Abstract
A safe and reliable battery operation needs effective diagnostic tools. A quantitative failure analysis (FA) to enable cell qualification and quantify its effectiveness for reliable and safe operation of rechargeable Li batteries (RLB) is shown here. The method can identify and quantify potential failure based on the state of charge (SOC) under any operating conditions. A precise and accurate electrochemical analytic diagnosis (eCAD) of 14 rechargeable Li || NMC-622 cells of the same build are used as an example. The FA by eCAD can quantitatively decipher good, bad and ugly cells in cycle aging. The cell qualification is based on thermodynamic SOC, not experimental conditions. The method provides a quantitative failure mode and effect analysis to reveal diverse ‘dead Li’ formation that affects the reversibility of the Li anode and charge retention in the cell. This cell qualification method highlights the potential to improve cell quality for safe operation, with strong implications for early fault detection, FA, risk mitigation, state estimation and life prediction for reliable and safe RLB operations.
Highlights
Significant advancements in the rechargeable Li battery (RLB) technology propel the use of RLB in portable electronic devices, as recognized in the award of the 2019 Nobel Prize in Chemistry.[1]
We have shown the charge retention of 14 rechargeable Li || NMC-622 cells of the same build and their classification into good, bad and ugly classes based on performance in cycle aging
Via the illustration of an electrochemical analytic diagnosis-based failure mode and effect analysis (FMEA) on three sample cells, one from each class; we show cell qualification can be reliably achieved by this method based on thermodynamic state of charge (SOC) determination
Summary
Significant advancements in the rechargeable Li battery (RLB) technology propel the use of RLB in portable electronic devices, as recognized in the award of the 2019 Nobel Prize in Chemistry.[1]. We have recently developed a quantitative electrochemical analytic diagnosis (eCAD) method[21] that can accurately and precisely determine the attributes and the amount of capacity loss in LME-based RLB during cycle aging.
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