4.7 Article

State-of-Health Estimation for Lithium-Ion Batteries Based on Decoupled Dynamic Characteristic of Constant-Voltage Charging Current

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TTE.2021.3125932

Keywords

Batteries; Estimation; Current measurement; Electronic countermeasures; Parameter estimation; Integrated circuit modeling; Degradation; Constant-voltage (CV) charge; feature-of-interest (FoI); lithium-ion battery; parameter identification; state-of-health (SoH)

Funding

  1. National Science Foundation, USA [1507198]
  2. China Postdoctoral Science Foundation [2020M671356]
  3. Natural Science Foundation of Jiangsu Province [BK20210773]
  4. National Natural Science Foundation of China [U1764257]

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In this study, a method based on the decoupled characteristic of the charging current is proposed to calculate the SoH of batteries. The method involves parameter identification of an equivalent circuit model and the use of a time constant feature to reflect the battery capacity degradation. Experimental results show that the proposed method has a reduced computational cost and provides satisfactory fitting performance.
State-of-health (SoH) is one of the critical battery states that must be estimated and closely monitored by the on- board battery management system in electric vehicles (EVs). In this study, the battery SoH, especially the capacity fade, is calculated based on the decoupled characteristic of the charging current under the constant-voltage (CV) scenario. First, a dynamic-decoupled parameter identification method is proposed to extract the parameters of the simplified second-order resistor-inductor (RL) network-based equivalent circuit model (ECM), developed by the authors. Second, the dynamic characteristics of the decoupled CV charging currents at different aging states are qualitatively investigated, and the corresponding time constant is selected as a feature-of-interest (FoI) to reflect the battery capacity degradation. Third, the aging data based on two types of lithium-ion batteries are employed to evaluate the performance of the proposed method. Verification results demonstrate that the proposed parameter identification method yields a reduced computational cost with a satisfactory fitting performance, compared to the conventional methods. The proposed parameterization method and the selected FoI guarantee the root-mean-square errors of the estimated SoH less than 2%, and the comparative results further validate the superiority of the selected FoI in terms of the SoH estimation accuracy.

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