Full
Outline
The state-of-charge (SOC) is key to the safe operation and energy management of electric vehicles. The traditional SOC estimation algorithm based on Kalman filter and recurrent neural network requires a period of data to ensure the convergence of estimation results and cannot accurately estimate the SOC value near the starting point. A convolutional neural network SOC estimation method using U-Net structure is proposed, which can deal with variable-length input data and output the equal-length SOC estimation results. Meanwhile, it can also accurately estimate SOC at the starting point. In addition, a full-variance loss function is put forward, which can improve the estimation stability only by optimizing the loss function without increasing the model complexity and significantly reduce the maximum error. This model is trained with dynamic driving cycle data under five constant-temperature conditions, and the SOC estimation results are highly accurate under both the constant-temperature and variable-temperature conditions. Under the constant-temperature condition, the mean absolute error (MAE) and root mean square error (RMSE) of SOC estimation were within 1.1% and 1.4%, respectively. Under the variable-temperature condition, the MAE and RMSE were within 1.5% and 1.8%, 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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