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Journal of Automotive Safety and Energy ›› 2025, Vol. 16 ›› Issue (2): 294-302.DOI: 10.3969/j.issn.1674-8484.2025.02.013

• Intelligent Driving and Intelligent Transportation • Previous Articles     Next Articles

Identification of battery polarization parameters based on initial charging segment of cloud data

WANG Limei1(), CUI Yanwei1, SUN Jingjing1, ZHAO Xiuliang2,*(), LIU Liang1, PAN Chaofeng1   

  1. 1. Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
    2. School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
  • Received:2024-09-29 Revised:2024-12-12 Online:2025-04-30 Published:2025-04-22

Abstract:

A benchmark polarization parameter identification method was proposed based on cloud data to enhance the accuracy and the speed of online identification of battery polarization parameters. The characteristics of battery polarization parameters were investigated by conducting charge-discharge pulse experiments. A method analogous was employed by utilizing the initial charging segment from cloud data through the Hybrid Pulse Power Characterization (HPPC) tests to obtain the charging polarization parameters. The Variable Forgetting Factor Recursive Least Squares (VFFRLS) algorithm was applied with the identified charging polarization parameters as constraints to compute the discharging polarization parameters. The results indicated that this method yielded battery time constants ranging from 34~53 s, and the polarization parameters remained invariant with respect to the current rate under corresponding low current rates in the cloud environment. The calculated charging polarization resistance and polarization capacitance aligned well with laboratory results. The convergence speed of the proposed constrained online identification method was improved by at least 6% compared with the unconstrained identification method.

Key words: battery charging and discharging, polarization parameter, cloud data, off-line identification, hybrid pulse power characterization (HPPC) analogy, variable forgetting factor recursive least square (VFFRLS) algorithm

CLC Number: