Rapid estimation of lithium-ion battery capacity and resistances from short duration current pulses

Rapid onboard diagnosis of battery state of health enables the use of real-time control strategies that can improve product safety and maximize battery lifetime. However, onboard prediction of battery state-of-health is challenging due to the limitations imposed on diagnostic tests so as not to nega...

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Bibliographic Details
Published in:Journal of power sources 2025-02, Vol.628, p.235813, Article 235813
Main Authors: Nowacki, Benjamin, Ramamurthy, Jayanth, Thelen, Adam, Tischer, Chad, Pint, Cary L., Hu, Chao
Format: Article
Language:English
Subjects:
Battery aging data
Lithium-ion batteries
Machine learning
Rapid diagnostics
Rapid SOH estimation
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Summary:Rapid onboard diagnosis of battery state of health enables the use of real-time control strategies that can improve product safety and maximize battery lifetime. However, onboard prediction of battery state-of-health is challenging due to the limitations imposed on diagnostic tests so as not to negatively affect the user experience and impede normal operation. To this end, we demonstrate a lightweight machine learning model capable of predicting a lithium-ion battery’s discharge capacity and internal resistance at various states of charge using only the raw voltage-capacity time-series data recorded during short-duration (100 s) current pulses. Tested on two battery aging datasets, one publicly available and the other newly collected for this work, we find that the best models can accurately predict cell discharge capacity with an average mean-absolute-percent-error of 1.66%. Additionally, we quantize and embed the machine learning model onto a microcontroller and show comparable accuracy to the computer-based model, further demonstrating the practicality of on-board rapid capacity and resistance estimation. •SOH estimation is examined using voltage-capacity data during short-duration current pulses.•Discharge capacity and pulse resistances at three SOCs are used as SOH indicators.•A lightweight neural network produces rapid estimates of these seven SOH indicators.•Method validation uses a publicly available NMC dataset and a new LFP dataset.•Hardware-software integration is demonstrated by embedding the network onto a microcontroller.
ISSN:0378-7753
DOI:10.1016/j.jpowsour.2024.235813