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Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique
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Zhang, Cheng, Allafi, Walid, Dinh, Quang Truong, Ascencio Ormeno, Pedro and Marco, James (2018) Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique. Energy, 142 . pp. 678-688. doi:10.1016/j.energy.2017.10.043 ISSN 0360-5442.
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Official URL: https://doi.org/10.1016/j.energy.2017.10.043
Abstract
Battery equivalent circuit models (ECMs) are widely employed in online battery management applications. The model parameters are known to vary according to the operating conditions, such as the battery state of charge (SOC) and the ambient temperature. Therefore, online recursive ECM parameter estimation is one means that may help to improve the modelling accuracy. Because a battery system consists of both fast and slow dynamics, the classical least squares (LS) method, that estimates together all the model parameters, is known to suffer from numerical problems and poor accuracy. The aim of this paper is to overcome this problem by proposing a new decoupled weighted recursive least squares (DWRLS) method, which estimates separately the parameters of the battery fast and slow dynamics. Another issue is that, the ECM-based SOC estimator generally requires a full-order state observer, which will increase the algorithm’s complexity and the time required for the filter tuning. In this work, the battery SOC estimation is achieved based on the parameter estimation results. This circumvents the additional full-order observer, leading to a reduced complexity. An extensive simulation study is conducted to compare the proposed method against the traditional LS technique. The proposed approach is also applied to estimate the parameters of ECM where the experimental data are collected using a cylindrical 3Ah 18650-type Li ion NCA cell. Finally, both the simulation and experimental results in this study have demonstrated that the proposed DWRLS approach can improve not only the modelling accuracy but also the SOC estimation performance compared with the LS algorithm.
| Item Type: | Journal Article | ||||||||
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| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | ||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
| Library of Congress Subject Headings (LCSH): | Lithium ion batteries -- Mathematical models, Least squares | ||||||||
| Journal or Publication Title: | Energy | ||||||||
| Publisher: | Elsevier Ltd | ||||||||
| ISSN: | 0360-5442 | ||||||||
| Official Date: | January 2018 | ||||||||
| Dates: |
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| Volume: | 142 | ||||||||
| Page Range: | pp. 678-688 | ||||||||
| DOI: | 10.1016/j.energy.2017.10.043 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||
| Date of first compliant deposit: | 7 November 2017 | ||||||||
| Funder: | Warwick Manufacturing Group, Innovate UK, Engineering and Physical Sciences Research Council (EPSRC) | ||||||||
| Grant number: | EP/M009394/1 (EPSRC) | ||||||||
| RIOXX Funder/Project Grant: |
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