An Enhanced Equivalent Circuit Model With Real-Time Parameter Identification for Battery State-of-Charge Estimation

• Published in: IEEE transactions on industrial electronics (1982) Vol. 69; no. 4; pp. 3743 - 3751
• Main Authors: Naseri, Farshid, Schaltz, Erik, Stroe, Daniel-Ioan, Gismero, Alejandro, Farjah, Ebrahim
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Batteries; Computational modeling; Electric cells; Electronic countermeasures; Equivalent circuit model (ECM); Equivalent circuits; Estimation; Extended Kalman filter; extended Kalman filter (EKF); Integrated circuit modeling; Iterative methods; least squares; lithium-ion (Li-ion) battery; Lithium-ion batteries; Manganese; Mathematical model; Mathematical models; Nonlinearity; Parameter estimation; Parameter identification; Real time; Real-time systems; Rechargeable batteries; State of charge; state of charge (SoC); Wiener model
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2021.3071679

This article introduces an efficient modeling approach based on the Wiener structure to reinforce the capacity of classical equivalent circuit models (ECMs) in capturing the nonlinearities of lithium-ion (Li-ion) batteries. The proposed block-oriented modeling architecture is composed of a simple linear ECM followed by a static output nonlinearity block, which helps achieving a superior nonlinear mapping property while maintaining the real-time efficiency. The observability of the established battery model is analytically proven. This article also introduces an efficient parameter estimator based on extended-kernel iterative recursive least squares algorithm for real-time estimation of the parameters of the proposed Wiener model. The proposed approach is applied for state-of-charge (SoC) estimation of 3.4-Ah 3.6-V nickel-manganese-cobalt-based Li-ion cells using the extended Kalman filter (EKF). The results show about 1.5% improvement in SoC estimation accuracy compared with the EKF algorithm based on the second-order ECM. A series of real-time tests are also carried out to demonstrate the computational efficiency of the proposed method.