An Improved Collaborative Estimation Method for Determining The SOC and SOH of Lithium-Ion Power Batteries for Electric Vehicles

Yixin Liu, Ao Lei, Chunyang Yu, Tengfei Huang and Yuanbin Yu ()
Additional contact information
Yixin Liu: China FAW Group Co., Ltd., Changchun 130013, China
Ao Lei: China FAW Group Co., Ltd., Changchun 130013, China
Chunyang Yu: China FAW Group Co., Ltd., Changchun 130013, China
Tengfei Huang: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
Yuanbin Yu: State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China

Energies, 2024, vol. 17, issue 13, 1-22

Abstract: With the increase in the amount of actual operating data on electric vehicles, how to analyze and process useful information from existing battery charging and discharging data and apply it to subsequent state estimation is worthy of in-depth thinking and practice by researchers. This article proposes a collaborative estimation architecture for SOC and SOH based on the 1RC equivalent circuit model, recursive least squares, and adaptive extended Kalman filtering algorithms (AEKF), which combine offline data processing with online applications. By applying offline data processing, OCV–SOC polynomial fitting and average polarization resistance were determined, which reduced the time required for basic data measurement and improved the accuracy of model parameter identification, while a recursive estimation combining micro- and macro-time-scales of AEKF was used for the online real-time estimation of the SOC and actual available capacity of batteries, in order to eliminate interference from measurement and process noise. The results of the simulated and experimental data validation indicate that the proposed algorithm is applicable to the lithium-ion batteries studied in this paper, the average SOC deviation is less than 1.5%, the maximum deviation is less than 2.02%, and the SOH estimation deviation is less than 1% under different driving conditions in the multi-temperature range. This study lays the foundation for further utilizing offline data and improving SOC and SOH collaborative estimation algorithms.

Keywords: electric vehicles; lithium battery; model parameter identification; state of charge; state of health; adaptive extended Kalman filter; recursive least square method (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/13/3287/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/13/3287/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:13:p:3287-:d:1428975

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().