# Lithium-ion Battery Online Knee Onset Detection by Matrix Profile

arXiv: https://arxiv.org/abs/2304.00691 · HTML: https://mlsanity.com/paper/2304.00691/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [Machine learning pipeline for battery state of health estimation](https://mlsanity.com/paper/2102.00837/) (2021)
- [A Transfer Learning-based State of Charge Estimation for Lithium-Ion Battery at Varying Ambient Temperatures](https://mlsanity.com/paper/2101.03704/) (2021)
- [Slow-varying Dynamics Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation](https://mlsanity.com/paper/2203.16373/) (2022)
- [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)
- [IIoT-Enabled Health Monitoring for Integrated Heat Pump System Using Mixture Slow Feature Analysis](https://mlsanity.com/paper/2104.09876/) (2021)
- [Lithium-ion Battery State of Health Estimation based on Cycle Synchronization using Dynamic Time Warping](https://mlsanity.com/paper/2109.13448/) (2021)
- [Invariant learning based multi-stage identification for Lithium-ion battery performance degradation](https://mlsanity.com/paper/2008.05123/) (2020)
- [Transfer Learning-based State of Health Estimation for Lithium-ion Battery with Cycle Synchronization](https://mlsanity.com/paper/2208.11204/) (2022)
- [A Transferable Multi-stage Model with Cycling Discrepancy Learning for Lithium-ion Battery State of Health Estimation](https://mlsanity.com/paper/2209.00190/) (2022)
- [Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-ion Batteries](https://mlsanity.com/paper/2101.00035/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Lightweight Transfer Learning-Based State-of-Health Monitoring with Application to Lithium-ion Batteries in Autonomous Air Vehicles](https://mlsanity.com/paper/2512.08512/) (2025)

## Abstract (arXiv)

Lithium-ion batteries (LiBs) degrade slightly until the knee onset, after which the deterioration accelerates to end of life (EOL). The knee onset, which marks the initiation of the accelerated degradation rate, is crucial in providing an early warning of the battery's performance changes. However, there is only limited literature on online knee onset identification. Furthermore, it is good to perform such identification using easily collected measurements. To solve these challenges, an online knee onset identification method is developed by exploiting the temporal information within the discharge data. First, the temporal dynamics embedded in the discharge voltage cycles from the slight degradation stage are extracted by the dynamic time warping. Second, the anomaly is exposed by Matrix Profile during subsequence similarity search. The knee onset is detected when the temporal dynamics of the new cycle exceed the control limit and the profile index indicates a change in regime. Finally, the identified knee onset is utilized to categorize the battery into long-range or short-range categories by its strong correlation with the battery's EOL cycles. With the support of the battery categorization and the training data acquired under the same statistic distribution, the proposed SOH estimation model achieves enhanced estimation results with a root mean squared error as low as 0.22%.