Accurate Lifetime Prediction Research Articles

Feature selection and data‐driven model for predicting the remaining useful life of lithium‐ion batteries

AbstractTo ensure long and reliable operation of lithium‐ion battery storage workstations, accurate, fast, and stable lifetime prediction is crucial. However, due to the complex and interrelated ageing mechanisms of Li‐ion batteries, using physical model‐based methods for accurate description is challenging. Therefore, building data‐driven models based on direct measurement data (voltage, current, capacity, etc.) during battery operation may be a more effective approach. This paper employs a time series analysis of discharge capacity/voltage curves to perform feature predication. The goal is to predict the state of health using a short‐term model and the remaining useful life of batteries using a long‐term iterative model. The validity of this method is verified using the open‐source MIT battery dataset. Comparisons with models reported in the literature demonstrate that this method is generalisable and ensures accuracy across a wider range of predictions.

Long Time Dynamics for Platinum Surface Area Loss in PEM Fuel Cells

Proton exchange membrane (PEM) fuel cells may emerge as a dominant clean energy source for heavy duty transport vehicles. Amongst other considerations, the lifetime performance of the fuel cell cathode catalyst layer is a key component of their future viability and a focus in the development of many proposed membrane electrode assemblies (MEAs). Typical targets of the lifetime of a PEMFC stack are on the order of thousands of hours of operation. However, running degradation tests spanning those long timescales is often impractical; and consequently, extensive work has been done developing and implementing accelerated stress tests (AST's) in order to screen proposed cathode catalyst materials for fuel cells.Much value is obtained by accurate and robust lifetime prediction models which are able bridge the gap between degradation from AST's in differential cells, and degradation of the catalyst layer in a fuel cell stack under realistic operating conditions. In other words, by parameterizing a model on short-time, subscale ASTs and extrapolating to a stack after tens of thousands of hours of operation. Commonly in such models, the electrochemically active surface area (ECSA) of platinum is used as the primary state-of-health parameter and predicted using either data-driven models or physics-based models. On one hand, data-driven models may be well suited to utilize large sets of stack data, but offer weaker extrapolation potential, conversely fitting physics-based models on global measurements of stack data is rarely a well-conditioned inverse problem.In this talk we present a scaling law on the long-time behavior of the ECSA obtained from analysis of governing physics equations from the literature. It is assumed that the primary loss mechanism is the coarsening of platinum particles, and the chief technique used is the method of averaging, which allows for the separation of two timescales: the short timescale of platinum oxide formation and dissolution, and the longer timescale over which the particle size distribution changes and a performance loss can be detected. The physics-derived asymptote for the ECSA evolution is then applied to several experiments and found to be in good agreement, allowing for a simple, streamlined lifetime prediction method for PEM fuel cells.

Attention Mechanism-Based Neural Network for Prediction of Battery Cycle Life in the Presence of Missing Data

As batteries become widespread applications across various domains, the prediction of battery cycle life has attracted increasing attention. However, the intricate internal mechanisms of batteries pose challenges to achieving accurate battery lifetime prediction, and the inherent patterns within temporal data from battery experiments are often elusive. Meanwhile, the commonality of missing data in real-world battery usage further complicates accurate lifetime prediction. To address these issues, this article develops a self-attention-based neural network (NN) to precisely forecast battery cycle life, leveraging an attention mechanism that proficiently manages time-series data without the need for recurrent frameworks and adeptly handles the data-missing scenarios. Furthermore, a two-stage training approach is adopted, where certain network hyperparameters are fine-tuned in a sequential manner to enhance training efficacy. The results show that the proposed self-attention-based NN approach not only achieves superior predictive precision compared with the benchmarks including Elastic Net and CNN-LSTM but also maintains resilience against missing-data scenarios, ensuring reliable battery lifetime predictions. This work highlights the superior performance of utilizing attention mechanism for battery cycle life prognostics.

A Physics-Based Tweedie Exponential Dispersion Process Model for Metal Fatigue Crack Propagation and Prognostics

Most structural faults in metal parts can be attributed to fatigue crack propagation. The analysis and prognostics of fatigue crack propagation play essential roles in the health management of mechanical systems. Due to the impacts of different uncertainty factors, the crack propagation process exhibits significant randomness, which causes difficulties in fatigue life prediction. To improve prognostic accuracy, a physics-based Tweedie exponential dispersion process (TEDP) model is proposed via integrating Paris Law and the stochastic process. This TEDP model can capture both the crack growth mechanism and uncertainty. Compared with other existing models, the TEDP taking Wiener process, Gamma process, and inverse process as special cases is more general and flexible in modeling complex degradation paths. The probability density function of the model is derived based on saddle-joint approximation. The unknown parameters are calculated via maximum likelihood estimation. Then, the analytic expressions of the distributions of lifetime and product reliability are presented. Significant findings include that the proposed TEDP model substantially enhances predictive accuracy in lifetime estimations of mechanical systems under varying operational conditions, as demonstrated in a practical case study on fatigue crack data. This model not only provides highly accurate lifetime predictions, but also offers deep insights into the reliability assessments of mechanically stressed components.

Lifespan prediction procedure of volume nanogratings imprinted by femtosecond laser in optical glasses

Volume nanogratings imprinted by infrared femtosecond laser in oxide glasses exhibit a characteristic birefringent signature, which translates into measurable retardance. Upon thermal annealing, such signature is progressively erased, typical of nanograting erasure. In this work, we propose a procedure to predict the lifespan of nanogratings by two main approaches: 1/ numerical modeling of optical retardance ageing using the Rayleigh-Plesset equation, and 2/exploiting VAREPA (VAriable REaction PAthways) framework fed by simulated ageing data. By considering experimental time – temperature annealing conditions, the modeled retardance is gathered as a function of demarcation energy to build a so-called Master Curve and then compared to accelerated ageing experiments. The erasure constant rate k0 can be determined for 8 commercial optical glasses. Based on a distributed Rayleigh-Plesset model, k0 and activation energy distribution are linked to glass viscosity and its temperature dependency. Finally, we discussed the restrictions on VAREPA application for an accurate lifetime prediction. This work provides guidelines for the future development of nanogratings based devices and applications, including optical data storage, birefringent devices, and optical sensors, through a judicious choice of glass composition and associated properties.

Improved lifetime estimation of shot-peened shaft bores using a numerical approach

Shot peening is commonly used to improve the fatigue strength of mechanical components. The peening process involves the use of high-energy mechanical impacts to create compressive residual stresses and a material hardening on the surface of the component, which can significantly increase its resistance to fatigue. Accurate lifetime prediction is important for optimizing the design of shot-peened components and ensuring their reliability and safety. Due to nonlinear material behavior and the simulation of contact, estimating the lifetime of shot-peened shaft bores under cyclic loading conditions remains a challenge.In this study, the lifetime estimation of shot-peened shaft bores using a combination of experimental testing and finite element analysis is investigated. A series of experiments was conducted on shot-peened shaft bores made of EN-GJS-700 and 34CrNiMo6, using different peening parameters such as intensity and coverage. The specimens with shot-peened shaft bores were subjected to cyclic loading in a fatigue testing machine and the lifetime was experimentally identified.The results were used to develop a methodology based on finite element analysis, which considers the effect of the shot peening parameters on the residual stress distribution, hardening and the resulting fatigue life. The results of the proposed methodology were validated against the experimental data and showed good agreement with the experimental results.The derived methodology can be used to estimate the lifetime of shot-peened shaft bores under different loading and peening conditions and provides a useful tool for optimizing the design of shot-peened components in fatigue engineering applications.

Fatigue strength assessment of aluminium welded joints under variable amplitude loading using the Peak Stress Method

One of the simplest and most efficient ways to design lightweight structural components is the combination of welding and aluminum alloys. However, welded joints are extremely sensitive to fatigue failure and making accurate lifetime predictions is still challenging when Variable Amplitude (VA) loading conditions are involved. Among all design criteria available in the literature, the present investigation focuses on the Peak Stress Method (PSM), an engineering finite element (FE)-based approach to rapidly assess the fatigue strength of welded joints. In more detail, the PSM suggests modelling both weld toe and weld root as sharp V-notches having null tip radius and correlates their fatigue strength using the intensity of the local linear elastic asymptotic stress distributions described by the Notch Stress Intensity Factors (NSIFs). The theoretical formulation of the PSM for the fatigue strength assessment of welded joints subjected to VA loadings has been recently proposed by combining its Constant Amplitude (CA) formulation with the Palmgren-Miner's cumulative linear damage rule. Such VA formulation has been successfully validated against a large bulk of experimental fatigue results generated by testing welded joints made of structural steels under uniaxial as well as multiaxial loadings. In the present investigation, the VA formulation of the PSM has been further validated against experimental data relevant to welded joints made of aluminium alloy under VA loadings.

Microtexture effects on fatigue and dwell-fatigue lifetimes of Ti-6Al-4V

The influence of microtexture on the fatigue and dwell-fatigue response of Ti-6Al-4V was investigated considering two regions of a part showing different degrees of microtexture. A high degree of microtexture resulted in a significant reduction in fatigue lifetime. No difference in crack initiation mechanism was observed, as main cracks formed along basal twist grain boundaries (BTGB) connected to the specimen surface. However, a key contribution of faster small crack growth in the strongly microtextured region is suggested. A 120 s dwell time resulted in a dwell-fatigue lifetime debit exceeding a factor of 10 in this region while it remained below a factor of 3 in the weakly microtextured region. In the dwell-sensitive region, internal nucleation of the main crack occurred at a BTGB and long range facetted growth was enabled by the adjacent microtextured region with a dominant [0001] orientation. In contrast, surface crack nucleation typical of fatigue failures dominated in the dwell-insensitive region. The limited growth of internal cracks suggests that the lack of path for rapid crack growth has a pivotal effect for the low dwell sensitivity. The competition between fatigue and dwell-fatigue damage highlights the importance of considering both in modeling approaches to obtain accurate lifetime predictions.

Degradation and damage analysis of composite pressure vessels via experimental modal analysis

For mobile gas storage systems, the application of type IV pressure vessels is state of the art. Type IV tanks consist of an inner polymer liner fully wrapped with fibre-reinforced plastic (FRP). Because of the complex fabric of the FRP as well as a difficulty estimable interaction behaviour between the single components under load, there are still no satisfying non-destructive testing methods to assess the current state of failure nor to estimate the level of degradation accurately and economically. At BAM division 3.5, analysing the ageing process of mobile composite pressure vessels is a major task to ensure safe usage over the whole lifetime. In this context, key aspects of our ongoing research activities are the invention of new test procedures and the development of accurate lifetime prediction models. In order to determine the level of degradation or damage, one meaningful non-destructive approach is to analyse the structural dynamic behaviour via an experimental modal analysis (EMA). Over the last few years, different types and sizes of composite pressure vessels have been tested in several research projects. The presented paper gives an insight into how to extract and interpret modal parameters and how to fit them to the results of residual strength tests.

Data-driven rapid lifetime prediction method for lithium-ion batteries under diverse fast charging protocols

Accurate lithium-ion battery lifetime prediction is essential for equipment maintenance and safety assurance in practical applications. The influence of different charge protocols on the lifetime varies greatly, which raises tremendous challenges for rapid lifetime prediction. In this paper, a comprehensive data-driven rapid lifetime prediction method for batteries under diverse fast charging protocols is proposed. It straightforwardly establishes the relationship between the charging conditions and the impacts on battery lifetime. A deep neural network model with the Bayesian optimization algorithm for hyperparameter search (BOA-DNN) and three traditional shallow machine learning algorithms consisting of support vector machine (SVM), Gaussian process regression (GPR), and extreme gradient boosting (XGBoost) are employed. The verification is performed on an authoritative and popular dataset with 69 types of distinct fast charging conditions. The eventual results indicate that the proposed approach has attained reliable performance of battery lifetime degradation trajectory and battery end-of-life (EOL) prediction, in which the BOA-DNN model outperforms SVM, GPR, and XGBoost. Especially in battery EOL prediction, the prediction mean absolute percentage error of the BOA-DNN model is below 3.58%.

Early prediction of battery lifetime based on graphical features and convolutional neural networks

Accurate lifetime prediction of lithium-ion batteries in the early cycles is critical for timely failure warning and effective quality grading. Convolutional neural network (CNN), with excellent performance in feature extraction, has gained increasingly attentions in battery prognostics. However, since degradation test normally takes years to complete, employing end-to-end CNNs directly for battery lifetime prediction is impractical due to the limited number of available training samples and the scarcity of features in the early cycles. Instead of directly feeding the raw data, in this work, we propose to use graphical features for early lifetime prediction. Three feature curves, including capacity-voltage curve, incremental capacity curve, and capacity difference curve are used to construct graphical features. Specifically, the incremental capacity curve and capacity difference curve are derived from capacity-voltage curve, aiming to extract more information from both intra-cycle and inter-cycle perspectives. The evolution patterns of these feature curves over the initial 100 cycles show evident correlations with battery lifetime, and are termed as the graphical features. The three graphical features, after some proper transformation, are stacked into a three-channel image before feeding to the CNN model. Five classical CNNs, with different structures and key parameters, are investigated for battery lifetime prediction. Comparative experiments are conducted to study the influence of different feature combinations, voltage segments, and discharge cycles on the prediction performance. Experimental results demonstrate that simple CNNs with only a few convolutional layers can achieve satisfying prediction results. Additionally, networks with rectified linear unit are shown to outperform those with other activation functions.

Lifetime prediction and reliability analysis for aluminum electrolytic capacitors in EV charging module based on mission profiles

The charging modules of Electric vehicles (EVs) always run in a complex and variable state. As the weakness in the reliable operation of charging modules, the accurate lifetime prediction of aluminum electrolytic capacitors (Al-caps) is important for the later maintenance and reliability design. The hotspot temperature calculation method and lifetime model limit the accuracy of aluminum electrolytic capacitors lifetime prediction methods, which cannot meet the increasing requirements for reliability. In order to solve the problems above, this paper has proposed a hotspot temperature calculation method based on the ripple current with frequency characteristics and the cooling conditions on the heat generation and thermal conductivity of the capacitors. Furthermore, the lifetime model under reference voltage has been constructed with 3D surface fitting toolbox, which describes the trends of capacitor lifetime with ambient temperature and hotspot temperature under the constant voltage condition. Considering the variation of voltage, the multiple lifetime model of capacitor is established with a voltage correction coefficient. With the proposed method, it can be realized about the real-time lifetime prediction of capacitors under multiple operating profiles such as ripple current, thermal dissipation conditions, ambient temperature and operating voltage. Finally, the effectiveness of the proposed method is verified with the annual profiles of a 30 kW EV charging module.

Long-Term Lifetime Prediction of Power MOSFET Devices Based on LSTM and GRU Algorithms

Predicting the long-term lifetime of power MOSFET devices plays a central role in the prevention of unprecedented failures for power MOSFETs used in safety-critical applications. The various traditional model-based approaches and statistical and filtering algorithms for prognostics have limitations in terms of handling the dynamic nature of failure precursor degradation data for these devices. In this paper, a prognostic model based on LSTM and GRU is developed that aims at estimating the long-term lifetime of discrete power MOSFETs using dominant failure precursor degradation data. An accelerated power cycling test has been designed and executed to collect failure precursor data. For this purpose, commercially available power MOSFETs passed through power cycling tests at different temperature swing conditions and potential failure precursor data were collected using an automated curve tracer after certain intervals. The on-state resistance degradation data identified as one of the dominant failure precursors and potential aging precursors has been analyzed using RNN, LSTM, and GRU-based algorithms. The LSTM and GRU models have been found to be superior compared to RNN, with MAPE of 0.9%, 0.78%, and 1.72% for MOSFET 1; 0.90%, 0.66%, and 0.6% for MOSFET 5; and 1.05%, 0.9%, and 0.78%, for MOSFET 9, respectively, predicted at 40,000 cycles. In addition, the robustness of these methods is examined using training data at 24,000 and 54,000 cycles of starting points and is able to predict the long-term lifetime accurately, as evaluated by MAPE, MSE, and RMSE metrics. In general, the prediction results showed that the prognostics algorithms developed were trained to provide effective, accurate, and useful lifetime predictions and were found to address the reliability concerns of power MOSFET devices for practical applications.

Probabilistic machine learning aided transformer lifetime prediction framework for wind energy systems

Accurate lifetime prediction of transformers operated in power grids with renewable energy systems is a challenging task because it requires a large amount of data that is not usually available. In the case of wind energy, this complexity is intensified with the stochastic ageing process influenced by the intermittency of the wind and weather conditions. Existing models make use of detailed power topologies to evaluate transformer stress profiles and associated degradation. However, this modelling approach requires high computational resources and long simulation times. In this context, this paper presents a lifetime prediction model for transformers designed through probabilistic machine learning, thermal modelling and ageing analysis. The proposed model is compared with synthetic wind-to-power detailed simulations of a wind farm and validated with real data. The lifetime prediction is evaluated with different mission profile estimates and results show that the accuracy of the probabilistic machine learning model is very high, with an error of 0.47% for the median value and 80% prediction interval errors within 6%–7% with respect to observations. Moreover, there is a substantial reduction in the simulation time and memory requirements when compared to the synthetic model. A detailed sensitivity analysis demonstrates the influence on transformer ageing of different overloading strategies, thermal constants and the geographic location of the wind farm.

Battery health prognostic using efficient and robust aging trajectory matching with ensemble deep transfer learning

Accurate aging trajectory and lifetime prediction are key objectives for battery prognostic and health management. However, effective health prognostic is difficult to realize due to the different aging characteristics at different aging stages and incomplete aging data for practical applications. To embrace these challenges, we propose an intelligent battery health prognostic method using efficient and robust aging trajectory matching with ensemble deep transfer learning (EDTL). Firstly, dispensing with expertise in battery degradation, spectral clustering is offline performed for unsupervised degradation pattern recognition. Secondly, to ensure the accuracy of aging trajectory matching, lifetime indicator-based candidate cell selection fusing with similarity assessment using dynamic time warping provides multi-dimensional information in finding the optimally matched battery aging trajectory from the historical data. Finally, to robustly and accurately capture the long-term dependencies of battery aging, the bi-directional long short-term memory network with EDTL is employed. Experimental results demonstrate that the proposed method could achieve accurate and reliable health prognostic at different battery aging stages or in the presence of incomplete data with a maximum lifetime prediction error of 6.35%. Compared with traditional methods, our method could provide a more accurate battery health prognostic, which is of great significance for battery predictive maintenance.

A modified energy‐based fatigue parameter for short fiber reinforced polymers: Performance analysis with varying thicknesses, load ratios, and fiber orientations

AbstractThe durability of short fiber reinforced polymers (SFRPs) is affected by several variables. Accurate unifying fatigue parameters are thus essential for an efficient characterization campaign. This research investigated the fatigue behavior of a Polyphtalamide PA6T/6I reinforced with 50% of glass fibers. Two thicknesses (1.6 and 3 mm), two orientations from the injection molding direction (0° and 90°), and three load rations (−0.5, 0.1, 0.5) were investigated. A new fatigue parameter, called alternating energy density (AED), was presented; its ability to correlate the fatigue tests results was compared to other known fatigue parameters—cyclic mean strain rate and cyclic creep energy density. All the fatigue parameters were found to be independent of specimen thickness. Furthermore, the prediction of the fatigue lifetime based on AED showed greater accuracy with respect to the other known investigated methods. AED could thus be used for accurate and efficient lifetime prediction of SFRPs.

A Computationally Efficient IGBT Lifetime Prediction Method Based on Successive Initiation Technique by Iteratively Using Clech Algorithm

Thermal-mechanical coupled models built by finite element analysis (FEA) software are attractive due to its excellent capability to calculate thermal/mechanical behaviors of the material interfaces inside the power module. However, these FEA-based approaches will consume enormous computational resources, resulting in unacceptably long simulation times. Herein, a computationally efficient and accurate IGBT lifetime prediction method enabling explicit emulation of solder layer degradation is proposed with the assistance of Clech algorithm. The variation of assembly stiffness <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> and imposed strain <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</i> in Clech algorithm with the geometrical variation during the solder layer degradation is particularly considered. A look-up table of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</i> changing with crack propagation is established to calculate viscoplastic strain energy density Δ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</i> (VSED) of the critical elements across the crack path. This improved process is applied for lifetime consumption calculation of each successive run. It remarkably reduces the simulation costs compared to that based on FEA. Finally, two sets of experimental results verify high prediction accuracy and low time-consuming cost of the proposed method.

A Universal Scaling Formulation of the Generalized Peck’s Equation for Corrosion Limited Lifetime in Self-Heated ICs

Since the early 1980s, the highly accelerated stress test in conjunction with conventional Temperature-Humidity-Bias testing has been the industry-standard method for interpreting relative humidity (RH) and temperature (T) dependent failure modes in non-hermetically packaged ICs, solar modules, and other electronic components. In recent years, however, testing requirements based on JEDEC standards and the corresponding lifetime extrapolation to end-use conditions using Peck’s equation have been criticized as overly conservative for modern self-heated logic/power ICs. In practice, the self-heated ICs correlate RH, and T in a way that is not explicitly accounted for by Peck’s equation. In this study, we investigate the IC lifetime due to electrochemical failures – Cu-Al bond wire corrosion – by considering the effects of ambient RH, T, duty cycle, and self-heating temperature rise using an integrated self-consistent finite element-based Multiphysics model and propose a generalized Peck’s equation for self-heated ICs. Our study reveals how the asymmetry between On and Off-state heating leads to dramatic suppression of the effective RH experienced by an integrated circuit and, most importantly, identifies that the corrosion of Cu-Al bond wire occurs in spikes during the on-off transition when both RH and T are high. The generalized Peck’s equation also predicts an elegant scaling relationship among the fundamental variables associated with a self-heated IC lifetime. The prediction is validated by experimental lifetime data from self-heated IC published in the literature. The experimental validation of the physics-informed generalized Peck’s equation suggests the possibility of more accurate (and optimistic) lifetime predictions of modern ICs and solar modules.

Semi-Supervised Self-Learning-Based Lifetime Prediction for Batteries

Accurate and reliable degradation and lifetime prediction for lithium-ion batteries is the main challenge for smart prognostic and health management. This paper proposes a novel semi-supervised self-learning method for battery lifetime prediction. Firstly, three health indicators are extracted from the partial capacity-voltage curve. Secondly, the capacity estimation model and lifetime prediction model are built using data from three randomly selected batteries in the source domain. Then, the health indicators are used to reconstruct the historical capacities to provide pseudo values for self-training of the lifetime model. Finally, the self-trained lifetime model is used to predict future degradation. The uncertainty expression is also included to provide the probabilistic prediction of future capacities. Different application scenarios are considered in the verification. The mean lifetime prediction error is less than 23 cycles with only three known checkpoints for batteries aging under different profiles. Predictions for different battery types show that the errors are less than 50 cycles with relative errors less than 4.1% for long lifespan batteries, and less than 20 cycles with relative errors less than 5.21% for short lifespan batteries. This paper guides proper solutions for lifetime prediction when the labeled capacities in the real world are limited.

An Overview of Multilevel Inverters Lifetime Assessment for Grid-Connected Solar Photovoltaic Applications

Nowadays, due to advancements in power electronic devices as well as the rise in consumer awareness of the need to protect the environment on a global scale, many people are turning to the use of solar photovoltaic (PV) technology in the distributed power generation side. In the field of power electronics, manufacturers need to develop products that have high lifespans. Power electronic device reliability is important for the maintenance of the device and may be scheduled under that information. Rather than preventing failures, reliability can be improved by predicting them. Even though some research has been conducted over the past few years to investigate the reliability of power electronic devices, the reliability is many common circuits has not been investigated and this leads to a big challenge for researchers. In this review paper, an overview of the grid-connected multilevel inverters for PV systems with motivational factors, features, assessment parameters, topologies, modulation schemes of the multilevel inverter, and the selection process for specific applications are presented. In this paper, the findings of a comprehensive reliability analysis of fundamental multilevel inverters are studied. To evaluate the reliability of three basic multilevel inverters, a calculation is made using each component’s mean time before its failure. Two techniques of computation approximate and exact were used to arrive at the final result. To calculate power losses in temperature-sensitive components such as diodes and switches, MATLAB Simulink is employed. In addition, the concept of oversizing photovoltaic (PV) arrays is presented in this study. This concept proposes that energy output may be increased by increasing the size of the PV array under conditions of poor solar irradiation. Finally, the mission-profile-based and Monte Carlo simulation-based methods process flows are discussed for the accurate lifetime prediction and reliability assessments of PV inverters in a real-time scenario, followed by a conclusion with future work.