A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles

被引:407
作者
Moura, Scott Jason [1 ]
Fathy, Hosam K. [1 ]
Callaway, Duncan S. [2 ]
Stein, Jeffrey L. [1 ]
机构
[1] Univ Michigan, Dept Mech Engn, Ann Arbor, MI 48109 USA
[2] Univ Calif Berkeley, Energy & Resources Grp, Berkeley, CA 94720 USA
基金
美国国家科学基金会;
关键词
Dynamic programming; Markov process; plug-in hybrid electric vehicles (PHEV); power management; powertrain control; powertrain modeling; ENERGY MANAGEMENT; PREDICTIVE CONTROL; SYSTEMS; OPTIMIZATION; ALGORITHM; ADVISER; POLICY;
D O I
10.1109/TCST.2010.2043736
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper examines the problem of optimally splitting driver power demand among the different actuators (i.e., the engine and electric machines) in a plug-in hybrid electric vehicle (PHEV). Existing studies focus mostly on optimizing PHEV power management for fuel economy, subject to charge sustenance constraints, over individual drive cycles. This paper adds three original contributions to this literature. First, it uses stochastic dynamic programming to optimize PHEV power management over a distribution of drive cycles, rather than a single cycle. Second, it explicitly trades off fuel and electricity usage in a PHEV, thereby systematically exploring the potential benefits of controlled charge depletion over aggressive charge depletion followed by charge sustenance. Finally, it examines the impact of variations in relative fuel-to-electricity pricing on optimal PHEV power management. The paper focuses on a single-mode power-split PHEV configuration for mid-size sedans, but its approach is extendible to other configurations and sizes as well.
引用
收藏
页码:545 / 555
页数:11
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