Pub Date : 2022-09-01DOI: 10.1109/DMC55175.2022.9906467
Peter Wilson, C. Vagg
The use of vehicle scale data for the parameter characterization of electric vehicle battery packs is a challenging topic. This paper describes the implementation of a design tool that carries out both simulated annealing and genetic optimization of model parameters for a modified Nernst Open Circuit Voltage Battery model (K0, K1, K2, K3), concurrently with the dynamic transient model parameters (R0,R1,RP,C1,CP) and pack level parameters including the initial state of charge and capacity (SOCINIT and AH). This paper describes the model for the battery pack implemented in the Saber simulator and the optimization tool (written in TCL-TK) also integrated with the Saber simulator. Results were collected from rolling road tests of of a BMW i8 to validate the fidelity of the model.
{"title":"Optimization Tool for the Characterization of Electric Vehicle Battery Packs","authors":"Peter Wilson, C. Vagg","doi":"10.1109/DMC55175.2022.9906467","DOIUrl":"https://doi.org/10.1109/DMC55175.2022.9906467","url":null,"abstract":"The use of vehicle scale data for the parameter characterization of electric vehicle battery packs is a challenging topic. This paper describes the implementation of a design tool that carries out both simulated annealing and genetic optimization of model parameters for a modified Nernst Open Circuit Voltage Battery model (K0, K1, K2, K3), concurrently with the dynamic transient model parameters (R0,R1,RP,C1,CP) and pack level parameters including the initial state of charge and capacity (SOCINIT and AH). This paper describes the model for the battery pack implemented in the Saber simulator and the optimization tool (written in TCL-TK) also integrated with the Saber simulator. Results were collected from rolling road tests of of a BMW i8 to validate the fidelity of the model.","PeriodicalId":245908,"journal":{"name":"2022 IEEE Design Methodologies Conference (DMC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114250540","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 : 2022-09-01DOI: 10.1109/DMC55175.2022.9906544
Jacob Reynvaan, Monika Stipsitz, Philipp Skoff, Thomas Langbauer, A. Connaughton
This paper presents an approach for reproducing key characteristics of non-linear, high frequency switching transients using a multilayer perceptron neural network. Training data is generated using variable time-step transient simulations of a half-bridge switching cell of SPICE transistor models together with constrained yet randomized combinations of DC-link voltage, drain currents and lumped loop inductances. Using the example of peak turn-OFF voltage overshoot for SiC and Si power transistors, the multilayer perceptrons show a mean error of less than (0.9 ± 1.3)%. The predictions of the multilayer perceptron are then compared to preliminary measurements made using a SiC half-bridge test-bench where good agreement is observed especially for higher drain currents. With continued development, such a neural network could be used in coarse, fixed-time-step simulations of any “half-bridge-based” circuit to offer typically unavailable high-fidelity information with negligible computation time. For example, a designer could choose a transistor and quickly see the limits on allowable loop inductance to avoid excessive voltage overshoot for their simulated current waveforms, or see an estimate for voltage overshoot if the loop inductances are known.
{"title":"A Preliminary Investigation into Approximating Power Transistor Switching Behavior using a Multilayer Perceptron","authors":"Jacob Reynvaan, Monika Stipsitz, Philipp Skoff, Thomas Langbauer, A. Connaughton","doi":"10.1109/DMC55175.2022.9906544","DOIUrl":"https://doi.org/10.1109/DMC55175.2022.9906544","url":null,"abstract":"This paper presents an approach for reproducing key characteristics of non-linear, high frequency switching transients using a multilayer perceptron neural network. Training data is generated using variable time-step transient simulations of a half-bridge switching cell of SPICE transistor models together with constrained yet randomized combinations of DC-link voltage, drain currents and lumped loop inductances. Using the example of peak turn-OFF voltage overshoot for SiC and Si power transistors, the multilayer perceptrons show a mean error of less than (0.9 ± 1.3)%. The predictions of the multilayer perceptron are then compared to preliminary measurements made using a SiC half-bridge test-bench where good agreement is observed especially for higher drain currents. With continued development, such a neural network could be used in coarse, fixed-time-step simulations of any “half-bridge-based” circuit to offer typically unavailable high-fidelity information with negligible computation time. For example, a designer could choose a transistor and quickly see the limits on allowable loop inductance to avoid excessive voltage overshoot for their simulated current waveforms, or see an estimate for voltage overshoot if the loop inductances are known.","PeriodicalId":245908,"journal":{"name":"2022 IEEE Design Methodologies Conference (DMC)","volume":" 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113948298","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 : 2022-09-01DOI: 10.1109/DMC55175.2022.9906470
Itziar Alzuguren, A. Garcia‐Bediaga, A. Avila, A. Rujas, M. Vasić
This paper presents an optimized modulation scheme for a single-stage Dual Active Bridge (DAB) |AC|-DC converter developed by means of a multiobjective genetic algorithm, in particular the nondominated sorting genetic algorithm II (NSGA-II), for an Electric Vehicle (EV) On-Board Charger (OBC) application. The proposed methodology has the aim of reaching the best sequence of control parameters across the whole range of the input voltage, by optimizing the current across the series inductance and the turn-on currents of the semiconductors. The proposed methodology and the optimal solutions are validated with simulation results and compared with a well-known phase-shift modulation, in order to observe the improvements in the power losses.
{"title":"Genetic Algorithm-Based Optimized Modulation For Dual Active Bridge PFC Circuit For Electric Vehicle Application","authors":"Itziar Alzuguren, A. Garcia‐Bediaga, A. Avila, A. Rujas, M. Vasić","doi":"10.1109/DMC55175.2022.9906470","DOIUrl":"https://doi.org/10.1109/DMC55175.2022.9906470","url":null,"abstract":"This paper presents an optimized modulation scheme for a single-stage Dual Active Bridge (DAB) |AC|-DC converter developed by means of a multiobjective genetic algorithm, in particular the nondominated sorting genetic algorithm II (NSGA-II), for an Electric Vehicle (EV) On-Board Charger (OBC) application. The proposed methodology has the aim of reaching the best sequence of control parameters across the whole range of the input voltage, by optimizing the current across the series inductance and the turn-on currents of the semiconductors. The proposed methodology and the optimal solutions are validated with simulation results and compared with a well-known phase-shift modulation, in order to observe the improvements in the power losses.","PeriodicalId":245908,"journal":{"name":"2022 IEEE Design Methodologies Conference (DMC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132846434","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 : 2022-09-01DOI: 10.1109/DMC55175.2022.9906471
Susan Oluropo Adedokun, Zhenhua Luo, Patrick Luk, N. Balta-Ozkan, Mohammad Farhan Khan, Xin Zhang
Power systems resiliency studies focus largely on operational planning, optimization, and control strategies to restore critical loads, after blackouts from extreme incidents, and natural disasters, which characterize high-impact, low-probability events. There is a lacuna of resiliency studies of other events, including blackouts with high-impact, high-probability, which classify technical faults. However, the highest percentage of blackouts are from equipment failure technical related faults. Few ML studies cover both outage forecasting and restoration, including resiliency methods for all types of power outages. This study presents a resiliency management system framework, incorporating maintenance indicators, for all types of outages from different events, particularly in developing countries, where up to 60% of blackouts are technical related. A novel framework, with machine learning classification and regression is applied. The model is validated with real historic load flows and outage interruptions of four Nigeria states. Results reveal complex multiple power outages due to different causes at different locations. A relay target indication of 91.8%, an outage type classification accuracy of 85%, and a start time regression (R) value of one, signify that the onset of all types of power outages can be predicted accurately, including indication of maintenance targets where self-sufficient, sustainable energy resources can be applied to enhance power system resilience.
{"title":"ML Self-Sufficient Sustainable Energy Resiliency Management System: Outage Forecasting, Classification and Restoration with Maintenance Indicators for All Types of Power Outages","authors":"Susan Oluropo Adedokun, Zhenhua Luo, Patrick Luk, N. Balta-Ozkan, Mohammad Farhan Khan, Xin Zhang","doi":"10.1109/DMC55175.2022.9906471","DOIUrl":"https://doi.org/10.1109/DMC55175.2022.9906471","url":null,"abstract":"Power systems resiliency studies focus largely on operational planning, optimization, and control strategies to restore critical loads, after blackouts from extreme incidents, and natural disasters, which characterize high-impact, low-probability events. There is a lacuna of resiliency studies of other events, including blackouts with high-impact, high-probability, which classify technical faults. However, the highest percentage of blackouts are from equipment failure technical related faults. Few ML studies cover both outage forecasting and restoration, including resiliency methods for all types of power outages. This study presents a resiliency management system framework, incorporating maintenance indicators, for all types of outages from different events, particularly in developing countries, where up to 60% of blackouts are technical related. A novel framework, with machine learning classification and regression is applied. The model is validated with real historic load flows and outage interruptions of four Nigeria states. Results reveal complex multiple power outages due to different causes at different locations. A relay target indication of 91.8%, an outage type classification accuracy of 85%, and a start time regression (R) value of one, signify that the onset of all types of power outages can be predicted accurately, including indication of maintenance targets where self-sufficient, sustainable energy resources can be applied to enhance power system resilience.","PeriodicalId":245908,"journal":{"name":"2022 IEEE Design Methodologies Conference (DMC)","volume":"67 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133018460","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 : 2022-09-01DOI: 10.1109/DMC55175.2022.9906537
Chang Wang, Yi Dou, Gabriel Zsurzsan, Z. Ouyang, Zhe Zhang, M. Andersen
This paper presents design concepts, design implementation and results evaluation of two antithetical design methodologies for transmitter coils in inductive power transfer (IPT) systems. The IPT transmitter coil design targets at achieving homogeneous-flux distribution at the receivers’ position, thus, the limited misalignment can be fully countered by passive magnetic design. However, different design effort is expected based on the coil’s structure, and corresponding modelling and design methods. In this investigation, we adopted two methodologies for the homo-flux transmitter coil design: one is to derive an analytical solution and the other is to explore a Finite Element Analysis (FEA) based solution. By implementing the genetic algorithm (GA) as the optimization tool, both design methods can generate the optimal structure of transmitter coils. However, different design complexity, calculating efforts and slightly different results are observed. The result evaluation is presented in this paper with finite-element-analysis (FEA) simulation and experimental results. A 6.78 MHz 48V-12V 20W experimental prototype is demonstrated to verify the analysis.
{"title":"Antithetical Design Methodologies of Position-Free Transmitter Coils in Wireless Power Transfer","authors":"Chang Wang, Yi Dou, Gabriel Zsurzsan, Z. Ouyang, Zhe Zhang, M. Andersen","doi":"10.1109/DMC55175.2022.9906537","DOIUrl":"https://doi.org/10.1109/DMC55175.2022.9906537","url":null,"abstract":"This paper presents design concepts, design implementation and results evaluation of two antithetical design methodologies for transmitter coils in inductive power transfer (IPT) systems. The IPT transmitter coil design targets at achieving homogeneous-flux distribution at the receivers’ position, thus, the limited misalignment can be fully countered by passive magnetic design. However, different design effort is expected based on the coil’s structure, and corresponding modelling and design methods. In this investigation, we adopted two methodologies for the homo-flux transmitter coil design: one is to derive an analytical solution and the other is to explore a Finite Element Analysis (FEA) based solution. By implementing the genetic algorithm (GA) as the optimization tool, both design methods can generate the optimal structure of transmitter coils. However, different design complexity, calculating efforts and slightly different results are observed. The result evaluation is presented in this paper with finite-element-analysis (FEA) simulation and experimental results. A 6.78 MHz 48V-12V 20W experimental prototype is demonstrated to verify the analysis.","PeriodicalId":245908,"journal":{"name":"2022 IEEE Design Methodologies Conference (DMC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122284009","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 : 2022-09-01DOI: 10.1109/DMC55175.2022.9906475
J. M. Baron, Alejandro García, Fermin Vergara, Pedro J. Arnaiz, M. Vasić
This paper presents a design methodology for Real- Time Digital Twin Electrothermal Models on SoC (Systems on a Chip) developing an analysis of the most relevant constraints for Real-Time Operation and their classification. Moreover, the electrothermal model layout within the architecture of the PYNQ Z1 SoC will be presented exposing the advantages of using an external interface such as Jupyter for Forecasting and the role of parallel structures to accelerate the computation of numerical integration algorithms in Programmable Logic (PL). The proposed electrothermal models will be initially validated with simulation results and later compared with experimental data provided by CMCS (Compact Motor Control System), in which 4 asynchronous engines are supplied by 4 DC/AC IPBB (Inverter Power Building Block).
本文提出了一种基于SoC(片上系统)的实时数字双电热模型的设计方法，并分析了实时操作的最相关约束及其分类。此外，PYNQ Z1 SoC架构内的电热模型布局将展示使用外部接口(如Jupyter for Forecasting)的优势，以及并行结构在可编程逻辑(PL)中加速数值积分算法计算的作用。提出的电热模型将首先通过仿真结果进行验证，然后与CMCS(紧凑型电机控制系统)提供的实验数据进行比较，CMCS(紧凑型电机控制系统)由4个DC/AC IPBB(逆变电源模块)提供4个异步发动机。
{"title":"Methodology for Designing Embedded Real-Time Electrothermal Models in PYNQ Z1 System on Chip","authors":"J. M. Baron, Alejandro García, Fermin Vergara, Pedro J. Arnaiz, M. Vasić","doi":"10.1109/DMC55175.2022.9906475","DOIUrl":"https://doi.org/10.1109/DMC55175.2022.9906475","url":null,"abstract":"This paper presents a design methodology for Real- Time Digital Twin Electrothermal Models on SoC (Systems on a Chip) developing an analysis of the most relevant constraints for Real-Time Operation and their classification. Moreover, the electrothermal model layout within the architecture of the PYNQ Z1 SoC will be presented exposing the advantages of using an external interface such as Jupyter for Forecasting and the role of parallel structures to accelerate the computation of numerical integration algorithms in Programmable Logic (PL). The proposed electrothermal models will be initially validated with simulation results and later compared with experimental data provided by CMCS (Compact Motor Control System), in which 4 asynchronous engines are supplied by 4 DC/AC IPBB (Inverter Power Building Block).","PeriodicalId":245908,"journal":{"name":"2022 IEEE Design Methodologies Conference (DMC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132165550","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 : 2022-09-01DOI: 10.1109/DMC55175.2022.9906536
Han Wang, X. Zeng, X. Pei, R. Burke
PI controllers are essential in electric motor drive systems in terms of speed control and torque control. This paper introduces a simplified gain and phase margin (SGPM) method for PI tuning used for surface mounted permanent magnet synchronous motor (SPMSM) control. The paper compares the differences between Ziegler Nichols (ZN), gain and phase margin (GPM) and SGPM under different operating situations. The results show that SGPM has similar control with GPM method whilst simplifying the solving procedure and would have potential to significantly reduce computation time especially for self-adapting algorithm.
{"title":"Simplified Gain and Phase Margin PI Tuning Method for SPMSM Control","authors":"Han Wang, X. Zeng, X. Pei, R. Burke","doi":"10.1109/DMC55175.2022.9906536","DOIUrl":"https://doi.org/10.1109/DMC55175.2022.9906536","url":null,"abstract":"PI controllers are essential in electric motor drive systems in terms of speed control and torque control. This paper introduces a simplified gain and phase margin (SGPM) method for PI tuning used for surface mounted permanent magnet synchronous motor (SPMSM) control. The paper compares the differences between Ziegler Nichols (ZN), gain and phase margin (GPM) and SGPM under different operating situations. The results show that SGPM has similar control with GPM method whilst simplifying the solving procedure and would have potential to significantly reduce computation time especially for self-adapting algorithm.","PeriodicalId":245908,"journal":{"name":"2022 IEEE Design Methodologies Conference (DMC)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114766983","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 : 2022-09-01DOI: 10.1109/DMC55175.2022.9906474
Skye Reese, Thomas Byrd, J. Haddon, D. Maksimović
This paper is focused on a data-driven approach to capturing figures of merit and features of semiconductor switches and passive components used in switched-mode power converters. Extensive amounts of component data available on commercial distributor sites are gathered and processed to provide insights into relationships among component characteristics beyond what is commonly available in physics-based models. The data is used to train supervised regression machine learning (ML) models that can be used to predict component parameters. One practical use of these ML-based models is in an optimization tool that advises power converter designers on component selection to achieve an optimal specified objective function.
{"title":"Machine Learning-based Component Figures of Merit and Models for DC-DC Converter Design","authors":"Skye Reese, Thomas Byrd, J. Haddon, D. Maksimović","doi":"10.1109/DMC55175.2022.9906474","DOIUrl":"https://doi.org/10.1109/DMC55175.2022.9906474","url":null,"abstract":"This paper is focused on a data-driven approach to capturing figures of merit and features of semiconductor switches and passive components used in switched-mode power converters. Extensive amounts of component data available on commercial distributor sites are gathered and processed to provide insights into relationships among component characteristics beyond what is commonly available in physics-based models. The data is used to train supervised regression machine learning (ML) models that can be used to predict component parameters. One practical use of these ML-based models is in an optimization tool that advises power converter designers on component selection to achieve an optimal specified objective function.","PeriodicalId":245908,"journal":{"name":"2022 IEEE Design Methodologies Conference (DMC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126894909","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 : 2022-09-01DOI: 10.1109/DMC55175.2022.9906542
Michel Nagel, S. Race, Ivana Kovacevic-Badstuebner, T. Ziemann, U. Grossner
This paper presents a virtual prototype of a power electronics switching cell realized on a 4-layer printed circuit board (PCB) with a discrete SiC power MOSFET and a SiC Schottky diode. The main goal is to determine the modeling requirements for an accurate prediction of the actual switching losses and the potential coupling between the gate signal and the power loop due to PCB parasitic capacitances and inductances. The results point out that not only parasitic inductances are of interest but also parasitic capacitances, and that gate driver models have to be included for reliable virtual prototyping and layout design of power electronic PCBs.
{"title":"Virtual PCB Layout Prototyping: Importance of Modeling Gate Driver and Parasitic Capacitances","authors":"Michel Nagel, S. Race, Ivana Kovacevic-Badstuebner, T. Ziemann, U. Grossner","doi":"10.1109/DMC55175.2022.9906542","DOIUrl":"https://doi.org/10.1109/DMC55175.2022.9906542","url":null,"abstract":"This paper presents a virtual prototype of a power electronics switching cell realized on a 4-layer printed circuit board (PCB) with a discrete SiC power MOSFET and a SiC Schottky diode. The main goal is to determine the modeling requirements for an accurate prediction of the actual switching losses and the potential coupling between the gate signal and the power loop due to PCB parasitic capacitances and inductances. The results point out that not only parasitic inductances are of interest but also parasitic capacitances, and that gate driver models have to be included for reliable virtual prototyping and layout design of power electronic PCBs.","PeriodicalId":245908,"journal":{"name":"2022 IEEE Design Methodologies Conference (DMC)","volume":"518 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116243142","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 : 2022-09-01DOI: 10.1109/DMC55175.2022.9906473
J. Baxter, D. Costinett
Schematic-level optimization and steady-state loss modeling play a vital role in the design of advanced power converters. Recently, discrete time state-space modeling has shown merits in rapid analysis and generality to arbitrary circuit topologies but has not yet been utilized under rapid optimization techniques. In this work, we investigate methods for the incorporation of rapid gradient-based optimization techniques leveraging discrete time state-space modeling and showcase the utility of the approach for use in the converter design process.
{"title":"Broad-Scale Converter Optimization Utilizing Discrete Time State-Space Modeling","authors":"J. Baxter, D. Costinett","doi":"10.1109/DMC55175.2022.9906473","DOIUrl":"https://doi.org/10.1109/DMC55175.2022.9906473","url":null,"abstract":"Schematic-level optimization and steady-state loss modeling play a vital role in the design of advanced power converters. Recently, discrete time state-space modeling has shown merits in rapid analysis and generality to arbitrary circuit topologies but has not yet been utilized under rapid optimization techniques. In this work, we investigate methods for the incorporation of rapid gradient-based optimization techniques leveraging discrete time state-space modeling and showcase the utility of the approach for use in the converter design process.","PeriodicalId":245908,"journal":{"name":"2022 IEEE Design Methodologies Conference (DMC)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131785041","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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