A Modified Extend Kalman Filter Based Approach for Lithium-ion Battery RUL Prognosis

Accurate and efficient accessing the Lithiumion battery remaining useful life (RUL) can assist engineer to adjust the battery using mode, expect charging time and set battery maintenance and replacement plan. Owing to the complex and noisy of the Lithium-ion batteries systems interior, this paper proposed a modified extend Kalman filter to predict the RUL. The internal state variables which have strong nonlinear characteristics are linearized with the Taylor expansion, and using the intercepted first-order linear part to approximate the battery system charging and discharging process. Numerical experiments with the Lithium-ion battery test data published by the National Aeronautics and Space Administration (NASA) Research Center confirm the effectiveness and robustness of the proposed prediction method, and the prediction errors are within 5%.