2022
DOI: 10.1016/j.energy.2022.125083
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Robust state of charge estimation for Li-ion batteries based on cubature kalman filter with generalized maximum correntropy criterion

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Cited by 34 publications
(13 citation statements)
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“…There are two main stages in CKF: first, to convert the necessary form to a more recognizable spherical radial integral format, and second, to establish third‐order spherical radial criteria (Arasaratnam & Haykin, 2009). With its high computational efficiency and capability of processing nonlinear systems (Ma et al, 2022), CKF is considered an excellent choice for LIB's SoC estimation accuracy compared with the EKF (J. Luo et al, 2019). Despite outperforming the UKF, it has certain drawbacks.…”
Section: Soc Estimation Methodsmentioning
confidence: 99%
“…There are two main stages in CKF: first, to convert the necessary form to a more recognizable spherical radial integral format, and second, to establish third‐order spherical radial criteria (Arasaratnam & Haykin, 2009). With its high computational efficiency and capability of processing nonlinear systems (Ma et al, 2022), CKF is considered an excellent choice for LIB's SoC estimation accuracy compared with the EKF (J. Luo et al, 2019). Despite outperforming the UKF, it has certain drawbacks.…”
Section: Soc Estimation Methodsmentioning
confidence: 99%
“…[6] Accurate state of charge (SOC) estimation is essential for the charge/discharge control and thermal management of BMS. [7,8] Currently, SOC estimation methods can be divided into three categories: direct measurement method, [9] data-driven method, [10] and model-driven method. [11,12] The Ampere hour DOI: 10.1002/adts.202301022 (AH) method estimates SOC by integrating the charge/discharge current, but it relies on accurate initial SOC information and is susceptible to error accumulation.…”
Section: Introductionmentioning
confidence: 99%
“…For example, Ma et al, to enhance the volume of Kalman robustness and the estimation precision of the non-Gaussian noise environment, put forward a SOC estimation model based on cubature KF with generalized maximum correntropy criterion (GMCC-CKF). 22 Maheshwari and Nageswari use the sunflower optimization (SFO) algorithm to find the optimal values of the noise covariance matrices before applying extended KF (EKF) for online SOC estimation, the experimental results show that the algorithm has a high SOC estimation accuracy and a relatively high convergence rate under static and dynamic working conditions. 23 Qi et al proposed the adaptive spherical unscented KF (AS-UKF) algorithm based on the equivalent circuit model of second-order RC cells, which improved accuracy and became more stable.…”
Section: Introductionmentioning
confidence: 99%
“…Researchers have recently developed a series of improved KF algorithms to obtain more accurate SOC estimates. For example, Ma et al, to enhance the volume of Kalman robustness and the estimation precision of the non‐Gaussian noise environment, put forward a SOC estimation model based on cubature KF with generalized maximum correntropy criterion (GMCC‐CKF) 22 . Maheshwari and Nageswari use the sunflower optimization (SFO) algorithm to find the optimal values of the noise covariance matrices before applying extended KF (EKF) for online SOC estimation, the experimental results show that the algorithm has a high SOC estimation accuracy and a relatively high convergence rate under static and dynamic working conditions 23 .…”
Section: Introductionmentioning
confidence: 99%