Name
Abstract | ||
|---|---|---|
The frequent stop-and-go operations require high and fast burst driving power, which accelerates the electric vehicle batteries degradation. Hybrid electric storage system (HESS) is a promising solution, which supplements the battery with supercapacitor for rapid charging/discharging. If future power demand is available, effective power management can be done by fully exploiting the HESS benefits. Recent advances in the Internet of Things (IoT) have made the future information prediction practical, since surroundings information is obtainable. In this paper, a proactive energy management strategy is developed for the HESS with the IoT support. By analyzing the traffic data, a probabilistic graphical model, i.e., the conditional linear Gaussian (CLG), is designed for future driving information prediction. Since, the CLG prediction results are probability distributions, a scenario-tree method is developed to approximate the future power demand by sampling the possible future velocity profiles from the results. A stochastic model predictive control problem is established by incorporating the sampled trajectories. A fast dual proximal gradient method is proposed to solve the problem and facilitate real-time implementation. Simulation results demonstrate that the magnitude and fluctuation of the battery discharging power are reduced by 46.4% and 27.7%, respectively, compared with the battery only case. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/JIOT.2019.2899928 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Batteries,Power demand,Energy management,Internet of Things,Mathematical model,Stochastic processes,Predictive models | Journal | 6 |
Issue | ISSN | Citations |
5 | 2327-4662 | 1 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuying Hu | 1 | 2 | 1.40 |
| Cai-Lian Chen | 2 | 831 | 98.98 |
| Jianping He | 3 | 19 | 6.39 |
| Bo Yang | 4 | 361 | 40.37 |
| Xinping Guan | 5 | 127 | 17.80 |