Full
Outline
Aimed at the phenomenon of capacity recovery and the problem of accurate prediction during the cyclic usage of lithium-ion batteries, an approach integrating feature analysis with an innovative variational pyramid split attention long short-term memory (V-PSA-LSTM) network model is proposed. This method performs the feature analysis of lithium-ion batteries, and Pearson correlation coefficients are employed to screen out the health features that are closely associated with the battery’s state of health (SOH), thereby revealing the key characteristics such as dynamic capacity restoration. Then, a novel V-PSA-LSTM network model is put forward, in which a periodic variation activation mechanism is incorporated into the pyramid split attention (PSA) algorithm to optimize the long short-term memory (LSTM) network for the first time. This design aims at enhancing the LSTM model’s capability to precisely capture the evolution patterns of SOH in lithium-ion batteries, particularly the fluctuating changes in capacity during the cyclic process. It is verified that the V-PSA-LSTM model significantly reduces prediction errors during the SOH prediction for lithium-ion batteries, e.g., the root mean square error (RMSE) and mean absolute error (MAE) are 0.002 2 and 0.001 8, respectively, indicating the exceptional performance in solving the problems of capacity recovery and accurate prediction.
40
5
| 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 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
