Initial Proof-of-Concept for an Accelerated Galvanostatic Intermittent Titration Technique via Embedded Machine Learning

David S. Mebane; Roksana Jackowska; Michael W. Fouts; Ferran Brosa Planella; Emma Kendrick; Mohit R. Mehta; John W. Lawson

doi:10.1149/1945-7111/ae26cd

Initial Proof-of-Concept for an Accelerated Galvanostatic Intermittent Titration Technique via Embedded Machine Learning

David S. Mebane^*
, Roksana Jackowska
, Michael W. Fouts
, Ferran Brosa Planella
, Emma Kendrick
, Mohit R. Mehta
, John W. Lawson

^*Corresponding author for this work

Metallurgy and Materials

Research output: Contribution to journal › Article › peer-review

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Abstract

This work conceptually proves and demonstrates in silico a new method and statistical analysis for the Galvanostatic Intermittent Titration Technique (GITT) that may decrease the experiment time by an order of magnitude. The method utilizes embedded Gaussian processes and focuses on the relaxation portion of the pulse-relaxation series. These innovations enable much higher current draws at each pulse, while shortening relaxation periods. The in silico proof-of-concept was achieved using simulated conditions similar to a published GITT experiment run on a lithium-lanthanum titanate half cell, while a validation of the relaxation-based method was performed on a porous Li_ζNi_0.92Co_0.03Mn_0.05O₂ cathode.

Original language	English
Article number	120527
Number of pages	4
Journal	Journal of the Electrochemical Society
Volume	172
Issue number	12
DOIs	https://doi.org/10.1149/1945-7111/ae26cd
Publication status	Published - 22 Dec 2025

Bibliographical note

Publisher Copyright: © 2025 The Electrochemical Society (“ECS”). Published on behalf of ECS by IOP Publishing Limited. All rights, including for text and data mining, AI training, and similar technologies, are reserved.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

batteries - Li-ion
energy storage
theory and modelling

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Renewable Energy, Sustainability and the Environment
Condensed Matter Physics
Surfaces, Coatings and Films
Electrochemistry
Materials Chemistry

Access to Document

10.1149/1945-7111/ae26cdLicence: None: All rights reserved

MebaneDS2025Initial_AAMAccepted author manuscript, 874 KBLicence: Creative Commons: Attribution (CC BY)

https://wrap.warwick.ac.uk/195695/Licence: Creative Commons: Attribution (CC BY)

Multi-Scale Modelling - MSM
Kendrick, E. (Principal Investigator)
The Faraday Institution
1/04/25 → 31/03/26
Project: Research
Cell Designs and Testing Methodologies for Parameterisation
Drakopoulos, S. (Researcher), Slater, P. (Co-Investigator), Kendrick, E. (Principal Investigator) & Chen, Y. (Researcher)
The Faraday Institution
1/10/21 → 31/12/25
Project: Research

Cite this

@article{e3466c51820043009360240f7c66f5e3,

title = "Initial Proof-of-Concept for an Accelerated Galvanostatic Intermittent Titration Technique via Embedded Machine Learning",

abstract = "This work conceptually proves and demonstrates in silico a new method and statistical analysis for the Galvanostatic Intermittent Titration Technique (GITT) that may decrease the experiment time by an order of magnitude. The method utilizes embedded Gaussian processes and focuses on the relaxation portion of the pulse-relaxation series. These innovations enable much higher current draws at each pulse, while shortening relaxation periods. The in silico proof-of-concept was achieved using simulated conditions similar to a published GITT experiment run on a lithium-lanthanum titanate half cell, while a validation of the relaxation-based method was performed on a porous Liζ Ni0.92Co0.03Mn0.05O2 cathode.",

keywords = "batteries - Li-ion, energy storage, theory and modelling",

author = "Mebane, \{David S.\} and Roksana Jackowska and Fouts, \{Michael W.\} and Planella, \{Ferran Brosa\} and Emma Kendrick and Mehta, \{Mohit R.\} and Lawson, \{John W.\}",

note = "Publisher Copyright: {\textcopyright} 2025 The Electrochemical Society (“ECS”). Published on behalf of ECS by IOP Publishing Limited. All rights, including for text and data mining, AI training, and similar technologies, are reserved.",

year = "2025",

month = dec,

day = "22",

doi = "10.1149/1945-7111/ae26cd",

language = "English",

volume = "172",

journal = "Journal of the Electrochemical Society",

issn = "0013-4651",

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T1 - Initial Proof-of-Concept for an Accelerated Galvanostatic Intermittent Titration Technique via Embedded Machine Learning

AU - Mebane, David S.

AU - Jackowska, Roksana

AU - Fouts, Michael W.

AU - Planella, Ferran Brosa

AU - Kendrick, Emma

AU - Mehta, Mohit R.

AU - Lawson, John W.

N1 - Publisher Copyright: © 2025 The Electrochemical Society (“ECS”). Published on behalf of ECS by IOP Publishing Limited. All rights, including for text and data mining, AI training, and similar technologies, are reserved.

PY - 2025/12/22

Y1 - 2025/12/22

N2 - This work conceptually proves and demonstrates in silico a new method and statistical analysis for the Galvanostatic Intermittent Titration Technique (GITT) that may decrease the experiment time by an order of magnitude. The method utilizes embedded Gaussian processes and focuses on the relaxation portion of the pulse-relaxation series. These innovations enable much higher current draws at each pulse, while shortening relaxation periods. The in silico proof-of-concept was achieved using simulated conditions similar to a published GITT experiment run on a lithium-lanthanum titanate half cell, while a validation of the relaxation-based method was performed on a porous Liζ Ni0.92Co0.03Mn0.05O2 cathode.

AB - This work conceptually proves and demonstrates in silico a new method and statistical analysis for the Galvanostatic Intermittent Titration Technique (GITT) that may decrease the experiment time by an order of magnitude. The method utilizes embedded Gaussian processes and focuses on the relaxation portion of the pulse-relaxation series. These innovations enable much higher current draws at each pulse, while shortening relaxation periods. The in silico proof-of-concept was achieved using simulated conditions similar to a published GITT experiment run on a lithium-lanthanum titanate half cell, while a validation of the relaxation-based method was performed on a porous Liζ Ni0.92Co0.03Mn0.05O2 cathode.

KW - batteries - Li-ion

KW - energy storage

KW - theory and modelling

U2 - 10.1149/1945-7111/ae26cd

DO - 10.1149/1945-7111/ae26cd

M3 - Article

AN - SCOPUS:105033986317

SN - 0013-4651

VL - 172

JO - Journal of the Electrochemical Society

JF - Journal of the Electrochemical Society

IS - 12

M1 - 120527

ER -

Initial Proof-of-Concept for an Accelerated Galvanostatic Intermittent Titration Technique via Embedded Machine Learning

Abstract

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Projects

Multi-Scale Modelling - MSM

Cell Designs and Testing Methodologies for Parameterisation

Cite this