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Outline
Lithium-ion batteries are usually taken as energy storage components to realize the storage and utilization of electrical energy, and an accurate estimation of their state-of-health (SOH) can provide a theoretical basis for their actual applications. However, SOH is difficult to measure directly. To estimate the SOH of lithium-ion batteries accurately and conveniently, a model for estimating the SOH of lithium-ion batteries is proposed based on residual connection and convolutional long short-term memory (Conv-LSTM) networks from the perspective of data driven. This method is based on residual connection, Conv-LSTM and convolutional neural networks, and it uses the voltage, current and capacity during the charging process of a lithium-ion battery to realize the SOH estimation in its service cycle. The experimental results on multiple battery test datasets show that compared with those of the existing models with better estimation effects, the estimation result of the SOH estimation model based on residual connection and Conv-LSTM was more accurate, and the maximum percentage error and mean absolute percentage error of SOH estimation on the test dataset were less than 3.3% and 1.7%, respectively.
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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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