State of health battery estimator enabling degradation diagnosis: Model and algorithm description

Dubarry, Matthieu; Berecibar, Maitane; Devie, Arnaud; Anseán, David; Omar, Noshin; Villarreal, I.

doi:10.1016/j.jpowsour.2017.05.121

Cited by 127 publications

(107 citation statements)

References 64 publications

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“…The qualitative way is the easiest and involves comparing the curves. Different features of interest (FOI) [53] (Figure 7a) can be discussed and characterized without a complete understanding of the underlying electrochemical process. To properly discuss changes, it is extremely important to describe the peaks properly ( Figure 7b).…”

Section: An Example Of the Pseudo-ocv Calculation And Validation Is Pmentioning

confidence: 99%

“…The typical way of performing the analysis of IC or DV curves is to first select some FOIs [53,62], then compare their experimental evolution to predicted ones for individual degradation modes, and finally validate by simulating the full degradation to match the entire voltage response of the cell. The full match might only be possible with the exact same positive and negative electrodes as the ones used in the considered cells.…”

Section: An Example Of the Pseudo-ocv Calculation And Validation Is Pmentioning

confidence: 99%

“…However, in that case, only trends can be used to compare the mechanistic simulations to the experimental data. For analysis, the FOI selection must only be made after an exhaustive sensibility analysis [10,53]. It is highly recommended to first simulate a degradation map with the voltage changes and capacity loss associated with all the individual modes to gain an understanding of which FOI is more sensible to a degradation mode.…”

Section: An Example Of the Pseudo-ocv Calculation And Validation Is Pmentioning

confidence: 99%

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Perspective on Commercial Li-ion Battery Testing, Best Practices for Simple and Effective Protocols

Dubarry

Baure

2020

Electronics

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Validation is an integral part of any study dealing with modeling or development of new control algorithms for lithium ion batteries. Without proper validation, the impact of a study could be drastically reduced. In a perfect world, validation should involve testing in deployed systems, but it is often unpractical and costly. As a result, validation is more often conducted on single cells under control laboratory conditions. Laboratory testing is a complex task, and improper implementation could lead to fallacious results. Although common practice in open literature, the protocols used are usually too quickly detailed and important details are left out. This work intends to fully describe, explain, and exemplify a simple step-by-step single apparatus methodology for commercial battery testing in order to facilitate and standardize validation studies.

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Section: An Example Of the Pseudo-ocv Calculation And Validation Is Pmentioning

confidence: 99%

Section: An Example Of the Pseudo-ocv Calculation And Validation Is Pmentioning

confidence: 99%

Section: An Example Of the Pseudo-ocv Calculation And Validation Is Pmentioning

confidence: 99%

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Perspective on Commercial Li-ion Battery Testing, Best Practices for Simple and Effective Protocols

Dubarry

Baure

2020

Electronics

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“…In addition, Dubarry et al proposed a novel mechanistic model which can enable cell diagnosis and simulate various “what‐if” scenarios of cell degradation modes . Dubarry et al also introduced a new approach for SOH estimation via an automated diagnosis method that could be implemented directly into BMS . Methods offered by Dubarry et al provides quantitative and comparative analyses on aging behaviors of commercially Li‐ion cells .…”

Section: Introductionmentioning

confidence: 99%

“…17 Dubarry et al also introduced a new approach for SOH estimation via an automated diagnosis method that could be implemented directly into BMS. 18 Methods offered by Dubarry et al provides quantitative and comparative analyses on aging behaviors of commercially Li-ion cells. 19 On the basis of Dubarry's research, Carlos et al analyzed the effects of the degradation modes using incremental capacity and differential voltage (IC-DV) analysis and EIS.…”

Section: Introductionmentioning

confidence: 99%

Study on degradation behavior of commercial 18650 LiAlNiCoO₂cells in over‐charge conditions

et al. 2018

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Summary From the battery life and safety perspective, an investigation was conducted to analyze the degradation behavior of 18650 LiAlNiCoO2 (NCA/C) cells with different cut‐off voltages (4.30, 4.50, 4.80 and 5.00 V) and different cycle numbers. Electrochemical Impedance Spectroscopy (EIS) and Incremental Capacity (IC) analysis were employed to identify the fading mechanism for overcharged NCA/C cells. Key insights were that major side reactions for the overcharged NCA cells are the oxidation and decomposition of electrolyte, the thickening of the passivation layer, the deposition of lithium and the structure change of cathode. These side reactions accounted for the increase of impedance for the cells with the cut‐off voltages and the cycle numbers, especially for the charge transfer impedance. An EIS‐distribution of relaxation time (DRT) method was applied to identify and quantify the influences of various degradation modes during overcharging process. The loss of lithium inventory (LLI) was more pertinent to the degradation than the conductivity loss (CL) and the loss of active material (LAM). Conspicuous variations in the cell can assist in self‐test and failure warning, such as the rapid rise of the temperature change rate, the evident transformation of peaks in IC results and the irreversible increase of CL.

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Investigation of Path‐Dependent Degradation in Lithium‐Ion Batteries**

Raj

Wang

Monroe

et al. 2020

Batteries & Supercaps

102

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Models that predict battery lifetime require knowledge of the causes of degradation and operating conditions that accelerate it. Batteries experience two ageing modes: calendar ageing at rest and cyclic ageing during the passage of current. Existing empirical ageing models treat these as independent, but degradation may be sensitive to their order and periodicity-a phenomenon that has been called "path dependence". This experimental study of path dependence probes whether interactions between ageing conditions can impact a battery's state of health. Groups of graphite/NCA 18650 lithium-ion cells were exposed to load profiles consisting of similar proportions of calendar and cyclic ageing applied in various orders. Load profiles at higher Crates exhibited path dependence, which differential voltage analysis correlates with increased anode degradation. These results suggest that more accurate ageing models should include the possible coupling between calendar and cyclic ageing modes.

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State of health battery estimator enabling degradation diagnosis: Model and algorithm description

Cited by 127 publications

References 64 publications

Perspective on Commercial Li-ion Battery Testing, Best Practices for Simple and Effective Protocols

Perspective on Commercial Li-ion Battery Testing, Best Practices for Simple and Effective Protocols

Study on degradation behavior of commercial 18650 LiAlNiCoO₂cells in over‐charge conditions

Investigation of Path‐Dependent Degradation in Lithium‐Ion Batteries**

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State of health battery estimator enabling degradation diagnosis: Model and algorithm description

Cited by 127 publications

References 64 publications

Perspective on Commercial Li-ion Battery Testing, Best Practices for Simple and Effective Protocols

Perspective on Commercial Li-ion Battery Testing, Best Practices for Simple and Effective Protocols

Study on degradation behavior of commercial 18650 LiAlNiCoO2cells in over‐charge conditions

Investigation of Path‐Dependent Degradation in Lithium‐Ion Batteries**

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Product

Resources

About

Study on degradation behavior of commercial 18650 LiAlNiCoO₂cells in over‐charge conditions