thermal polyethylene covering. The model’s validity was demonstrated by comparing its temperature profiles with a study conducted in West Bengal, India. The results indicate the adequate size for the studied case is 16 kW. Sensitivity analysis showed the required power for different temperature settings (12-18ºC) and the effect of wind speed by analyzing the heat transfer coefficient of the cover, which doubled the power when the heat transfer coefficient increased from 4 to 10 W/m2.K.

This review paper offers a comprehensive examination of the various types of faults that occur in inverters and the methods used for their identification. The introductory segment investigates the internal component failures of voltage-source inverters (VSIs), examining their failure rates and the consequent effects on the overall system performance. Subsequently, this paper classifies and clarifies the potential malfunctions in components and sensors, placing particular emphasis on their frequency of occurrence and the severity of their impact. The examination encompasses issues associated with transistors, including open circuits, short circuits, gate firing anomalies, as well as failures in capacitors, diodes, and sensors. Following this, the paper delivers a comparative assessment of fault diagnosis techniques pertinent to each type of component, appraised against specific criteria. The concluding section encapsulates the findings for each fault category, delineates the fault detection and diagnosis (FDD) methodologies, analyzes the outcomes, and provides recommendations for future scholarly investigation.