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The state of health(SOH) and remaining useful life(RUL) of a battery are core indicators for evaluating battery performance degradation and potential lifespan. Accurately predicting the SOH and RUL of batteries is crucial in practical applications. To capture changes in battery performance and make predictions, operational data of the battery is typically relied on to train machine learning algorithms, such as neural networks or deep learning methods. However, traditional machine learning models often adopt a single architecture to adapt to the entire dataset, which is insufficient when dealing with complex and highly heterogeneous big data. Such models generally have the risk of insufficient generalization ability and overfitting, and are inefficient in big data processing. Therefore, Hierarchical sparse mixture of experts(HS-MoE) and multi head mixture of experts(MH-MoE) models were used to construct predictive models for battery State of Health(SOH) and Remaining Useful Life(RUL), respectively. Comparative experiments were conducted on publicly available datasets from NASA and EIS, and the results showed that the MH-MoE model outperformed the HS-MoE model in predicting SOH and RUL on both datasets.
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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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