State of Charge Estimation for Lithium‐Ion Batteries Based on an Adaptive Fractional‐Order Cubature Kalman Filter

Abstract

Based on the fractional-order model (FOM), this paper proposes an adaptive fractional-order cubature Kalman filter (AFCKF) method for state of charge (SOC) estimation of a lithium-ion battery (LIB). Firstly, a FOM with two constant phase elements is built, which can accurately represent the dynamic features of a LIB with a higher accuracy. Secondly, the adaptive estimations of the coefficients in the measurement equation are achieved by a linear Kalman filter algorithm, which avoids the calculation of the relationship between the open-circuit voltage and SOC. Thirdly, an augmented state equation including the SOC, the fractional-orders and parameters in the FOM is investigated by introducing the augmented vector method, and the state information is estimated online via the AFCKF algorithm. The algorithm requires a little computational burden while ensuring the estimation accuracy and is well adapted to complex working conditions. Besides, this study fully considers the impact of noises on the estimation effect. To better overcome the disturbances caused by unknown noises and further improve the precision and stability of the algorithm, an adaptive estimation method of the noise covariance matrices is achieved. Finally, the experimental findings are given to reveal that the proposed method can be effectively used to different working conditions and the estimation accuracy is better than the adaptive integer-order cubature Kalman filter.

An Initial Value Problem for State of Charge Estimation of Lithium‐Ion Batteries with an Adaptive Fractional‐Order Unscented Particle Filter

“…The issue of the SOC estimation for LIBs was addressed using cubature Kalman filter algorithms based on the FO models in refs. [26,27]. According to an adaptive UKF method based on the FO models, SOC was calculated for LIBs in refs.…”

An improved fractional‐order state estimation algorithm based on an unscented particle filter for state of charge estimation of lithium‐ion batteries with adaptive estimations of unknown parameters

“…Many academics have employed various methodologies to estimate the SOC of LIBs in recent years. [7][8][9] The ampere-hour integration, 10,11 the opencircuit voltage, 12,13 and the internal resistance techniques 14 are the SOC estimation techniques that are not based on the battery model. These techniques typically have drawbacks, such as the poor noise immunity and the low precision.…”

“…Therefore, the methods to obtain the state of charge (SOC) for LIBs and avoid overcharging and overdischarging in using LIBs have become one of the most critical issues in the field of power batteries. Many academics have employed various methodologies to estimate the SOC of LIBs in recent years 7–9 . The ampere‐hour integration, 10,11 the open‐circuit voltage, 12,13 and the internal resistance techniques 14 are the SOC estimation techniques that are not based on the battery model.…”