State-of-charge estimation for onboard LiFePO4 batteries with adaptive state update in specific open-circuit-voltage ranges

Rui Xiong, Yanzhou Duan, Kaixuan Zhang, Da Lin, Jinpeng Tian*, Cheng Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Accurate estimation of the state-of-charge (SOC) is crucial for efficient and safe battery applications. However, existing SOC estimation methods fail to provide accurate SOC estimation for LiFePO4 batteries that have a flat voltage-SOC relationship. The analysis of the voltage-SOC characteristics shows that the failure of the present model-based methods can be ascribed to their inability to simultaneously accommodate the differences in voltage characteristics between different open-circuit-voltage (OCV) ranges. To overcome this limitation, an adaptive recursive square root algorithm is used to online identify OCV and other battery model parameters. Then, the parameters of the extended Kalman filter are adaptively updated in different OCV ranges, which are distinguished based on the identified OCV. Additional filtering methods are employed to enhance the stability of the estimation. Large-scale experiments are conducted at different temperatures with various driving profiles for method validation. While conventional methods fail to converge, the proposed method ensures both high accuracy and stability, with a maximum absolute error of <2%. The viability of the proposed method is further verified using data collected from real battery systems. Our work lays a foundation for the reliable management of LiFePO4 batteries in electric vehicles.

Original languageEnglish
Article number121581
JournalApplied Energy
Volume349
DOIs
Publication statusPublished - 1 Nov 2023

Keywords

  • Adaptive recursive square root (ARSR)
  • Extended Kalman filter (EKF)
  • LiFePO battery
  • Open circuit voltage
  • State of charge (SOC)

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