國立虎尾科技大學 |

Performance Optimization of Onboard Lithium Ion Batteries for Electric Vehicles.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Performance Optimization of Onboard Lithium Ion Batteries for Electric Vehicles./
作者:	Ugle, Rohit.
面頁冊數:	1 online resource (164 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
Contained By:	Dissertation Abstracts International78-10B(E).
標題:	Mechanical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781369825107

Performance Optimization of Onboard Lithium Ion Batteries for Electric Vehicles.
Ugle, Rohit.

Performance Optimization of Onboard Lithium Ion Batteries for Electric Vehicles. - 1 online resource (164 pages)

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

Thesis (Ph.D.)

Includes bibliographical references

Next generation of transportation in the form of electric vehicles relies on better operation and control of large battery packs. The individual modules in large battery packs generally do not have identical characteristics and may degrade differently due to manufacturing variability and other factors. Degraded battery modules waste more power, affecting the performance and economy for the whole battery pack. Also, such impact varies with different trip patterns. It will be cost effective if we evaluate the performance of the battery modules prior to replacing the complete battery pack. The knowledge of the driving cycle and battery internal resistance will help to make decision to replace the worst battery modules and directly cut down on user expenditure to replace the battery.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369825107Subjects--Topical Terms:

557493
Mechanical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Performance Optimization of Onboard Lithium Ion Batteries for Electric Vehicles.
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245 1 0 $a Performance Optimization of Onboard Lithium Ion Batteries for Electric Vehicles.
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300 $a 1 online resource (164 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-10(E), Section: B.
500 $a Adviser: Anoop K. Dhingra.
502 $a Thesis (Ph.D.) $c The University of Wisconsin - Milwaukee $d 2017.
504 $a Includes bibliographical references
520 $a Next generation of transportation in the form of electric vehicles relies on better operation and control of large battery packs. The individual modules in large battery packs generally do not have identical characteristics and may degrade differently due to manufacturing variability and other factors. Degraded battery modules waste more power, affecting the performance and economy for the whole battery pack. Also, such impact varies with different trip patterns. It will be cost effective if we evaluate the performance of the battery modules prior to replacing the complete battery pack. The knowledge of the driving cycle and battery internal resistance will help to make decision to replace the worst battery modules and directly cut down on user expenditure to replace the battery.
520 $a Also, optimizing the performance of battery during the driving trip is the challenging task to achieve. The knowledge of energy prices of the grid, internal resistance of the lithium ion battery pack on the electric vehicle, the age of the battery and distance travelled by the electric vehicle are very important factors on which the cost of daily driving cycle is dependent.
520 $a In near future, the energy consumed by the electric vehicles will create a major consumer market for the smart grids. The smart grid system is complemented by the renewable energy sources that contribute and support the grid. The electric vehicles are not only predicted as energy consumers but also as dynamic sources of energy. These vehicles can now travel more than 100 miles with a single charging cycle whereas average day to day commute is well below the maximum capacity of these vehicles. This leaves the driver with the extra energy on the battery pack which can be used later for supporting energy requirement from the grid. As we know that cells/modules in large battery packs do not have identical properties and these degrade at different rates during the course of their lifespan. It is beneficial for the user to quantify the amount of energy that can be used to support the grid.
520 $a The improvement of the electric grid to the next generation infrastructure ie 'Smart Grid' will enable diverse opportunities to contribute the energy and balance the load on the grid. The information about the grid like price quality, load etc. will be available to the people very easily. This information can be useful to make the energy grid more economical and environment friendly. We have used the information for price of energy on the grid to optimize the cost of daily driving cycle.
520 $a The goal of this research is to accurately predict the battery behavior for the daily driving cycle. The prediction of battery behavior will help the driver to decide the optimum charging patterns, energy consumed during driving and the surplus energy available in the batteries. The prior knowledge of the battery behavior, price of the energy on the grid and the trip travel will help the driver to minimize the cost of travel on daily basis as well as throughout the life of the battery.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Mechanical engineering. $3 557493
650 4 $a Mathematics. $3 527692
655 7 $a Electronic books. $2 local $3 554714
690 $a 0548
690 $a 0405
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Wisconsin - Milwaukee. $b Engineering. $3 1178927
773 0 $t Dissertation Abstracts International $g 78-10B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10219018 $z click for full text (PQDT)