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Li-ion Battery State of Charge (SOC) and State of Health (SOH) Online Estimations Using Partial Charge or Discharge Data

  • Author / Creator
    Bavand, Amin
  • Estimating the state of health (SOH) and state of charge (SOC) of Lithium-ion (Li-ion) batteries is crucial for lifetime and performance optimizations. Many existing online estimation methods are not practical in many applications as they may need offline training, take too much time for estimations, or need a full discharge or charge cycle for accurate results. In this thesis, a fast online SOH/SOC estimation method for batteries with partial charge/discharge condition is introduced that can provide accurate results in various operating and temperature conditions.

    First, a method for online estimation of SOH/SOC is introduced. Based on only two consecutive partial discharge intervals, the battery equivalent circuit model (ECM) parameters and the open circuit voltage (OCV) relation with the battery charge are estimated using Adam optimization algorithm. By comparing the estimated OCV curve at each interval with the reference OCV curve of the brand new battery, the battery capacity and therefore its SOH along with SOC are estimated.

    In many applications the temperature changes in a wide range that may create relatively large errors in state estimations. The proposed method is further refined to guarantee accurate results and estimations in various temperature conditions. In this modification, the SOC-OCV curve is extracted from the battery datasheet and is predicted for different temperatures, which are then used to estimate SOH/SOC at any given temperature.

    In this thesis, the proposed methods are validated using NASA dataset. The proposed method results in root mean square error (RMSE) below 1% for SOH and 1.07% for SOC on average. Moreover, it is shown that using the refined method, the SOH estimation RMSE is improved by 2.55% when the datasheet's SOC-OCV curves are adjusted according to their test temperatures.

  • Subjects / Keywords
  • Graduation date
    Fall 2021
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/r3-eyvv-qh93
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.