A multi-model probability SOC fusion estimation approach using an improved adaptive unscented Kalman filter technique

Li, Yanwen; Wang, Chao; Gong, Jinfeng

doi:10.1016/j.energy.2017.11.079

Cited by 87 publications

(32 citation statements)

References 45 publications

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“…Model-based filtering methods, such as the extended Kalman filter (EKF), [13][14][15] unscented Kalman filter [16][17][18] and H infinity filter, 1,[19][20][21] can achieve high accuracy online with low-cost hardware; many studies on these methods have been conducted, exhibiting remarkable potential. EKF is one of the most popular approaches used for battery SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

A normal‐gamma‐based adaptive dual unscented Kalman filter for battery parameters and state‐of‐charge estimation with heavy‐tailed measurement noise

2020

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Summary This study simultaneously considers the state‐of‐charge (SOC) estimation and model parameter identification of lithium‐ion batteries with outliers in measurements. Conventional Kalman‐type filters may degrade performance in this case since they assume Gaussian‐distributed measurement noise. To improve the SOC estimation accuracy under this condition, a robust normal‐gamma (NG)‐based adaptive dual unscented Kalman filter (NG‐ADUKF) is proposed. First, by modeling the joint distribution of the state and auxiliary variables of the measurement noise as the NG distribution, the unscented Kalman filter (UKF) is integrated with the NG filter to deal with the heavy‐tailed measurement noise. Second, the online parameter identification and SOC estimation are realized simultaneously by alternatively using two NG‐based adaptive UKFs. The performance of the proposed algorithm is validated by the New European Driving Cycle and Urban Dynamometer Driving Schedule tests. Experimental results show that the proposed NG‐ADUKF algorithm has more accurate SOC estimations compared with the dual UKF (DUKF) and the variational Bayes‐based adaptive DUKF (VB‐ADUKF) in the case of mistuning and outliers. Moreover, the proposed method is more computationally efficient than VB‐ADUKF.

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Section: Introductionmentioning

confidence: 99%

A normal‐gamma‐based adaptive dual unscented Kalman filter for battery parameters and state‐of‐charge estimation with heavy‐tailed measurement noise

2020

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“…The unscented Kalman filter (UKF) with unscented transformation (UT) method is utilized to handle the SOC estimation problem in strong nonlinear battery systems [25,26], and the estimation results show that the UKF has better robustness and higher precision than the EKF. The adaptive EKF (AEKF) and adaptive UKF (AUKF) based on the dynamic adaptive strategy of adjusting noise were adopted in state estimator to achieve the goal of highprecision and better stability than EKF and UKF [27,28]. In [29][30][31][32][33], an H-Infinity Filter (HIF) based on the minimax residual error criterion observer is designed for model parameters identification and state estimation, which has more robust to model error uncertainty without assumptions of accurate battery model.…”

Section: Introductionmentioning

confidence: 99%

“…In other words, the uncertain covariance of noise can result in unavoidable estimation error of the HIF. One common feather of the aforementioned methods is that OCV can be calculated from OCV-SOC fitting model [23][24][25][26][27][28][29][30][31][32][33][34][35]. However, due to fitting error in OCV-SOC curve [9], any small perturbation in OCV calculation by OCV-SOC fitting model may cause a large deviation in SOC estimation, especially a wide flat area existing in the OCV-SOC curve with a strong nonlinear relationship.…”

Section: Introductionmentioning

confidence: 99%

Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter

Liu

Dang

2018

Mathematical Problems in Engineering

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The state of charge (SOC) estimation is one of the most important features in battery management system (BMS) for electric vehicles (EVs). In this article, a novel equivalent-circuit model (ECM) with an extra noise sequence is proposed to reduce the adverse effect of model error. Model parameters identification method with variable forgetting factor recursive extended least squares (VFFRELS), which combines a constructed incremental autoregressive and moving average (IARMA) model with differential measurement variables, is presented to obtain the ECM parameters. The independent open circuit voltage (OCV) estimator with error compensation factors is designed to reduce the OCV error of OCV fitting model. Based on the IARMA battery model analysis and the parameters identification, an SOC estimator by adaptive H-infinity filter (AHIF) is formulated. The adaptive strategy of the AHIF improves the numerical stability and robust performance by synchronous adjusting noise covariance and restricted factor. The results of experiment and simulation have verified that the proposed approach has superior advantage of parameters identification and SOC estimation to other estimation methods.

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“…The unscented Kalman filter (UKF) is utilized to estimate lithium-ion battery SOC [18,19], and the estimation results show that the UKF has better robustness and higher precision than the EKF. The adaptive EKF (AEKF) and adaptive UKF (AUKF) are adopted in state estimator to achieve the goal of higher precision and better stability than EKF and UKF [20,21]. The H infinity filter (HIF) is also applied to the model parameters identification and state estimation [22][23][24][25][26][27] and has better performance than UKF and EKF.…”

Section: Introductionmentioning

confidence: 99%

A New Method for State of Charge and Capacity Estimation of Lithium-Ion Battery Based on Dual Strong Tracking Adaptive H Infinity Filter

Liu

Dang

2018

Mathematical Problems in Engineering

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As one of the most important features representing the operating state of power battery in electric vehicles (EVs), state of charge (SOC) and capacity estimation is a crucial assessment index in battery management system (BMS). This paper presents a fusion method of SOC and capacity estimation with identified model parameters. The equivalent circuit model (ECM) parameters are obtained online by variable forgetting factor recursive least squares (VFFRLS), which is based on incremental ECM analysis to respond to the inconsistent rates of parameters variation. The independent open-circuit voltage (OCV) estimation way is designed to reduce the effect of mutual coupling between OCV and ECM parameters. Based on the identified ECM parameters and OCV, a dual adaptive H infinity filter (AHIF) combined with strong tracking filter (STF) is proposed to estimate battery SOC and capacity. A new quadratic function as capacity error compensation is introduced to represent the relationship between capacity and OCV. The adaptive strategy of the AHIF can adjust noise covariance and restricted factor, while the STF can regulate prior state covariance by adding suboptimum fading factor. The results of experiment and simulation show the merits of proposed approach in SOC and capacity estimation.

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A multi-model probability SOC fusion estimation approach using an improved adaptive unscented Kalman filter technique

Cited by 87 publications

References 45 publications

A normal‐gamma‐based adaptive dual unscented Kalman filter for battery parameters and state‐of‐charge estimation with heavy‐tailed measurement noise

A normal‐gamma‐based adaptive dual unscented Kalman filter for battery parameters and state‐of‐charge estimation with heavy‐tailed measurement noise

Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter

A New Method for State of Charge and Capacity Estimation of Lithium-Ion Battery Based on Dual Strong Tracking Adaptive H Infinity Filter

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