4.7 Article

A comprehensive equivalent circuit model for lithium-ion batteries, incorporating the effects of state of health, state of charge, and temperature on model parameters

Journal

JOURNAL OF ENERGY STORAGE
Volume 43, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.est.2021.103252

Keywords

Lithium-ion batteries; Battery modeling; Equivalent circuit model; State of health; State of charge; Battery management system

Categories

Funding

  1. Department of Chemical Engineering at the University of Waterloo
  2. Canada Research Chair Tier I -ZeroEmission Vehicles and Hydrogen Energy Systems [950232215]
  3. Natural Sciences and Engineering Research Council of Canada (NSERC) [RGPIN-2020-04149]

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The research found that state of health (SOH) has an impact on Thevenin ECM parameters, and it is recommended to use a new empirical model to represent this impact to improve parameter identification accuracy, with experimental validation yielding favorable results.
The equivalent circuit model (ECM) is a battery model often used in the battery management system (BMS) to monitor and control lithium-ion batteries (LIBs). The accuracy and complexity of the ECM, hence, are very important. State of charge (SOC) and temperature are known to affect the parameters of the ECM and have been integrated into the model effectively. However, the effect of the state of health (SOH) on these parameters has not been widely investigated. Without a good understanding of the effect of SOH on ECM parameters, parameter identification would have to be done manually through calibration, which is inefficient. In this work, experiments were performed to investigate the effect of SOH on Thevenin ECM parameters, in addition to the effect of SOC and temperature. The results indicated that with decreasing SOH, the ohmic resistance and the polarization resistance increase while the polarization capacitance decreases. An empirical model was also proposed to represent the effect of SOH, SOC, and temperature on the ECM parameters. The model was then validated experimentally, yielding good results, and found to improve the accuracy of the Thevenin model significantly. With low complexity and high accuracy, this model can be easily integrated into real-world BMS applications.

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