# Lithium-ion Battery State of Health Estimation based on Cycle Synchronization using Dynamic Time Warping

arXiv: https://arxiv.org/abs/2109.13448 · HTML: https://mlsanity.com/paper/2109.13448/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Time-Series Regeneration with Convolutional Recurrent Generative Adversarial Network for Remaining Useful Life Estimation](https://mlsanity.com/paper/2101.03678/) (2021)
- [A Transfer Learning-based State of Charge Estimation for Lithium-Ion Battery at Varying Ambient Temperatures](https://mlsanity.com/paper/2101.03704/) (2021)
- [Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial Machinery](https://mlsanity.com/paper/1810.05644/) (2018)
- [IIoT-Enabled Health Monitoring for Integrated Heat Pump System Using Mixture Slow Feature Analysis](https://mlsanity.com/paper/2104.09876/) (2021)
- [Invariant learning based multi-stage identification for Lithium-ion battery performance degradation](https://mlsanity.com/paper/2008.05123/) (2020)
- [On the Identification of Electrical Equivalent Circuit Models Based on Noisy Measurements](https://mlsanity.com/paper/2101.05349/) (2021)

## Built on by (most cited first, 6 of 6)

- [Transfer Learning-based State of Health Estimation for Lithium-ion Battery with Cycle Synchronization](https://mlsanity.com/paper/2208.11204/) (2022)
- [Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2303.02880/) (2023)
- [Lithium-ion Battery Online Knee Onset Detection by Matrix Profile](https://mlsanity.com/paper/2304.00691/) (2023)
- [Graph neural network-based lithium-ion battery state of health estimation using partial discharging curve](https://mlsanity.com/paper/2409.00141/) (2024)
- [GAETS: A Graph Autoencoder Time Series Approach Towards Battery Parameter Estimation](https://mlsanity.com/paper/2111.09314/) (2021)
- [Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation](https://mlsanity.com/paper/2408.07624/) (2024)

## Abstract (arXiv)

The state of health (SOH) estimation plays an essential role in battery-powered applications to avoid unexpected breakdowns due to battery capacity fading. However, few studies have paid attention to the problem of uneven length of degrading cycles, simply employing manual operation or leaving to the automatic processing mechanism of advanced machine learning models, like long short-term memory (LSTM). As a result, this causes information loss and caps the full capability of the data-driven SOH estimation models. To address this challenge, this paper proposes an innovative cycle synchronization way to change the existing coordinate system using dynamic time warping, not only enabling the equal length inputs of the estimation model but also preserving all information. By exploiting the time information of the time series, the proposed method embeds the time index and the original measurements into a novel indicator to reflect the battery degradation status, which could have the same length over cycles. Adopting the LSTM as the basic estimation model, the cycle synchronization-based SOH model could significantly improve the prediction accuracy by more than 30% compared to the traditional LSTM.