Dual particle swarm optimization based data-driven state of health estimation method for lithium-ion battery
Accurate estimation of Li-ion battery state of health (SOH) is essential to ensure battery safety and vehicle operation. Here, this paper proposes a dual particle swarm optimization algorithm-extreme gradient boosting algorithm (DP-X) with the battery's charging voltage and incremental capacity...
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| Published in: | Journal of energy storage Vol. 56; p. 105908 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
10.12.2022
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| Subjects: | |
| ISSN: | 2352-152X, 2352-1538 |
| Online Access: | Get full text |
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| Summary: | Accurate estimation of Li-ion battery state of health (SOH) is essential to ensure battery safety and vehicle operation. Here, this paper proposes a dual particle swarm optimization algorithm-extreme gradient boosting algorithm (DP-X) with the battery's charging voltage and incremental capacity (IC) data. First, the features are extracted from the voltage curve and the IC curve of each charging cycle through curve compression and interpolation. Then, this paper utilizes the PSO-XGBoost (P-X) algorithm to optimize the selected features and reduce the dimensionality of the features. Finally, the P-X algorithm was applied to combine with the optimized features to adjust the model's hyperparameters and estimate the SOH. Experimental results show that the maximum SOH estimation error of the dual P-X algorithm is less than 2 %.
•Dimensionality reduction of the sampled data is realized via curve compression.•Particle Swarm Optimization (PSO) is utilized to feature optimization.•PSO algorithm is again used to optimize the parameters of the model.•Coordination of features and model parameters is achieved by the proposed method. |
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| ISSN: | 2352-152X 2352-1538 |
| DOI: | 10.1016/j.est.2022.105908 |