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oai:mountainscholar.org:10217/2353362025-12-30T03:26:35Zcom_10217_100532com_10217_100000com_10217_199888com_10217_100303col_10217_182111col_10217_199889

Pirani, Badruddin, author Cale, James, advisor Simske, Steven, committee member Miller, Erika, committee member Keller, Josh, committee member 2022-05-30T10:22:53Z 2022-05-30T10:22:53Z 2022 Pirani_colostate_0053A_17154.pdf https://hdl.handle.net/10217/235336 https://doi.org/10.25675/3.02439 Battery systems are used as critical power sources in a wide variety of advanced platforms (e.g., ships, submersibles, aircraft). These platforms undergo unique and extreme mission profiles that necessitate high reliability and maintainability. Battery system failures and non-optimal maintenance strategies have a significant impact on total fleet lifecycle costs and operational capability. Previous research has applied various approaches to improve battery system reliability and maintainability. Machine learning methodologies have applied data-driven and physics-based approaches to model battery decay and predict battery state-of-health, estimation of battery state-of-charge, and prediction of future performance. Queuing theory has been used to optimize battery charging resources ensure service and minimize cost. However, these approaches do not focus on pre-acceptance reliability improvements or platform operational requirements. This research introduces a two-faceted approach for enhancing the overall maintainability of platforms with battery systems as critical components. The first facet is the implementation of an advanced inspection and classification methodology for automating the acceptance/rejection decision for batteries prior to entering service. The purpose of this "pre-screening" step is to increase the reliability of batteries in service prior to deployment. The second facet of the proposed approach is the optimization of several critical maintenance plan design attributes for battery systems. Together, the approach seeks to simultaneously enhance both aspects of maintainability (inherent reliability and cost-effectiveness) for battery systems, with the goal of decreasing total lifecycle cost and increasing operational availability. born digital doctoral dissertations English eng Colorado State University. Libraries 2020- Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. A combined classification and queuing system optimization approach for enhanced battery system maintainability Text Doctor of Philosophy (Ph.D.) Doctoral Systems Engineering Colorado State University This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). open.access