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The state of charge （SOC） of batteries is one of the most important parameters in lithium-ion battery management technology，and high-precision SOC estimation is beneficial for the grid connection and control of energy storage stations. Battery charge and discharge data are not only time-series in nature，but also have certain spatial relationships between feature variables. To improve the accuracy and generality of the estimation method，a SOC estimation method was proposed for lithium-ion batteries based on a joint convolutional neural networks-long short term memory networks（CNN-LSTM） network structure. Firstly，the feature relationships between different dimensions of lithium-ion battery data were obtained through CNN feature extraction，and then the time series relationships were extracted through the LSTM network structure. The joint network fully captures the spatial and temporal characteristics of the battery dataset. The experimental results show that the average error of predicting battery SOC based on the CNN-LSTM joint network model is controlled at 0.65%，which is about 4.4% lower than the average error predicted by a single CNN network and about 0.2% lower than the average error predicted by a single LSTM network. It has good application prospects.
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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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