Abstract
The simulation and evaluation of land vehicle performance requires accurate representation of driving conditions. The ability to effectively interpret this condition to estimate the power demand is important for the energy management and plays crucial role in the battery utilization. After collecting driving status of vehicle, two algorithms (K-means Clustering and EM Algorithm) are employed for clustering the working condition blocks. Finally, compare the clustering effects of the two algorithms, and draw the conclusion that EM Algorithm is better.
| Original language | English |
|---|---|
| Title of host publication | IET Conference Proceedings |
| Publisher | Institution of Engineering and Technology |
| Pages | 1281-1285 |
| Number of pages | 5 |
| Volume | 2020 |
| Edition | 3 |
| ISBN (Electronic) | 9781839534195 |
| DOIs | |
| Publication status | Published - 2020 |
| Event | 2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020 - Virtual, Online Duration: 18 Sept 2020 → 21 Sept 2020 |
Conference
| Conference | 2020 CSAA/IET International Conference on Aircraft Utility Systems, AUS 2020 |
|---|---|
| City | Virtual, Online |
| Period | 18/09/20 → 21/09/20 |
Keywords
- CLUSTER ANALYSIS
- DRIVING PATTERN
- EM ALGORITHM
- K-MEANS ALGORITHM
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Yan, Q., Ma, Y., & Li, Y. (2020). CLUSTER ANALYSIS OF VEHICLE DRIVING CONDITIONS BASED ON K-MEANS ALGORITHM AND EM ALGORITHM. In IET Conference Proceedings (3 ed., Vol. 2020, pp. 1281-1285). Institution of Engineering and Technology. https://doi.org/10.1049/icp.2021.0381