‘Alawa: A Unique Mechanistic Model for Battery Diagnosis and Prognosis

‘Alawa: A Unique Mechanistic Model for Battery Diagnosis and Prognosis hneieditor February 26, 2024

‘Alawa is a software suite based on the mechanistic model developed by the Hawai‘i Natural Energy Institute. The toolbox enables the emulation of the impact of all degradation modes (loss of ion inventory, loss of active material, and kinetic hindrance) on single phased or blended electrodes.

Though relatively young, the mechanistic modeling approach was proven to be extremely versatile and effective for lithium-ion battery diagnosis and prognosis. The approach relies on assembling digital twins by matching the individual voltage response of each electrode. Changing the matching, via scaling or translations, enables replication of the degradation modes electrochemical signature. Degradation modes comprise the loss of lithium inventory, the loss of active material, and kinetic changes and refer to the impact of degradation mechanisms on the electrodes rather than their root cause as degradation mechanisms will only affect, to some extent, the amount of material able to react, the amount of lithium able to go back and forth between the electrodes, and the overall reaction kinetics. Quantifying degradation modes open the gate for material-based diagnosis and prognosis without the need for complex models.

Since its inception in the early 2010s, ‘alawa has been used to diagnose the degradation of several hundred cells of multiple chemistries and blends. It made it possible to explain and predict the apparition of knees with the concept of hidden mechanisms and to emulate the impact of kinetics. The approach also proved useful to investigate plating, overdischarge and overcharge, and to generate big data with millions of synthetic voltage curves, constant current or not, enabling the development of advanced diagnosis and prognosis tools. Recent proof-of-concept implementations opened new directions to enhance the modeling of blended and inhomogeneous electrodes and packs, voltage fade, and calculations outside of constant current.

An interactive web-based demonstration version of the toolbox can be found at https://www.hnei.hawaii.edu/alawa-toolbox/.

‘Alawa is available for free for academic uses and licensing is available for any other applications. For academic applications, please download and fill in the license agreement form at https://www.hnei.hawaii.edu/wp-content/uploads/Alawa-License-Agreement-Terms-and-Conditions.pdf with your name and institution and send the form to Matthieu Dubarry at matthieu@hawaii.edu.

The toolbox is available at the Matlab © format as a graphical user interface which user’s manual can be downloaded at https://www.hnei.hawaii.edu/wp-content/uploads/Alawa-Toolbox-User-Manual.pdf. For academic collaborations, additional code will be made available at the Matlab © or python formats to perform batch calculations which enables synthetic data generation.

For non-academic application, please contact matthieu@hawaii.edu for availability and pricing information.

More than 125 organizations worldwide have already licensed the toolbox. This includes U.S. national laboratories, prestigious universities as well as battery manufacturers, battery pack assemblers, and electronic giants.

Interested readers can find a full description of the approach in the following publications:

Synthesize battery degradation modes via a diagnostic and prognostic model.
M. Dubarry, C. Truchot, and B. Y. Liaw, J. Power Sources, 219, p.204 (2012).

State of Health Battery Estimator Enabling Degradation Diagnosis: Model and Algorithm description
M. Dubarry, M. Berecibar, A. Devie, D. Ansean, N. Omar and I. Villarreal
Journal of Power Sources, 360, 59-69 (2017).

Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis
M. Dubarry and D. Beck
J. Power Sources, 479, 228806 (2020).

Perspective on Mechanistic Modeling of Li-Ion Batteries,
M. Dubarry and D. Beck
Accounts of Material Research 3(8), 843-853 (2022).
doi: 10.1021/accountsmr.2c00082

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