Time-domain fitting of battery electrochemical impedance models

By S. Alavi, Christoph R. Birkl, David A. Howey. 2015

144 citations recorded by the source corpus.

Abstract Electrochemical impedance spectroscopy (EIS) is an effective technique for diagnosing the behaviour of electrochemical devices such as batteries and fuel cells, usually by fitting data to an equivalent circuit model (ECM). The common approach in the laboratory is to measure the impedance spectrum of a cell in the frequency domain using a single sine sweep signal, then fit the ECM parameters in the frequency domain. This paper focuses instead on estimation of the ECM parameters directly from time-domain data. This may be advantageous for parameter estimation in practical applications such as automotive systems including battery-powered vehicles, where the data may be heavily corrupted by noise. The proposed methodology is based on the simplified refined instrumental variable for continuous-time fractional systems method (‘srivcf’), provided by the Crone toolbox [1,2], combined with gradient-based optimisation to estimate the order of the fractional term in the ECM. The approach was tested first on synthetic data and then on real data measured from a 26650 lithium-ion iron phosphate cell with low-cost equipment. The resulting Nyquist plots from the time-domain fitted models match the impedance spectrum closely (much more accurately than when a Randles model is assumed), and the fitted parameters as separately determined through a laboratory potentiostat with frequency domain fitting match to within 13%.

Subfields: Automotive Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/c74f4b0299331c3548c46a567e2a9b196cbb8e29

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

  1. Xiaosong Hu
  2. Lei Zhang
  3. Zhenpo Wang
  4. Michael G. Pecht
  5. Changfu Zou
  6. Torsten Wik
  7. Xianke Lin
  8. Simona Onori

In Control and Systems Engineering

  1. Xiaosong Hu
  2. Michael G. Pecht

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