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  <doc>
    <id>4989</id>
    <completedYear>2024</completedYear>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>64, XIX</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>bachelorthesis</type>
    <publisherName/>
    <publisherPlace>Ingolstadt</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-08-12</completedDate>
    <publishedDate>2024-06-19</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Development of machine learning models for estimating battery state-of-health (SOH)</title>
    <abstract language="eng">This study focuses on the development of predictive models for the State of Health (SoH) of lithium-ion batteries, specifically Nickel Manganese Cobalt (NMC) cells, which are crucial for the advancement of Electric Vehicles (EVs). With the growing adoption of EVs, efficient battery management systems (BMS) are essential to ensure optimal performance and longevity. This research addresses the challenges associated with SoH prediction, including data collection, processing, and the nonlinear characteristics of battery degradation.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-49896</identifier>
    <enrichment key="opus.import.date">2024-08-12T12:12:12+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">primuss</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Urheberrechtsschutz</licence>
    <advisor>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </advisor>
    <author>
      <first_name>Yaowei</first_name>
      <last_name>Wang</last_name>
    </author>
    <advisor>
      <first_name>Klaus-Uwe</first_name>
      <last_name>Moll</last_name>
    </advisor>
    <collection role="institutes" number="15985">Fakultät Maschinenbau</collection>
    <collection role="degree_programme" number="19333">Fahrzeugtechnik (B. Eng.)</collection>
    <collection role="Import" number="deepgreen">DeepGreen</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <thesisGrantor>Technische Hochschule Ingolstadt</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-haw/files/4989/I002026235Thesis.pdf</file>
  </doc>
</export-example>
