Prognostics of Battery State of Health Using Hybrid Modeling of Electric Vehicle Charging Data

Accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for ensuring the safety, reliability, and performance optimization of electric vehicles. In practical operating environments, however, data quality is often compromised by noise interference, frequent fluctuations in load conditions, and the inherently non-stationary nature of battery degradation features. These challenges reduce the effectiveness of conventional modeling approaches, which often struggle to maintain both high prediction accuracy and strong generalization capability. To address these issues, this study develops a comprehensive SOH estimation approach encompassing data quality enhancement, degradation feature extraction, and hybrid deep learning-based modeling. In the first stage, multi-stage anomaly detection techniques are applied to remove noisy or inconsistent measurements. A week-based indexing strategy is introduced to generate temporally coherent labels, ensuring that time-series dependencies are preserved. This procedure ensures a minimum per-vehicle valid-data ratio of 87.59%, thereby guaranteeing consistent data availability across all vehicles and significantly improving both reliability and temporal alignment. In the second stage, a set of degradation-sensitive health indicators is extracted from raw sensor measurements, including voltage, current, and temperature profiles. These features are then aggregated at a weekly resolution to enhance stability and reduce short-term variability. In the third stage, a hybrid deep learning model is constructed by combining a Temporal Convolutional Network (TCN) for local pattern extraction, a Bidirectional Gated Recurrent Unit (BiGRU) for long-term dependency modeling, and an attention mechanism for adaptive feature weighting. Experimental results under 10-fold cross-validation show that the proposed approach achieves a root mean square error of 1.30% and a mean absolute error of 1.03% on the test set, outperforming single-model baselines in both accuracy and robustness. The proposed framework offers a practical and scalable solution for high-precision SOH estimation in real-world scenarios and provides a strong basis for deployment under diverse and extreme operating conditions.