Revisiting the dual extended Kalman filter for battery state-of-charge and state-of-health estimation: A use-case life cycle analysis
•Algorithm's capability for accurate and reliable state and parameter estimation is investigated at different dynamics and aging stages.•Initial setup and parameterization of the filter is performed application-oriented.•Strengths and weaknesses of the algorithm are emphasized. One of the most...
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| Vydáno v: | Journal of energy storage Ročník 19; s. 73 - 87 |
|---|---|
| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.10.2018
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| Témata: | |
| ISSN: | 2352-152X, 2352-1538 |
| On-line přístup: | Získat plný text |
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| Abstract | •Algorithm's capability for accurate and reliable state and parameter estimation is investigated at different dynamics and aging stages.•Initial setup and parameterization of the filter is performed application-oriented.•Strengths and weaknesses of the algorithm are emphasized.
One of the most discussed topics in battery research is the state-of-charge (SOC) and state-of-health (SOH) determination of traction batteries. Unfortunately, neither is directly measurable and both must be derived from sensor signals using model-based algorithms. These signals can be noisy and erroneous, leading to an inaccurate estimate and, hence, to a limitation of usable battery capacity. A popular approach tackling these difficulties is the dual extended Kalman filter (DEKF). It consists of two extended Kalman filters (EKFs), that synchronously estimate both the battery states and parameters. An analysis of the reliability of the DEKF estimation against realistically fading battery parameters is still a widely discussed subject. This work investigates the DEKF performance from a high-level perspective, involving different load dynamics and SOH stages. A numerical optimization-based approach for the crucial filter parameterization is employed. We show that the DEKF partly improves the accuracy of the SOC estimation compared to the simple EKF over battery lifetime within the operational limits of an automotive application. However, capacity and internal resistance estimation becomes unreliable and partly diverges from the reference under constant and realistic load scenarios coupled with advanced degradation. As a consequence, a downstream use of both parameters in a SOC or SOH estimation is hampered over the battery lifetime. Extensions are needed to improve reliability and enable employment in real-world applications. |
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| AbstractList | •Algorithm's capability for accurate and reliable state and parameter estimation is investigated at different dynamics and aging stages.•Initial setup and parameterization of the filter is performed application-oriented.•Strengths and weaknesses of the algorithm are emphasized.
One of the most discussed topics in battery research is the state-of-charge (SOC) and state-of-health (SOH) determination of traction batteries. Unfortunately, neither is directly measurable and both must be derived from sensor signals using model-based algorithms. These signals can be noisy and erroneous, leading to an inaccurate estimate and, hence, to a limitation of usable battery capacity. A popular approach tackling these difficulties is the dual extended Kalman filter (DEKF). It consists of two extended Kalman filters (EKFs), that synchronously estimate both the battery states and parameters. An analysis of the reliability of the DEKF estimation against realistically fading battery parameters is still a widely discussed subject. This work investigates the DEKF performance from a high-level perspective, involving different load dynamics and SOH stages. A numerical optimization-based approach for the crucial filter parameterization is employed. We show that the DEKF partly improves the accuracy of the SOC estimation compared to the simple EKF over battery lifetime within the operational limits of an automotive application. However, capacity and internal resistance estimation becomes unreliable and partly diverges from the reference under constant and realistic load scenarios coupled with advanced degradation. As a consequence, a downstream use of both parameters in a SOC or SOH estimation is hampered over the battery lifetime. Extensions are needed to improve reliability and enable employment in real-world applications. |
| Author | Lohmann, Boris Reiter, Christoph Wassiliadis, Nikolaos Pak, Mikhail Frericks, Alexander Adermann, Jörn Lienkamp, Markus |
| Author_xml | – sequence: 1 givenname: Nikolaos surname: Wassiliadis fullname: Wassiliadis, Nikolaos email: wassiliadis@ftm.mw.tum.de organization: Chair of Automotive Technology, Department of Mechanical Engineering, Technical University of Munich (TUM), Germany – sequence: 2 givenname: Jörn surname: Adermann fullname: Adermann, Jörn organization: Chair of Automotive Technology, Department of Mechanical Engineering, Technical University of Munich (TUM), Germany – sequence: 3 givenname: Alexander surname: Frericks fullname: Frericks, Alexander organization: Chair of Automatic Control, Department of Mechanical Engineering, Technical University of Munich (TUM), Germany – sequence: 4 givenname: Mikhail surname: Pak fullname: Pak, Mikhail organization: Chair of Automatic Control, Department of Mechanical Engineering, Technical University of Munich (TUM), Germany – sequence: 5 givenname: Christoph surname: Reiter fullname: Reiter, Christoph organization: Chair of Automotive Technology, Department of Mechanical Engineering, Technical University of Munich (TUM), Germany – sequence: 6 givenname: Boris surname: Lohmann fullname: Lohmann, Boris organization: Chair of Automatic Control, Department of Mechanical Engineering, Technical University of Munich (TUM), Germany – sequence: 7 givenname: Markus surname: Lienkamp fullname: Lienkamp, Markus organization: Chair of Automotive Technology, Department of Mechanical Engineering, Technical University of Munich (TUM), Germany |
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| Keywords | Battery state estimation State-of-charge estimation State-of-health estimation Battery management systems Dual extended Kalman filter |
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