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Prognostics of Lithium-Ion Batteries using Extended Kalman Filtering
Keywords: Lithium-ion batteries, Prognostic and health management, Extended Kalman Filtering
Lithium-ion batteries have become a chosen energy solution for many systems. In certain applications, such as those in aerospace field, a failure in batteries could lead to catastrophic system failure. Prognostic and health management (PHM) can provide essential cost saving benefits and mitigate risk factors by providing the assessment of battery degradation and the prediction of battery failure time. This paper presents a real-time PHM algorithm for the state-of-health (SOH) estimation and remaining useful life (RUL) prediction of lithium-ion batteries. An exponential model is developed based on the degradation behavior of lithium-ion batteries. The model parameters are estimated by extended Kalman filtering based on the in-situ monitoring data of batteries. Once the model converge to the real system response, the degradation level and the RUL of batteries can be calculated, which will provide useful information for decision-making of condition-based maintenance and operation planning. This approach is validated using experimental data from Lithium-ion batteries.
Wei He, Graduate Research Assistant
University of Maryland - CALCE
College Park, MD
USA


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