Version 1
: Received: 26 April 2024 / Approved: 27 April 2024 / Online: 30 April 2024 (06:58:36 CEST)
How to cite:
Byg Vilsen, S.; Stroe, D.-I. On the Use of Randomly Selected Partial Charges to Predict Battery State-of-Health. Preprints2024, 2024041968. https://doi.org/10.20944/preprints202404.1968.v1
Byg Vilsen, S.; Stroe, D.-I. On the Use of Randomly Selected Partial Charges to Predict Battery State-of-Health. Preprints 2024, 2024041968. https://doi.org/10.20944/preprints202404.1968.v1
Byg Vilsen, S.; Stroe, D.-I. On the Use of Randomly Selected Partial Charges to Predict Battery State-of-Health. Preprints2024, 2024041968. https://doi.org/10.20944/preprints202404.1968.v1
APA Style
Byg Vilsen, S., & Stroe, D. I. (2024). On the Use of Randomly Selected Partial Charges to Predict Battery State-of-Health. Preprints. https://doi.org/10.20944/preprints202404.1968.v1
Chicago/Turabian Style
Byg Vilsen, S. and Daniel-Ioan Stroe. 2024 "On the Use of Randomly Selected Partial Charges to Predict Battery State-of-Health" Preprints. https://doi.org/10.20944/preprints202404.1968.v1
Abstract
As society becomes more reliant on Lithium-ion (Li-ion) batteries, state-of-health (SOH) estimation will need to become more accurate and reliable. Therefore, SOH modelling is in the process of shifting from using simple and continuous charge/discharge profiles, to more dynamic profiles constructed to mimic real operation, when ageing the Li-ion batteries. However, in most cases, when ageing the batteries, the same exact profile is just repeated until the battery reaches its end-of-life. Using data from batteries aged in this fashion to build a model, there is a very real possibility that the model will rely on the built-in repetitiveness of the profile. Therefore, this work will examine the dependence of the performance of a multiple linear regression on the number of charges used to train the model, and their location within the profile used to age the batteries. The investigation shows that it is possible to build models using randomly selected partial charges while still reaching errors as low as 0.5%. Furthermore, it shows that only two randomly sampled partial charges are needed to achieve errors of less than 1%. Lastly, as the number of randomly sampled partial charges used to create the model increases, then the dependence on particular partial charges tends to decrease.
Keywords
Lithium-ion; Battery state-of-health; dynamic operation profile; partial charges; random selection; linear regression
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
