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  <record>
   <ref-type name="Journal Article">17</ref-type>
   <contributors>
    <authors>
     <author></author>
     <author></author>
     <author></author>
     <author></author>
     <author></author>
     <author></author>
     <author></author>
    </authors>
   </contributors>
   <titles>
    <title></title>
   </titles>
   <dates>
    <year>2023</year>
    <pub-dates>
     <date>2023-05-15</date>
    </pub-dates>
   </dates>
   <doi>10.3390/e25040646</doi>
   <abstract>In this paper, an adaptive remaining useful life prediction model is proposed for electric&#13;
vehicle lithium batteries. Capacity degradation of the electric car lithium batteries is modeled by the&#13;
multi-fractal Weibull motion. The varying degree of long-range dependence and the 1/f characteristics in the frequency domain are also analyzed. The age and state-dependent degradation model&#13;
is derived, with the associated adaptive drift and diffusion coefficients. The adaptive mechanism&#13;
considers the quantitative relations between the drift and diffusion coefficients. The unit-to-unit&#13;
variability is considered a random variable. To facilitate the application, the convergence of the RUL&#13;
prediction model is proved. Replacement of the lithium battery in the electric car is recommended&#13;
according to the remaining useful life prediction results. The effectiveness of the proposed model is&#13;
shown in the case study.</abstract>
   <urls>
    <web-urls>
     <url>https://repo.bashgmu.ru/publication/2789</url>
    </web-urls>
    <pdf-urls>
     <url>https://repo.bashgmu.ru/files/2965</url>
    </pdf-urls>
   </urls>
  </record>
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