A stochastic model predictive control strategy for energy management of series PHEV

被引：0
作者：
Xie, Haiming [1 ]
Chen, Hongxu [1 ]
Tian, Guangyu [1 ]
Wang, Jing [1 ]
机构：
[1] State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing
来源：
World Electric Vehicle Journal | 2015年 / 7卷 / 02期
关键词：
Energy management; Hybrid Electric Vehicle; Hybrid systems; Markov prediction; Model predictive control;
D O I：
10.3390/wevj7020299
中图分类号：
学科分类号：
摘要：
Splitting power is a tricky problem for series plug-in hybrid electric vehicles (SPHEVs) for the multi-working modes of powertrain and the hard prediction of future power request of the vehicle. In this work, we present a methodology for splitting power for a battery pack and an auxiliary power unit (APU) in SPHEVs. The key steps in this methodology are (a) developing a hybrid automaton (HA) model to capture the power flows among the battery pack, the APU and a drive motor (b) forecasting a power request sequence through a Markov prediction model and the maximum likeli-hood estimation approach (c) formulating a constraint stochastic optimal control problem to minimize fuel consumption and at the same time guarantee the dynamic performance of the vehicle (d) solving the optimal control problem using the model predictive control technique and the YALMIP toolbox. Our simulation experimental results show that with our stochastic model predictive control strategy a series plug-in hybrid electric vehicle can save 1.544 L gasoline per 100 kilometers compared to another existing power splitting strategy. © 2015 WEVA.
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收藏
页码：299 / 310
页数：11
相关论文
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