4.6 Article

State-of-health (SOH) evaluation on lithium-ion battery by simulating the voltage relaxation curves

Journal

ELECTROCHIMICA ACTA
Volume 303, Issue -, Pages 183-191

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.electacta.2019.02.055

Keywords

Lithium ion battery; State of health; Voltage stabilization model; Aging mechanism

Funding

  1. National Nature Science Foundation of China [51872157]
  2. Shenzhen Technical Plan Project [KQJSCX20160226191136, JCYJ20170817161753629, JCYJ20170412170911187]
  3. Guangdong Technical Plan Project [2015TX01N011]
  4. Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program [2017BT01N111]
  5. Shenzhen Key Laboratory of Power Battery Safety Research [ZDSYS201707271615073]

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State-of-health evaluation of lithium ion batteries is one of the key challenges to construct efficient battery system with enhanced performance. In this paper, we demonstrate a facile evaluation method which is based on the voltage stabilization behaviour during relaxation. A second-order equivalent circuit is employed to describe the voltage relaxation. By simulating the voltage relaxation curves, the characteristic parameters that can distinguish the ohmic, activation and concentration depolarizations are acquired. The obtained parameters are sensitive to electrochemical states, which provide comprehensive evaluation to the state-of-health of batteries and aging mechanism analysis. As an example, the proposed method identifies two aging stages of Li(Ni1/3Co1/3Mn1/3)O-2/graphite pouch cells during accelerated aging. Namely, a loss of lithium inventory in the first stage of aging, and the loss of active material in the next stage. Postmodern analysis including X-ray diffraction and high-resolution transmission electron microscope well agrees with the voltage relaxation simulation. The proposed state-of-health evaluation method is non-destructive and accessible for on-board aging diagnosis, which is of great significance for developing better battery management system. (c) 2019 Elsevier Ltd. All rights reserved.

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