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Outline
The state-of-charge (SOC) prediction for lithium-ion batteries is an important part of a battery management system, and accurately predicting their SOC is of significance for ensuring their safe operation. On this basis, empirical mode decomposition (EMD), long short-term memory (LSTM) recurrent neural network and Attention mechanism are used to build an SOC prediction model for a lithium-ion battery. The voltage, current, surface temperature and the voltage decomposed by EMD in its charging and discharging processes are taken as the characteristics of the model. The LSTM layer of the model can capture the battery time information from the historical cycle data of the lithium-ion battery, and the long-term dependence relationship is also summarized. The Attention layer of the model can capture the nonlinear and dynamic relationship between feature input and SOC output, so as to optimize the weight relationship between each cycle stage and each feature and further improve the SOC prediction accuracy. Experiments were carried out based on the public battery data set of University of Maryland, and results show that compared with that of a model which only uses LSTM, the average prediction errors of the proposed model under three different working conditions were reduced by 49.36%, 45.61% and 46.93%, 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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