Uncertainty management in data-driven state estimation: Taxonomy, methods, and battery applications

초록

Driven by advances in machine learning and sensors, data-driven state estimation has become a key technology for supporting operational decisions in safety-critical systems. However, a single point estimate fails to reveal the uncertainty and confidence associated with a prediction, which can lead to overconfident decisions; as reliance on such estimates grows, the quantification and management of uncertainty becomes a prerequisite for reliable operation. This comprehensive review analyzes uncertainty management in data-driven state estimation, taking battery state estimation, in which current state estimation and future state prediction coexist, as a representative case study. This paper organizes uncertainty along two axes, its source (Aleatoric/Epistemic) and the mechanism that handles it (memory-based before modeling, model-based within the model), and applies this taxonomy to analyze how the dominant source and the management strategy suited to it shift along the prediction horizon. In doing so, it goes beyond a simple classification of techniques to present the correspondence between uncertainty sources and management strategies, and it summarizes the main open challenges and future research directions. The insights of this paper provide a foundation for reliable and risk-aware decision-making in predictive maintenance and safety-critical systems beyond batteries.

키워드