State-of-power estimation for lithium-ion batteries based on a frequency-dependent integer-order model

Xin LAI, Ming YUAN, Xiaopeng TANG*, Yuejiu ZHENG, Jiajun ZHU, Yuedong SUN, Yuanqiang ZHOU, Furong GAO

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

5 Citations (Scopus)

Abstract

Power capability of lithium-ion batteries is strongly correlated with electric vehicle’s accelerating and braking performance. However, the estimate of state-of-power relies highly on battery models, whose accuracy usually increases with the complexity. We here propose a simple and accurate frequency-dependent integer-order model for battery state-of-power estimation. First, a random search-pseudo gradient descent algorithm is proposed to identify the parameters of our model from electrochemical impedance spectroscopy in the frequency domain. Then, the proposed model is mathematically derived in the time domain. Next, two strategies are developed to estimate battery state-of-power under different constraints — using particle swarm optimization and direct inversion algorithms. Finally, our method is experimentally verified: the proposed frequency-dependent model shares similar complexity compared with the conventional integer-order model, while its accuracy is competitive to that of the fractional-order model. With such a simple and accurate model, our state-of-power estimation error is 90% smaller than that based on the conventional integer order model, and the computational time is 99.8% lower than that corresponds to the fractional-order model. Since the proposed method is developed upon the conventional integer-order model, it has a strong potential for real-life application and can be easily integrated into the existing battery management systems.
Original languageEnglish
Article number234000
JournalJournal of Power Sources
Volume594
Early online date30 Dec 2023
DOIs
Publication statusPublished - 28 Feb 2024

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

  • Fractional-order model
  • Frequency-dependent integer-order model
  • Integer-order model
  • Lithium-ion battery
  • State-of-power estimation

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