東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Self-redundant Real-time Fault Diagn...

Xia, Bing.

FindBook

Google Book

Amazon

博客來

Self-redundant Real-time Fault Diagnosis of Battery Systems in Electrified Vehicles.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Self-redundant Real-time Fault Diagnosis of Battery Systems in Electrified Vehicles./
作者:	Xia, Bing.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	155 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
Contained By:	Dissertation Abstracts International78-10B(E).
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10258857
ISBN:	9781369808940

Self-redundant Real-time Fault Diagnosis of Battery Systems in Electrified Vehicles.
Xia, Bing.

Self-redundant Real-time Fault Diagnosis of Battery Systems in Electrified Vehicles. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 155 p.

Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2017.

As electrified vehicles penetrate the market, consumers have been gradually experiencing the benefits of their high performance and contribution towards green living. However, the benefits of the new powertrain system bring with them many severe safety hazards, which hinder their development. The early detection of electric faults is an essential approach to identifying the occurrence of hazards and ensuring safe operation. This thesis studies state-of-the-art fault diagnosis methods for the battery system in electrified vehicles. Based on the uniqueness of the battery system, the self-redundant fault detection methods are proposed and studied in detail.

ISBN: 9781369808940Subjects--Topical Terms:

649834
Electrical engineering.

Self-redundant Real-time Fault Diagnosis of Battery Systems in Electrified Vehicles.
LDR:04608nmm a2200373 4500 001 2156360
005 20180517112608.5
008 190424s2017 ||||||||||||||||| ||eng d
020 $a 9781369808940
035 $a (MiAaPQ)AAI10258857
035 $a (MiAaPQ)ucsd:16288
035 $a AAI10258857
040 $a MiAaPQ $c MiAaPQ
100 1 $a Xia, Bing. $3 1030722
245 1 0 $a Self-redundant Real-time Fault Diagnosis of Battery Systems in Electrified Vehicles.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 155 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
500 $a Advisers: Chris Mi; Truong Nguyen.
502 $a Thesis (Ph.D.)--University of California, San Diego, 2017.
520 $a As electrified vehicles penetrate the market, consumers have been gradually experiencing the benefits of their high performance and contribution towards green living. However, the benefits of the new powertrain system bring with them many severe safety hazards, which hinder their development. The early detection of electric faults is an essential approach to identifying the occurrence of hazards and ensuring safe operation. This thesis studies state-of-the-art fault diagnosis methods for the battery system in electrified vehicles. Based on the uniqueness of the battery system, the self-redundant fault detection methods are proposed and studied in detail.
520 $a First, abundant experiments are conducted to capture the electrical and thermal behaviors of the lithium ion battery cells, serving as the basic energy storage elements in the commercial battery packs nowadays, and the simple threshold-based fault detection method is implemented to identify electric faults, including over charge, over discharge, external short circuit and internal short circuit.
520 $a Then, the model-based fault detection method is investigated to compensate the drawback of the threshold-based diagnosis method, in which the input information is ignored. The continuous-time system identification methods are introduced to estimate model parameters of the equivalent circuit model. The estimated model provides more accurate and robust fault detection performance compared with that of the traditional discrete-time system identification methods.
520 $a Next, the correlation-based fault diagnosis method is proposed for short circuit detections in the battery system. This method does not require the preliminary effort in battery modeling, identification and validation. More importantly, the correlation coefficient is not sensitive to variations in open circuit voltages and internal resistances, and thus is robust to cell inconsistencies in real applications.
520 $a After that, the theory of the interleaved voltage measurement method is developed to distinguish between sensor and cell failure without extra hardware components or battery models. The theory is then improved such that the constraint in sensor topology is removed by varying the sensor matrix. The feasibility of the measurement method is validated by simulation and experiment.
520 $a At last, the interleaved voltage measurement method is integrated with the correlation-based fault diagnosis method to achieve both the advantages. The viability of the integration is confirmed by experiment validation.
520 $a In summary, this thesis develops the self-redundant fault diagnosis approaches, including the correlation-based short circuit detection method and the interleaved voltage measurement method. These methods are specifically designed for the battery system, in which duplicative components and similar measurements are needed. The advantages of the proposed self-redundant fault diagnosis methods over the state-of-the-art redundancy-based methods are listed as follows, 1) The implementation of hardware redundancy is not necessary. 2) No cell testing, modeling and validation work are required. 3) The method is robust to cell inconsistencies in state of charge and state of health. 4) The ambiguity in cell and sensor failure is resolved upon fault occurrence. The disadvantages of the proposed fault detection methods are, 1) More computation power is needed to calculate the correlation coefficient and solve the sensor matrices online. 2) The noise level of the voltage measurements is increased.
590 $a School code: 0033.
650 4 $a Electrical engineering. $3 649834
650 4 $a Mechanical engineering. $3 649730
690 $a 0544
690 $a 0548
710 2 $a University of California, San Diego. $b Electrical Engineering (Intelsys, Robotics and Cont). $3 1044063
773 0 $t Dissertation Abstracts International $g 78-10B(E).
790 $a 0033
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10258857