Comparison of machine learning techniques for SoC and SoH evaluation from impedance data of an aged lithium ion battery
DOI:
https://doi.org/10.21014/acta_imeko.v10i2.1043Abstract
State of charge estimation and ageing evolution of lithium ion (Li-Ion) batteries are key points for their massive applications in the market. However, the battery behavior is very complex to understand because many parameters act in determining their ageing evolution. Therefore, traditional analytical models employed for this purpose are often affected by inaccuracy. In this context, machine learning techniques can provide a viable alternative to traditional models and a useful tool to characterize the batteries behavior.
In this work, different machine learning techniques were applied to model the impedance evolution over time of an aged cobalt based Li-Ion battery, cycled under a stationary frequency regulation profile for grid application. The different ML techniques were compared in terms of accuracy to determine the state of charge and the state of health over the battery ageing phenomena. Experimental results showed that ML based on Random Forest algorithm can be profitably used for this purpose.
Downloads
Additional Files
Published
Issue
Section
License
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under the CC BY 4.0, Creative Commons Attribution 4.0 International License.
Users are free to
- share, i.e. copy and redistribute the material in any medium or format for any purpose, even commercially;
- adapt, i.e. remix, transform, and build upon the material for any purpose, even commercially.
At the same time, the user must give appropriate credit, provide a link to the license, and indicate if changes were made.
Additional information about the license can be found at: https://creativecommons.org/licenses/by/4.0/.
Authors are
- able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
