Cross-Stitch Networks for Joint State of Charge and State of Health Online Estimation of Lithium-Ion Batteries
Abstract
:1. Introduction
2. Methodology
2.1. Multi-Task Learning
2.2. Model Overview
2.3. Long Short-Term Memory
2.4. Cross-Stitch Unit
2.5. Multi-Scale Online Estimation
2.6. Dataset and Preprocessing
3. Results
3.1. Loss Weighting
3.1.1. Empirical Weighting
3.1.2. Uncertainty Weighting
3.1.3. Dynamic Weight Average
3.2. Training Paradigm
3.3. Comparison with Single-Task Model
3.4. Generalization Test on SOC Estimation
4. Discussion
4.1. Model
4.2. Training
4.3. Performance
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
BMS | Battery Management System |
CNN | Convolutional Neural Network |
DOD | Depth of Discharge |
DWA | Dynamic Weight Average |
EKF | Extended Kalman Filter |
FC | Fully Connected |
EFC | Equivalent Full Cycle |
GRU | Gated Recurrent Unit |
HPPC | Hybrid Pulse Power Characterization |
KF | Kalman Filter |
LSTM | Long Short-Term Memory |
MAE | Mean Absolute Error |
MSE | Mean Squared Error |
OCV | Open-Circuit Voltage |
RMSE | Root Mean Square Error |
RNN | Recurrent Neural Network |
SOC | State of Charge |
SOH | State of Health |
UKF | Unscented Kalman Filter |
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Parameter | Data |
---|---|
Nominal Capacity | Ah |
Nominal Voltage | V |
Charging Cutoff Voltage | V |
Discharging Cutoff Voltage | V |
Max. Charging Current | 4 A |
Max. Discharging Current | 20 A |
Layer | Input Size | Output Size |
---|---|---|
Shared LSTM | 3 | 256 |
Task-Specific LSTM (both) | 256 | 256 |
FC Layer 1 (both) | 256 | 256 |
FC Layer 2 (both) | 256 | 128 |
FC Layer 3 (both) | 128 | 64 |
FC Layer 4 (both) | 64 | 1 |
Weighting Method | SOC | SOH | ||
---|---|---|---|---|
MAE [%] | RMSE [%] | MAE [%] | RMSE [%] | |
Empirical Weighting | 0.743 | 0.961 | 0.062 | 0.080 |
Uncertainty Weighting | 1.470 | 1.777 | 0.177 | 0.204 |
Dynamic Weight Average | 1.107 | 1.407 | 0.119 | 0.151 |
Training Paradigm | SOC | SOH | ||
---|---|---|---|---|
MAE [%] | RMSE [%] | MAE [%] | RMSE [%] | |
End-to-End Training | 0.743 | 0.961 | 0.062 | 0.080 |
Fine-Tuning with Cross-Stitch Units | 0.554 | 0.759 | 0.063 | 0.078 |
Separate (Single-Task Model) | 1.324 | 1.717 | 0.147 | 0.185 |
Training Paradigm | SOC | |
---|---|---|
MAE [%] | RMSE [%] | |
End-to-End Training | 0.924 | 1.560 |
Fine-Tuning with Cross-Stitch Units | 0.786 | 1.270 |
Separate (Single-Task Model) | 1.690 | 2.355 |
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Yao, J.; Neupert, S.; Kowal, J. Cross-Stitch Networks for Joint State of Charge and State of Health Online Estimation of Lithium-Ion Batteries. Batteries 2024, 10, 171. https://doi.org/10.3390/batteries10060171
Yao J, Neupert S, Kowal J. Cross-Stitch Networks for Joint State of Charge and State of Health Online Estimation of Lithium-Ion Batteries. Batteries. 2024; 10(6):171. https://doi.org/10.3390/batteries10060171
Chicago/Turabian StyleYao, Jiaqi, Steven Neupert, and Julia Kowal. 2024. "Cross-Stitch Networks for Joint State of Charge and State of Health Online Estimation of Lithium-Ion Batteries" Batteries 10, no. 6: 171. https://doi.org/10.3390/batteries10060171
APA StyleYao, J., Neupert, S., & Kowal, J. (2024). Cross-Stitch Networks for Joint State of Charge and State of Health Online Estimation of Lithium-Ion Batteries. Batteries, 10(6), 171. https://doi.org/10.3390/batteries10060171