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Outline
Aiming at the problems of difficulty in extracting feature parameters of the life assessment model of retired power batteries and low accuracy and low assessment efficiency in model improvement, the life assessment model combining convolutional neural network (CNN) and long short-term memory (LSTM) network is improved by adding input feature parameters that are easy to extract, without increasing the computational burden. The equal voltage drop discharge time, equal temperature rise discharge time, and charging time during cycling of retired batteries are selected as the input feature parameters of the CNN-LSTM life assessment model, and the proposed model has improved accuracy and stability and is more efficient compared with the existing CNN-LSTM life assessment model. In addition, in order to improve the efficiency of the model in evaluating the life of retired batteries, the effect of reducing the proportion of training resources on the assessment results is explored, and it is found that when only 50% of the total number of battery cycles is selected as the training set of the model, the accuracy and stability of the model assessment can still be maintained at more than 99.00%.
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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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