Battery State of Health Estimation with Improved Generalization Using Parallel Layer Extreme Learning Machine
Abstract
:1. Introduction
2. Methodology
2.1. Deterministic Extreme Learning Machine
2.2. Deterministic Parallel Layer Extreme Learning Machine
2.3. Experimental Dataset Description
2.4. State of Health (SOH)
2.5. Characterization and Feature Selection
2.6. Summary of Scheme Setup Procedure
Model Input: [V, SOC, E] |
Model Output:SOH |
|
3. Results and Discussion
3.1. PL-ELM Model Training
3.2. PL-ELM Model Validation
3.3. Model Comparison with Deterministic ELM and Demonstration of the Drift Problem
4. Summary and Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Indicator Variable | Model Train Features | Unit |
---|---|---|
Training Feature | ||
Voltage (V) | ΔV | [V] |
State of Charge (SOC) | ΔSOC | [%] |
Energy (E) | ΔE | [Wh] |
Model Output | ||
State of Health (SOH) |
PL-ELM | ICA Model | |||||
---|---|---|---|---|---|---|
RMSE | MAE | EB | % | RMSE | ||
Training | B0007 | 0.046 | 0.034 | (−0.14, 0.14) | 1.570 | 0.66 |
Validation | B0005 | 0.362 | 0.345 | ( 0.02, 0.67) | 2.037 | 0.87 |
B0006 | 0.473 | 0.355 | (−0.62, 1.31) | 3.240 | 2.49 | |
B0018 | 0.170 | 0.158 | (−0.04, 0.36) | 0.250 | - |
RMSE | MAE | EB | % | ||
---|---|---|---|---|---|
Training | B0007 | 0.245 | 0.191 | (−0.74, 0.74) | 0.615 |
Validation | B0005 | 1.117 | 0.762 | (−1.70, 3.22) | 1.24 |
B0006 | 1.563 | 0.907 | (−3.18, 4.82) | 0.19 | |
B0018 | 0.501 | 0.361 | (−0.79, 1.46 ) | 0.89 |
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Ezemobi, E.; Tonoli, A.; Silvagni, M. Battery State of Health Estimation with Improved Generalization Using Parallel Layer Extreme Learning Machine. Energies 2021, 14, 2243. https://doi.org/10.3390/en14082243
Ezemobi E, Tonoli A, Silvagni M. Battery State of Health Estimation with Improved Generalization Using Parallel Layer Extreme Learning Machine. Energies. 2021; 14(8):2243. https://doi.org/10.3390/en14082243
Chicago/Turabian StyleEzemobi, Ethelbert, Andrea Tonoli, and Mario Silvagni. 2021. "Battery State of Health Estimation with Improved Generalization Using Parallel Layer Extreme Learning Machine" Energies 14, no. 8: 2243. https://doi.org/10.3390/en14082243