Name
Abstract | ||
|---|---|---|
This brief addresses the energy management problem with the framework of receding horizon optimization. For power-split plug-in hybrid electric vehicles (HEVs), the real-time power-split decision is formulated as a nonlinear receding horizon optimization problem. Then, an online iterative algorithm to solve the optimization problem is proposed based on the continuation/generalized minimum residual algorithm. It should be noted that the proposed energy management strategy aims for optimality of the targeted horizon, but the solution is not optimal for the full driving route, unlike many solutions presented using the dynamic programming approaches. At each decision step, only the initial value of the optimal solution is implemented according to the receding horizon optimization approach. Finally, to demonstrate a comparison of the proposed scheme with other schemes, numerical validations conducted on a full-scale GT-SUITE HEV simulator are presented. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TCST.2016.2517130 | IEEE Trans. Contr. Sys. Techn. |
Keywords | Field | DocType |
Plug-in hybrid electric vehicles,Fuel economy,Optimization,Real-time systems,Predictive control | Residual,Dynamic programming,Energy management,Nonlinear system,Iterative method,Control theory,Model predictive control,Control engineering,Initial value problem,Optimization problem,Mathematics | Journal |
Volume | Issue | ISSN |
24 | 6 | 1063-6536 |
Citations | PageRank | References |
4 | 0.51 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiangyan Zhang | 1 | 27 | 7.02 |
| Tielong Shen | 2 | 243 | 40.52 |