Engineering reports : open access最新文献

{"title":"A Robust, Resilience Machine Learning With a Risk Approach for Project Scheduling","authors":"Reza Lotfi,&nbsp;Soheila Sadeghi,&nbsp;Sadia Samar Ali,&nbsp;Fatemeh Ramyar,&nbsp;Ehsan Ghafourian,&nbsp;Ebrahim Farbod","doi":"10.1002/eng2.70161","DOIUrl":"https://doi.org/10.1002/eng2.70161","url":null,"abstract":"<p>This study proposes a novel Robust, Resilient, and Risk-Based approach in Machine Learning (3RML) that emphasizes the application of project scheduling for the first time. A robust stochastic LASSO regression model is proposed to predict project duration. This model seeks to enhance a traditional LASSO regression by minimizing the expected value and the Weighted Value at Risk (WVaR) of the Mean Absolute Deviation (MAD) while penalizing the regression coefficients. The 3R requirements, which prioritize robustness, resilience, and risk aversion, are integrated into the mathematical model to ensure flexibility and disaster consideration. A comparative analysis was carried out between the square root, logarithm, and mixed linear/square root models and the baseline model. The Robust, Resilience MAD with Risk-Averse (RRMADR) and <i>R</i>-squared values were computed. The square root regression model demonstrated a 36% enhancement compared with the primary model. The conservatism coefficient affects risk levels, where a 5% increase results in a 2% decrease in the RRMADR. Varying confidence levels influence the model. The penalty coefficient in the lasso regression affects RRMADR and <i>R</i>-squared. The resiliency coefficient impacts both the RRMADR and <i>R</i>-squared. Probability scenarios influence RRMADR but do not affect <i>R</i>-squared. The type of probability density influences the RRMADR but does not impact <i>R</i>-squared.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Polymer Lightweight Aggregate Concrete With Coal Fly Ash for Biomedical Infrastructure: Mechanical, Physical, and Microstructural Investigation","authors":"Ani Firda,&nbsp;Rosmalinda Permatasari,&nbsp;Hendrik Jimmyanto,&nbsp;Muhammad Imam Ammarullah","doi":"10.1002/eng2.70196","DOIUrl":"https://doi.org/10.1002/eng2.70196","url":null,"abstract":"<p>Aggregates constitute ~60%–80% of concrete volume and play a crucial role in determining its mechanical and durability properties. In the context of sustainable construction, artificial aggregates derived from industrial by-products are gaining prominence as environmentally responsible alternatives to natural aggregates. This study presents the development and performance evaluation of a novel lightweight concrete incorporating artificial polymer lightweight aggregate synthesized from coal fly ash (CFA), epoxy resin, and a hardener in varying CFA-to-resin ratios (70:30, 74:26, and 80:20 by weight). The proposed mix design aims to address the increasing demand for lightweight, durable, and sustainable materials suitable for biomedical infrastructure applications, which require enhanced thermal insulation, fire resistance, and seismic performance. Concrete mixtures were designed to achieve target compressive strengths of 17.5, 20, and 30 MPa, with both lightweight (BR series) and normal weight (BN series) concrete formulations evaluated. Results demonstrated that the incorporation of polymer lightweight aggregates reduced the bulk density of concrete by up to 15.36%, while meeting or exceeding the required compressive strength thresholds for BR_17.5 and BR_20 mixtures. Although the BR_30 mix did not meet the target strength, polymer lightweight aggregate-based concrete exhibited significantly improved flexural strength (up to 60.57% higher than conventional mixes) and enhanced chemical durability when exposed to acidic and saline environments. However, its resistance to elevated temperatures was lower compared to that of conventional concrete. The findings suggest that polymer lightweight aggregate concrete offers a promising sustainable material solution for biomedical infrastructure and other applications demanding lightweight, durable, and thermally efficient construction materials. The utilization of industrial waste in polymer lightweight aggregate production not only contributes to environmental conservation but also advances the development of next-generation building materials aligned with circular economy principles.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70196","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementing Supply Chain Management 4.0: Potential Driving Forces and Strategies From an Empirical Study of Pharmaceutical Industries","authors":"Ismail W. R. Taifa,&nbsp;Johnson Subby Nzowa","doi":"10.1002/eng2.70190","DOIUrl":"https://doi.org/10.1002/eng2.70190","url":null,"abstract":"<p>This study investigates the potential driving forces and strategies for implementing supply chain management 4.0 (SCM 4.0) in pharmaceutical manufacturing industries (PMIs). Pertinent data were collected from 111 related companies using a mixed-methods research approach. The study used IBM SPSS and AMOS version 21 for exploratory and confirmatory factor analysis, respectively. The driving forces include regulatory and compliance, market, technological, and economic drivers, while research, development, and innovation emerged as the first-ranked strategy. With the manufacturing landscape in Tanzania transitioning towards digital transformation, implementing SCM 4.0 is essential. Digital transformation in PMIs can improve supply chain performance by enabling predictive analytics, real-time tracking, and better resource optimisation. Incorporating digital technologies like the Internet of Things, artificial intelligence, blockchain technology, and big data analytics is crucial for PMIs to maintain competitiveness and resilience in a globalized market. The digital transformation can boost efficiency, precision, and regulatory compliance while mitigating SCM risks. The transformation in deploying advanced robotics and automating the production systems within the PMIs can assist in streamlining the manufacturing workflows, diminishing human errors, and ultimately increasing the PMI outputs. Likewise, collaboration between PMIs, academia, research institutions, and government agencies is essential for knowledge sharing and addressing common PMIs' challenges. PMIs should be customer-focused and use SCM 4.0 technologies to improve competitiveness and satisfy changing customer demands. Likewise, to develop new technology and business models, it is essential to support innovation and entrepreneurship through funding programs, incubators, and hubs.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70190","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DBSCAN-Based Electricity Consumption Anomaly Detection Method Integrated With VAE","authors":"Zhang Bohan,&nbsp;Qi Bin,&nbsp;Sun Xiaoming,&nbsp;Li Weihan,&nbsp;Zhang Lingyan,&nbsp;Sun Guoqi,&nbsp;Wei Xiaobin,&nbsp;Wu Qiong","doi":"10.1002/eng2.70183","DOIUrl":"https://doi.org/10.1002/eng2.70183","url":null,"abstract":"<p>With the large-scale deployment of smart grid technologies in China and rapid progress in power system informatization, power utilities have accumulated vast amounts of operational data through automated management systems, which serves as a critical foundation for driving the digital transformation and intelligent modernization of the national grid infrastructure. During the operational process of electromechanical equipment, anomalies may arise due to various potential non-standard electricity consumption behaviors, equipment malfunctions, and other factors. Failing to preprocess the contaminated raw data prior to analysis can significantly compromise the accuracy of data analysis. Anomaly detection technology enables the timely detection and localization of abnormal data, while also revealing electricity consumption trends. This aids staff in proactively responding to special or unexpected situations, thereby maintaining the safe operation of equipment. This paper introduces a DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm integrated with VAE (Variational Autoencoder) (VAE-DBSCAN), utilizing local electricity consumption data provided by Shandong DeYou Electric Company. By incorporating a channel attention model, the method can detect anomalies in the electricity consumption data, and the anomalies can then be cleaned up. The ADF testing method is used to quantify the disparities between VAE-DBSCAN and other algorithms, verifying the superiority of VAE-DBSCAN in anomaly detection.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70183","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hydrothermal Liquefaction of Jatropha curcas (J. curcas) Under Subcritical Water Conditions: Water and Palm Oil Mill Effluent as Solvents","authors":"Rohazriny Rohim,&nbsp;Khairuddin Md Isa,&nbsp;Umi Fazara Md Ali,&nbsp;Mohd. Aizudin Abd. Aziz,&nbsp;Naimah Ibrahim,&nbsp;Muhammad Auni Hairunnaja,&nbsp;Saiful Azhar Saad,&nbsp;Nur Amira Fatihah Bashari","doi":"10.1002/eng2.70145","DOIUrl":"https://doi.org/10.1002/eng2.70145","url":null,"abstract":"<p>In this study, palm oil mill effluent (POME) and water were used as the medium or hydrogen donor solvent in the hydrothermal liquefaction (HTL) of <i>Jatropha curcas</i>. POME, with its high organic compound content, is seen as a promising solvent to be investigated. The POME analysis using GC–MS showed the existence of palmitic and oleic acids. The HTL was performed for both solvents using a batch reactor, and three parameters were varied (biomass-to-solvent ratio, temperature, and reaction time). The results showed that POME could be used as a medium for the HTL of <i>J. curcas</i>. It was found that the HTL of <i>J. curcas</i> with POME produced a higher oil yield (63.4%) than the one using water (43.2%) with a lower biomass-to-solvent ratio (1:2) and lower temperature (300°C) with an optimum reaction time (60 min), and with 30.8% of solid and 5.8% of gas recorded. The results of gas chromatography–mass spectrometry (GC–MS) showed that high ester content and the lowest acid content were obtained at 350°C for both solvents. As for POME, the ester content increased at 350°C while the acid content decreased at the same temperature. High hydrocarbon content was obtained for the experiment at 300°C for both solvents. The highest oil yield under POME conditions recorded a good HHV value of 39.07 MJ/kg with an oxygen content of ~11%, with 81% carbon recovered, indicating high energy recovery. The extra hydrogen generated through the reforming of POME in the liquefaction of <i>J. curcas</i> leads to stabilizing the free radicals and producing high oil yields.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70145","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Abnormal Hosts in Data Streams Using Reversible Sketch","authors":"Aiping Zhou,&nbsp;Jin Qian","doi":"10.1002/eng2.70193","DOIUrl":"https://doi.org/10.1002/eng2.70193","url":null,"abstract":"<p>Significant cardinality change is an important sign of the beginning of network attacks. Hosts associated with significant cardinality changes usually exhibit abnormal behavior. Identifying abnormal hosts is meaningful for many applications such as anomaly detection. High-speed data streams remain a great challenge to accurately estimate cardinality changes and detect abnormal hosts in real-time. Sketches are a type of probability data structure, which are widely used to compress high-rate data streams and estimate their statistics. However, most existing studies cannot simultaneously measure two kinds of cardinality changes in a distributed manner and efficiently reconstruct addresses of abnormal hosts in a centralized manner because of high calculation and memory overhead. In this paper, we propose reversible sketch-based abnormal host identification. It constructs a reversible data structure and estimates cardinality changes using a probabilistic counting approach, so that abnormal sources and destinations are simultaneously identified based on their cardinality changes between consecutive measurement periods. Moreover, addresses of abnormal hosts can be reconstructed by only simple inverse calculation to find out attackers and victims. The experimental results illustrate that the proposed approach obtains superior performance for cardinality change estimation and addresses of abnormal host reconstruction in accuracy and performance compared with the existing approaches.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70193","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Blade Fault Diagnosis Using Hybrid Deep Learning: A Comparative Analysis of Traditional Machine Learning and 1D Convolutional Transformer Architecture","authors":"Syed Asad Imam,&nbsp;Meng Hee Lim,&nbsp;Ahmed Mohammed Abdelrhman,&nbsp;Iftikhar Ahmad,&nbsp;Mohd Salman Leong","doi":"10.1002/eng2.70202","DOIUrl":"https://doi.org/10.1002/eng2.70202","url":null,"abstract":"<p>Artificial intelligence offers a promising solution for the precise identification of faults in rotating machinery. The severe repercussions of turbomachinery blade failures, including fatalities and extensive damage, necessitate robust diagnostic tools. Early fault detection and diagnosis are vital and significant concerns for preventing these incidents and are particularly crucial in gas turbines and compressors to avoid costly downtime and maintain optimal plant performance. The financial consequences of unplanned downtime due to blade failures can be substantial, leading to loss of production, costly repairs, and potential legal liabilities. Effective fault diagnosis (FD) plays a key role in mitigating these financial liabilities by minimizing downtime and facilitating optimized maintenance planning. By investigating blade fault patterns and using appropriate diagnostic techniques, it becomes possible to predict potential failures and schedule maintenance proactively. This approach reduces operational failure and extends the lifespan of the equipment. Diagnosing blade failure is more challenging than bearing and gear faults, which exhibit standard fault characteristics observable in the time and frequency domain. Noise and complex design in multistage rotors can mask blade faults in vibration signals, necessitating automated feature extraction and expert diagnosis. This research investigates blade FD, comparing traditional machine learning approaches with a novel hybrid deep learning fused model based on a one-dimensional (1D) convolutional transformer. Tested on an in-house fabricated multistage rotor, the hybrid model demonstrated exceptional diagnostic accuracy, exceeding 93% for various fault scenarios. This represents a significant enhancement over existing traditional methods, which achieved 49.81%–86.75% accuracy, and also shows appreciable improvement compared with established artificial neural networks, which typically range from 88.43% to 90%. This enhanced performance was achieved with minimal human intervention and without complex signal processing. The Implementation of this approach within complex rotor systems offers a significant improvement in both the efficiency and reliability of blade FD.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Behavior of Fixed Offshore Jackets in Failure Condition, Evaluation in Ultimate State to Improve Structural Safety","authors":"Hamid Reza Karimi,&nbsp;Nima Shahni Karamzadeh,&nbsp;Etemad Odin Rabei Gholami","doi":"10.1002/eng2.70179","DOIUrl":"https://doi.org/10.1002/eng2.70179","url":null,"abstract":"<p>The safety of offshore fixed jacket platforms is a critical concern in offshore engineering, given their vulnerability to extreme environmental conditions and operational stresses. Despite ongoing advancements, the structural integrity of these platforms remains a challenge, particularly under failure conditions induced by storms or other severe environmental loads. This study aims to assess the failure modes and structural resilience of four offshore jacket platforms from the South Pars Gas Field, using pushover analysis to simulate the response of these platforms under both operational and storm conditions. The motivation for this research stems from the increasing frequency of structural failures in offshore platforms, underscoring the need for improved safety standards and proactive maintenance strategies. Our primary objective is to identify key structural vulnerabilities and propose design enhancements to improve safety margins and extend the service life of existing platforms. We examine the behavior of these platforms under various load combinations, focusing on failure mechanisms such as deck toppling, excessive deformation, and buckling of the structural members. Key findings indicate that safety factors for the platforms under typical operational conditions range from 1.85 to 2.5, which is below the desired safety threshold of 3.0 for new platforms. Proposed reinforcement strategies, including increasing member thickness and adjusting bracing configurations, resulted in significant safety improvements, with some platforms demonstrating up to a 45% increase in safety factors and improved ductility.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70179","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The SADRA Algorithm as a Framework for Sustainable Innovation: A Review of Case Studies on 3D-Printed Reinforced Concrete Beam and Fiber-Reinforced Concrete","authors":"Mohammad Hematibahar,&nbsp;Makhmud Kharun,&nbsp;Abhijit Bhowmik,&nbsp;Valentin Romanovski","doi":"10.1002/eng2.70208","DOIUrl":"https://doi.org/10.1002/eng2.70208","url":null,"abstract":"<p>The absence of a structured and evolutionary approach in civil engineering research often leads to fragmented innovations and limited progress. This study introduces the SADRA Algorithm, a philosophical and practical framework inspired by Mulla Sadra's concept of substantial motion, which emphasizes continuous development and refinement in engineering projects. The effectiveness of this algorithm is demonstrated through two comparative case studies: 3D-Printed Reinforced Concrete Beams and Fiber-Reinforced Concrete. In the 3D printing case, an evolutionary sequence of projects shows a transition from ineffective hyperboloid shell structures (which reduced compressive and tensile strength) to optimized truss patterns like the Warren and Howe trusses, which improved flexural strength by over 14%. Further refinements led to innovations in print geometry and placement, such as the honeycomb infill at 10 mm from the beam's bottom, which enhanced flexural strength by 25%. Each project built upon previous insights, illustrating SADRA's principle of iterative progress. In contrast, fiber-reinforced concrete studies (e.g., basalt and steel fiber integration) yielded isolated findings. For instance, while 0.5%–1.2% basalt fiber improved tensile and flexural strength, and steel fibers increased ductility and compressive performance, the lack of methodological continuity hindered broader evolution. The primary conclusion is that the SADRA Algorithm fosters sustainable engineering innovation by enabling project-based evolution, offering a transformative methodology for civil engineering design, materials development, and project management.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70208","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized Control of Hybrid Energy Storage Systems Using Whale Optimization Algorithm for Enhanced Battery Longevity and Stability in Microgrids","authors":"Nouman Alam Siddiqui,&nbsp;Hira Tahir,&nbsp;Muhammad Akram,&nbsp;Habib Ullah Manzoor","doi":"10.1002/eng2.70199","DOIUrl":"https://doi.org/10.1002/eng2.70199","url":null,"abstract":"<p>The target of achieving net-zero emissions by 2050 requires integrating a significant share of renewable energy. However, this integration can cause instability in microgrid operations. Hybrid energy storage systems (HESS), consisting of battery energy storage systems (BESS) and supercapacitors, address these challenges but necessitate complex control strategies. Traditional frequency-based methods (FBM) enhance HESS performance but do not guarantee continuous operation and may lead to BESS degradation. This article proposes an optimized FBM control approach using the whale optimization algorithm (WOA) to improve HESS operation. The method optimizes two key variables: current sharing coefficients and the smoothing constant, enabling continuous HESS functionality. The proposed FBM-WOA reduces high-frequency current stress on BESS, minimizes BESS usage, and ensures supercapacitor state-of-charge levels remain within safe limits. The proposed approach achieves the lowest BESS life loss and voltage fluctuations in both test load and microgrid load cases. It decreases BESS life loss by 11.59% and 0.25% compared to rule-based (FB-RB) and current sharing coefficient (FB-COEFF) methods, respectively, for test load cases. Similarly, it reduces average BESS life loss by 1.45% and 2.35% compared to FB-RB and FB-COEFF methods for real load cases over five different days.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 5","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70199","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144091637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}