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020 ▼a 9780438050600
035 ▼a (MiAaPQ)AAI10824718
035 ▼a (MiAaPQ)princeton:12642
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 621
1001 ▼a Davies, Gregory.
24510 ▼a Characterization of Batteries Using Ultrasound: Applications for Battery Management and Structural Determination.
260 ▼a [S.l.]: ▼b Princeton University., ▼c 2018.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2018.
300 ▼a 206 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 ▼a Adviser: Daniel Steingart.
5021 ▼a Thesis (Ph.D.)--Princeton University, 2018.
506 ▼a This item is not available from ProQuest Dissertations & Theses.
520 ▼a Ultrasound has been an invaluable and widely used tool in the medical and non-destructive testing (NDT) sectors. Much of its success is attributable to its low-cost, compact size, the speed and ease of its application, and the useful qualitative
520 ▼a More specifically, in Chapter 2 ultrasonic measurements were combined with a supervised machine-learning technique, which was used to predict the SOC and SOH of lithium-ion cells that had been operated for several hundred cycles. Excellent resul
590 ▼a School code: 0181.
650 4 ▼a Mechanical engineering.
650 4 ▼a Energy.
650 4 ▼a Acoustics.
690 ▼a 0548
690 ▼a 0791
690 ▼a 0986
71020 ▼a Princeton University. ▼b Mechanical and Aerospace Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-10B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0181
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998697 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 201812 ▼f 2019
990 ▼a ***1012033