Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result
Abstract
:1. Introduction
2. Proposed SOC Estimation and SOH Diagnosis Method
2.1. Definition of SOC and SOH
2.2. Proposed SOH Monitoring and the SOC Estimation Algorithm Based on the SOH Results
2.2.1. Proposed SOH Diagnosis Method
2.2.2. Proposed SOC Estimation Based on the SOH
2.3. MNN
2.4. LSTM
3. Experiment and Results
3.1. Experiment Setup
3.2. SOC Estimation Based on the SOH Result
3.2.1. Structure of SOH Diagnosis Using MNN
3.2.2. Structure of SOC Estimation Using a NN Model Bank
3.2.3. Comparison of the Proposed and General Methods
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Method | Advantages | Disadvantages | Example Model |
---|---|---|---|
Model-based [9,10] | Reliable and accurate Has universal validity | Requires extensive domain knowledge Longer development time | Equivalent circuit model Electrochemical model Kalman filter |
Data-driven [11,12,13,14,15,16,17,18] | Shorter development time Does not require very much specialized knowledge. | Requires a large amount of data | Neural network Deep learning Look-up table |
Coulomb-counter [19,20] | Simple to implement | Errors accumulate over time. | Coulomb-counter |
Battery Type | Li-Po Battery |
---|---|
Capacity | 1.3 Ah |
Voltage range | 2.4–4.28 V |
Nominal voltage | 3.7 V |
Methods | Pattern 1 | Pattern 2 | Pattern 3 | Pattern 4 | Pattern 5 | Pattern 6 |
---|---|---|---|---|---|---|
SOH diagnosis model | normal | normal | caution | caution | fault | fault |
Methods | Pattern 1 | Pattern 2 | Pattern 3 | Pattern 4 | Pattern 5 | Pattern 6 |
---|---|---|---|---|---|---|
Proposed method using LSTM | 1.31% | 0.18% | 1.59% | 1.19% | 1.13% | 1.68% |
Proposed method using MNN | 1.59% | 0.33% | 1.09% | 1.65% | 1.23% | 1.55% |
Only LSTM | 1.96% | 4.12% | 2.65% | 3.19% | 5.83% | 6.05% |
Only MNN | 1.93% | 4.45% | 2.18% | 2.96% | 6.23% | 6.67% |
Methods | Pattern 1 | Pattern 2 | Pattern 3 | Pattern 4 | Pattern 5 | Pattern 6 |
---|---|---|---|---|---|---|
Proposed method using LSTM | 0.31% | 0.3% | 0.95% | 1.04% | 0.63% | 0.24% |
Proposed method using MNN | 1.59% | 0.28% | 1.48% | 1.08% | 0.76% | 1.57% |
Only LSTM | 2.27% | 4.37% | 2.52% | 2.94% | 5.33% | 5.88% |
Only MNN | 1.94% | 4.65% | 1.73% | 2.7% | 6.68% | 6.96% |
Methods | Pattern 1 | Pattern 2 | Pattern 3 | Pattern 4 | Pattern 5 | Pattern 6 |
---|---|---|---|---|---|---|
Proposed method using LSTM | 1.23% | 0.2% | 1.3% | 1.37% | 0.29% | 1.1% |
Proposed method using MNN | 1.18% | 0.39% | 1.03% | 1.62% | 0.34% | 1.28% |
Only LSTM | 2.35% | 5.41% | 2.91% | 2.81% | 4.89% | 5.55% |
Only MNN | 2.05% | 5.15% | 2.07% | 2.98% | 5.86% | 5.99% |
Methods | Pattern 1 | Pattern 2 | Pattern 3 | Pattern 4 | Pattern 5 | Pattern 6 |
---|---|---|---|---|---|---|
Proposed method using LSTM | 1.46% | 0.19% | 0.88% | 1.87% | 0.47% | 0.99% |
Proposed method using MNN | 1.43% | 0.32% | 1.47% | 0.87% | 0.81% | 1.21% |
Only LSTM | 2.53% | 5.26% | 2.4% | 2.8% | 5.53% | 5.64% |
Only MNN | 2% | 4.81% | 1.47% | 2.66% | 6.56% | 6.65% |
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Lee, J.-H.; Lee, I.-S. Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result. Energies 2021, 14, 4506. https://doi.org/10.3390/en14154506
Lee J-H, Lee I-S. Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result. Energies. 2021; 14(15):4506. https://doi.org/10.3390/en14154506
Chicago/Turabian StyleLee, Jong-Hyun, and In-Soo Lee. 2021. "Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result" Energies 14, no. 15: 4506. https://doi.org/10.3390/en14154506
APA StyleLee, J. -H., & Lee, I. -S. (2021). Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result. Energies, 14(15), 4506. https://doi.org/10.3390/en14154506