There is considerable interest in developing new energy storage technologies for the electric grid, but economic viability will require that manufacturers provide warranties guaranteeing 15+ years of life. Although there are extensive efforts to make early predictions for the expected life of new storage technologies, we argue here that for the purposes of pricing warranties and valuing second-life potential—considerations that are crucial to whether the technologies can be commercialized—the full failure probability distribution, not just the expected life, is required. We use published battery cycle-life data to suggest efficient statistical and machine learning-based testing and analysis strategies that can rapidly estimate and also take advantage of the failure probability distribution. One approach is a Weibull analysis, which can (1) reduce the number of testing machine hours required for setting a warranty, (2) quickly determine whether a new technology is better than a baseline technology, and (3) estimate the maximum intensity of testing acceleration that does not change the failure mode. A second approach is driven by the idea that all measured data—such as capacity or energy as a function of time or cycle number—are valuable and generated by an underlying latent function. This analysis employs a Gaussian process to find the underlying latent function, together with its uncertainties, which can be used to calculate the failure distribution.

人们对为电网开发新的储能技术非常感兴趣，但经济可行性要求制造商提供 15 年以上使用寿命的保修。尽管人们为对新存储技术的预期寿命做出早期预测做出了广泛的努力，但我们在此认为，出于定价保证和评估二次使用潜力的目的——这些因素对技术是否可以商业化至关重要——完全失败需要概率分布，而不仅仅是预期寿命。我们使用已发布的电池循环寿命数据来建议有效的基于统计和机器学习的测试和分析策略，这些策略可以快速估计并利用故障概率分布。一种方法是威布尔分析，它可以（1）减少设置保修所需的测试机小时数，（2）快速确定新技术是否优于基准技术，以及（3）估计不改变测试加速度的最大强度故障模式。第二种方法的驱动思想是，所有测量数据（例如作为时间或周期数函数的容量或能量）都是有价值的，并且由潜在的潜在函数生成。该分析采用高斯过程来查找潜在的潜在函数及其不确定性，可用于计算故障分布。(3) 估算不改变失效模式的最大试验加速度强度。第二种方法的驱动思想是，所有测量数据（例如作为时间或周期数函数的容量或能量）都是有价值的，并且由潜在的潜在函数生成。该分析采用高斯过程来查找潜在的潜在函数及其不确定性，可用于计算故障分布。(3) 估算不改变失效模式的最大试验加速度强度。第二种方法的驱动思想是，所有测量数据（例如作为时间或周期数函数的容量或能量）都是有价值的，并且由潜在的潜在函数生成。该分析采用高斯过程来查找潜在的潜在函数及其不确定性，可用于计算故障分布。