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SOH Estimation of Lithium Iron Phosphate Batteries Based on Electro-Thermal-Acoustic Multi-Source Feature
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DOI：10.1149/1945-7111/ae5ea8.png)
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Abstract
En 中文
The complex nonlinear behavior of lithium iron phosphate batteries cannot be fully characterized by conventional electrical-thermal parameters. Ultrasonic techniques provide complementary insights through acoustic signature analysis. Accurate state-of-health (SOH) estimation during long-term cycling is addressed through ultrasonic-electrical-thermal data fusion. Unlike conventional SOH estimation methods that rely solely on electrical-thermal signals or purely data-driven models, this study further integrates physically interpretable acoustic-derived aging indicators to enhance structural sensitivity and robustness. Voltage, temperature, time-of-flight (TOF), and signal amplitude (SA) variations are analyzed during charging. Five key aging indicators are identified. An improved Bidirectional Gate Recurrent Unit (IBiGRU) model with enhanced temporal feature representation is developed to extract multi-source degradation characteristics adaptively. Only 30% of the initial cycling data is required for accurate prediction. Mean absolute error (MAE) is maintained below 0.0066 in single-battery tests. Root mean square error (RMSE) remains under 0.0078 in these tests. Cross-validation yields an MAE of 0.0195 and an RMSE of 0.0216. These results validate the effectiveness and robustness of the proposed multi-source data fusion framework for accurate state-of-health (SOH) estimation of lithium iron phosphate batteries.
Keywords:
lithium iron phosphate batteries
electro-thermal-acoustic feature fusion
state of health estimation
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3.3
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3.3W
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9.4W
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