Degradation diagnostics for lithium ion cells

By Christoph R. Birkl, M. Roberts, Euan McTurk, P. Bruce, David A. Howey. 2017

1,527 citations recorded by the source corpus.

Abstract Degradation in lithium ion (Li-ion) battery cells is the result of a complex interplay of a host of different physical and chemical mechanisms. The measurable, physical effects of these degradation mechanisms on the cell can be summarised in terms of three degradation modes, namely loss of lithium inventory, loss of active positive electrode material and loss of active negative electrode material. The different degradation modes are assumed to have unique and measurable effects on the open circuit voltage (OCV) of Li-ion cells and electrodes. The presumptive nature and extent of these effects has so far been based on logical arguments rather than experimental proof. This work presents, for the first time, experimental evidence supporting the widely reported degradation modes by means of tests conducted on coin cells, engineered to include different, known amounts of lithium inventory and active electrode material. Moreover, the general theory behind the effects of degradation modes on the OCV of cells and electrodes is refined and a diagnostic algorithm is devised, which allows the identification and quantification of the nature and extent of each degradation mode in Li-ion cells at any point in their service lives, by fitting the cells' OCV.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2ba82c5842308c5645782ad1db0902528b309cce

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Citations in the corpus

A Comparison between Electrochemical Impedance Spectroscopy and Incremental Capacity-Differential Voltage as Li-ion Diagnostic Techniques to Identify and Quantify the Effects of Degradation Modes within Battery Management Systems (2017, methodology): “Equivalent Circuit Model (AR-ECM) components with DMs; (c) DMs and ageing mechanisms in LIBs, adapted from [1,2,22]; (d) pseudo-OCV curve, (e) relationship of changes in IC and DV curves with the DMs.”
Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, background): “Other factors include chemical decomposition and dissolution reactions of transition metals into the electrolyte and SEI modification [23,28].”
Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems (2020, background): “Maximising battery lifetime is a particular hallenge due to the strong coupling between LIB longevity and operatng conditions such as: temperature, load-profile and operating state-ofharge (SOC) range [1] .”
Online capacity estimation of lithium-ion batteries with deep long short-term memory networks (2021, methodology): “…experimental approach is the first method for SOH estimation, and this can be done using trivial methods such as coulomb counting or more sophisticated approaches such as electrochemical impedance spectroscopy (EIS) [4,5] and incremental capacity and differential voltage (IC/DV) analysis [6 – 9].”
Machine learning pipeline for battery state-of-health estimation (2021, methodology): “Several studies [23], [24], [25], [26], [27] showed that incremental capacity (IC) and differential voltage (DV) curves, a method developed for use in cell aging mechanism analysis [23], can also be used for ofﬂine and online capacity fade estimation.”
A Comprehensive Review on Second-Life Batteries: Current State, Manufacturing Considerations, Applications, Impacts, Barriers & Potential Solutions, Business Strategies, and Policies (2019, background): “Cracks in the SEI provide space for its continued growth, which results in increased impedance and further loss of lithium inventory [49].”