XGBoost–random forest stacking with dual-state Kalman filtering for real-time battery SOC estimation

Accurate real-time state-of-charge estimation remains a bottleneck for e-bike battery management because firmware must deliver sub-Image 1 updates while drawing less than Image 2. Classical observers drift under sensor bias, and purely data-driven models exceed the timing and memory ceilings of low-...

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Bibliographic Details
Main Authors:	Robin K.E. Tau, Abid Yahya, Mmoloki Mangwala, Nonofo M.J. Ditshego
Format:	Article
Language:	English
Published:	Elsevier 2025-09-01
Series:	Results in Engineering
Subjects:	Machine learning Battery management systems (BMS) Lithium-ion batteries SOC estimation Edge AI Embedded systems
Online Access:	http://www.sciencedirect.com/science/article/pii/S2590123025024971
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XGBoost–random forest stacking with dual-state Kalman filtering for real-time battery SOC estimation

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