Accurate battery life prediction is a critical part of the business case for electric vehicles, stationary energy storage, and nascent applications such as electric aircraft. Existing methods are based on relatively small but well-designed lab datasets and controlled test conditions but incorporating field data is crucial to build a complete picture of how cells age in real-world situations. This comes with additional challenges because end-use applications have uncontrolled operating conditions, less accurate sensors, data collection and storage concerns, and infrequent access to validation checks. We explore a range of techniques for estimating lifetime from lab and field data and suggest that combining machine learning approaches with physical models is a promising method, enabling inference of battery life from noisy data, assessment of second-life condition, and extrapolation to future usage conditions. This work highlights the opportunity for insights gained from field data to reduce battery costs and improve designs.

准确的电池寿命预测是电动汽车、固定式储能和电动飞机等新兴应用的业务案例的关键部分。现有方法基于相对较小但设计良好的实验室数据集和受控测试条件，但结合现场数据对于构建细胞在现实世界中如何老化的完整画面至关重要。这带来了额外的挑战，因为最终用途的应用程序具有不受控制的操作条件、不太准确的传感器、数据收集和存储问题以及很少访问验证检查。我们探索了一系列从实验室和现场数据估计寿命的技术，并建议将机器学习方法与物理模型相结合是一种很有前景的方法，可以从嘈杂的数据中推断电池寿命，评估第二次使用条件，并推断未来的使用条件。这项工作突出了从现场数据中获得洞察力的机会，以降低电池成本和改进设计。