Abstract
Equivalent circuit models (ECM) of lithium ion batteries are used in many applications because of their ease of implementation and low complexity. The accuracy of an ECM is critical to the functionality and usefulness of the battery management system (BMS). The ECM accuracy depends on the parametrization method, and therefore different experimental techniques and model parameter identification methods (PIM) have been widely studied. Yet, how to account for significant changes in time constants between operation under load and during relaxation has not been resolved. In this work a novel PIM and modified ECM is presented that increases accuracy by 77.4% during drive cycle validation and 87.6% during constant current load validation for a large format lithium iron phosphate prismatic cell. The modified ECM uses switching RC network values for each phase, which is significant for this cell and particularly at low state-of-charge for all lithium ion batteries. Different characterisation tests and the corresponding experimental data have been trained together across a complete State-of-Charge (SoC) and temperature range, which enables a smooth transition between identified parameters. Ultimately, the model created using parameters captured by the proposed PIM shows an improved model accuracy in comparison with conventional PIM techniques.
Original language | English |
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Article number | 229117 |
Number of pages | 11 |
Journal | Journal of Power Sources |
Volume | 484 |
Early online date | 24 Nov 2020 |
DOIs | |
Publication status | Published - 1 Feb 2021 |
Bibliographical note
NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Power Sources. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Power Sources, 484, (2021)DOI: 10.1016/j.jpowsour.2020.229117
© 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Funder
Envision AESC China Ltd, Project TRENDS (reference number EP/R020973/1 ), Faraday Institution (faraday.ac.uk; EP/S003053/1), grant number FIRG003Keywords
- Lithium-ion battery
- Equivalent circuit model parameterization
- Parameter identification method
- Lithium iron phosphate
- Electric vehicle
- Stationary energy storage
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
- Renewable Energy, Sustainability and the Environment
- Physical and Theoretical Chemistry