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A solution was proposed to address the issue of low generalization ability of diagnostic models for electric vehicle power battery faults caused by sparse data, which utilized a data augmentation method based on Generative Adversarial Networks (GAN). According to the augmented data, a fault diagnosis scheme was designed using the Random Forest (RF) model combined with the Bayesian Optimization (BO) method to form a GAN-RF-BO battery fault diagnosis framework. The proposed fault diagnosis approach was compared with the common Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT) model on a real fault dataset. The results show that the accuracy of the proposed method is improved by 19.66%, 19.71% and 16.31% compared to the MLP, SVM, and GBDT models respectively. The GAN-RF-BO framework can better utilize sparse data to troubleshoot problems with power batteries.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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