A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model. (15th October 2017)

• Record Type: Journal Article
• Authors: Wei, Zhongbao; Zhao, Jiyun; Ji, Dongxu; Tseng, King Jet
• Is Part Of: Applied energy. Volume 204(2017)
• Page Start: 1264
• Page End: 1274
• Publication Date: 2017-10-15
• Subjects: Model parameters identification -- State of charge -- Capacity -- State of health -- Multi-timescale -- Battery model; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.02.016
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: SOC and capacity are dually estimated with online adapted battery model. Model identification and state dual estimate are fully decoupled. Multiple timescales are used to improve estimation accuracy and stability. The proposed method is verified with lab-scale experiments. The proposed method is applicable to different battery chemistries. Abstract: Reliable online estimation of state of charge (SOC) and capacity is critically important for the battery management system (BMS). This paper presents a multi-timescale method for dual estimation of SOC and capacity with an online identified battery model. The model parameter estimator and the dual estimator are fully decoupled and executed with different timescales to improve the model accuracy and stability. Specifically, the model parameters are online adapted with the vector-type recursive least squares (VRLS) to address the different variation rates of them. Based on the online adapted battery model, the Kalman filter (KF)-based SOC estimator and RLS-based capacity estimator are formulated and integrated in the form of dual estimation. Experimental results suggest that the proposed method estimates the model parameters, SOC, and capacity in real time with fast convergence and high accuracy. Experiments on both lithium-ion battery and vanadium redox flow battery (VRB) verify the generality of the proposed method on multiple battery chemistries. The proposed method is also compared with other existing methods on the computational cost to reveal its superiority for practical application.