Pub Date : 2022-10-17DOI: 10.1109/IECON49645.2022.9968514
Ivan Choque, M. Pérez, J. Guzmán
The wireless power transfer technology for electric vehicles charging has been largely improved during the last years in terms of system configurations, coil design, and control schemes with the aim to achieve higher power, higher efficiency, and longer transmission distance. The compensation stage is usually designed considering sinusoidal voltages and currents. However, the full-bridge converters used to generate the controlled voltage generate a square waveform, requiring design adjustments, reducing the efficiency of the compensation, and impacting the control performance. In this paper, a design guideline for the compensation stage and the evaluation of the performance of the wireless power sources system using power converters instead of sinusoidal sources is given.
{"title":"Analysis of Bidirectional Wireless Power Transfer for EV applications","authors":"Ivan Choque, M. Pérez, J. Guzmán","doi":"10.1109/IECON49645.2022.9968514","DOIUrl":"https://doi.org/10.1109/IECON49645.2022.9968514","url":null,"abstract":"The wireless power transfer technology for electric vehicles charging has been largely improved during the last years in terms of system configurations, coil design, and control schemes with the aim to achieve higher power, higher efficiency, and longer transmission distance. The compensation stage is usually designed considering sinusoidal voltages and currents. However, the full-bridge converters used to generate the controlled voltage generate a square waveform, requiring design adjustments, reducing the efficiency of the compensation, and impacting the control performance. In this paper, a design guideline for the compensation stage and the evaluation of the performance of the wireless power sources system using power converters instead of sinusoidal sources is given.","PeriodicalId":125740,"journal":{"name":"IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133485764","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 : 2022-10-17DOI: 10.1109/IECON49645.2022.9968704
Maxime Selingue, A. Olabi, Stéphane Thiery, Richard Béarée
Industrial robots are known to have good repeatability and poor accuracy. However, accuracy can be improved through calibration process. Different methods of calibration can be found in the literature. In this paper, a hybrid calibration approach was applied to improve the accuracy of a lightweight collaborative robot. The approach is based on an analytical model to compensate geometric errors and on an artificial neural network to compensate residual errors (stiffness, gear errors,…. etc). The suggested approach is analysed and optimised in the work. The approach can reduce the positioning error from 3.10mm to 0.13mm on a lightweight collaborative robot in a specific sub-workspace.
{"title":"Experimental Analysis of Robot Hybrid Calibration Based on Geometrical Identification and Artificial Neural Network","authors":"Maxime Selingue, A. Olabi, Stéphane Thiery, Richard Béarée","doi":"10.1109/IECON49645.2022.9968704","DOIUrl":"https://doi.org/10.1109/IECON49645.2022.9968704","url":null,"abstract":"Industrial robots are known to have good repeatability and poor accuracy. However, accuracy can be improved through calibration process. Different methods of calibration can be found in the literature. In this paper, a hybrid calibration approach was applied to improve the accuracy of a lightweight collaborative robot. The approach is based on an analytical model to compensate geometric errors and on an artificial neural network to compensate residual errors (stiffness, gear errors,…. etc). The suggested approach is analysed and optimised in the work. The approach can reduce the positioning error from 3.10mm to 0.13mm on a lightweight collaborative robot in a specific sub-workspace.","PeriodicalId":125740,"journal":{"name":"IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133301864","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 : 2022-10-17DOI: 10.1109/IECON49645.2022.9968513
G. Pellecuer, T. Martiré, L. Daridon
This paper deals with the design of a power intercell transformer (ICT) for a marine drone application. This on-board converter, with a power of the order of kW, is intended to adapt the energy coming from the solar panels to recharge the on-board battery of the vehicle. The constrained application requires special attention to the design of this intercell power transformer. The dimensioning approach by 3D finite element modeling is then essential to apprehend the complexity of the realization of this type of component and to determine the electromagnetic parameters such as the serial resistances of the windings or the proper and mutual inductances of the ICT. The component, thus dimensioned, has been manufactured and the experimental measurements are compared with those from the modeling.
{"title":"Power Inter Cell Transformer Modelling for ASV Application","authors":"G. Pellecuer, T. Martiré, L. Daridon","doi":"10.1109/IECON49645.2022.9968513","DOIUrl":"https://doi.org/10.1109/IECON49645.2022.9968513","url":null,"abstract":"This paper deals with the design of a power intercell transformer (ICT) for a marine drone application. This on-board converter, with a power of the order of kW, is intended to adapt the energy coming from the solar panels to recharge the on-board battery of the vehicle. The constrained application requires special attention to the design of this intercell power transformer. The dimensioning approach by 3D finite element modeling is then essential to apprehend the complexity of the realization of this type of component and to determine the electromagnetic parameters such as the serial resistances of the windings or the proper and mutual inductances of the ICT. The component, thus dimensioned, has been manufactured and the experimental measurements are compared with those from the modeling.","PeriodicalId":125740,"journal":{"name":"IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133661296","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 : 2022-10-17DOI: 10.1109/IECON49645.2022.9968539
S. Suarez, D. Chabane, A. N'Diaye, Y. Ait-Amirat, A. Djerdir
Hydrogen has been identified as one of the main axes of the energy transition. Its large-scale development faces several technological barriers. This is the case for the storage method, although today there are generally three methods: high gaseous pressure storage, liquid storage and metal hydride storage. However, only the first two have achieved technological maturity, yet they present several disadvantages in terms of the calorific value used for the compression and liquefaction of hydrogen as well as from the point of view of pressure of use and social acceptance. On the other hand, metal hydride storage offers the technical possibility to operate at low pressure and ambient temperature, with a higher volumetric energy density than the other two methods. Hydrogen storage in metal hydrides is an exothermic process and its release is an endothermic reaction, hence the idea of recovering the waste heat produced by a fuel cell to provide calories to the metal hydride hydrogen reservoir to extract the hydrogen that will in turn feed the fuel cell. This article presents the experimental results of the coupling of a PEMFC fuel cell and a hydrogen metal hydride hydrogen tank. The results obtained highlight the great interest of this solution in terms of improving energy efficiency and safety for stationary and mobile applications.
{"title":"Feasibility of efficiency improvement in a fuel cell system powered by a metal hydride tank","authors":"S. Suarez, D. Chabane, A. N'Diaye, Y. Ait-Amirat, A. Djerdir","doi":"10.1109/IECON49645.2022.9968539","DOIUrl":"https://doi.org/10.1109/IECON49645.2022.9968539","url":null,"abstract":"Hydrogen has been identified as one of the main axes of the energy transition. Its large-scale development faces several technological barriers. This is the case for the storage method, although today there are generally three methods: high gaseous pressure storage, liquid storage and metal hydride storage. However, only the first two have achieved technological maturity, yet they present several disadvantages in terms of the calorific value used for the compression and liquefaction of hydrogen as well as from the point of view of pressure of use and social acceptance. On the other hand, metal hydride storage offers the technical possibility to operate at low pressure and ambient temperature, with a higher volumetric energy density than the other two methods. Hydrogen storage in metal hydrides is an exothermic process and its release is an endothermic reaction, hence the idea of recovering the waste heat produced by a fuel cell to provide calories to the metal hydride hydrogen reservoir to extract the hydrogen that will in turn feed the fuel cell. This article presents the experimental results of the coupling of a PEMFC fuel cell and a hydrogen metal hydride hydrogen tank. The results obtained highlight the great interest of this solution in terms of improving energy efficiency and safety for stationary and mobile applications.","PeriodicalId":125740,"journal":{"name":"IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132580918","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 : 2022-10-17DOI: 10.1109/IECON49645.2022.9968992
Jeongmin Kim, JaeDuck Lee, Zoo-Hwan Hah, Y. Park
Three-dimensional (3D) measurement is an essential procedure in various manufacturing industries including shipbuilding. Since binocular systems are convenient and time-saving, they are proposed for ship block measurement. However, because of the very large scale of the ship blocks, working distance is about 10 m, resulting in a fatal limitation of calibration: the size of image portion corresponding to the checkerboard for calibration included in the whole image is extremely small. This prevents the distortion parameter of camera lens which most affects 3D reconstruction accuracy, from being accurately estimated in the calibration. To overcome this limitation, this paper proposes a method that pre-estimates the distortion correction map that covers the entire image area. A phase-shift circular grating (PCG) pattern displayed on a monitor is captured by the camera set to large scale. Since PCG patterns are generated by computer software, infinite number of patterns corresponding to desired orientations and positions can be generated, which are useful to measure the center of distortion and more accurate vanishing points. Based on the estimated vanishing points, the accurate distortion correction is performed using perspective projection invariants, and the distortion values in pixels are measured for each grid points to estimate the distortion correction map of the entire image area. An experimental 3D measurement was conducted with an estimated distortion correction map. As a result, Mean and standard deviation of 3D reconstruction error by proposed method were improved by 15.84% and 6.77% compared with the Zhang’s method, respectively.
{"title":"Distortion Correction using Virtual PCG Pattern for Precise Stereo-based Large-scale 3D Measurement","authors":"Jeongmin Kim, JaeDuck Lee, Zoo-Hwan Hah, Y. Park","doi":"10.1109/IECON49645.2022.9968992","DOIUrl":"https://doi.org/10.1109/IECON49645.2022.9968992","url":null,"abstract":"Three-dimensional (3D) measurement is an essential procedure in various manufacturing industries including shipbuilding. Since binocular systems are convenient and time-saving, they are proposed for ship block measurement. However, because of the very large scale of the ship blocks, working distance is about 10 m, resulting in a fatal limitation of calibration: the size of image portion corresponding to the checkerboard for calibration included in the whole image is extremely small. This prevents the distortion parameter of camera lens which most affects 3D reconstruction accuracy, from being accurately estimated in the calibration. To overcome this limitation, this paper proposes a method that pre-estimates the distortion correction map that covers the entire image area. A phase-shift circular grating (PCG) pattern displayed on a monitor is captured by the camera set to large scale. Since PCG patterns are generated by computer software, infinite number of patterns corresponding to desired orientations and positions can be generated, which are useful to measure the center of distortion and more accurate vanishing points. Based on the estimated vanishing points, the accurate distortion correction is performed using perspective projection invariants, and the distortion values in pixels are measured for each grid points to estimate the distortion correction map of the entire image area. An experimental 3D measurement was conducted with an estimated distortion correction map. As a result, Mean and standard deviation of 3D reconstruction error by proposed method were improved by 15.84% and 6.77% compared with the Zhang’s method, respectively.","PeriodicalId":125740,"journal":{"name":"IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127646230","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 : 2022-10-17DOI: 10.1109/IECON49645.2022.9968369
N. Tashakor, F. Naseri, Jingyang Fang, H. Schotten, S. Goetz
Modular reconfigurable batteries, also known as smart batteries, are gaining significant traction, mainly due to the large environmental incentives and falling price of electronic components. Although they have many advantages compared to a hard-wired battery pack, complex monitoring circuit and numerous sensor requirement make it harder to compete with conventional systems in a cost-driven application. This paper proposes a novel approach to estimate parameters of each individual battery module without any direct measurement at their terminals. The proposed algorithm uses the output voltage and current of the load combined with the exact knowledge of the modules’ states to estimate the open-circuit voltage, ohmic resistance, and polarization resistance in the electric circuit model for each battery module. The method combined with Kalman filter demonstrates the feasibility of this method through simulations, where the proposed method achieves above 98% and 96% accuracies for estimation of the open-circuit voltage and equivalent resistance of the battery, respectively. Additionally, the method can decouple the two resistances with <0.015 Ω.
{"title":"Voltage and Resistance Estimation of Battery-Integrated Cascaded Converters","authors":"N. Tashakor, F. Naseri, Jingyang Fang, H. Schotten, S. Goetz","doi":"10.1109/IECON49645.2022.9968369","DOIUrl":"https://doi.org/10.1109/IECON49645.2022.9968369","url":null,"abstract":"Modular reconfigurable batteries, also known as smart batteries, are gaining significant traction, mainly due to the large environmental incentives and falling price of electronic components. Although they have many advantages compared to a hard-wired battery pack, complex monitoring circuit and numerous sensor requirement make it harder to compete with conventional systems in a cost-driven application. This paper proposes a novel approach to estimate parameters of each individual battery module without any direct measurement at their terminals. The proposed algorithm uses the output voltage and current of the load combined with the exact knowledge of the modules’ states to estimate the open-circuit voltage, ohmic resistance, and polarization resistance in the electric circuit model for each battery module. The method combined with Kalman filter demonstrates the feasibility of this method through simulations, where the proposed method achieves above 98% and 96% accuracies for estimation of the open-circuit voltage and equivalent resistance of the battery, respectively. Additionally, the method can decouple the two resistances with <0.015 Ω.","PeriodicalId":125740,"journal":{"name":"IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127843475","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 : 2022-10-17DOI: 10.1109/IECON49645.2022.9968677
Thushara R. Bandara, M. Halgamuge
The complete life cycle management of complex equipment is seen as critical to the smart transformation and upgrading of today’s industrial industry. In recent years, digital twin (DT) technology and machine learning (ML) have arisen as emerging technologies. Developing technologies like DT technology and ML in entire battery life cycle management may make each stage of the life cycle more predictable and proactive. We propose a hybrid DT model based on ML that can enhance the performance of an existing DT mathematical model formulated to simulate lithium-ion battery deterioration behavior using DT technology. Firstly, we develop a long short-term memory (LSTM)-based model to forecast the error term of battery capacity enumerated for each charge and discharge cycle from the existing DT model. In this work, we use 18,650 lithium-ion battery discharge data from NASA Ames’ prognostics data repository as our experimental data. The LSTM model is configured with Adam optimizer and the mean absolute error (MAE) loss function. The early stopping criterion is also employed as a regularization technique to overcome model overfitting. Secondly, we develop our proposed hybrid DT by integrating both the existing DT and the LSTM model. Thirdly, we formulate an empirical mathematical model, which allows us to better replicate behavior of battery degradation of any lithium-ion battery. Finally, we evaluate the performance of the proposed hybrid DT in terms of the MAE metric. Compared with the existing model, our proposed model reduces the error of battery capacity during the entire degradation period by 68.42%.
{"title":"Modeling a Digital Twin to Predict Battery Deterioration with Lower Prediction Error in Smart Devices: From the Internet of Things Sensor Devices to Self-Driving Cars","authors":"Thushara R. Bandara, M. Halgamuge","doi":"10.1109/IECON49645.2022.9968677","DOIUrl":"https://doi.org/10.1109/IECON49645.2022.9968677","url":null,"abstract":"The complete life cycle management of complex equipment is seen as critical to the smart transformation and upgrading of today’s industrial industry. In recent years, digital twin (DT) technology and machine learning (ML) have arisen as emerging technologies. Developing technologies like DT technology and ML in entire battery life cycle management may make each stage of the life cycle more predictable and proactive. We propose a hybrid DT model based on ML that can enhance the performance of an existing DT mathematical model formulated to simulate lithium-ion battery deterioration behavior using DT technology. Firstly, we develop a long short-term memory (LSTM)-based model to forecast the error term of battery capacity enumerated for each charge and discharge cycle from the existing DT model. In this work, we use 18,650 lithium-ion battery discharge data from NASA Ames’ prognostics data repository as our experimental data. The LSTM model is configured with Adam optimizer and the mean absolute error (MAE) loss function. The early stopping criterion is also employed as a regularization technique to overcome model overfitting. Secondly, we develop our proposed hybrid DT by integrating both the existing DT and the LSTM model. Thirdly, we formulate an empirical mathematical model, which allows us to better replicate behavior of battery degradation of any lithium-ion battery. Finally, we evaluate the performance of the proposed hybrid DT in terms of the MAE metric. Compared with the existing model, our proposed model reduces the error of battery capacity during the entire degradation period by 68.42%.","PeriodicalId":125740,"journal":{"name":"IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131873580","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 : 2022-10-17DOI: 10.1109/IECON49645.2022.9969098
Akihito Nishikawa, K. Hattori, Motomasa Tanaka, Hiroaki Muranami, Hiroaki Nishi
Anomaly detection is one of the key applications of data utilization in smart factories, particularly in monitoring factory facilities. Early detection and resolution of anomalies, such as system failures, can lead to cost reduction and quality stabilization. One of the targets of abnormality detection applications in the industry section is a refrigerator unit used in food processing factories and warehouses. Anomalies in the early stages in refrigerator units appear in the operating sounds, which can enable their detection. In this study, we propose a method for detecting abnormal sound, extracting abnormal frequency components, and identifying the direction of the abnormal sound source. To identify the direction of the anomalous sound source, multi-channel sound recorded by a microphone array is used. To the best of our knowledge, no method has yet been proposed for anomaly sound detection using multi-channel acoustic data. In the proposed method, anomaly scores calculated in each channel of the microphone array are aggregated to determine whether the entire data is anomalous or not. Anomalous sounds were extracted from the anomaly data using a deep generative model. The extracted anomalous sounds were used to localize the sound source and the direction of the anomalous source was identified. The proposed method improved the precision of anomaly sound detection while maintaining the recall rate of a conservative comparison method. Using the proposed method, anomalous sounds were extracted from the anomaly data, and their arrival directions were identified.
{"title":"Anomalous Sound Detection, Extraction, and Localization for Refrigerator Units Using a Microphone Array","authors":"Akihito Nishikawa, K. Hattori, Motomasa Tanaka, Hiroaki Muranami, Hiroaki Nishi","doi":"10.1109/IECON49645.2022.9969098","DOIUrl":"https://doi.org/10.1109/IECON49645.2022.9969098","url":null,"abstract":"Anomaly detection is one of the key applications of data utilization in smart factories, particularly in monitoring factory facilities. Early detection and resolution of anomalies, such as system failures, can lead to cost reduction and quality stabilization. One of the targets of abnormality detection applications in the industry section is a refrigerator unit used in food processing factories and warehouses. Anomalies in the early stages in refrigerator units appear in the operating sounds, which can enable their detection. In this study, we propose a method for detecting abnormal sound, extracting abnormal frequency components, and identifying the direction of the abnormal sound source. To identify the direction of the anomalous sound source, multi-channel sound recorded by a microphone array is used. To the best of our knowledge, no method has yet been proposed for anomaly sound detection using multi-channel acoustic data. In the proposed method, anomaly scores calculated in each channel of the microphone array are aggregated to determine whether the entire data is anomalous or not. Anomalous sounds were extracted from the anomaly data using a deep generative model. The extracted anomalous sounds were used to localize the sound source and the direction of the anomalous source was identified. The proposed method improved the precision of anomaly sound detection while maintaining the recall rate of a conservative comparison method. Using the proposed method, anomalous sounds were extracted from the anomaly data, and their arrival directions were identified.","PeriodicalId":125740,"journal":{"name":"IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133842998","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 : 2022-10-17DOI: 10.1109/iecon49645.2022.9968988
Rui Qiang, Yang Wu, T. Soeiro, Pierpaolo Granello, Zian Qin, P. Bauer
This paper proposes a solution to the circuit topology of heavy-duty electric vehicle (HDEV) chargers. In light of the original hybrid rectifier, a new unidirectional Input-Parallel-Output-Series (IPOS) three-phase hybrid rectifier is proposed and analyzed. The IPOS topology is advantageous at ultra-high power rating to interface the next-generation HDEV batteries which require a high and wide output voltage range of 800~1500 V with available 600/1200V commercial semiconductors. Moreover, the proposed topology is efficient, cost-effective, and scalable with the grid input current harmonic components in compliance with the IEEE-519 standard. The benefits of the IPOS topology are supported by circuit derivation, control strategy, analytical modelling, simulation, and experimental verification.
{"title":"A New Input-Parallel-Output-Series Three-Phase Hybrid Rectifier for Heavy-Duty Electric Vehicle Chargers","authors":"Rui Qiang, Yang Wu, T. Soeiro, Pierpaolo Granello, Zian Qin, P. Bauer","doi":"10.1109/iecon49645.2022.9968988","DOIUrl":"https://doi.org/10.1109/iecon49645.2022.9968988","url":null,"abstract":"This paper proposes a solution to the circuit topology of heavy-duty electric vehicle (HDEV) chargers. In light of the original hybrid rectifier, a new unidirectional Input-Parallel-Output-Series (IPOS) three-phase hybrid rectifier is proposed and analyzed. The IPOS topology is advantageous at ultra-high power rating to interface the next-generation HDEV batteries which require a high and wide output voltage range of 800~1500 V with available 600/1200V commercial semiconductors. Moreover, the proposed topology is efficient, cost-effective, and scalable with the grid input current harmonic components in compliance with the IEEE-519 standard. The benefits of the IPOS topology are supported by circuit derivation, control strategy, analytical modelling, simulation, and experimental verification.","PeriodicalId":125740,"journal":{"name":"IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society","volume":"401 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115918878","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 : 2022-10-17DOI: 10.1109/IECON49645.2022.9968435
Jorge Pereira, Jérôme Mendes, J. S. Junior, C. Viegas, J. Paulo
Every year, wildfires cause significant losses and destruction around the globe. In order to attempt to reduce their damages, resources have been put into developing fire propagation prediction systems. In a real wildfire event, these systems provide the authorities with information about the fire propagation in the near future, thus allowing them to make better decisions. Wildfire spread prediction systems are based on fire propagation models, from which the most used and accepted model is the Rothermel model. However, given the complexity of the wildfire phenomena and the uncertainty of some of its input parameter values, the Rothermel model can produce misleading results of fire propagation. This paper uses 3 metaheuristic algorithms, genetic algorithm (GA), differential evolution (DE) and simulated annealing (SA), for calibration of input parameters from the Rothermel model. These algorithms were validated using 37 datasets containing data from controlled experimental fires. Results have shown that these algorithms provide a precise wildfire spread prediction accounting for the uncertainties in the model’s selected parameters.
{"title":"Wildfire Spread Prediction Model Calibration Using Metaheuristic Algorithms","authors":"Jorge Pereira, Jérôme Mendes, J. S. Junior, C. Viegas, J. Paulo","doi":"10.1109/IECON49645.2022.9968435","DOIUrl":"https://doi.org/10.1109/IECON49645.2022.9968435","url":null,"abstract":"Every year, wildfires cause significant losses and destruction around the globe. In order to attempt to reduce their damages, resources have been put into developing fire propagation prediction systems. In a real wildfire event, these systems provide the authorities with information about the fire propagation in the near future, thus allowing them to make better decisions. Wildfire spread prediction systems are based on fire propagation models, from which the most used and accepted model is the Rothermel model. However, given the complexity of the wildfire phenomena and the uncertainty of some of its input parameter values, the Rothermel model can produce misleading results of fire propagation. This paper uses 3 metaheuristic algorithms, genetic algorithm (GA), differential evolution (DE) and simulated annealing (SA), for calibration of input parameters from the Rothermel model. These algorithms were validated using 37 datasets containing data from controlled experimental fires. Results have shown that these algorithms provide a precise wildfire spread prediction accounting for the uncertainties in the model’s selected parameters.","PeriodicalId":125740,"journal":{"name":"IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124312510","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}
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