Operational Cycle Detection for Mobile Mining Equipment: An Integrative Scoping Review with Narrative Synthesis

Background: Operational cycle detection underpins a range of important tasks, such as predictive maintenance, energy consumption prediction, and energy management for mobile equipment in mining. Yet, no review has investigated the landscape of methods that segment mobile mining vehicle telemetry into discrete operating modes—a task termed operational cycle detection. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, Scoping Review extension (PRISMA-ScR) framework, we searched The Lens database on 27 June 2025, for records published between 2000 and 2025 that apply cycle detection to mobile mining vehicles. After de-duplication and two-stage screening, 20 empirical studies met all criteria (19 diesel, 1 electric-drive). Due to the sparse research involving battery electric vehicles (BEVs) in mining, three articles performing cycle detection on heavy-duty vehicles in a similar operational context to mining are synthesized. Results: Early diesel work used single-sensor thresholds, often achieving >90% site-specific accuracy, while recent studies increasingly employ neural networks using multivariate datasets. While the cycle detection research on mining BEVs, even supplemented with additional heavy-duty BEV studies, is sparse, similar approaches are favored. Conclusions: Persisting gaps in the literature include the absence of public mining datasets, inconsistent evaluation metrics, and limited real-time validation.