{"title":"基于双层模糊映射的电氢混合储能系统动态功率分配策略","authors":"Xibeng Zhang;Shun Zhou;Yang Lu;Feng Huo;Feixiang Jiao;Yanyu Zhang;Yi Zhou;Abhisek Ukil","doi":"10.1109/TIE.2025.3555046","DOIUrl":null,"url":null,"abstract":"Integrating a hydrogen energy storage system into the traditional lead-acid battery-supercapacitor energy storage architecture can significantly enhance the energy density and facilitate long-term electricity storage. The main challenge of dynamic power allocation for an electric-hydrogen hybrid energy storage system (EHESS) lies in considering the different characteristics of multiple energy storage devices within a short control period. To address this issue, this article proposes a dynamic power allocation strategy based on a dual-layer fuzzy mapping mechanism for EHESS. The state of charge (SOC) and state of hydrogen storage (SOHS) are mapped to a uniform EHESS state, considering the lifespan and safety of lead-acid battery and hydrogen storage. Subsequently, the SOHS is dynamically adjusted based on energy conversion efficiency. Finally, adaptive droop coefficients are designed based on the mapped EHESS states to achieve safe power distribution with high energy conversion efficiency. The effectiveness of the proposed method is validated using an actual EHESS hardware experimental setup.","PeriodicalId":13402,"journal":{"name":"IEEE Transactions on Industrial Electronics","volume":"72 10","pages":"10316-10326"},"PeriodicalIF":7.2000,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dual-Layer Fuzzy Mapping-Based Dynamic Power Allocation Strategy for Electric-Hydrogen Hybrid Energy Storage System\",\"authors\":\"Xibeng Zhang;Shun Zhou;Yang Lu;Feng Huo;Feixiang Jiao;Yanyu Zhang;Yi Zhou;Abhisek Ukil\",\"doi\":\"10.1109/TIE.2025.3555046\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Integrating a hydrogen energy storage system into the traditional lead-acid battery-supercapacitor energy storage architecture can significantly enhance the energy density and facilitate long-term electricity storage. The main challenge of dynamic power allocation for an electric-hydrogen hybrid energy storage system (EHESS) lies in considering the different characteristics of multiple energy storage devices within a short control period. To address this issue, this article proposes a dynamic power allocation strategy based on a dual-layer fuzzy mapping mechanism for EHESS. The state of charge (SOC) and state of hydrogen storage (SOHS) are mapped to a uniform EHESS state, considering the lifespan and safety of lead-acid battery and hydrogen storage. Subsequently, the SOHS is dynamically adjusted based on energy conversion efficiency. Finally, adaptive droop coefficients are designed based on the mapped EHESS states to achieve safe power distribution with high energy conversion efficiency. The effectiveness of the proposed method is validated using an actual EHESS hardware experimental setup.\",\"PeriodicalId\":13402,\"journal\":{\"name\":\"IEEE Transactions on Industrial Electronics\",\"volume\":\"72 10\",\"pages\":\"10316-10326\"},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2025-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Industrial Electronics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10950082/\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Industrial Electronics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10950082/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Dual-Layer Fuzzy Mapping-Based Dynamic Power Allocation Strategy for Electric-Hydrogen Hybrid Energy Storage System
Integrating a hydrogen energy storage system into the traditional lead-acid battery-supercapacitor energy storage architecture can significantly enhance the energy density and facilitate long-term electricity storage. The main challenge of dynamic power allocation for an electric-hydrogen hybrid energy storage system (EHESS) lies in considering the different characteristics of multiple energy storage devices within a short control period. To address this issue, this article proposes a dynamic power allocation strategy based on a dual-layer fuzzy mapping mechanism for EHESS. The state of charge (SOC) and state of hydrogen storage (SOHS) are mapped to a uniform EHESS state, considering the lifespan and safety of lead-acid battery and hydrogen storage. Subsequently, the SOHS is dynamically adjusted based on energy conversion efficiency. Finally, adaptive droop coefficients are designed based on the mapped EHESS states to achieve safe power distribution with high energy conversion efficiency. The effectiveness of the proposed method is validated using an actual EHESS hardware experimental setup.
期刊介绍:
Journal Name: IEEE Transactions on Industrial Electronics
Publication Frequency: Monthly
Scope:
The scope of IEEE Transactions on Industrial Electronics encompasses the following areas:
Applications of electronics, controls, and communications in industrial and manufacturing systems and processes.
Power electronics and drive control techniques.
System control and signal processing.
Fault detection and diagnosis.
Power systems.
Instrumentation, measurement, and testing.
Modeling and simulation.
Motion control.
Robotics.
Sensors and actuators.
Implementation of neural networks, fuzzy logic, and artificial intelligence in industrial systems.
Factory automation.
Communication and computer networks.