  Lithium-ion batteries are ubiquitous in modern day applications ranging from
portable electronics to electric vehicles. Irrespective of the application,
reliable real-time estimation of battery state of health (SOH) by on-board
computers is crucial to the safe operation of the battery, ultimately
safeguarding asset integrity. In this paper, we design and evaluate a machine
learning pipeline for estimation of battery capacity fade - a metric of battery
health - on 179 cells cycled under various conditions. The pipeline estimates
battery SOH with an associated confidence interval by using two parametric and
two non-parametric algorithms. Using segments of charge voltage and current
curves, the pipeline engineers 30 features, performs automatic feature
selection and calibrates the algorithms. When deployed on cells operated under
the fast-charging protocol, the best model achieves a root mean squared percent
error of 0.45\%. This work provides insights into the design of scalable
data-driven models for battery SOH estimation, emphasising the value of
confidence bounds around the prediction. The pipeline methodology combines
experimental data with machine learning modelling and can be generalized to
other critical components that require real-time estimation of SOH.
