Online state of charge and model parameter co-estimation based on a novel multi-timescale estimator for vanadium redox flow battery. (15th June 2016)

• Record Type: Journal Article
• Authors: Wei, Zhongbao; Lim, Tuti Mariana; Skyllas-Kazacos, Maria; Wai, Nyunt; Tseng, King Jet
• Is Part Of: Applied energy. Volume 172(2016)
• Page Start: 169
• Page End: 179
• Publication Date: 2016-06-15
• Subjects: State of charge estimation -- Parameters identification -- Battery model -- Multi-timescale -- Vanadium redox flow battery -- Lithium-ion battery; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2016.03.103
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Battery model parameters and SOC co-estimation is investigated. The model parameters and OCV are decoupled and estimated independently. Multiple timescales are adopted to improve precision and stability. SOC is online estimated without using the open-circuit cell. The method is robust to aging levels, flow rates, and battery chemistries. Abstract: A key function of battery management system (BMS) is to provide accurate information of the state of charge (SOC) in real time, and this depends directly on the precise model parameterization. In this paper, a novel multi-timescale estimator is proposed to estimate the model parameters and SOC for vanadium redox flow battery (VRB) in real time. The model parameters and OCV are decoupled and estimated independently, effectively avoiding the possibility of cross interference between them. The analysis of model sensitivity, stability, and precision suggests the necessity of adopting different timescales for each estimator independently. Experiments are conducted to assess the performance of the proposed method. Results reveal that the model parameters are online adapted accurately thus the periodical calibration on them can be avoided. The online estimated terminal voltage and SOC are both benchmarked with the reference values. The proposed multi-timescale estimator has the merits of fast convergence, high precision, and good robustness against the initialization uncertainty, aging states, flow rates, and also battery chemistries.