Mechanical Engineering Research Works

Permanent URI for this collectionhttp://hdl.handle.net/1903/1661

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Author: search.filters.author.Saxena, SaurabhSubject: search.filters.subject.lithium-ion batteries

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    Algorithm to Determine the Knee Point on Capacity Fade Curves of Lithium-Ion Cells
    (MDPI, 2019-07-29) Diao, Weiping; Saxena, Saurabh; Han, Bongtae; Pecht, Michael
    Lithium-ion batteries typically exhibit a transition to a more rapid capacity fade trend when subjected to extended charge–discharge cycles and storage conditions. The identification of the knee point can be valuable to identify the more severe degradation trend, and to provide guidance when scheduling battery replacements and planning secondary uses of the battery. However, a concise and repeatable determination of a knee point has not been documented. This paper provides a definition of the knee point which can be used as a degradation metric, and develops an algorithm to identify it. The algorithm is implemented on various data cases, and the results indicate that the approach provides repeatable knee point identification.
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    Battery Stress Factor Ranking for Accelerated Degradation Test Planning Using Machine Learning
    (MDPI, 2021-01-30) Saxena, Saurabh; Roman, Darius; Robu, Valentin; Flynn, David; Pecht, Michael
    Lithium-ion batteries power numerous systems from consumer electronics to electric vehicles, and thus undergo qualification testing for degradation assessment prior to deployment. Qualification testing involves repeated charge–discharge operation of the batteries, which can take more than three months if subjected to 500 cycles at a C-rate of 0.5C. Accelerated degradation testing can be used to reduce extensive test time, but its application requires a careful selection of stress factors. To address this challenge, this study identifies and ranks stress factors in terms of their effects on battery degradation (capacity fade) using half-fractional design of experiments and machine learning. Two case studies are presented involving 96 lithium-ion batteries from two different manufacturers, tested under five different stress factors. Results show that neither the individual (main) effects nor the two-way interaction effects of charge C-rate and depth of discharge rank in the top three significant stress factors for the capacity fade in lithium-ion batteries, while temperature in the form of either individual or interaction effect provides the maximum acceleration.