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To improve the state-of-charge(SOC) prediction accuracy of lithium battery, a prediction method based on the fusion model of Attention mechanism and convolution neural network-long short-term memory(CNN-LSTM) is proposed. This model uses one-dimensional CNN and LSTM neural network to learn the nonlinear relationship between SOC and lithium battery discharge data, as well as the long-term dependence existing in SOC sequences. At the same time, it adopts a "many-to-one" structure and establishes a mapping relationship between the SOC at the present moment and the discharge data at multiple historical moments, and pays attention to the historical discharge data which has a greater influence on the SOC at the present moment through the Attention mechanism, thus further improving the SOC prediction accuracy. The SOC prediction experiments under dynamic conditions show that the average prediction error of the proposed method is 0.89% under different temperature conditions, which is 81.2%, 66.7% and 56.5% lower than those of SVM, GRU and XGBoost algorithms, respectively. In addition, this method is also superior to LSTM and CNN-LSTM models that do not combine the Attention mechanism, showing a higher prediction accuracy and higher application values.
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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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