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Health Usage and Monitoring Systems in Aviation Batteries.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Health Usage and Monitoring Systems in Aviation Batteries./
作者:	Martin, Jon Ander.
面頁冊數:	1 online resource (184 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-09, Section: B.
Contained By:	Dissertations Abstracts International85-09B.
標題:	Mechanics. -
電子資源:	click for full text (PQDT)
ISBN:	9798381701449

Health Usage and Monitoring Systems in Aviation Batteries.
Martin, Jon Ander.

Health Usage and Monitoring Systems in Aviation Batteries. - 1 online resource (184 pages)

Source: Dissertations Abstracts International, Volume: 85-09, Section: B.

Thesis (Ph.D.)--University of Cincinnati, 2023.

Includes bibliographical references

Electric propulsion is expected to revolutionize aviation in the following years. Multiple research groups, public agencies and private companies are working towards incorporating electric powered aircraft to the national airspace. The implementation of such technology necessitates increased trust and confidence in its performance from regulators, operators and the end users. This work focuses on enhancing the safety and reliability of emerging electric power technology in airborne vehicles within the context of Advanced Air Mobility and fixed-wing aircraft. Batteries are targeted as the most critical component of the propulsion system of an electric aircraft. Support Vector Machine, Relevance Vector Machine and a Genetic Fuzzy Inference System are presented as viable methodologies to monitor the degradation of a battery in an aviation context. The Support Vector Machine provides the best accuracy, but the errors obtained with Relevance Vector Machine are marginally higher and its training is much faster. The Fuzzy Inference System provides good accuracy with good generalization capabilities and excellent explainability properties. These methodologies can be combined with a Particle Filter algorithm to perform real-time predictions of the End of Life and End of Discharge of battery cells and modules. Further, the Particle Filter provides an assessment of the uncertainty of the prediction in the form of a probability distribution function. This offers an intuitive way for pilots and remote pilots to visualize the remaining flight time. The Particle Filter can also be combined with an electric equivalent circuit model to have very accurate predictions of single discharge cycles in battery modules. We demonstrate that this technique can predict the behavior of faulty battery cells within a module, a critical aspect in large battery ensembles. Another issue encountered in batteries is the possibility of a Thermal Runaway when a cell is overheated. A model that can effectively predict the propagation of a Thermal Runaway within a battery module is presented. This model is utilized to optimize the design of a system that is capable of containing the Thermal Runaway of a cell and avoiding its propagation while minimizing the mass and volume of the system.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381701449Subjects--Topical Terms:

527684
Mechanics.
Subjects--Index Terms:

PrognosticsIndex Terms--Genre/Form:

554714
Electronic books.

Health Usage and Monitoring Systems in Aviation Batteries.
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