# Hybrid physics-based and data-driven modeling with calibrated uncertainty for lithium-ion battery degradation diagnosis and prognosis

arXiv: https://arxiv.org/abs/2110.13661 · HTML: https://mlsanity.com/paper/2110.13661/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 25 Oct 2021.

## Builds on (most cited first, 3 of 3)

- [Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems](https://mlsanity.com/paper/2003.04919/) (2020)
- [Statistical learning for accurate and interpretable battery lifetime prediction](https://mlsanity.com/paper/2101.01885/) (2021)
- [Identifiability and parameter estimation of the single particle lithium-ion battery model](https://mlsanity.com/paper/1702.02471/) (2017)

## Built on by (most cited first, 3 of 3)

- [Requirements Engineering for Machine Learning: A Review and Reflection](https://mlsanity.com/paper/2210.00859/) (2022)
- [Boosting AI Reliability with an FSM-Driven Streaming Inference Pipeline: An Industrial Case](https://mlsanity.com/paper/2603.01528/) (2026)
- [Identifying Culprits Through Deep Deterministic Policy Gradient Deep Learning Investigation](https://mlsanity.com/paper/2605.14774/) (2026)

## Abstract (arXiv)

Advancing lithium-ion batteries (LIBs) in both design and usage is key to promoting electrification in the coming decades to mitigate human-caused climate change. Inadequate understanding of LIB degradation is an important bottleneck that limits battery durability and safety. Here, we propose hybrid physics-based and data-driven modeling for online diagnosis and prognosis of battery degradation. Compared to existing battery modeling efforts, we aim to build a model with physics as its backbone and statistical learning techniques as enhancements. Such a hybrid model has better generalizability and interpretability together with a well-calibrated uncertainty associated with its prediction, rendering it more valuable and relevant to safety-critical applications under realistic usage scenarios.