A novel real-time battery state estimation using data-driven prognostics and health management
Journal article
Pimentel, J., McEwan, A. and Yu, H. 2025. A novel real-time battery state estimation using data-driven prognostics and health management. Applied Sciences. 15 (5), pp. 1-21. https://doi.org/10.3390/app15158538
| Authors | Pimentel, J., McEwan, A. and Yu, H. |
|---|---|
| Abstract | This paper presents a novel data-driven framework for real-time State of Charge (SOC) estimation in lithium-ion battery systems using a data-driven Prognostics and Health Management (PHM) approach. The method leverages an optimized bidirectional Long Short-Term Memory (Bi-LSTM) network, trained with enhanced datasets filtered via exponentially weighted moving averages (EWMAs) and refined through SHAP-based feature attribution. Compared against a Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) across ten diverse drive cycles, the proposed model consistently achieved superior performance, with mean absolute errors (MAEs) as low as 0.40%, outperforming EKF (0.66%) and UKF (1.36%). The Bi-LSTM model also demonstrated higher R2 values (up to 0.9999) and narrower 95% confidence intervals, confirming its precision and robustness. Real-time implementation on embedded platforms yielded inference times of 1.3–2.2 s, validating its deployability for edge applications. The framework’s model-free nature makes it adaptable to other nonlinear, time-dependent systems beyond battery SOC estimation. |
| Keywords | PHM; SOC estimation; Li-ion battery; data-driven; machine learning; bidirectional LSTM; Kalman Filter; embedded systems; EWMA; SHAP; EKF; UKF; drive cycles |
| Year | 2025 |
| Journal | Applied Sciences |
| Journal citation | 15 (5), pp. 1-21 |
| Publisher | MDPI |
| ISSN | 2076-3417 |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/app15158538 |
| Web address (URL) | https://www.mdpi.com/2076-3417/15/15/8538 |
| Publisher's version | License File Access Level Open |
| Output status | Published |
| Publication dates | 31 Jul 2025 |
| Publication process dates | |
| Accepted | 27 Jul 2025 |
| Deposited | 28 Aug 2025 |
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