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LiFePO4 battery pack capacity estimation for electric vehicles based on charging cell voltage curve transformation

Authors
Journal
Journal of Power Sources
0378-7753
Publisher
Elsevier
Publication Date
Volume
226
Identifiers
DOI: 10.1016/j.jpowsour.2012.10.057
Keywords
  • Electric Vehicle
  • Battery Pack Capacity
  • Charging Cell Voltage Curves
  • Cell Variations
  • Genetic Algorithm

Abstract

Abstract Because of the diversiform driving conditions and the cell variations, it is difficult to accurately determine battery pack capacities in electric vehicles (EVs) by model prediction or direct measurement. This paper studies the charging cell voltage curves (CCVC) for the estimation of the LiFePO4 battery pack capacities in EVs. We propose the uniform CCVC hypothesis and estimate cell capacities by overlapping CCVCs using CCVC transformation. CCVCs of two LiFePO4 cells with large capacity difference are used to verify the hypothesis. We further develop an equivalent simplified approach using voltage-capacity rate curve (VCRC) and implement Genetic Algorithm (GA) to find the optimum transformation parameter for overlapping VCRCs. A small battery pack with four LiFePO4 cells in series is employed to verify the method and the result shows that the estimation errors of both pack capacity and cell capacities are less than 1%. With the proposed method, the battery pack capacity can be precisely estimated which could be used for the driving range prediction. Meanwhile, the estimated cell capacities in battery packs will significantly support the study of cell degradation and cell variations in vehicle driving conditions.

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