{"title":"基于在线参数识别和改进粒子滤波算法的锂电池 SOC 估算","authors":"Zhongqiang Wu, Xiaoyu Hu","doi":"10.1177/09576509241260085","DOIUrl":null,"url":null,"abstract":"This paper proposes an SOC estimation method for lithium battery, which combines the online parameter identification and an improved particle filter algorithm. Targeted at the particle degradation issue in particle filtering, grey wolf optimization is introduced to optimize particle distribution. Its strong global optimization ability ensures particle diversity, effectively suppresses particle degradation, and improves the filtering accuracy. The recursive least square method with forgetting factor is also introduced to update the model parameters in a real-time manner, which further improves the estimation accuracy of SOC alternately with the improved particle filter algorithm. Experimental results validate the proposed method, with an average estimation error less than ±0.15%. Compared with conventional extended Kalman filter and unscented Kalman filter algorithms, the proposed algorithm has higher estimation accuracy and stability for battery SOC estimation.","PeriodicalId":20705,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SOC estimation of lithium battery based on online parameter identification and an improved particle filter algorithm\",\"authors\":\"Zhongqiang Wu, Xiaoyu Hu\",\"doi\":\"10.1177/09576509241260085\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes an SOC estimation method for lithium battery, which combines the online parameter identification and an improved particle filter algorithm. Targeted at the particle degradation issue in particle filtering, grey wolf optimization is introduced to optimize particle distribution. Its strong global optimization ability ensures particle diversity, effectively suppresses particle degradation, and improves the filtering accuracy. The recursive least square method with forgetting factor is also introduced to update the model parameters in a real-time manner, which further improves the estimation accuracy of SOC alternately with the improved particle filter algorithm. Experimental results validate the proposed method, with an average estimation error less than ±0.15%. Compared with conventional extended Kalman filter and unscented Kalman filter algorithms, the proposed algorithm has higher estimation accuracy and stability for battery SOC estimation.\",\"PeriodicalId\":20705,\"journal\":{\"name\":\"Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2024-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1177/09576509241260085\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/09576509241260085","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
SOC estimation of lithium battery based on online parameter identification and an improved particle filter algorithm
This paper proposes an SOC estimation method for lithium battery, which combines the online parameter identification and an improved particle filter algorithm. Targeted at the particle degradation issue in particle filtering, grey wolf optimization is introduced to optimize particle distribution. Its strong global optimization ability ensures particle diversity, effectively suppresses particle degradation, and improves the filtering accuracy. The recursive least square method with forgetting factor is also introduced to update the model parameters in a real-time manner, which further improves the estimation accuracy of SOC alternately with the improved particle filter algorithm. Experimental results validate the proposed method, with an average estimation error less than ±0.15%. Compared with conventional extended Kalman filter and unscented Kalman filter algorithms, the proposed algorithm has higher estimation accuracy and stability for battery SOC estimation.
期刊介绍:
The Journal of Power and Energy, Part A of the Proceedings of the Institution of Mechanical Engineers, is dedicated to publishing peer-reviewed papers of high scientific quality on all aspects of the technology of energy conversion systems.