Trip-oriented Predictive Energy Management for a Plug-in Hybrid Electric Vehicle Based on Real-time Traffic Information,"Presenter"
- Delivery
- Available on this site
- Format
- Price
- Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880
- Publication code
- 20181827
- Paper/Info type
- AVEC
No.ThE1-4
- Pages
- 1-6(Total 6 p)
- Date of publication
- Jul 2018
- Publisher
- Others, Unknown
- Language
- English
- Event
- AVEC '18
Detailed Information
| Category(E) | Hybrid EV Control I |
|---|---|
| Author(E) | 1) Junjun Liu |
| Abstract(E) | The energy management system has a pivotal role in fuel economy improvement for the Plug-in hybrid electric vehicle (PHEV). To realize the near optimal global energy distribution of PHEV, this paper develops a trip-oriented predictive energy management strategy based on real-time traffic information. Compared with the traditional exponentially varying prediction model, an exponentially varying prediction model based on tunable decay coefficient using Support Vector Machine (SVM) is proposed. The traffic environment in VISSIM platform to simulate the actual traffic is established, in which the trip modeling is finished and the real-time traffic information is used to generate the battery state of charge (SOC) reference. The Dynamic Programming (DP) algorithm is used to solve the nonlinear rolling optimization problem to minimum the trip cost in prediction horizon. The model predictive control (MPC) simulation under the established traffic environment is implemented and the results show that the MPC-traffic can reach the 92.83% optimality as DP, which is 6.18% higher than the MPC-duration knowing the trip duration and the linearly decreased SOC reference has the limitation in the actual driving cycle. The MPC-traffic has a tendency to have a better total cost performance with the decreasing of updating unit, which means the timely updating of traffic information help to improve the economy performance of the energy management strategy. |
