A strong tracking adaptive fading‐extended Kalman filter for the state of charge estimation of lithium‐ion batteries. (29th June 2022)

• Record Type: Journal Article
• Authors: Takyi‐Aninakwa, Paul; Wang, Shunli; Zhang, Hongying; Appiah, Emmanuel; Bobobee, Etse Dablu; Fernandez, Carlos
• Is Part Of: International journal of energy research. Volume 46:Number 12(2022)
• Page Start: 16427
• Page End: 16444
• Publication Date: 2022-06-29
• Subjects: lithium‐ion battery -- second‐order resistor–capacitor equivalent circuit model -- state of charge estimation -- strong tracking adaptive fading‐extended Kalman filter -- voltage traction; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.8307
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: Lithium‐ion batteries are widely used as rechargeable energy and power storage system in smart devices and electric vehicles because of their high specific energy, high power densities, etc. The state of charge (SOC) serves as a vital feature that is monitored by the battery management system to optimize the performance, safety, and lifespan of lithium‐ion batteries. In this paper, a strong tracking adaptive fading‐extended Kalman filter (STAF‐EKF) based on the second‐order resistor–capacitor equivalent circuit model (2RC‐ECM) is proposed for accurate SOC estimation of lithium‐ion batteries under different working conditions and ambient temperatures. The characteristic parameters of the established 2RC‐ECM for the lithium‐ion battery are identified offline using the least‐squares curve fitting method with an average R‐squared value of 0.99881. Experimental data from the hybrid pulse power characterization (HPPC) is used for the estimation and verification of the proposed STAF‐EKF method under the complex Beijing bus dynamic stress test (BBDST) and the dynamic stress test (DST) working conditions at varying ambient temperatures. The results show that the established 2RC‐ECM tracks the actual voltage of the battery with a maximum error of 28.44 mV under the BBDST working condition. For the SOC estimation, the results show that the proposed STAF‐EKF has a maximum mean absolute error (MAE) and root mean square error (RMSE) values of 1.7159% and 1.8507%, while the EKF has 6.7358% and 7.2564%, respectively, at an ambient temperature of −10°C under the BBDST working condition. The proposed STAF‐EKF delivers optimal performance improvement compared to the EKF under different working conditions and ambient temperatures, serving as a basis for an accurate and robust SOC estimation method with quick convergence for the real‐time applications of lithium‐ion batteries.