Batteries are dynamic systems with complicated nonlinear aging, highly
dependent on cell design, chemistry, manufacturing, and operational conditions.
Prediction of battery cycle life and estimation of aging states is important to
accelerate battery R&D, testing, and to further the understanding of how
batteries degrade. Beyond testing, battery management systems rely on real-time
models and onboard diagnostics and prognostics for safe operation. Estimating
the state of health and remaining useful life of a battery is important to
optimize performance and use resources optimally.
This tutorial begins with an overview of first-principles, machine learning,
and hybrid battery models. Then, a typical pipeline for the development of
interpretable machine learning models is explained and showcased for cycle life
prediction from laboratory testing data. We highlight the challenges of machine
learning models, motivating the incorporation of physics in hybrid modeling
approaches, which are needed to decipher the aging trajectory of batteries but
require more data and further work on the physics of battery degradation. The
tutorial closes with a discussion on generalization and further research
directions.

电池是动态系统，受到电池设计、化学成分、制造和工作条件等复杂的非线性老化影响。预测电池的循环寿命和估算老化状态对于加快电池的研发和测试，以及进一步了解电池的退化过程非常重要。本教程首先概述了循环寿命预测的基本原理、机器学习和混合电池模型。然后，解释了从实验室测试数据中开发可解释的机器学习模型的典型流程，并且强调了机器学习模型所面临的挑战，以此来激发将物理学引入混合建模方法的动机，后者对于解读电池的老化轨迹至关重要，但需要更多的数据和进一步研究电池退化的物理学。本教程最后讨论了普适性和进一步的研究方向。