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