March 3, 2026Open Access

Pattern-aware transformer for SOC estimation in IoT-based battery management systems: Toward energy-efficient and interpretable modeling

Key Points

Accurate SOC estimation is critical for improving battery management system efficiency, with PATM showing significant performance improvements.
The PATM achieves an RMSE of 2.08 × 10 −3 and an R 2 of 0.9998, markedly outpacing LSTM and GRU benchmarks.
Feature selection using mutual information ranking and principal component analysis preserves essential variables while reducing redundancy.
SHAP analysis and attention visualizations confirm the model's reliance on key factors like temperature gradients and voltage fluctuations.

Abstract

Accurate estimation of the State of Charge (SOC) is essential for enhancing the efficiency and reliability of Battery Management Systems (BMS) in Internet of Things (IoT) applications. This study introduces the Pattern-Aware Transformer Model (PATM), an interpretable framework for SOC prediction in Float-Nominal (FN), Constant-Current (CC), and Energy Release (ER) scenarios. PATM extends the standard Transformer architecture by incorporating a pattern embedding mechanism that explicitly encodes operating conditions and directs adaptive attention allocation. A feature engineering pipeline that combines mutual information (MI) ranking and principal component analysis (PCA) reduces dimensionality while preserving physically relevant variables. On real-world data, PATM achieves an RMSE of 2.08 × 10 −3 and an R 2 of 0.9998, outperforming the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) baselines. Compared with single-scenario CC modeling, multi-scenario learning reduces RMSE, MAE, MSE, and MAPE by 54.9%, 80.1%, 79.6%, and 75.9%, respectively. Ablation studies further demonstrate that removing the embedding module increases RMSE by 2.4%, MAE by 17.8%, and MSE by 4.9%, while leaving R 2 nearly unchanged. This indicates that the embedding mechanism enhances cross-scenario robustness and error stability. SHapley Additive exPlanations (SHAP) analysis and attention visualizations reveal the model’s dependence on physically relevant factors, including temperature gradients, voltage fluctuations, and internal resistance. • This study combines MI ranking and PCA for efficient feature selection, preserving interpretability while reducing redundancy. • This study introduces a multi-scenario approach that integrates data from diverse operating phases, enhancing adaptability and accuracy. • This study introduces the Pattern-Aware Transformer Model (PATM), and experimental results show that it outperforms LSTM and GRU across multiple metrics, demonstrating its accuracy and robustness.

Pattern-aware transformer for SOC estimation in IoT-based battery management systems: Toward energy-efficient and interpretable modeling

Key Points

Abstract

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