Pub Date : 2025-10-15DOI: 10.1109/JESTIE.2025.3600856
{"title":"IEEE Industrial Electronics Society Information","authors":"","doi":"10.1109/JESTIE.2025.3600856","DOIUrl":"https://doi.org/10.1109/JESTIE.2025.3600856","url":null,"abstract":"","PeriodicalId":100620,"journal":{"name":"IEEE Journal of Emerging and Selected Topics in Industrial Electronics","volume":"6 4","pages":"C4-C4"},"PeriodicalIF":4.0,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11204803","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290210","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}
Pub Date : 2025-10-15DOI: 10.1109/JESTIE.2025.3600854
{"title":"Officers and Vice Presidents of Co-Sponsoring Societies Information","authors":"","doi":"10.1109/JESTIE.2025.3600854","DOIUrl":"https://doi.org/10.1109/JESTIE.2025.3600854","url":null,"abstract":"","PeriodicalId":100620,"journal":{"name":"IEEE Journal of Emerging and Selected Topics in Industrial Electronics","volume":"6 4","pages":"C3-C3"},"PeriodicalIF":4.0,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11204804","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290263","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}
The operational safety of lithium-ion batteries is heavily dependent on accurate battery state of health (SOH) assessments. This article introduces a novel hybrid algorithm: A multifaceted temporal convolutional network with dynamic weight adaptation (MFDWA) combined with a gated recurrent unit (GRU) to estimate the SOH of lithium-ion batteries across various experimental setups. The MFDWA architecture includes multiple temporal convolutional network (TCN) branches, each containing a dilated TCN block, attention blocks, and dynamic weight adaptation components. This multibranch structure captures multiscale patterns through different dilation rates, while attention blocks select essential input features. The dynamic weight adaptation mechanism further enhances performance by addressing evolving battery dynamics. The GRU component tracks temporal information sequences by retaining relevant past states. The proposed hybrid algorithm performs better than the existing state-of-the-art methods, achieving lower mean absolute error and root mean square error values across multiple datasets while maintaining parameter compatibility. These results indicate the model’s robustness across different initial conditions, battery combinations, and varying prediction horizons. In addition, the integration of explainable artificial intelligence through the SHapley Additive exPlanations technique in the proposed hybrid model prioritizes input features, improves model performance, and enhances interpretability, thus fostering user trust.
{"title":"Explainable MFDWA-GRU Model for Battery State of Health Estimation","authors":"Anantha Padmanabhan;Shubham Vijay Mate;Rajeev Kumar Singh;Sanjay Kumar Singh","doi":"10.1109/JESTIE.2025.3618621","DOIUrl":"https://doi.org/10.1109/JESTIE.2025.3618621","url":null,"abstract":"The operational safety of lithium-ion batteries is heavily dependent on accurate battery state of health (SOH) assessments. This article introduces a novel hybrid algorithm: A multifaceted temporal convolutional network with dynamic weight adaptation (MFDWA) combined with a gated recurrent unit (GRU) to estimate the SOH of lithium-ion batteries across various experimental setups. The MFDWA architecture includes multiple temporal convolutional network (TCN) branches, each containing a dilated TCN block, attention blocks, and dynamic weight adaptation components. This multibranch structure captures multiscale patterns through different dilation rates, while attention blocks select essential input features. The dynamic weight adaptation mechanism further enhances performance by addressing evolving battery dynamics. The GRU component tracks temporal information sequences by retaining relevant past states. The proposed hybrid algorithm performs better than the existing state-of-the-art methods, achieving lower mean absolute error and root mean square error values across multiple datasets while maintaining parameter compatibility. These results indicate the model’s robustness across different initial conditions, battery combinations, and varying prediction horizons. In addition, the integration of explainable artificial intelligence through the SHapley Additive exPlanations technique in the proposed hybrid model prioritizes input features, improves model performance, and enhances interpretability, thus fostering user trust.","PeriodicalId":100620,"journal":{"name":"IEEE Journal of Emerging and Selected Topics in Industrial Electronics","volume":"7 1","pages":"300-310"},"PeriodicalIF":4.0,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-30DOI: 10.1109/JESTIE.2025.3615965
Dong Zhao;Ahmad W. Al-Dabbagh;Changsheng Hua;Yang Shi
{"title":"Guest Editorial: Enhancing Safety and Security in Industrial Cyber-Physical Systems Through Machine Learning","authors":"Dong Zhao;Ahmad W. Al-Dabbagh;Changsheng Hua;Yang Shi","doi":"10.1109/JESTIE.2025.3615965","DOIUrl":"https://doi.org/10.1109/JESTIE.2025.3615965","url":null,"abstract":"","PeriodicalId":100620,"journal":{"name":"IEEE Journal of Emerging and Selected Topics in Industrial Electronics","volume":"6 4","pages":"1795-1798"},"PeriodicalIF":4.0,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11184759","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290244","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}
Pub Date : 2025-09-24DOI: 10.1109/JESTIE.2025.3614002
Reyhaneh Eskandari;Hossein Khoun Jahan;Prasanth Venugopal;Alan J. Watson;Patrick Wheeler;Thiago Batista Soeiro
This letter introduces a three-phase multilevel converter for the integration of multiple battery submodules. The circuit comprises a synergetic modulated quasi-single stage design that includes a three-phase three-level voltage source converter operating at low switching frequency and two modular series-connected half-bridge converters operating with high switching frequency. Therein, all dc-link voltage rated devices switch at low frequency and zero voltage while sinusoidal currents are ensured without a complicated control. Furthermore, it provides a multilevel conversion and consequently smaller voltage transients enhancing power quality. In addition, the modular configuration enables flexible management of the series-connected battery submodules, eliminating the need for an extra balancer between the battery submodules. This letter provides an explanation of circuit operation and the synergistic modulation technique. Simulations and experimental results are presented to validate the feasibility of the proposed circuit.
{"title":"Three-Phase Multilevel DC/AC Converter and Synergetic Modulation for Split Batteries","authors":"Reyhaneh Eskandari;Hossein Khoun Jahan;Prasanth Venugopal;Alan J. Watson;Patrick Wheeler;Thiago Batista Soeiro","doi":"10.1109/JESTIE.2025.3614002","DOIUrl":"https://doi.org/10.1109/JESTIE.2025.3614002","url":null,"abstract":"This letter introduces a three-phase multilevel converter for the integration of multiple battery submodules. The circuit comprises a synergetic modulated quasi-single stage design that includes a three-phase three-level voltage source converter operating at low switching frequency and two modular series-connected half-bridge converters operating with high switching frequency. Therein, all dc-link voltage rated devices switch at low frequency and zero voltage while sinusoidal currents are ensured without a complicated control. Furthermore, it provides a multilevel conversion and consequently smaller voltage transients enhancing power quality. In addition, the modular configuration enables flexible management of the series-connected battery submodules, eliminating the need for an extra balancer between the battery submodules. This letter provides an explanation of circuit operation and the synergistic modulation technique. Simulations and experimental results are presented to validate the feasibility of the proposed circuit.","PeriodicalId":100620,"journal":{"name":"IEEE Journal of Emerging and Selected Topics in Industrial Electronics","volume":"7 1","pages":"381-385"},"PeriodicalIF":4.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-22DOI: 10.1109/JESTIE.2025.3613267
Prarthana Pillai;Smeet Desai;Sooraj Sunil;Krishna R. Pattipati;Balakumar Balasingam
This article focuses on standardized approaches for estimating battery open-circuit voltage (OCV) and internal resistance, which rely on low-fidelity models and simple estimation techniques, yet offer repeatable and widely used results in practical applications. In particular, we examine the common method of computing internal resistance as the ratio of the voltage response to an applied current excitation pulse. When applied to batteries, this method implicitly assumes that the OCV remains constant during the pulse, which can introduce significant estimation errors when this assumption is violated. To address this limitation, we propose a novel observation model that explicitly accounts for OCV variation during the excitation window. The model introduces a single parameter representing the OCV–SOC gradient, which is estimated using a linear least-squares approach from the current excitation and voltage response data. This leads to a closed-form solution that requires no prior knowledge of battery-specific parameters such as capacity or equivalent circuit elements. The proposed approach retains the simplicity and short excitation duration of standardized methods, yet yields significantly improved accuracy in estimating both OCV and resistance. Simulation and experimental results show that the method reduces voltage prediction error by up to 85% and estimates internal resistance within 0.04% of the true value under standardized pulsed conditions. Additional validation under dynamic load scenarios further demonstrates the robustness and accuracy of the approach, making it well suited for embedded applications and standardized diagnostic protocols.
{"title":"Least Squares Approach to Improve Standardized Estimation of OCV and Internal Resistance in Batteries","authors":"Prarthana Pillai;Smeet Desai;Sooraj Sunil;Krishna R. Pattipati;Balakumar Balasingam","doi":"10.1109/JESTIE.2025.3613267","DOIUrl":"https://doi.org/10.1109/JESTIE.2025.3613267","url":null,"abstract":"This article focuses on standardized approaches for estimating battery open-circuit voltage (OCV) and internal resistance, which rely on low-fidelity models and simple estimation techniques, yet offer repeatable and widely used results in practical applications. In particular, we examine the common method of computing internal resistance as the ratio of the voltage response to an applied current excitation pulse. When applied to batteries, this method implicitly assumes that the OCV remains constant during the pulse, which can introduce significant estimation errors when this assumption is violated. To address this limitation, we propose a novel observation model that explicitly accounts for OCV variation during the excitation window. The model introduces a single parameter representing the OCV–SOC gradient, which is estimated using a linear least-squares approach from the current excitation and voltage response data. This leads to a closed-form solution that requires no prior knowledge of battery-specific parameters such as capacity or equivalent circuit elements. The proposed approach retains the simplicity and short excitation duration of standardized methods, yet yields significantly improved accuracy in estimating both OCV and resistance. Simulation and experimental results show that the method reduces voltage prediction error by up to 85% and estimates internal resistance within 0.04% of the true value under standardized pulsed conditions. Additional validation under dynamic load scenarios further demonstrates the robustness and accuracy of the approach, making it well suited for embedded applications and standardized diagnostic protocols.","PeriodicalId":100620,"journal":{"name":"IEEE Journal of Emerging and Selected Topics in Industrial Electronics","volume":"7 1","pages":"311-324"},"PeriodicalIF":4.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Inductive power transfer (IPT) converters suffer efficiency degradation when operating away from the optimal load point during battery charging. This challenge is exacerbated in fast constant power (CP) charging, where higher power levels enable faster charging but often result in load resistances below the optimal value, making load matching more difficult. To address this, a novel load matching scheme is proposed, employing a half-wave rectifier to step up the load resistance effectively. In addition, a switch-controlled inductor is integrated into a passive impedance matching network, forming an active impedance matching network to achieve load matching and maintain a stable CP output. The scheme operates at a fixed frequency with secondary-side control, eliminating the need for wireless feedback communication. Compared to existing single-stage CP charging methods, the proposed single-stage charger design reduces the charging time by 50%, significantly improving user convenience. A 500-W hardware prototype is constructed to validate the performance of the proposed charger, achieving a maximum efficiency of 90.6%. The system’s robustness under open-circuit load conditions is further demonstrated through experimental results.
{"title":"Active Impedance Matching Network Based Single-Stage Constant Power Wireless Charger With Reduced Battery Charging Time","authors":"Rohan Sandeep Burye;Sheron Figarado;Akshay Kumar Rathore","doi":"10.1109/JESTIE.2025.3610926","DOIUrl":"https://doi.org/10.1109/JESTIE.2025.3610926","url":null,"abstract":"Inductive power transfer (IPT) converters suffer efficiency degradation when operating away from the optimal load point during battery charging. This challenge is exacerbated in fast constant power (CP) charging, where higher power levels enable faster charging but often result in load resistances below the optimal value, making load matching more difficult. To address this, a novel load matching scheme is proposed, employing a half-wave rectifier to step up the load resistance effectively. In addition, a switch-controlled inductor is integrated into a passive impedance matching network, forming an active impedance matching network to achieve load matching and maintain a stable CP output. The scheme operates at a fixed frequency with secondary-side control, eliminating the need for wireless feedback communication. Compared to existing single-stage CP charging methods, the proposed single-stage charger design reduces the charging time by 50%, significantly improving user convenience. A 500-W hardware prototype is constructed to validate the performance of the proposed charger, achieving a maximum efficiency of 90.6%. The system’s robustness under open-circuit load conditions is further demonstrated through experimental results.","PeriodicalId":100620,"journal":{"name":"IEEE Journal of Emerging and Selected Topics in Industrial Electronics","volume":"7 1","pages":"359-368"},"PeriodicalIF":4.0,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-05DOI: 10.1109/JESTIE.2025.3595978
Junjie Jiang;Liqun He;Xiaohui Li;Xueliang Fan;Chudi Lin;Shengfang Fan;Cheng Wang
The X-ray tube is a crucial and costly component of X-ray machines. It generates tube current through filament heating and a high-voltage electric field, both of which are related to the X-ray dose rate. However, changing conditions within the tube can lead to deviations and overshoots in tube current control, which in turn affects the X-ray dose rate. Manual calibration of the tube is occasionally necessary to adjust the mathematical relationship prestored in the microcontroller, but this process can reduce the lifespan of the X-ray tube. To address this issue, this article presents a thermionic-emission model for the X-ray tube current. A heat transfer model for the tungsten cathode has been developed, and the unknown parameters in the tube current model are identified using a dynamic neighborhood particle swarm optimization algorithm. The proposed tube current model is utilized to enhance the control of filament current. Experiments were conducted using a 1 kW/100 kV C-arm X-ray generator equipped with an XD56 tube. The results indicate that the proposed identification method significantly improves the accuracy of the derived model in comparison to the actual tube current. The improvements in filament current control have led to a reduction in tube current overshoot, a shorter tube current establishment time, and fewer instances of tube current dropping below a specified threshold.
{"title":"Thermionic-Emission Modeling and DN-PSO Based Parameter Identification of X-Ray Tube","authors":"Junjie Jiang;Liqun He;Xiaohui Li;Xueliang Fan;Chudi Lin;Shengfang Fan;Cheng Wang","doi":"10.1109/JESTIE.2025.3595978","DOIUrl":"https://doi.org/10.1109/JESTIE.2025.3595978","url":null,"abstract":"The X-ray tube is a crucial and costly component of X-ray machines. It generates tube current through filament heating and a high-voltage electric field, both of which are related to the X-ray dose rate. However, changing conditions within the tube can lead to deviations and overshoots in tube current control, which in turn affects the X-ray dose rate. Manual calibration of the tube is occasionally necessary to adjust the mathematical relationship prestored in the microcontroller, but this process can reduce the lifespan of the X-ray tube. To address this issue, this article presents a thermionic-emission model for the X-ray tube current. A heat transfer model for the tungsten cathode has been developed, and the unknown parameters in the tube current model are identified using a dynamic neighborhood particle swarm optimization algorithm. The proposed tube current model is utilized to enhance the control of filament current. Experiments were conducted using a 1 kW/100 kV C-arm X-ray generator equipped with an XD56 tube. The results indicate that the proposed identification method significantly improves the accuracy of the derived model in comparison to the actual tube current. The improvements in filament current control have led to a reduction in tube current overshoot, a shorter tube current establishment time, and fewer instances of tube current dropping below a specified threshold.","PeriodicalId":100620,"journal":{"name":"IEEE Journal of Emerging and Selected Topics in Industrial Electronics","volume":"6 4","pages":"1766-1775"},"PeriodicalIF":4.0,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145290237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-18DOI: 10.1109/JESTIE.2025.3585473
{"title":"Journal of Emerging and Selected Topics in Industrial Electronics Publication Information","authors":"","doi":"10.1109/JESTIE.2025.3585473","DOIUrl":"https://doi.org/10.1109/JESTIE.2025.3585473","url":null,"abstract":"","PeriodicalId":100620,"journal":{"name":"IEEE Journal of Emerging and Selected Topics in Industrial Electronics","volume":"6 3","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11085032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657509","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}
Pub Date : 2025-07-18DOI: 10.1109/JESTIE.2025.3585477
{"title":"IEEE Industrial Electronics Society Information","authors":"","doi":"10.1109/JESTIE.2025.3585477","DOIUrl":"https://doi.org/10.1109/JESTIE.2025.3585477","url":null,"abstract":"","PeriodicalId":100620,"journal":{"name":"IEEE Journal of Emerging and Selected Topics in Industrial Electronics","volume":"6 3","pages":"C4-C4"},"PeriodicalIF":0.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11085139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657496","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}
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