Mission-Specific Prognosis of Li-ion Batteries using Hybrid Physics-Informed Neural Networks

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Published Oct 26, 2023
kajetanfricke

Abstract

New transportation modalities such as electric powered vertical
takeoff and landing aircraft and logistic applications
like delivery of packages with drones require highly reliable
and powerful electric batteries for operation. A challenging
but very important task hereby is the precise forecasting of
the degradation of battery state-of-health (SOH) and stateof-
charge (SOC). While high-fidelity electrochemistry based
models can provide precise predictions of the SOC, they can
be computationally expensive. On the other hand, purely datadriven
approaches require a large amount of training data in
order to learn the input to output relation. In this research an
improved hybrid physics-informed machine learning model
is introduced, that conserves the electrochemistry based laws
and is implemented with data-driven layers to compensate for
unknown portions of internal voltage drop during discharge.
Preliminary results indicate that the model can predict discharge
for a large variety of loads, accurately predicts capacity
degradation over age and can be enhanced through extracting
information from cell temperature data as surrogate for aging.

How to Cite

kajetanfricke. (2023). Mission-Specific Prognosis of Li-ion Batteries using Hybrid Physics-Informed Neural Networks. Annual Conference of the PHM Society, 15(1). https://doi.org/10.36001/phmconf.2023.v15i1.3796
Abstract 95 | PDF Downloads 84

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Keywords

Physics-Informed Neural Networks, Li-ion Battery Prognostics, Scientific Machine Learning, Battery Aging

References
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Nascimento, R. G., Corbetta, M., Kulkarni, C. S., & Viana, F. A. C. (2021). Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis. Journal of Power Sources, 230526. doi: 10.1016/j.jpowsour.2021.230526

Nascimento, R. G., Fricke, K., & Viana, F. A. C. (2020). A tutorial on solving ordinary differential equations using Python and hybrid physics-informed neural networks. Engineering Applications of Artificial Intelligence, 96.
Section
Doctoral Symposium Summaries