Title page for etd-0726116-160207


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URN etd-0726116-160207
Author Yi-Min Liang
Author's Email Address No Public.
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Department Electrical Engineering
Year 2015
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title On-line State-of-Health Estimation for LiFePO4 Battery
Date of Defense 2016-07-15
Page Count 79
Keyword
  • Artificial Neural Network
  • SOH Estimation
  • LiFePO4 Battery
  • State-of-Health
  • Abstract The demand of batteries for electric vehicle (EV) and Energy Storage System (ESS) is increasing. After battery has been used for a long time, the actual available capacity of battery will decrease, so State-of-Health (SOH) estimation is important in EV and ESS operations. An on-line SOH estimation method is proposed in the thesis. It is different from the conventional off-line estimation methods that need to remove battery from the system and connect to other devices. The key component in the proposed SOH estimation procedure is to obtain aging indicators according to the data from aging experiment performed off-line, and then use the indicators, including model parameters in a battery equivalent circuit to estimate SOH. Test data are used to determine the model parameter values during different battery ages by least square error method. The battery characteristic parameters computed at each age of the battery are then used in an Artificial Neural Network (ANN) to train and setup the automatic SOH estimator. In the proposed procedure, a regression model is used to determine the relationship of battery open-circuit voltage with State-of-Charge (SOC) and SOH. An on-line SOH estimation can be achieved after the battery open-circuit voltage and the equivalent circuit model parameters are calculated real time and fed into the ANN model. Test results indicate that the average absolute error of the proposed SOH estimator under different usage scenarios is 1.7732% based on 5 LiFePO4 batteries.
    Advisory Committee
  • Le-Ren Chang-Chien - chair
  • Min-Siong Liang - co-chair
  • Chan-Nan Lu - advisor
  • Files
  • etd-0726116-160207.pdf
  • Indicate in-campus at 3 year and off-campus access at 3 year.
    Date of Submission 2016-08-26

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