A linear recursive state of power estimation method based on fusion model of voltage and state of charge limitations. (August 2021)

• Record Type: Journal Article
• Authors: Li, Bowen; Wang, Shunli; Fernandez, Carlos; Yu, Chunmei; Xia, Lili; Fan, Yongcun
• Is Part Of: Journal of energy storage. Volume 40(2021)
• Publication Date: 2021-08
• Subjects: lithium ion batteries -- electric vehicles;state of power -- fusion model -- adaptive forgetting factor recursive least square algorithm -- linear recursion algorithm; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2021.102583
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: A parameter analysis method under complex changes is established. A compound model with multiple limiting factors is created. The dynamic online tracking method of multi-state integration is proposed innovatively. Abstract: As the main candidate of energy storage system for electric vehicles and hybrid electric vehicles, lithium-ion battery has attracted extensive attention. The working characteristics of the battery under dynamic stress stimulation are complex and changeable. To solve the problem of high-precision state of power estimation, a fusion model based on adaptive forgetting factor recursive least squares identification and voltage and charge state constraints was proposed, and a continuous discharge state of power analysis model for lithium-ion batteries was established. The adaptive forgetting factor recursive least square method based on battery model provides accurate and reliable online parameter identification feedback. The results show that the accuracy error of online parameter identification is less than 0.02V; the combination of the linear recursive algorithm of state of power analysis and the fusion model of voltage and current limit makes the power state estimation more reliable and accurate. The results show that when the battery is t=10s, the peak discharge power error is less than 80W.