A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile

• Published in: Applied energy Vol. 164; pp. 387 - 399
• Main Authors: Yang, Fangfang, Xing, Yinjiao, Wang, Dong, Tsui, Kwok-Leung
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.02.2016
• Subjects: algorithms; ambient temperature; batteries; Degradation; Extended Kalman filter; Lithium-ion batteries; management systems; Particle filter; State of charge; uncertainty; Unscented Kalman filter; vehicles (equipment); Lithium-ion batteries; Degradation; State of charge; Unscented Kalman filter; Extended Kalman filter; Particle filter
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2015.11.072

•Three different model-based filtering algorithms for SOC estimation are compared.•A combined dynamic loading profile is proposed to evaluate the three algorithms.•Robustness against uncertainty of initial states of SOC estimators are investigated.•Battery capacity degradation is considered in SOC estimation. Accurate state-of-charge (SOC) estimation is critical for the safety and reliability of battery management systems in electric vehicles. Because SOC cannot be directly measured and SOC estimation is affected by many factors, such as ambient temperature, battery aging, and current rate, a robust SOC estimation approach is necessary to be developed so as to deal with time-varying and nonlinear battery systems. In this paper, three popular model-based filtering algorithms, including extended Kalman filter, unscented Kalman filter, and particle filter, are respectively used to estimate SOC and their performances regarding to tracking accuracy, computation time, robustness against uncertainty of initial values of SOC, and battery degradation, are compared. To evaluate the performances of these algorithms, a new combined dynamic loading profile composed of the dynamic stress test, the federal urban driving schedule and the US06 is proposed. The comparison results showed that the unscented Kalman filter is the most robust to different initial values of SOC, while the particle filter owns the fastest convergence ability when an initial guess of SOC is far from a true initial SOC.