Abstract
Electric city buses have potential to reduce greenhouse gases emission in case the majority of the electric power used in electric buses originate from the renewable sources or nuclear power plants. Their charging behaviors analysis is critical to their development and mass-adoption. To analyze charging behavior characteristics of electric city buses at different locations, the datasets collected from 17576 electric buses operating in 14 cities are used based on the probability statistics method. Then, the characteristic parameters including the charging power and charging duration are utilized to cluster the cities into 5 clusters based on the K-means algorithm. The results enrich the traditional research conducted only under limited test routes and provide the comparison of key characteristic parameters among different clusters. The analysis results are useful in studying the connection between the operational efficiency and the charging behaviors, optimizing the charging scheduling, evaluation of charging load and planning charging infrastructures construction.
| Original language | English |
|---|---|
| Article number | 8946603 |
| Pages (from-to) | 4466-4474 |
| Number of pages | 9 |
| Journal | IEEE Access |
| Volume | 8 |
| DOIs | |
| Publication status | Published - 2020 |
Keywords
- Charging behaviors
- K-means algorithm
- big data analytic
- electric city bus
