Approximate Stochastic Differential Dynamic Programming for Hybrid Vehicle Energy Management

被引：4
|
作者：
Williams, Kyle [1 ,3 ]
Ivantysynova, Monika [2 ]
机构：
[1] Purdue Univ, Sch Mech Engn, W Lafayette, IN 47906 USA
[2] Purdue Univ, Sch Mech Engn, Maha Fluid Power Res Ctr, W Lafayette, IN 47906 USA
[3] Caterpillar Large Power Syst Div, Lafayette, IN 47905 USA
来源：
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME | 2019年 / 141卷 / 05期
关键词：
25;
D O I：
10.1115/1.4042253
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 develops a new computational approach for energy management in a hydraulic hybrid vehicle. The developed algorithm, called approximate stochastic differential dynamic programming (ASDDP) is a variant of the classic differential dynamic programming algorithm. The simulation results are discussed for two Environmental Protection Agency drive cycles and one real world cycle based on collected data. Flexibility of the ASDDP algorithm is demonstrated as real-time driver behavior learning, and forecasted road grade information are incorporated into the control setup. Real-time potential of ASDDP is evaluated in a hardware-in-the-loop (HIL) experimental setup.
引用
收藏
页数：9
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