A novel model-based state of charge estimation for lithium-ion battery using adaptive robust iterative cubature Kalman filter

Liu, Zheng; Dang, Xuanju; Jing, Benqin; Ji, Jianbo

doi:10.1016/j.epsr.2019.105951

Cited by 46 publications

(21 citation statements)

References 33 publications

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“…Δ is difference operator, n is the different order for x kþ1 . Combining with the state Equation (6), the linear fractional-order stochastic discrete state-space equation can be obtained in Equation (10).…”

Section: Fractional-order Adaptive Extended Kalman Correctionmentioning

confidence: 99%

“…9 Now, more and more scholars pay attention to SOC estimation, thanks to that accurate SOC ensures the high efficiency and safety of BMS. 10 Scholars have proposed various methods of estimating the state of charge of batteries, such as, the open-circuit voltage method, Ah method, neural network method, and Kalman filtering method, but every method has its limitations and advantages. 11,12 By comparison, the Kalman method can correct the initial error and can effectively suppress the random interference from the system.…”

Section: Introductionmentioning

confidence: 99%

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An adaptive fractional‐order extended Kalman filtering for state of charge estimation of high‐capacity lithium‐ion battery

Peng

Wang

et al. 2021

Intl J of Energy Research

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The state of charge (SOC) estimation of lithium-ion battery is a crucial portion of the battery management system (BMS). The high-precision estimation is the foundation of BMS safety and efficiency. To that extent, a fractional-order algorithm with time-varying parameters model is proposed to ensure the accuracy of the SOC. Since the battery state changes slowly and is related to the state in the past, this study proposes a memory factor M containing the battery state in the past to estimate the SOC. Moreover, by comparing the experimental results of different orders, the most appropriate fractional order is determined. In order to eliminate the influence of noises introduced into historical data processing, an adaptive noise factor is added to the algorithm. The experimental results confirm that the maximum error of the adaptive fractional-order extended Kalman (AFEKF) estimation is less than 2%, which indicates that the estimation method provides a higher accuracy than the extended Kalman filter.

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Section: Fractional-order Adaptive Extended Kalman Correctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An adaptive fractional‐order extended Kalman filtering for state of charge estimation of high‐capacity lithium‐ion battery

Peng

Wang

et al. 2021

Intl J of Energy Research

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“…Various forms of Kalman filters (KF) are widely applied for ECM-based state estimation by regarding the SOC as one of the state observers [19,20]. Since the basic KF is essentially unsuitable for LIB with a strong nonlinear feature, some general improved KF are used for SOC estimation, such as extended Kalman filter (EKF) [21], unscented Kalman filter (UKF) [22], cubature Kalman filter (CKF) [23], and H-Infinite filter (HIF) [17]. e EKF with proportional-integral regulation is used for SOC estimation based on ECM with a resistance-capacitance network [24].…”

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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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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“…[ 23 ] A called a cubature Kalman filter (CKF) with a set of cubature points was developed to obtain state estimation of LIBs. [ 24–25 ] The key idea of CKF is to use the radial sphere to acquire the state expectation and variance of the non‐linear system with a simple Gaussian function. The corresponding adaptive strategy was integrated into the CKF to approximate the parameter identification and SOC estimation of LIBs.…”

Section: Introductionmentioning

confidence: 99%

A Combined State of Charge Estimation Method for Lithium‐Ion Batteries Using Cubature Kalman Filter and Least Square with Gradient Correction

Liu

Chen

et al. 2021

Advcd Theory and Sims

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Reliable state of charge (SOC) is the core of the energy storage system for electric vehicles. An efficient method of parameter identification and SOC estimation for lithium‐ion batteries (LIBs) using cubature Kalman filter (CKF) and least square with gradient correction is proposed. Firstly, the recursive gradient correction (RGC) strategy is introduced based on the recursive least square with forgetting factor (FFRLS) method, and a novel way is proposed for a simplified equivalent circuit model (ECM) based LIBs parameter identification using the combination of RGC and FFRLS method. Then, on the basis of CKF, the singular value decomposition (SVD) is used instead of the Cholesky decomposition to solve the non‐positive definiteness matrix for cubature transformation in the priori estimated covariance, thereby improving the effect of SOC estimation. Finally, the dynamic stress test (DST) and the federal urban driving schedule (FUDS) are used to verify the effect of the proposed method. The test results reflect that the combination of RGCFFRLS‐SVDCKF method and simplified ECM have advantages in terms of LIBs SOC estimation convergence ability and tracking accuracy, and the SOC estimation error can converge to 1% and 1.5% error boundary when the initial SOC are 0.5 and 0.8, respectively. In addition, the robustness of the proposed method is verified. Compared with the FFRLS‐EKF method, the proposed RGCFFRLS‐SVDCKF method has better precision and robustness to noise disturbance.

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A novel model-based state of charge estimation for lithium-ion battery using adaptive robust iterative cubature Kalman filter

Cited by 46 publications

References 33 publications

An adaptive fractional‐order extended Kalman filtering for state of charge estimation of high‐capacity lithium‐ion battery

An adaptive fractional‐order extended Kalman filtering for state of charge estimation of high‐capacity lithium‐ion battery

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

A Combined State of Charge Estimation Method for Lithium‐Ion Batteries Using Cubature Kalman Filter and Least Square with Gradient Correction

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