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On the Identification of Electrical Equivalent Circuit Models Based on Noisy Measurements.

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Title: On the Identification of Electrical Equivalent Circuit Models Based on Noisy Measurements.
Authors: Balasingam, Balakumar1 singam@uwindsor.ca, Pattipati, Krishna R.2 krishna.pattipati@uconn.edu
Source: IEEE Transactions on Instrumentation & Measurement. 2021, Vol. 70, p1-16. 16p.
Subjects: Electric motors, Electric circuits, Kalman filtering, Signal-to-noise ratio, Electric batteries, System identification, Unbiased estimation (Statistics)
Abstract: Real-time identification of electrical equivalent circuit models (ECMs) is a critical requirement in many practical systems, such as batteries and electric motors. Significant work has been done in the past developing different types of algorithms for system identification using reduced-order ECMs. However, little work was done in analyzing the theoretical performance bounds of these system identification approaches. Given that both voltage and current are measured with error, proper understanding of theoretical bounds will help in designing a system that is economical in cost and robust in performance. In this article, we analyze the performance of a linear recursive least squares (RLS) approach to ECM identification and show that the LS approach is both unbiased and efficient when the signal-to-noise ratio is high enough. However, we show that when the signal-to-noise ratio is low–resembling the case in many practical applications–the LS estimator becomes significantly biased. Consequently, we develop a parameter estimation approach based on the total LS method and show it to be asymptotically unbiased and efficient at practically low signal-to-noise ratio regions. Further, we develop a recursive implementation of the total least square algorithm and find it to be slow to converge; for this, we employ a Kalman filter to improve the convergence speed of the total LS method. The resulting total Kalman filter (TKF) is shown to be both unbiased and efficient in ECM identification. The performance of this filter is analyzed using real-world current profiles under fluctuating signal-to-noise ratios. Finally, the applicability of the algorithms and analysis in this article in identifying higher-order electrical ECMs is explained. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Instrumentation & Measurement is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 170415325
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  Data: On the Identification of Electrical Equivalent Circuit Models Based on Noisy Measurements.
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  Data: <searchLink fieldCode="AR" term="%22Balasingam%2C+Balakumar%22">Balasingam, Balakumar</searchLink><relatesTo>1</relatesTo><i> singam@uwindsor.ca</i><br /><searchLink fieldCode="AR" term="%22Pattipati%2C+Krishna+R%2E%22">Pattipati, Krishna R.</searchLink><relatesTo>2</relatesTo><i> krishna.pattipati@uconn.edu</i>
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  Data: <searchLink fieldCode="DE" term="%22Electric+motors%22">Electric motors</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+circuits%22">Electric circuits</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+batteries%22">Electric batteries</searchLink><br /><searchLink fieldCode="DE" term="%22System+identification%22">System identification</searchLink><br /><searchLink fieldCode="DE" term="%22Unbiased+estimation+%28Statistics%29%22">Unbiased estimation (Statistics)</searchLink>
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  Data: Real-time identification of electrical equivalent circuit models (ECMs) is a critical requirement in many practical systems, such as batteries and electric motors. Significant work has been done in the past developing different types of algorithms for system identification using reduced-order ECMs. However, little work was done in analyzing the theoretical performance bounds of these system identification approaches. Given that both voltage and current are measured with error, proper understanding of theoretical bounds will help in designing a system that is economical in cost and robust in performance. In this article, we analyze the performance of a linear recursive least squares (RLS) approach to ECM identification and show that the LS approach is both unbiased and efficient when the signal-to-noise ratio is high enough. However, we show that when the signal-to-noise ratio is low–resembling the case in many practical applications–the LS estimator becomes significantly biased. Consequently, we develop a parameter estimation approach based on the total LS method and show it to be asymptotically unbiased and efficient at practically low signal-to-noise ratio regions. Further, we develop a recursive implementation of the total least square algorithm and find it to be slow to converge; for this, we employ a Kalman filter to improve the convergence speed of the total LS method. The resulting total Kalman filter (TKF) is shown to be both unbiased and efficient in ECM identification. The performance of this filter is analyzed using real-world current profiles under fluctuating signal-to-noise ratios. Finally, the applicability of the algorithms and analysis in this article in identifying higher-order electrical ECMs is explained. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Transactions on Instrumentation & Measurement is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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    Identifiers:
      – Type: doi
        Value: 10.1109/TIM.2021.3068171
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      – Code: eng
        Text: English
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        PageCount: 16
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      – SubjectFull: Electric motors
        Type: general
      – SubjectFull: Electric circuits
        Type: general
      – SubjectFull: Kalman filtering
        Type: general
      – SubjectFull: Signal-to-noise ratio
        Type: general
      – SubjectFull: Electric batteries
        Type: general
      – SubjectFull: System identification
        Type: general
      – SubjectFull: Unbiased estimation (Statistics)
        Type: general
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      – TitleFull: On the Identification of Electrical Equivalent Circuit Models Based on Noisy Measurements.
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            NameFull: Balasingam, Balakumar
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            NameFull: Pattipati, Krishna R.
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              M: 07
              Text: 2021
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              Value: 70
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