{"title":"用于优化和预测控制的固定式锂离子电池建模","authors":"Emma Raszmann, K. Baker, Ying Shi, D. Christensen","doi":"10.1109/PECI.2017.7935755","DOIUrl":null,"url":null,"abstract":"Accurately modeling stationary battery storage behavior is crucial to pursuing cost-effective distributed energy resource opportunities. In this paper, a lithium-ion battery model was derived for building-integrated battery use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. To achieve these goals, a mixed modeling approach incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. The proposed battery model is validated through comparison to manufacturer data. Additionally, a dynamic test case demonstrates the effects of using regression models to represent cycling losses and capacity fading. A proof-of-concept optimization test case with time-of-use pricing is performed to demonstrate how the battery model could be included in an optimization framework.","PeriodicalId":444586,"journal":{"name":"2017 IEEE Power and Energy Conference at Illinois (PECI)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"47","resultStr":"{\"title\":\"Modeling stationary lithium-ion batteries for optimization and predictive control\",\"authors\":\"Emma Raszmann, K. Baker, Ying Shi, D. Christensen\",\"doi\":\"10.1109/PECI.2017.7935755\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurately modeling stationary battery storage behavior is crucial to pursuing cost-effective distributed energy resource opportunities. In this paper, a lithium-ion battery model was derived for building-integrated battery use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. To achieve these goals, a mixed modeling approach incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. The proposed battery model is validated through comparison to manufacturer data. Additionally, a dynamic test case demonstrates the effects of using regression models to represent cycling losses and capacity fading. A proof-of-concept optimization test case with time-of-use pricing is performed to demonstrate how the battery model could be included in an optimization framework.\",\"PeriodicalId\":444586,\"journal\":{\"name\":\"2017 IEEE Power and Energy Conference at Illinois (PECI)\",\"volume\":\"79 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-02-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"47\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Power and Energy Conference at Illinois (PECI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PECI.2017.7935755\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Power and Energy Conference at Illinois (PECI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PECI.2017.7935755","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modeling stationary lithium-ion batteries for optimization and predictive control
Accurately modeling stationary battery storage behavior is crucial to pursuing cost-effective distributed energy resource opportunities. In this paper, a lithium-ion battery model was derived for building-integrated battery use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. To achieve these goals, a mixed modeling approach incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. The proposed battery model is validated through comparison to manufacturer data. Additionally, a dynamic test case demonstrates the effects of using regression models to represent cycling losses and capacity fading. A proof-of-concept optimization test case with time-of-use pricing is performed to demonstrate how the battery model could be included in an optimization framework.