2 research outputs found
Online estimation of lithium battery SOC based on fractional order FOUKFâFOMIUKF algorithm with multiple time scales
Abstract Aiming at the matter of poor precision in predicting the charge of lithium battery by applying conventional integerâorder models and offline parameter identification, this paper proposes a joint fractionalâorder multiâinnovations unscented Kalman filter (FOUKFâFOMIUKF) algorithm for predicting the cells' state of charge (SOC) online and uses the theory of singularâvalue decomposition to tackle the issue of failure of the traceless transformation. Initially, the circuitry model of fractional order is built. The parameters of the model are recognized online by fractionalâorder unscented Kalman filtering (FOUKF), and the obtained parameters are then transmitted to the method known as the fractional order multiâinnovations unscented Kalman filter (FOMIUKF) to calculate the SOC of the cell. The algorithm was validated under four working conditions such as FUDS (US Federal Urban Driving Distance), BJDST (Beijing Dynamic Stress Test), DST (Dynamic Stress Test), and US06 (Highway Driving Distance Test), respectively, and compared with the FOMIUKF, MIUKF, and FOUKF algorithms for offline identification. The conclusions demonstrate that the SOC estimated by the FOUKFâFOMIUKF method is controlled within 0.5% of the mean absolute error under the four conditions and the rootâmeanâsquare error is controlled within 0.8%. It is not difficult to find that the FOUKFâFOMIUKF algorithm estimates SOC with higher accuracy and robustness