Research on Deep Adaptive Clustering Method Based on Stacked Sparse Autoencoders for Concrete Truck Mixers Driving Conditions

Existing standard driving conditions fail to accurately characterize the complex characteristics of heavy-duty commercial vehicles such as concrete truck mixers (CTMs), while traditional dimensionality reduction methods have strong empirical dependence and an insufficient ability to capture nonlinear relationships. To address these issues, a novel method for constructing typical composite driving conditions that integrates deep feature learning and adaptive clustering is proposed. Firstly, a vehicle data monitoring system is used to collect real-world driving data, and a data processing and filtering criterion specific to CTMs is designed to provide effective input for feature extraction. Then, stacked sparse autoencoders (SSAE) are employed to extract deep features from normalized driving data. Finally, the K-means++ algorithm is improved using a nearest neighbor effective index minimization strategy to construct an adaptive driving condition clustering model. Validation results based on a real-world dataset of 8779 driving condition segments demonstrate that this method enables the precise extraction of complex driving condition features and optimal cluster partitioning. It provides a reliable basis for subsequent research on typical composite driving conditions construction and energy management strategies for heavy-duty commercial vehicles.