XGBoost–random forest stacking with dual-state Kalman filtering for real-time battery SOC estimation
Robin Kagiso Elang Tau, Abid Yahya, Mmoloki Mangwala, Nonofo M.J. Ditshego
Abstract
Accurate real-time state-of-charge estimation remains a bottleneck for e-bike battery management because firmware must deliver sub- updates while drawing less than . Classical observers drift under sensor bias, and purely data-driven models exceed the timing and memory ceilings of low-cost microcontrollers. This study therefore proposes the Hybrid Ensemble Dual-State Kalman Filter (HEAD-KF), which fuses Extreme Gradient Boosting and Random-Forest regressors through non-negative ridge stacking and smooths the fused output with a dual-state Kalman filter whose noise covariances are tuned online from residual statistics. The pipeline runs end-to-end on a Raspberry Pi 4 and is validated on a 20S Samsung INR18650-25R pack that uses NCA chemistry and is cycled between and . HEAD-KF yields a global mean-absolute error of SOC, keeps dynamic-discharge error to , and updates in while consuming per prediction. Covariance-perturbation and sensor-noise injections hold the estimator inside the ISO-12405 band, and ablation tests show that removing either the ensemble fusion or the adaptive Kalman loop doubles the error. These results indicate that HEAD-KF satisfies the accuracy, timing, and energy constraints of embedded battery-management systems on commodity hardware, and they motivate future work on cross-chemistry retraining, aggressive model compression for sub- targets, and on-device drift detection to preserve accuracy as packs age. • Real-time SOC estimate completes in ≤6 ms on Raspberry Pi 4 edge hardware. • XGBoost + Random Forest ensemble cuts SOC error by 47%. • Dual-state Kalman filter self-tunes noise for robust real-world accuracy. • HEAD-KF caps worst-case discharge error at 0.28% and global MAE 0.0004%. • Each inference uses only 60 μJ, staying well below the 100 mW budget.