Laboratory ageing campaigns elucidate the complex degradation behaviour of most technologies. In lithium-ion batteries, such studies aim to capture realistic ageing mechanisms to optimize cell chemistries and designs as well as to engineer reliable battery management systems. In this study, we systematically compared dynamic discharge profiles representative of electric vehicle driving to the well-accepted constant current profiles. Surprisingly, we found that dynamic discharge enhances lifetime substantially compared with constant current discharge. Specifically, for the same average current and voltage window, varying the dynamic discharge profile led to an increase of up to 38% in equivalent full cycles at end of life. Explainable machine learning revealed the importance of both low-frequency current pulses and time-induced ageing under these realistic discharge conditions. This work quantifies the importance of evaluating new battery chemistries and designs with realistic load profiles, highlighting the opportunities to revisit our understanding of ageing mechanisms at the chemistry, material and cell levels.

实验室老化活动阐明了大多数技术复杂的降解行为。在锂离子电池中，此类研究旨在捕捉真实的老化机制，以优化电池化学和设计，以及设计可靠的电池管理系统。在这项研究中，我们系统地将代表电动汽车驾驶的动态放电曲线与公认的恒流曲线进行了比较。令人惊讶的是，我们发现与恒流放电相比，动态放电大大延长了使用寿命。具体来说，对于相同的平均电流和电压窗口，改变动态放电曲线会导致使用寿命结束时等效全周期增加高达 38%。可解释的机器学习揭示了在这些实际放电条件下低频电流脉冲和时间诱导老化的重要性。这项工作量化了以现实负载曲线评估新电池化学和设计的重要性，突出了我们在化学、材料和电池层面重新审视我们对老化机制的理解的机会。