Mechanical Engineering Research Works

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

Browse

Author: search.filters.author.Saxena, Saurabh

Search Results

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    A Unique Failure Mechanism in the Nexus 6P Lithium-Ion Battery
    (MDPI, 2018-04-04) Saxena, Saurabh; Xing, Yinjiao; Pecht, Michael
    Nexus 6P smartphones have been beset by battery drain issues, which have been causing premature shutdown of the phone even when the charge indicator displays a significant remaining runtime. To investigate the premature battery drain issue, two Nexus 6P smartphones (one new and one used) were disassembled and their batteries were evaluated using computerized tomography (CT) scan analysis, electrical performance (capacity, resistance, and impedance) tests, and cycle life capacity fade tests. The “used” smartphone battery delivered only 20% of the rated capacity when tested in a first capacity cycle and then 15% of the rated capacity in a second cycle. The new smartphone battery exceeded the rated capacity when first taken out of the box, but exhibited an accelerated capacity fade under C/2 rate cycling and decreased to 10% of its initial capacity in just 50 cycles. The CT scan results revealed the presence of contaminant materials inside the used battery, raising questions about the quality of the manufacturing process.
  • Thumbnail Image
    Item
    Analysis of Manufacturing-Induced Defects and Structural Deformations in Lithium-Ion Batteries Using Computed Tomography
    (MDPI, 2018-04-13) Wu, Yi; Saxena, Saurabh; Xing, Yinjiao; Wang, Youren; Li, Chuan; Yung, Winco K. C.; Pecht, Michael
    Premature battery drain, swelling and fires/explosions in lithium-ion batteries have caused wide-scale customer concerns, product recalls, and huge financial losses in a wide range of products including smartphones, laptops, e-cigarettes, hoverboards, cars, and commercial aircraft. Most of these problems are caused by defects which are difficult to detect using conventional nondestructive electrical methods and disassembly-based destructive analysis. This paper develops an effective computed tomography (CT)-based nondestructive approach to assess battery quality and identify manufacturing-induced defects and structural deformations in batteries. Several unique case studies from commercial e-cigarette and smartphone applications are presented to show where CT analysis methods work.
  • Thumbnail Image
    Item
    Derating Guidelines for Lithium-Ion Batteries
    (MDPI, 2018-11-26) Sun, Yongquan; Saxena, Saurabh; Pecht, Michael
    Derating is widely applied to electronic components and products to ensure or extend their operational life for the targeted application. However, there are currently no derating guidelines for Li-ion batteries. This paper presents derating methodology and guidelines for Li-ion batteries using temperature, discharge C-rate, charge C-rate, charge cut-off current, charge cut-off voltage, and state of charge (SOC) stress factors to reduce the rate of capacity loss and extend battery calendar life and cycle life. Experimental battery degradation data from our testing and the literature have been reviewed to demonstrate the role of stress factors in battery degradation and derating for two widely used Li-ion batteries: graphite/LiCoO2 (LCO) and graphite/LiFePO4 (LFP). Derating factors have been computed based on the battery capacity loss to quantitatively evaluate the derating effects of the stress factors and identify the significant factors for battery derating.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.