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The aim is to enhance the accuracy of state-of-charge estimation for lithium-ion batteries in battery management systems.

State-of-charge estimation of batteries using parameterized physics-informed neural networks

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The aim is to enhance the accuracy of state-of-charge estimation for lithium-ion batteries in battery management systems.
Developed a hybrid neural network integrating parameterized physics-informed neural networks and LSTM for temporal sequence learning.
Employed a hypernetwork framework to generate adaptive weights based on SOC-dependent parameters and temporal predictions.
Integrated physical dynamics into the loss function to better represent battery behavior.
Achieved RMSE values between 0.83% and 1.59% for SOC estimation across a public dataset.
Demonstrated robustness to missing data with an RMSE as low as 0.92%, despite a 62% reduction in training data.
Maintained an RMSE of less than 2.51% in controlled lab conditions, even when data was scarce.

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Hybrid neural network approach estimates SOC in batteries, suggesting improved accuracy under varied conditions.

Jang et al. (2026) studied this question.

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