Lithium‐Ion Battery State of Charge Estimation Using a New Extended Nonlinear State Observer. Issue 3 (24th January 2022)

• Record Type: Journal Article
• Authors: Sakile, Rajakumar; Sinha, Umesh Kumar
• Is Part Of: Advanced theory and simulations. Volume 5:Issue 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-01-24
• Subjects: extended nonlinear state observer -- Lyapunov's stability -- open‐circuit voltage -- state of charge; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.202100552
• Languages: English
• ISSNs: 2513-0390

Abstract

Abstract: In this paper, an extended nonlinear state observer (ENSO) is proposed to estimate lithium‐ion batteries' accurate state of charge (SOC). The two RC equivalent circuit model is used to describe the dynamic behavior of the lithium‐ion battery. Based on the circuit state equations, the proposed ENSO is designed to estimate SOC. The ninth‐order polynomial fitting curve shows a nonlinear relationship between open‐circuit voltage and SOC. Furthermore, the proposed ENSO's stability and convergence rate are guaranteed by Lyapunov's stability analysis. The performance of the proposed observer is compared to that of conventional techniques such as the unscented Kalman filter and sliding mode observer. The proposed method has given better dynamic performance with accurate SOC, reducing computational cost and enhancing the convergence capability compared to conventional methods. The proposed model's effectiveness is validated through the MATLAB/Simulink platform. Abstract : The lithium‐ion battery's state of charge (SOC) is estimated accurately from the proposed extended nonlinear state observer. The state equations are obtained from the second‐order RC equivalent circuit model. Based on the state equations, the proposed method has given better dynamic performance with accurate SOC, reducing computational cost and enhancing the convergence capability compared to conventional methods.