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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 …