Battery Lifetime Prediction via Neural Networks with Discharge Capacity and State of Health
Abstract
:1. Introduction
2. Methodology
2.1. Proposed Method
2.2. Structure Selection
2.2.1. Model Structure Selection
2.2.2. Architecture of Neural Networks
3. Experimental Results and Discussion
3.1. Comparative Analysis
3.2. Lifetime Prediction Based on Discharge Capacity and State of Health
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Capacity rating | 2600 mAh |
---|---|
Cell chemistry | LiFePO4 |
Weight | 80.5 g |
Length | 26.5 mm ± 0.2 mm |
Height | 65.2 mm ± 0.4 mm |
Nominal voltage | 3.2 V |
MSE | RMSE | NRMSE | RMSPE (%) | |
---|---|---|---|---|
Training | 4.5481 × 10 | 0.0213 | 0.0380 | 1.1871 |
Validation | 0.0245 | 0.1564 | 2.5069 | 10.8574 |
Test | 0.0413 | 0.2032 | 29.8817 | 14.4836 |
MSE | RMSE | NRMSE | RMSPE (%) | |
---|---|---|---|---|
Training | 3.5783 × 10 | 0.0189 | 0.0337 | 1.0486 |
Validation | 0.1281 | 0.3579 | 5.7363 | 24.8415 |
Test | 0.2130 | 0.4615 | 67.8708 | 32.8967 |
MSE | RMSE | NRMSE | RMSPE (%) | |
---|---|---|---|---|
Training | 3.2221 × 10 | 0.0057 | 0.0101 | 0.3176 |
Validation | 1.1290 × 10 | 0.0034 | 0.0538 | 0.2385 |
Test | 5.8381 × 10 | 0.0024 | 0.3553 | 0.1722 |
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Hemdani, J.; Degaa, L.; Soltani, M.; Rizoug, N.; Telmoudi, A.J.; Chaari, A. Battery Lifetime Prediction via Neural Networks with Discharge Capacity and State of Health. Energies 2022, 15, 8558. https://doi.org/10.3390/en15228558
Hemdani J, Degaa L, Soltani M, Rizoug N, Telmoudi AJ, Chaari A. Battery Lifetime Prediction via Neural Networks with Discharge Capacity and State of Health. Energies. 2022; 15(22):8558. https://doi.org/10.3390/en15228558
Chicago/Turabian StyleHemdani, Jamila, Laid Degaa, Moez Soltani, Nassim Rizoug, Achraf Jabeur Telmoudi, and Abdelkader Chaari. 2022. "Battery Lifetime Prediction via Neural Networks with Discharge Capacity and State of Health" Energies 15, no. 22: 8558. https://doi.org/10.3390/en15228558