Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and
  More
Joachim Schaeffer Giacomo Galuppini Jinwook Rhyu Patrick A. Asinger Robin Droop Rolf Findeisen Richard D. Braatz
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.