# A Transfer Learning-based State of Charge Estimation for Lithium-Ion Battery at Varying Ambient Temperatures

arXiv: https://arxiv.org/abs/2101.03704 · HTML: https://mlsanity.com/paper/2101.03704/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 11 Jan 2021.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Exploring Interpretable LSTM Neural Networks over Multi-Variable Data](https://mlsanity.com/paper/1905.12034/) (2019)
- [Real-Time Optimal Lithium-Ion Battery Charging Based on Explicit Model Predictive Control](https://mlsanity.com/paper/2004.14321/) (2020)

## Built on by (most cited first, 14 of 14)

- [A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extraction](https://mlsanity.com/paper/2305.07854/) (2023)
- [Slow-varying Dynamics Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation](https://mlsanity.com/paper/2203.16373/) (2022)
- [Lithium-ion Battery State of Health Estimation based on Cycle Synchronization using Dynamic Time Warping](https://mlsanity.com/paper/2109.13448/) (2021)
- [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)
- [A Change Point Detection Integrated Remaining Useful Life Estimation Model under Variable Operating Conditions](https://mlsanity.com/paper/2401.04351/) (2024)
- [Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2303.02880/) (2023)
- [Digital Twin for Real-time Li-ion Battery State of Health Estimation with Partially Discharged Cycling Data](https://mlsanity.com/paper/2212.04622/) (2022)
- [A Hybrid Deep Learning Model-based Remaining Useful Life Estimation for Reed Relay with Degradation Pattern Clustering](https://mlsanity.com/paper/2209.06429/) (2022)
- [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)
- [A Slow-Shifting Concerned Machine Learning Method for Short-term Traffic Flow Forecasting](https://mlsanity.com/paper/2303.17782/) (2023)
- [Transfer Learning Assisted XgBoost For Adaptable Cyberattack Detection In Battery Packs](https://mlsanity.com/paper/2504.10658/) (2025)
- [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)

Accurate and reliable state of charge (SoC) estimation becomes increasingly important to provide a stable and efficient environment for Lithium-ion batteries (LiBs) powered devices. Most data-driven SoC models are built for a fixed ambient temperature, which neglect the high sensitivity of LiBs to temperature and may cause severe prediction errors. Nevertheless, a systematic evaluation of the impact of temperature on SoC estimation and ways for a prompt adjustment of the estimation model to new temperatures using limited data have been hardly discussed. To solve these challenges, a novel SoC estimation method is proposed by exploiting temporal dynamics of measurements and transferring consistent estimation ability among different temperatures. First, temporal dynamics, which is presented by correlations between the past fluctuation and the future motion, is extracted using canonical variate analysis. Next, two models, including a reference SoC estimation model and an estimation ability monitoring model, are developed with temporal dynamics. The monitoring model provides a path to quantitatively evaluate the influences of temperature on SoC estimation ability. After that, once the inability of the reference SoC estimation model is detected, consistent temporal dynamics between temperatures are selected for transfer learning. Finally, the efficacy of the proposed method is verified through a benchmark. Our proposed method not only reduces prediction errors at fixed temperatures (e.g., reduced by 24.35% at -20degC, 49.82% at 25degC) but also improves prediction accuracies at new temperatures.