Estimation of the State-of-Charge of Lithium-ion Battery using Adaptive State Augmented Cubature Kalman Filter in Presence of Uncharacterized Coloured Noise in the Measurement
Estimation of the State-of-Charge of Lithium-ion Battery using Adaptive State Augmented Cubature Kalman Filter in Presence of Uncharacterized Coloured Noise in the Measurement
- Author(s): P. Sri mannarayana 1 ; A. Dey 1 ; J. Dey 1
- DOI: 10.1049/icp.2021.1188
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- Author(s): P. Sri mannarayana 1 ; A. Dey 1 ; J. Dey 1
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View affiliations
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Affiliations:
1:
Department of Electrical engineering, National Institute of Technology Durgapur , West Bengal , India
Source:
Michael Faraday IET International Summit 2020 (MFIIS 2020),
2021
p.
130 – 135
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Affiliations:
1:
Department of Electrical engineering, National Institute of Technology Durgapur , West Bengal , India
- Conference: Michael Faraday IET International Summit 2020 (MFIIS 2020)
- DOI: 10.1049/icp.2021.1188
- ISBN: 978-1-83953-523-9
- Location: Online Conference
- Conference date: 03-04 October 2020
- Format: PDF
This paper validates an adaptive State Augmented Cubature kalman Filter (adaptive SA-CKF) during estimation of the state-of-charge (SOC) of Lithium-ion (Li-ion) battery where the terminal voltage measurement is perturbed with an uncharacterized coloured noise, which is unavoidable and is usually assumed as a Gaussian white noise with zero mean. However, in real world scenario, this assumption may not hold always. In this work authors consider presence of coloured noise in the measurement where the noise statistics (the auto correlation coefficient and the noise covariance) remain unknown. The coloured noise and its unknown auto correlation coefficient are modelled as states, augmented with the state vector and subsequently estimated. In addition to this, as the noise covariance is also unknown; it is adapted using Q adaptation algorithm using Maximum Likelihood Estimation (MLE) method of parameter estimation. The Cubature Kalman filter (CKF) is preferred as the estimator over widely reported Extended Kalman filter (EKF) as CKF demonstrates improved estimation accuracy over EKF and completely avoids calculation of Jacobian matrix as in EKF. Efficacy of adaptive SA-CKF is exemplified over its non-Adaptive counterpart with the help of Monte Carlo simulation. It is also demonstrated that the proposed adaptive SA-CKF can accurately estimate the SOC of Li-ion battery even though the unknown noise covariance is time varying.
Inspec keywords: maximum likelihood estimation; secondary cells; nonlinear filters; Kalman filters; Monte Carlo methods; white noise
Subjects: Secondary cells; Filtering methods in signal processing; Secondary cells; Monte Carlo methods; Filters and other networks