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IJSRP, Volume 10, Issue 12, December 2020 Edition [ISSN 2250-3153]



      JAMSHER ALI, SHI YONG SHENG, ABDUL REHMAN, IMRAN AFZAL. SHOAIB UL HASSAN

Abstract: Prognostic and health management (PHM) verify the working safely and reliably of lithium batteries, PHM system determines the State of Health (SOH) and the Remaining Useful Life (RUL). To avoid severe negative consequences of the system, this paper presents the preliminary development of genetic algorithm, using an extreme learning machine (ELM) method to predict the State of Health (SOH) of the lithium-ion battery. The low prediction accuracy of the State of Health (SOH) of lithium battery, the extreme learning machine (ELM) algorithm improved by genetic algorithm can improve the prediction accuracy of SOH of lithium battery.

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JAMSHER ALI, SHI YONG SHENG, ABDUL REHMAN, IMRAN AFZAL. SHOAIB UL HASSAN (2020); Predictive Prognostic Model for Lithium Battery Based on A Genetic Algorithm (GA-ELM) Extreme Learning Machine; International Journal of Scientific and Research Publications (IJSRP) 10(12) (ISSN: 2250-3153), DOI: http://dx.doi.org/10.29322/IJSRP.10.12.2020.p10818

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