Research on Deep Adaptive Clustering Method Based on Stacked Sparse Autoencoders for Concrete Truck Mixers Driving Conditions
Abstract
1. Introduction
- (1)
- The vehicle real data experiment considering the vehicle monitoring platform is designed to collect the driving data, including speed profiles, vehicle mass and operation mode sequences.
- (2)
- A deep adaptive clustering method based on the SSAE algorithm is utilized for the precise extraction of complex driving condition features and the achievement of optimal cluster partitioning.
2. SSAE-Based Feature Extraction
2.1. Data Collection Experiment
2.2. Data Preprocessing
- (1)
- Since timestamps and corresponding trajectory points in the data file are in Greenwich Mean Time (GMT), they should be converted to Beijing time (GMT + 8);
- (2)
- The hexadecimal format of the ‘time’ keyword in the data file should be converted to a decimal time series and cross-day indexing appropriately processed.
- (1)
- Using the micro-trip partitioning method, the collected driving data of the CTM is divided into multiple micro-trips. After being chosen by criteria, a total of micro-trips are obtained. The following criteria are involved.
- (a)
- Due to equipment failures, abnormal driver operations, or weak GPS signals, some micro-trips have missing or discontinuous data. If a micro-trip has more than 10 consecutive missing sample points, it is discarded; otherwise, interpolation is used to supplement it.
- (b)
- According to relevant CTM standards, speed is limited to 50 km/h when fully loaded during transporting. However, no specific speed limit applies to empty returning.
- (c)
- All micro-trips in the established database for CTMs should be validated against the studied powertrain. Thus, the DC-side power demand sequence of the drive motor controller for each micro-trip is calculated, discarding any micro-trip where power demand exceeds the drive motor’s external characteristic curve at any point.
- (2)
- Due to the requirement of fixed input dimensions for SSAE, it is necessary to unify the duration of micro-trips. Divide the time interval and calculate the frequency distribution of the micro-trips, and select the average of the time interval with the highest frequency as time length .
- (3)
- Cut or fill driving data in each micro-trip according to the established division criteria: if the real-time length of the micro-trip is less than , cut the micro-trip and divide it into multiple sub-segments with time length ; otherwise, fill the micro-trip to time length with the last speed. Additionally, it should be noted to delete all segments with zero values.
2.3. Deep Feature Extraction
3. Deep Adaptive Clustering Method for CTMs Driving Conditions
3.1. Adaptive Cluster Method
- (1)
- Input the feature samples extracted by the SSAE algorithm, and initialize parameters, mainly including the maximum allowable error ε and the range of the number of clusters , where takes the value of [35].
- (2)
- Randomly initialize the cluster center . Then, update the cluster center by calculating the centers and the mean of the samples’ features of each class.
- (3)
- Check whether the change in cluster centers before and after updating is less than . If it does, output the optimal number of clusters , the corresponding CVNN under the optimal number, and the cluster labels; Otherwise, increase the number of clusters until the error condition is satisfied or the predefined maximum number of clusters is reached. The calculation method of the indicator CVNN is as follows,
3.2. Fine Turning Process
4. Simulation Analysis
4.1. Data Collection and Preprocessing
4.2. Feature Extraction Results Analysis
4.3. Clustering Results Analysis
4.4. Future Research
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
CTMs | Concrete truck mixers |
SSAE | Stacked sparse autoencoders |
PCA | Principal component analysis |
SAE | Stacked autoencoders |
DB | Davies–Bouldin index |
CH | Calinski–Harabasz index |
SH | Silhouette coefficient |
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Algorithm | Value |
---|---|
PCA | 0.41 |
Stacked AE | 1.03 |
Sparse AE | 0.44 |
SSAE | 2.10 |
Parameter | Value | Parameter | Value |
---|---|---|---|
Hidden layers | 4 | Sparsity rate | 0.1 |
Nodes | [70, 50, 40, 30] | Weight attenuation parameter | 0.001 |
Sparse penalty term | 3 | Maximum cluster number | 93 |
Minimum cluster number | 2 |
Index | EPKMC | DAKMC |
---|---|---|
DB | 1.0620 | 0.9140 |
CH | ||
SH | 0.5956 | 0.5562 |
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© 2025 by the authors. Published by MDPI on behalf of the World Electric Vehicle Association. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Huang, Y.; Jiang, F.; Xie, H. Research on Deep Adaptive Clustering Method Based on Stacked Sparse Autoencoders for Concrete Truck Mixers Driving Conditions. World Electr. Veh. J. 2025, 16, 581. https://doi.org/10.3390/wevj16100581
Huang Y, Jiang F, Xie H. Research on Deep Adaptive Clustering Method Based on Stacked Sparse Autoencoders for Concrete Truck Mixers Driving Conditions. World Electric Vehicle Journal. 2025; 16(10):581. https://doi.org/10.3390/wevj16100581
Chicago/Turabian StyleHuang, Ying, Fachao Jiang, and Haiming Xie. 2025. "Research on Deep Adaptive Clustering Method Based on Stacked Sparse Autoencoders for Concrete Truck Mixers Driving Conditions" World Electric Vehicle Journal 16, no. 10: 581. https://doi.org/10.3390/wevj16100581
APA StyleHuang, Y., Jiang, F., & Xie, H. (2025). Research on Deep Adaptive Clustering Method Based on Stacked Sparse Autoencoders for Concrete Truck Mixers Driving Conditions. World Electric Vehicle Journal, 16(10), 581. https://doi.org/10.3390/wevj16100581