Real-World SoC Estimation for Lithium-Ion Batteries in Electric Buses: Sampling Resolution and Machine Learning Efficiencys

Abstract

Accurate State of Charge (SoC) estimation is essential for safe and efficient lithium-ion battery operation in electric mobility. Although machine learning methods achieve high predictive capability, many studies rely on laboratory cycling data and overlook deployment constraints such as latency and embedded hardware. This paper presents a deployment-oriented evaluation of data-driven SoC estimation using large-scale operational data from a battery electric bus. The dataset includes more than 4.3 million field measurements of current, voltage, temperature, and SoC collected under realistic driving conditions. Five models are compared: Random Forest, LightGBM, XGBoost, Temporal Convolutional Networks (TCN), and Gated Recurrent Unit (GRU) networks. Models are evaluated across sampling intervals from 30 s to 210 s and multiple training-data fractions, considering accuracy, training time, and inference latency. Results show that temporal resolution strongly affects the accuracy-efficiency trade-off. The best configuration, LightGBM at 180 s using 75 % of training data, achieved a test MAE of 6.338, RMSE of 8.495, and median inference latency of 1.10 ms. Compared with the Random Forest baseline at 30 s, it reduced MAE by 34.4 %, RMSE by 35.6 %, and latency by 97.6 %, supporting efficient real-time battery management system deployment.