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Lithium-ion battery diagnostics and prognostics rely on measurements of electrical impedance, capacity, and voltage to infer the internal state of the battery. Mechanical changes to the cell structure represent an additional measure of the battery’s state because these changes are related to the overall battery health. As lithium-ion batteries are charged and discharged, lithium ions are inserted or removed from the anode and cathode, a process called intercalation and de-intercalation. As lithium ions intercalate and de-intercalate, they can cause changes to the lattice of the electrode particles, resulting in volumetric changes. These volumetric changes cause mechanical stresses and strains on the lithium-ion battery electrodes, and subsequently, the whole cell’s thickness varies as it is charged and discharged. This paper presents a study on the use of surface-mounted strain gauges for in-situ measurement of structural changes to lithium-ion batteries, along with a characterization of the unit-to-unit differences in strain response. A neural network modeling structure is then used to predict the battery’s depth of discharge under dynamic discharge conditions.

•The foundation of FIDES is on the erroneous assumption of a constant failure rate for electronics; its omission of critical and the subjectivity of certain inputs to the model make it inadequate for producing an accurate prediction.•Quality control and management of technical factors, such as part and supplier selection, during product realization impact the variability in the product contributing to variability in the reliability of the product population.•The FIDES prediction should not be used as input for product reliability improvement, designing reliability tests, or making decisions on the support and sustainment of electronic products.•There are alternative ways of predicting reliability beyond handbooks, as noted in IEEE 1413 and 1413.1, which account for the materials and geometry of components, such as physics of failure models or testing based on physics of failure. Investment should be made in developing and utilizing such methods for reliability prediction.

In the past we conducted experiments to measure the dielectric properties of PCB materials as per industry adopted methods, including preconditioning of test samples. It was observed that some measurement results were not in accordance with manufacturer datasheets. This paper presents follow-up experimental studies on the dependence of dielectric constant and dissipation factor on the moisture content of laminates. Four types of PCB materials from two manufacturers, including two halogen-free and two halogenated, were tested in this study. The paper further establishes whether preconditioning steps outlined in the IPC-TM-650 2.5.5.9 test method account for varying moisture contents in laminate samples.

In the past we conducted experiments to measure the dielectric properties of PCB materials as per industry adopted methods, including preconditioning of test samples. It was observed that some measurement results were not in accordance with manufacturer datasheets. This paper presents follow-up experimental studies on the dependence of dielectric constant and dissipation factor on the moisture content of laminates. Four types of PCB materials from two manufacturers, including two halogen-free and two halogenated, were tested in this study. The paper further establishes whether preconditioning steps outlined in the IPC-TM-650 2.5.5.9 test method account for varying moisture contents in laminate samples.