A Method for Predicting the Life of Lithium-Ion Batteries Based on Successive Variational Mode Decomposition and Optimized Long Short-Term Memory
Abstract
:1. Introduction
2. Methods
2.1. Successive Variational Mode Decomposition
2.2. Tuna Swarm Optimization
2.3. Long Short-Term Memory Network
2.4. Optimization Process of TSO-LSTM
2.5. SVMD and TSO-LSTM Prediction Framework
3. Results
3.1. Battery Datasets Description
3.2. Battery Capacity Sequence Decomposition Results
3.3. Evaluation Indicators
3.4. Battery Life Prediction Results
4. Discussion
4.1. Analysis of Battery Capacity Sequence Decomposition Results
4.2. Analysis of Battery Life Prediction Results
5. Conclusions
- The SVMD method is suitable for decomposing nonlinear and non-stationary signals. By applying this method, the capacity attenuation data of lithium-ion batteries are decomposed into modal functions with different scales, which reduces the impact of capacity fluctuations on the prediction accuracy during the actual use of lithium-ion batteries. This approach effectively accounts for the nonlinear and non-stationary characteristics of battery capacity data, leading to a more accurate prediction of the remaining life of the battery.
- The model for predicting the remaining life of lithium-ion batteries, based on SVMD and TSO-LSTM, demonstrates high prediction accuracy and stability. Compared to other models proposed in this study, the maximum MAPE for batteries of different types, discharge rates, and capacities using the SVMD and TSO-LSTM prediction model does not exceed 1%, and the average relative error is within 2.1%. This model exhibits good generalization performance and stable robustness.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Battery | Type | Size/mm | Capacity/(A·h) | Discharge Rate/C |
---|---|---|---|---|
CS2-34 | Prismatic | 5.4 × 33.6 × 50.6 | 1.1 | 0.5 |
CS2-36 | Prismatic | 5.4 × 33.6 × 50.6 | 1.1 | 1 |
CS2-37 | Prismatic | 5.4 × 33.6 × 50.6 | 1.1 | 1 |
CX2-37 | Prismatic | 6.6 × 33.8 × 50 | 1.35 | 0.5 |
B0007 | Cylindrical | 18 × 65 | 2 | 1 |
Model | Battery | ||||
---|---|---|---|---|---|
M1 | B0007 | 28 | 31 | 3 | 0.1071 |
CS2-34 | 156 | 158 | 2 | 0.0128 | |
CS2-36 | 121 | 113 | 8 | 0.0661 | |
CS2-37 | 196 | 211 | 15 | 0.0765 | |
CX2-37 | 138 | 146 | 8 | 0.0580 | |
M2 | B0007 | 28 | 36 | 8 | 0.2857 |
CS2-34 | 156 | 150 | 6 | 0.0385 | |
CS2-36 | 121 | 114 | 3 | 0.0248 | |
CS2-37 | 196 | 203 | 7 | 0.0357 | |
CX2-37 | 138 | 150 | 12 | 0.0870 | |
M3 | B0007 | 28 | 26 | 2 | 0.0714 |
CS2-34 | 156 | 155 | 1 | 0.0064 | |
CS2-36 | 121 | 122 | 1 | 0.0083 | |
CS2-37 | 196 | 200 | 4 | 0.0204 | |
CX2-37 | 138 | 134 | 4 | 0.0290 |
Model | Battery | ||||
---|---|---|---|---|---|
M1 | B0007 | 68 | 74 | 6 | 0.0882 |
CS2-34 | 296 | 293 | 3 | 0.0101 | |
CS2-36 | 254 | 259 | 5 | 0.0197 | |
CS2-37 | 336 | 352 | 16 | 0.0476 | |
CX2-37 | 338 | 353 | 15 | 0.0444 | |
M2 | B0007 | 68 | 72 | 4 | 0.0589 |
CS2-34 | 296 | 299 | 3 | 0.0101 | |
CS2-36 | 254 | 263 | 9 | 0.0354 | |
CS2-37 | 336 | 353 | 17 | 0.0506 | |
CX2-37 | 338 | 359 | 21 | 0.0621 | |
M3 | B0007 | 68 | 71 | 3 | 0.0441 |
CS2-34 | 296 | 294 | 2 | 0.0068 | |
CS2-36 | 254 | 255 | 1 | 0.0039 | |
CS2-37 | 336 | 345 | 9 | 0.0268 | |
CX2-37 | 338 | 345 | 7 | 0.0207 |
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Shi, Y.; Li, T.; Wang, L.; Lu, H.; Hu, Y.; He, B.; Zhai, X. A Method for Predicting the Life of Lithium-Ion Batteries Based on Successive Variational Mode Decomposition and Optimized Long Short-Term Memory. Energies 2023, 16, 5952. https://doi.org/10.3390/en16165952
Shi Y, Li T, Wang L, Lu H, Hu Y, He B, Zhai X. A Method for Predicting the Life of Lithium-Ion Batteries Based on Successive Variational Mode Decomposition and Optimized Long Short-Term Memory. Energies. 2023; 16(16):5952. https://doi.org/10.3390/en16165952
Chicago/Turabian StyleShi, Yongsheng, Tailin Li, Leicheng Wang, Hongzhou Lu, Yujun Hu, Beichen He, and Xinran Zhai. 2023. "A Method for Predicting the Life of Lithium-Ion Batteries Based on Successive Variational Mode Decomposition and Optimized Long Short-Term Memory" Energies 16, no. 16: 5952. https://doi.org/10.3390/en16165952