A Systematic Framework for Battery Performance Estimation Considering Model and Parameter Uncertainties



Published Nov 1, 2020
Rong Jing Zhimin Xi Xiao Guang Yang Ed Decker


Up to date, model and parameter uncertainties are generally overlooked by majority of researchers in the field of battery diagnostics and prognostics. As a consequence, accuracy of
the battery performance estimation is dominated by the model fidelity and may vary from cell-to-cell. This paper proposes a systematic framework to quantify battery model and parameter uncertainties for more effective battery performance estimation. Such a framework is generally applicable for estimating various battery performances of interest (e.g. state of charge (SOC), capacity, and power capability). Case studies for battery SOC estimation are conducted to demonstrate the effectiveness of the proposed framework.

Abstract 229 | PDF Downloads 249



extended Kalman filter, Battery SoC, model uncertainty, parameter uncertainty, battery diagnostics

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