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020 ▼a 9780438017016
035 ▼a (MiAaPQ)AAI10748410
035 ▼a (MiAaPQ)purdue:22363
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 621
1001 ▼a Williams, Kyle R.
24510 ▼a Real-Time Stochastic Predictive Control for Hybrid Vehicle Energy Management.
260 ▼a [S.l.]: ▼b Purdue University., ▼c 2018.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2018.
300 ▼a 144 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 ▼a Adviser: Monika M. Ivantysynova.
5021 ▼a Thesis (Ph.D.)--Purdue University, 2018.
520 ▼a This work presents three computational methods for real time energy management in a hybrid hydraulic vehicle (HHV) when driver behavior and vehicle route are not known in advance. These methods, implemented in a receding horizon control (aka mod
590 ▼a School code: 0183.
650 4 ▼a Mechanical engineering.
690 ▼a 0548
71020 ▼a Purdue University. ▼b Mechanical Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-10B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0183
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14996984 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 201812 ▼f 2019
990 ▼a ***1012033