{"title":"Auto-Tuning Control of PEM Water Electrolyzer","authors":"A. Keow, Zheng Chen","doi":"10.1115/dscc2019-9156","DOIUrl":null,"url":null,"abstract":"\n Proton exchange membrane (PEM) electrolyzers with the ability to produce gases at a pressure suitable for direct metal hydride storage are desirable because they do not require the use of compressors and other auxiliary components. Direct storage into metal hydride cylinders is made feasible when the pressure and flow rate of hydrogen is controlled. The nonlinear dynamics of the PEM electrolyzer change with temperature and pressure, both of which change with the hydrogen production rate, and are thus difficult to estimate. Therefore, a model-free, relay-feedback, auto-tuning approach is used to tune a proportional integral (PI) controller. This allows for the determination of the voltage supply to the electrolyzer by tracking the current set-point and correlating it to the hydrogen production rate. A gain scheduling approach is used to record the tuned controller’s parameters at different set-points, minimizing the frequency of tuning the device. A self-assessment test is used to determine situations where the auto-tuner should activate to update the PI parameters, thus, allowing for the system to operate without supervision. The auto-tuning PI control is successfully tested with a PEM electrolyzer setup. Experimental results showed that an auto-tuner can tune the controller parameters and produce favorable transient behaviors, allowing for a degree of adaptability for variations in system set-points.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":"5 1","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechatronic Systems and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/dscc2019-9156","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 1
Abstract
Proton exchange membrane (PEM) electrolyzers with the ability to produce gases at a pressure suitable for direct metal hydride storage are desirable because they do not require the use of compressors and other auxiliary components. Direct storage into metal hydride cylinders is made feasible when the pressure and flow rate of hydrogen is controlled. The nonlinear dynamics of the PEM electrolyzer change with temperature and pressure, both of which change with the hydrogen production rate, and are thus difficult to estimate. Therefore, a model-free, relay-feedback, auto-tuning approach is used to tune a proportional integral (PI) controller. This allows for the determination of the voltage supply to the electrolyzer by tracking the current set-point and correlating it to the hydrogen production rate. A gain scheduling approach is used to record the tuned controller’s parameters at different set-points, minimizing the frequency of tuning the device. A self-assessment test is used to determine situations where the auto-tuner should activate to update the PI parameters, thus, allowing for the system to operate without supervision. The auto-tuning PI control is successfully tested with a PEM electrolyzer setup. Experimental results showed that an auto-tuner can tune the controller parameters and produce favorable transient behaviors, allowing for a degree of adaptability for variations in system set-points.
期刊介绍:
This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.