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Optimization of a Hybrid Energy Stor...

Lesiuta, Eric J.

Optimization of a Hybrid Energy Storage System for Electric Vehicles Using Machine Learning Methods.

紀錄類型:	書目-電子資源 : Monograph/item
書名/作者:	Optimization of a Hybrid Energy Storage System for Electric Vehicles Using Machine Learning Methods.
作者:	Lesiuta, Eric J.
出版者:	Ann Arbor : : ProQuest Dissertations & Theses, , 2016
面頁冊數:	98 p.
附註:	Source: Masters Abstracts International, Volume: 56-01.
Contained By:	Masters Abstracts International56-01(E).
標題:	Electrical engineering.
標題:	Computer engineering.
ISBN:	9781369115031
摘要、提要註:	In electric vehicles, batteries are unable to entirely store the large amount of power from regenerative braking which is generated over a short time period. Batteries also have a lower efficiency when required to supply peaking power. Alternatively supercapacitors can handle peaking power at the expense of lower energy storage capacities. This is why hybrid energy storage systems using a battery and a supercapacitor are being researched. There exist multiple configurations and control strategies for these systems and recently some are beginning to take drive cycle data into consideration.

Optimization of a Hybrid Energy Storage System for Electric Vehicles Using Machine Learning Methods.
Lesiuta, Eric J.

Optimization of a Hybrid Energy Storage System for Electric Vehicles Using Machine Learning Methods. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 98 p.

Source: Masters Abstracts International, Volume: 56-01.

Thesis (M.A.Sc.)--University of Windsor (Canada), 2016.

In electric vehicles, batteries are unable to entirely store the large amount of power from regenerative braking which is generated over a short time period. Batteries also have a lower efficiency when required to supply peaking power. Alternatively supercapacitors can handle peaking power at the expense of lower energy storage capacities. This is why hybrid energy storage systems using a battery and a supercapacitor are being researched. There exist multiple configurations and control strategies for these systems and recently some are beginning to take drive cycle data into consideration.

ISBN: 9781369115031Subjects--Topical Terms:

183930
Electrical engineering.

Optimization of a Hybrid Energy Storage System for Electric Vehicles Using Machine Learning Methods.
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100 1 $a Lesiuta, Eric J. $3 686847
245 1 0 $a Optimization of a Hybrid Energy Storage System for Electric Vehicles Using Machine Learning Methods.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2016
300 $a 98 p.
500 $a Source: Masters Abstracts International, Volume: 56-01.
500 $a Adviser: Narayan Kar.
502 $a Thesis (M.A.Sc.)--University of Windsor (Canada), 2016.
520 $a In electric vehicles, batteries are unable to entirely store the large amount of power from regenerative braking which is generated over a short time period. Batteries also have a lower efficiency when required to supply peaking power. Alternatively supercapacitors can handle peaking power at the expense of lower energy storage capacities. This is why hybrid energy storage systems using a battery and a supercapacitor are being researched. There exist multiple configurations and control strategies for these systems and recently some are beginning to take drive cycle data into consideration.
520 $a The objective of this research is to design an intelligent algorithm for controlling the balancing of energy between a supercapacitor and a battery. By using machine learning methods, it's able to learn from offline data where the optimal balancing can be calculated. The algorithm can then operate online, predicting how to balance the system which should improve the overall efficiency.
590 $a School code: 0115.
650 4 $a Electrical engineering. $3 183930
650 4 $a Computer engineering. $3 411802
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710 2 0 $a University of Windsor (Canada). $b ELECTRICAL ENGINEERING. $3 686848
773 0 $t Masters Abstracts International $g 56-01(E).
790 $a 0115
791 $a M.A.Sc.
792 $a 2016
793 $a English

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