Vehicle Maintenance Demand Prediction: A Survey

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Abstract
The implementation of predictive maintenance (PdM) represents a pivotal strategy for enhancing operational efficiency and reducing costs within the automotive industry. While extensive research has yielded sophisticated machine learning and deep learning models capable of accurately predicting failures in specific vehicle components—including suspensions and batteries—these approaches predominantly operate in isolation. A principal challenge remains the transition from predicting failures in single components to managing the health of the vehicle as an integrated system. This review systematically examines recent advances in forecasting methodologies for vehicle service parts and predictive maintenance technologies, while critically evaluating the strengths and limitations of diverse approaches. The analysis highlights crucial unresolved technical and strategic challenges that must be addressed to realize fully integrated predictive maintenance systems for next-generation vehicles, with particular emphasis on the transition from isolated component monitoring to holistic Vehicle Health Management (VHM).
Funding Information
  • Central Public Welfare Scientific Research Institutes (2025-9044)

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