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State of Health Estimation Method for Lithium-Ion Batteries Based on BiLSTM-Transformer and Fusion Features

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Hybrid model predicts state of health in lithium-ion batteries using BiLSTM and transformer techniques, suggesting improved accuracy.

The hybrid model achieves MAE and RMSE values below 1.12×10-2, indicating high prediction accuracy.
Incremental capacity analysis and differential thermal voltammetry curves are utilized as key features for prediction.
Feature fusion combines insights from BiLSTM and transformers to address long-term sequence prediction challenges.
The model is validated with NASA and Oxford datasets, demonstrating robustness in battery SOH estimation.

Xu et al. (2025) studied this question.

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View Full Paper

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Authors

Hybrid model predicts state of health in lithium-ion batteries using BiLSTM and transformer techniques, suggesting improved accuracy.

The hybrid model achieves MAE and RMSE values below 1.12×10-2, indicating high prediction accuracy.
Incremental capacity analysis and differential thermal voltammetry curves are utilized as key features for prediction.
Feature fusion combines insights from BiLSTM and transformers to address long-term sequence prediction challenges.
The model is validated with NASA and Oxford datasets, demonstrating robustness in battery SOH estimation.

Xu et al. (2025) studied this question.

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