A Variational Bayesian-Based Unscented Kalman Filter With Both Adaptivity and Robustness

Li, Kailong; Chang, Lubin; Hu, Baiqing

doi:10.1109/jsen.2016.2591260

Cited by 100 publications

(63 citation statements)

References 26 publications

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“…where µ k is the residual error of the system measured output, and y k|k−1,i is the residual error of the system measured output estimated by the sigma points. Real-time updates of the process noise and measurement noise can be achieved by Equation (28). Thus, the establishment of the AUKF is completed.…”

Section: The Aukf Based On the Second-order Rc Equivalent Circuit Modelmentioning

confidence: 99%

SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization

Guo

Geng

et al. 2019

Applied Sciences

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State of charge (SOC) estimation is generally acknowledged to be one of the most important functions of the battery management system (BMS) and is thus widely studied in academia and industry. Based on an accurate SOC estimation, the BMS can optimize energy efficiency and protect the battery from being over-charged or over-discharged. The accurate online estimation of the SOC is studied in this paper. First, it is proved that the second-order resistance capacitance (RC) model is the most suitable equivalent circuit model compared with the Thevenin and multi-order models. The second-order RC equivalent circuit model is established, and the model parameters are identified. Second, the reasonable optimization of model parameters is studied, and a reasonable optimization method is proposed to improve the accuracy of SOC estimation. Finally, the SOC is estimated online based on the adaptive unscented Kalman filter (AUKF) with optimized model parameters, and the results are compared with the results of an estimation based on pre-optimization model parameters. Simulation experiments show that, without affecting the convergence of the initial error of the AUKF, the model after parameter optimization has a higher online SOC estimation accuracy.

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Section: The Aukf Based On the Second-order Rc Equivalent Circuit Modelmentioning

confidence: 99%

SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization

Guo

Geng

et al. 2019

Applied Sciences

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show abstract

“…But it is hard to get the analytical solution for most Bayesian approaches due to complex probability density function or high dimension of integration. Recently, the variational Bayesian (VB) inference method [27][28][29][30][31] has drawn extensive attentions, which utilizes a new simpler, analytically tractable distribution to approximate the true posterior distribution so as to avoid the direct complex calculation of multi-dimensional probability density function. Sarkka et al [27] adopted the VB method for joint recursive estimation of the dynamic state and the time-varying measurement noise parameters in linear state space models.…”

Section: Introductionmentioning

confidence: 99%

“…Sarkka et al [27] adopted the VB method for joint recursive estimation of the dynamic state and the time-varying measurement noise parameters in linear state space models. Li et al [28] employed VB approximation for the unscented Kalman filter to estimate the time-varying measurement noise covariance so as to improve algorithm adaptability. Sun et al [29] proposed a VB method to estimate the system states with unknown inputs.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Dual Extended Kalman Filter Based on Variational Bayesian Approximation for Joint Estimation of Lithium-Ion Battery State of Charge and Model Parameters

Hou

Yan

et al. 2019

Applied Sciences

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An accurate state of charge (SOC) estimation is vital for the safe operation and efficient management of lithium-ion batteries. At present, the extended Kalman filter (EKF) can accurately estimate the SOC under the condition of a precise battery model and deterministic noise statistics. However, in practical applications, the battery characteristics change with different operating conditions and the measurement noise statistics may vary with time, resulting in nonoptimal and even unreliable estimation of SOC by EKF. To improve the SOC estimation accuracy under uncertain measurement noise statistics, a variational Bayesian approximation-based adaptive dual extended Kalman filter (VB-ADEKF) is proposed in this paper. The variational Bayesian inference is integrated with the dual EKF (DEKF) to jointly estimate the lithium-ion battery parameters and SOC. Meanwhile, the measurement noise variances are simultaneously estimated in the SOC estimation process to compensate for the model uncertainties, so that the adaptability of the proposed algorithm to dynamic changes in battery characteristics is greatly improved. A constant current discharge test, a pulse current discharge test, and an urban dynamometer driving schedule (UDDS) test are performed to verify the effectiveness and superiority of the proposed algorithm by comparison with the DEKF algorithm. The experimental results show that the proposed VB-ADEKF algorithm outperforms the traditional DEKF algorithm in terms of SOC estimation accuracy, convergence rate, and robustness.

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“…Actually, the variational Bayesian (VB)-based filter [29][30][31][32][33] is one of the most general adaptive filters. Its adaptive strategy has a strong ability to track the time-varying measurement noise covariance.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it will be promising to combine VB approximation and Huber's M-estimation to achieve both adaptivity and robustness. Li et al [30] has acted out this idea in the framework of UKF. The efficiency of the proposed filter was verified through the numerical simulation test.…”

Section: Introductionmentioning

confidence: 99%

A Variational Bayesian and Huber-Based Robust Square Root Cubature Kalman Filter for Lithium-Ion Battery State of Charge Estimation

Hou

Yan

et al. 2019

Energies

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An accurate state of charge (SOC) estimation is vital for safe operation and efficient management of lithium-ion batteries. To improve the accuracy and robustness, an adaptive and robust square root cubature Kalman filter based on variational Bayesian approximation and Huber’s M-estimation (VB-HASRCKF) is proposed. The variational Bayesian (VB) approximation is used to improve the adaptivity by simultaneously estimating the measurement noise covariance and the SOC, while Huber’s M-estimation is employed to enhance the robustness with respect to the outliers in current and voltage measurements caused by adverse operating conditions. A constant-current discharge test and an urban dynamometer driving schedule (UDDS) test are performed to verify the effectiveness and superiority of the proposed algorithm by comparison with the square root cubature Kalman filter (SRCKF), the VB-based SRCKF, and the Huber-based SRCKF. The experimental results show that the proposed VB-HASRCKF algorithm outperforms the other three filters in terms of SOC estimation accuracy and robustness, with a little higher computation complexity.

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A Variational Bayesian-Based Unscented Kalman Filter With Both Adaptivity and Robustness

Cited by 100 publications

References 26 publications

SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization

SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization

Adaptive Dual Extended Kalman Filter Based on Variational Bayesian Approximation for Joint Estimation of Lithium-Ion Battery State of Charge and Model Parameters

A Variational Bayesian and Huber-Based Robust Square Root Cubature Kalman Filter for Lithium-Ion Battery State of Charge Estimation

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