This paper introduces a method to monitor battery state of health (SOH) by estimating the number of cyclable Li-ions, a health-relevant electrochemical variable. SOH monitoring is critical to battery management in particular for balancing the trade-off between maximizing system performance and minimizing battery degradation. However, SOH-related electrochemical variables cannot be directly measured non-invasively. Hence, estimation algorithms are needed to track those variables non-destructively while the battery is in use. In this paper, the extended Kalman filter (EKF) is used to estimate the number of cyclable Li-ions as an unknown battery parameter. Simulations are performed using an example parameter set for a hybrid-electric-vehicle battery whose cathode material is LiMn2O4 mixed with other Li-compounds to obtain estimation results under a typical electric vehicle current profile that consists of a 1 C constant current charge mode and a discharge current profile for an electric vehicle subject to the Urban Dynamometer Driving Schedule cycle. The simulations show promising results in estimation of the number of cyclable Li-ions using the EKF under the ideal conditions. Next, robustness of the algorithm under non-ideal conditions (i.e., with SOC estimation error, modeling error, and measurement noise) is analyzed, and it is shown that estimation of the number of cyclable Li-ions using the EKF preserves high accuracy even under these non-ideal conditions. The proposed estimation technique for the number of cyclable Li-ions can also be applied to other parameter sets and batteries with other cathode materials to monitor the SOH change resulting from any degradation mechanism that consumes cyclable Li-ions.
- Dynamic Systems and Control Division
Battery State of Health Monitoring by Estimation of the Number of Cyclable Li-Ions
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Zhou, X, Stein, JL, & Ersal, T. "Battery State of Health Monitoring by Estimation of the Number of Cyclable Li-Ions." Proceedings of the ASME 2016 Dynamic Systems and Control Conference. Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation. Minneapolis, Minnesota, USA. October 12–14, 2016. V001T08A001. ASME. https://doi.org/10.1115/DSCC2016-9730
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