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Parameter identification of electrolyte decomposition state in lithium-ion batteries based on a reduced pseudo two-dimensional model with Padé approximation

Research output: Contribution to journalArticlepeer-review

Abstract

The degradation of lithium-ion batteries resulted from electrolyte decomposition may be indicated by the change in diffusion coefficient, De, and concentration, Ce. Although De and Ce are included in the liquid-phase diffusion process of a pseudo two-dimensional (P2D) model, these two important parameters are always inundated or contaminated when the complete P2D model is reduced. To obtain De and Ce reliably, a new parameter-lumped model of concentration overpotential, based on the reduced liquid-phase diffusion partial differential equation (PDE)in P2D model by Padé approximation, is developed with two main contributions. First, as the basis of Padé-based approximation, new analytic solutions of liquid-phase diffusion PDE are derived under the improved boundary conditions with the mass conservation principle. Second, in the proposed parameter-lumped model, the target parameters of De and Ce are grouped with other unknown but fixed parameters, respectively, which guarantees that even if the specific values of target parameters cannot be obtained, their changing trajectories can also be independently and effectively observed by lumped parameters. Finally, the simulation accuracies of reduced dynamic electrolyte concentration expressions are compared with that from other simplified methods, and the identification accuracy of the lumped parameters is verified under different working conditions.

Original languageEnglish
Article number228093
JournalJournal of Power Sources
Volume460
DOIs
StatePublished - 1 Jun 2020
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Electrolyte decomposition
  • Lithium-ion battery
  • Padé approximation
  • Parameter identification
  • Parameter-lumped model

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