Health Monitoring of Lithium-Ion Batteries Using Dual Filters

Richard Bustos, Stephen Andrew Gadsden, Pawel Malysz, Mohammad Al-Shabi and Shohel Mahmud
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Richard Bustos: College of Engineering and Physical Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada
Stephen Andrew Gadsden: Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada
Pawel Malysz: Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada
Mohammad Al-Shabi: Department of Mechanical and Nuclear Engineering, University of Sharjah, Sharjah, United Arab Emirates
Shohel Mahmud: College of Engineering and Physical Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada

Energies, 2022, vol. 15, issue 6, 1-16

Abstract: Accurate estimation of a battery’s capacity is critical for determining its state of health (SOH) and retirement, as well as to ensure its reliable operation. In this paper, a dual filter architecture using the Kalman filter (KF) and the novel sliding innovation filter (SIF) was implemented to estimate the capacity and state of charge (SOC) of a lithium-ion battery. NASA’s Prognostic Center of Excellence (PCOE) B005 battery data set was selected for this experiment based on its wide use in academia and industry. This dataset contains cycling data of a 2 Ah lithium-ion battery until its capacity was measured at 1.3 Ah or less. The dual polarity equivalent circuit model (DP-ECM) was selected for modeling. The model parameter values were estimated using the least squares (LS) algorithm. Under normal operating conditions, both the dual-KF and dual-SIF performed similarly in terms of estimation accuracy. However, an uncertainty case was considered where the filters were subjected to rapid changing dynamics by cutting the data by 300 cycles. In this case, the battery capacity root-mean-square error (RMSE) for the dual-KF and the proposed dual-SIF were 0.1233 and 0.0675, respectively. Under rapidly changing dynamics and faulty conditions, the dual-SIF shows better convergence and robustness to disturbances.

Keywords: lithium battery; Kalman filter; dual Kalman filter; sliding innovation filter; state of charge; state of health; battery retirement (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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