自行车
电池(电)
快速循环
计算机科学
心理学
历史
神经科学
物理
功率(物理)
认知
考古
量子力学
双相情感障碍
作者
Alexis Geslin,Le Xu,Devi Ganapathi,Kevin Moy,William C. Chueh,Simona Onori
标识
DOI:10.26434/chemrxiv-2024-8fxl9
摘要
Laboratory aging campaigns benchmark and elucidate the complex degradation behavior of lithium-ion batteries, and are critical not only for developing new battery chemistries and cell designs but also for engineering reliable battery management systems. Critically, these laboratory experiments aim to quantify and capture realistic aging mechanisms. In this study, we systematically compare dynamic discharge profiles representative of electric vehicle driving to the well-accepted constant-current profiles. Surprisingly, we discovered that dynamic discharge enhances lifetime substantially compared to constant current discharge. Specifically, for the same average current and voltage window, varying the dynamic discharge profile leads to an increase of up to 38 % in equivalent full cycles at end-of-life. Explainable machine learning reveals the importance of low-frequency current pulses as well as time-induced aging under these realistic discharge conditions. Our work quantifies the importance of evaluating new battery chemistries and designs with realistic load profiles, and highlights the opportunities to revisit our understanding of aging mechanisms at the chemistry, materials, and cell levels.
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