The Library
Data-driven identification of lithium-ion batteries : a nonlinear equivalent circuit model with diffusion dynamics
Tools
Fan, Chuanxin, O'Regan, Kieran, Li, Liuying, Higgins, Matthew D., Kendrick, Emma and Widanage, Widanalage D. (2022) Data-driven identification of lithium-ion batteries : a nonlinear equivalent circuit model with diffusion dynamics. Applied Energy, 321 (119336). doi:10.1016/j.apenergy.2022.119336 ISSN 0306-2619.
|
PDF
WRAP-Data-driven-identification-of-lithium-ion-batteries-Fan-22.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (14Mb) | Preview |
Official URL: https://doi.org/10.1016/j.apenergy.2022.119336
Abstract
An accurate battery model is essential for battery management system (BMS) applications. However, existing models either don't describe battery physics or are too computationally intensive for practical applications. This paper presents a non-linear equivalent circuit model with diffusion dynamics (NLECM-diff) which phenomenologically describes the main electrochemical behaviours, such as ohmic, charge-transfer kinetics, and solid-phase diffusion. A multisine approach is applied to identify the elements for high frequency dynamics, as well as a distributed SoC dependent diffusion model block is optimised to account for long time dynamics. The model identification procedure is conducted on a three-electrode experimental cell, such that NLECM-diff models are developed for each electrode to then obtain the full cell voltage. Results imply that the NLECM-diff reduces the voltage root mean square error (RMSE) by 49.6% compared to a conventional ECM in the long duration discharge and has comparable accuracy to a parameterised SPMe in the NEDC driving cycle. Additionally, the variation of diffusion-related characteristics of the negative electrode under different currents is determined as the primary reason of the battery models' large low-SoC-range error. Furthermore, the diffusion process is determined as the dominant voltage loss contributor in the long duration discharge and the ohmic voltage loss is identified as the dominant dynamic under NEDC driving profile.
Item Type: | Journal Article | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Subjects: | Q Science > QD Chemistry T Technology > TJ Mechanical engineering and machinery 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 , Battery management systems , Electric batteries , Electrochemical analysis | ||||||||||||
Journal or Publication Title: | Applied Energy | ||||||||||||
Publisher: | Elsevier BV | ||||||||||||
ISSN: | 0306-2619 | ||||||||||||
Official Date: | 1 September 2022 | ||||||||||||
Dates: |
|
||||||||||||
Volume: | 321 | ||||||||||||
Number: | 119336 | ||||||||||||
DOI: | 10.1016/j.apenergy.2022.119336 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
Copyright Holders: | Elsevier Ltd. | ||||||||||||
Date of first compliant deposit: | 6 June 2022 | ||||||||||||
Date of first compliant Open Access: | 2 June 2023 | ||||||||||||
RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year