JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (5): 157-165.doi: 10.6040/j.issn.1672-3961.0.2017.268

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Performance assessment of lithium-ion battery based on geometric features and manifold distance

BAO Tala1,2, MA Jian1,2*, GAN Zuwang1,2   

  1. 1. School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China;
    2. Science &
    Technology Laboratory on Reliability and Environmental Engineering, Beihang University, Beijing 100191, China
  • Received:2017-02-10 Online:2017-10-20 Published:2017-02-10

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Abstract: The estimations of state of charge and state of health evolved in li-ion battery health management systems can help managing the reliability and safety of the fielded battery. Considering that many data-driven state of health estimations habituated to model the battery monitoring information in Euclid space with the purpose of assessing battery health status, which often brings about a poor adaptability to operation conditions, manifold learning was used to mine the health information hidden in the battery monitoring data and manifold distance was utilized to measure the battery health condition. At last, a case analysis was conducted to validate the proposed state of health estimation method for the li-ion battery.

Key words: lithium-ion battery, health condition, manifold learning, geometric features

CLC Number: 

  • TP277
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