Department of Physics, University of Cambridge, Cambridge, UK.
The Alan Turing Institute, London, UK.
Nat Commun. 2022 Aug 16;13(1):4806. doi: 10.1038/s41467-022-32422-w.
Accurate forecasting of lithium-ion battery performance is essential for easing consumer concerns about the safety and reliability of electric vehicles. Most research on battery health prognostics focuses on the research and development setting where cells are subjected to the same usage patterns. However, in practical operation, there is great variability in use across cells and cycles, thus making forecasting challenging. To address this challenge, here we propose a combination of electrochemical impedance spectroscopy measurements with probabilistic machine learning methods. Making use of a dataset of 88 commercial lithium-ion coin cells generated via multistage charging and discharging (with currents randomly changed between cycles), we show that future discharge capacities can be predicted with calibrated uncertainties, given the future cycling protocol and a single electrochemical impedance spectroscopy measurement made immediately before charging, and without any knowledge of usage history. The results are robust to cell manufacturer, the distribution of cycling protocols, and temperature. The research outcome also suggests that battery health is better quantified by a multidimensional vector rather than a scalar state of health.
准确预测锂离子电池的性能对于缓解消费者对电动汽车安全性和可靠性的担忧至关重要。大多数关于电池健康预测的研究都集中在研究和开发环境中，在这种环境下，电池会受到相同的使用模式的影响。然而，在实际操作中，电池之间的使用情况和循环次数存在很大的差异，因此预测具有挑战性。为了解决这一挑战，我们在这里提出了电化学阻抗谱测量与概率机器学习方法的结合。利用通过多阶段充电和放电生成的 88 个商用锂离子硬币电池数据集（电流在循环之间随机变化），我们表明，给定未来的循环协议和充电前立即进行的单次电化学阻抗谱测量，无需任何使用历史记录，就可以在有校准不确定性的情况下预测未来的放电容量。结果对电池制造商、循环协议的分布和温度具有鲁棒性。研究结果还表明，电池健康状况可以通过多维向量而不是健康状况的标量来更好地量化。
