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Title page for etd-0905114-135537


URN etd-0905114-135537 Statistics This thesis had been viewed 1166 times. Download 54 times.
Author Teng-yao Chang
Author's Email Address No Public.
Department Electrical Engineering
Year 2013 Semester 2
Degree Master Type of Document Master's Thesis
Language Chinese&English Page Count 74
Title THE STATE OF CHARGE ESTIMATION FOR LI-ION BATTERY ON CHARGE AND DISCHARGE STATE BY EXTENDED KALMAN FILTER
Keyword
  • lithium-ion battery
  • state-of-charge estimation
  • Extended Kalman Filter
  • equivalent circuit model
  • equivalent circuit model
  • Extended Kalman Filter
  • state-of-charge estimation
  • lithium-ion battery
  • Abstract To increase the life of the lithium-ion battery,and to improvethe safety andefficiency of the battery used, the accurate determination the battery's capacity is very important. Because of the complexity chemical and physical processes, the battery information that we can use is fewer and fewer. So estimation the state of charge is very difficult. In order to determine the state of charge, open-circuit voltage and battery's internal resistance are the important parameters for estimation the state of charge.
    Accurate estimation the state of charge can avoid over charging and discharging, improve battery performance and extend battery life. However, for batteries, the state during charging and discharging are not the same, this thesis using the equivalent circuit model(ECM) to approximate characteristic of the Li-ion battery. In order to obtain the unknown parameters of the ECM, the open circuit voltage (OCV) test and direct current internal resistance (DCIR)testare used to find the relationship between the parameters and SOC of the Li-ion battery.From the experiments results, we can find the relationship between the parameters and SOC of the Li-ion battery is a nonlinear, and the initial values will affect the accuracy of the SOC estimation. These problems can be effectively improved by the extended kalman filtering. Therefore, in this thesis we use the extended kalman filtering(EKF) to estimate the SOC of the Li-ion battery, andreduce the impact of SOC estimation by parameters errors and the dependence of the initial values. Finallythrough simulations and experimental results to validate the accuracy for ECM, and confirm the effective of EKF.
    Advisor Committee
  • Chung-chun Kung - advisor
  • Files indicate in-campus access immediately and off-campus access at 2 years
    Date of Defense 2014-07-30 Date of Submission 2014-09-06


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