A state of charge estimation method for lithium-ion batteries based on fractional order adaptive extended kalman filter

Zhu, Qiao; Xu, Mengen; Zheng, Mengqian

doi:10.1016/j.energy.2019.115880

Cited by 154 publications

(72 citation statements)

References 31 publications

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“…Consequently, the regulation parameters k P , k I , and k D of PID unit in the measurement correction step can be solved by equations (26) and (27).…”

Section: Complexitymentioning

confidence: 99%

“…e CKF with radial-spherical cubature rule is adopted to estimate LIB SOC, and the estimation results show that the CKF is better than EKF and UKF in terms of accuracy and stability [23,27]. Although the EKF is weaker than UKF and CKF in estimation precision and filter convergence, the symmetric nonnegative definition of prior information matrix in UKF and CKF is not guaranteed all the time.…”

Section: Introductionmentioning

confidence: 99%

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A State of Charge Estimation Method for Lithium‐Ion Battery Using PID Compensator‐Based Adaptive Extended Kalman Filter

et al. 2021

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With the widespread application of electric vehicles, the study of the power lithium-ion battery (LIB) has broad prospects and great academic significance. The state of charge (SOC) is one of the key parts in battery management system (BMS), which is used to provide guarantee for the safe and efficient operation of LIB. To obtain the reliable SOC estimation result under the influence of simple model and measurement noise, a novel estimation method with adaptive feedback compensator is presented in this paper. The simplified dynamic external electrical characteristic of LIB is represented by the one-order Thevenin equivalent circuit model (ECM) and then the ECM parameters are identified by the forgetting factor recursive least squares method (FFRLS). Fully taking into account the feedback effect of terminal voltage innovation, the combination of adaptive extended Kalman filter (AEKF) and innovation vector-based proportional-integral-derivative (PID) feedback is proposed to estimate the LIB SOC. The common single proportional feedback of Kalman filter (KF) is replaced by the innovation vector-based PID feedback, which means that the multiple prior terminal voltage innovation is used in the measurement correction step of KF. The results reveal that the AEKF with PID feedback compensation strategy can improve the SOC estimation performance compared with the common AEKF, and it reveals good robust capability and rapid convergence speed for initial SOC errors. The maximum absolute error and average absolute error for SOC estimation are close to 4% and 2.6%, respectively.

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“…Consequently, the regulation parameters k P , k I , and k D of PID unit in the measurement correction step can be solved by equations (26) and (27).…”

Section: Complexitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A State of Charge Estimation Method for Lithium‐Ion Battery Using PID Compensator‐Based Adaptive Extended Kalman Filter

et al. 2021

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“…Kalman filter-based methods (Kalman filter (KF), extended Kalman filter (EKF), Unscented Kalman filter (UKF), Dual EKF, etc.) have been successfully used to estimate battery SoH using different HIs [39][40][41]. Other widely used algorithms in adaptive filtering are the least square-based ones, specially the recursive least square (RLS) method because of its simple implementation and accuracy [42].…”

Section: State Of the Art Challenges And Outlookmentioning

confidence: 99%

Analysis of current density in the electrode and electrolyte of lithium‐ion cells for ageing estimation applications

Javadipour

Mehran

2021

IET Smart Grid

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Lithium-ion battery is the commonly used energy storage technology in electric vehicles (EVs) because of its inexpensive manufacturing cost and high energy capacity. For optimal utilization of its capacity and lifetime, reliable state of health (SoH) monitoring solutions have to be included in the battery management system (BMS). SoH of a cell is affected by several reasons such as internal degradation or external damages that need to be estimated. This article analyses the current density in electrode and electrolyte of an EV lithium-ion cell using a simulation assisted method that leads to improvement in SoH estimation accuracy. The experimental results are analysed through the fusion of the magnetic field images captured by quantum fluxgate magnetometers, installed on the surface of the cell, together with the real-time simulation of the multi-physics model of the cell. The magnetic field sensors measure the magnetic field intensity with an accuracy of ±2 mT. The real-time simulation input data is updated from the measurements of both the magnetic field sensors and the battery cycler. The multi-physics model of the cell is developed in COMSOL modelling software, and real-time data fusion process is implemented on dSPACE Microlabbox real-time simulator. Results confirm that the proposed monitoring solution provides useful insight that can be employed in ageing estimation of EV batteries. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…A hybrid estimation method combining ampere-hour counting and EKF is proposed in [18] to alleviate the errors caused by the model and sensors. Considering the dispersion of the internal parameters of lithium batteries, the author developed a fractional order adaptive EKF algorithm which combines the advantages of the fractional order model and adaptive strategy [19]. Considering the relationship between the battery model and the estimation accuracy, Xiao et al [20] proposed two SOC estimation methods based on fractional order, Thevenin and fractional order PNGV models, in which the genetic algorithm (GA) was utilized to identify battery model parameters, while the EKF was applied to estimate SOC.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation of Lithium Battery Based on Improved Correntropy Extended Kalman Filter

Duan

Wang

et al. 2020

Energies

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State of charge (SOC) estimation plays a crucial role in battery management systems. Among all the existing SOC estimation approaches, the model-driven extended Kalman filter (EKF) has been widely utilized to estimate SOC due to its simple implementation and nonlinear property. However, the traditional EKF derived from the mean square error (MSE) loss is sensitive to non-Gaussian noise which especially exists in practice, thus the SOC estimation based on the traditional EKF may result in undesirable performance. Hence, a novel robust EKF method with correntropy loss is employed to perform SOC estimation to improve the accuracy under non-Gaussian environments firstly. Secondly, a novel robust EKF, called C-WLS-EKF, is developed by combining the advantages of correntropy and weighted least squares (WLS) to improve the digital stability of the correntropy EKF (C-EKF). In addition, the convergence of the proposed algorithm is verified by the Cramér–Rao low bound. Finally, a C-WLS-EKF method based on an equivalent circuit model is designed to perform SOC estimation. The experiment results clarify that the SOC estimation error in terms of the MSE via the proposed C-WLS-EKF method can efficiently be reduced from 1.361% to 0.512% under non-Gaussian noise conditions.

show abstract

A state of charge estimation method for lithium-ion batteries based on fractional order adaptive extended kalman filter

Cited by 154 publications

References 31 publications

A State of Charge Estimation Method for Lithium‐Ion Battery Using PID Compensator‐Based Adaptive Extended Kalman Filter

A State of Charge Estimation Method for Lithium‐Ion Battery Using PID Compensator‐Based Adaptive Extended Kalman Filter

Analysis of current density in the electrode and electrolyte of lithium‐ion cells for ageing estimation applications

State of Charge Estimation of Lithium Battery Based on Improved Correntropy Extended Kalman Filter

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