Battery State of Health Estimation Using the Sliding Interacting Multiple Model Strategy

Richard Bustos, Stephen Andrew Gadsden (), Mohammad Biglarbegian, Mohammad AlShabi 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
Mohammad Biglarbegian: Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada
Mohammad AlShabi: Department of Mechanical and Nuclear Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates
Shohel Mahmud: College of Engineering and Physical Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada

Energies, 2024, vol. 17, issue 2, 1-22

Abstract: Due to their nonlinear behavior and the harsh environments to which batteries are subjected, they require a robust battery monitoring system (BMS) that accurately estimates their state of charge (SOC) and state of health (SOH) to ensure each battery’s safe operation. In this study, the interacting multiple model (IMM) algorithm is implemented in conjunction with an estimation strategy to accurately estimate the SOH and SOC of batteries under cycling conditions. The IMM allows for an adaptive mechanism to account for the decaying battery capacity while the battery is in use. The proposed strategy utilizes the sliding innovation filter (SIF) to estimate the SOC while the IMM serves as a process to update the parameter values of the battery model as the battery ages. The performance of the proposed strategy was tested using the well-known B005 battery dataset available at NASA’s Prognostic Data Repository. This strategy partitions the experimental dataset to build a database of different SOH models of the battery, allowing the IMM to select the most accurate representation of the battery’s current conditions while in operation, thus determining the current SOH of the battery. Future work in the area of battery retirement is also considered.

Keywords: lithium batteries; Kalman filters; sliding innovation filter; interacting multiple model; state of health; state of charge; battery monitoring system; B005 battery dataset (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: 2024
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