# Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More

arXiv: https://arxiv.org/abs/2404.04049 · HTML: https://mlsanity.com/paper/2404.04049/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 55 references that are in the map.
- First posted on arXiv 5 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [Automated feature extraction and selection for data-driven models of rapid battery capacity fade and end of life](https://mlsanity.com/paper/2101.04440/) (2021)
- [Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra](https://mlsanity.com/paper/2302.03362/) (2023)
- [Promise and Challenges of a Data-Driven Approach for Battery Lifetime Prognostics](https://mlsanity.com/paper/2010.07460/) (2020)
- [Latent Variable Method Demonstrator -- Software for Understanding Multivariate Data Analytics Algorithms](https://mlsanity.com/paper/2205.08132/) (2022)
- [Interpretation of High-Dimensional Linear Regression: Effects of Nullspace and Regularization Demonstrated on Battery Data](https://mlsanity.com/paper/2309.00564/) (2023)
- [Combining physics-based and machine learning methods to accelerate innovation in sustainable transportation and beyond: a control perspective](https://mlsanity.com/paper/2305.04840/) (2023)

## Built on by (most cited first, 4 of 4)

- [Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field data](https://mlsanity.com/paper/2406.19015/) (2024)
- [Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formation](https://mlsanity.com/paper/2410.07458/) (2024)
- [Learning Model Predictive Control Parameters via Bayesian Optimization for Battery Fast Charging](https://mlsanity.com/paper/2404.06125/) (2024)
- [Safe Learning-Based Optimization of Model Predictive Control: Application to Battery Fast-Charging](https://mlsanity.com/paper/2410.04982/) (2024)

## Abstract (arXiv)

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.