Full
Outline
To address the insufficient accuracy of State Of Health (SOH) prediction for power batteries under real-vehicle operating conditions, a prediction method integrating Temporal Convolutional Network (TCN) and Transformer is proposed. Using real-vehicle operation monitoring data as the research object, after removing abnormal values and selecting charging condition data, the current battery capacity is calculated via the ampere-hour integration method. The Whale Optimization Algorithm (WOA) is adopted to optimize the parameters of Variational Mode Decomposition (VMD), realizing the decomposition and denoising of the capacity sequence. Features are extracted by combining parameters such as current and temperature, and 6 strongly correlated features are selected to construct a dataset, which is input into the model to complete SOH prediction. Results show that the model significantly reduces prediction errors in the datasets of 4 real-vehicle batteries, with the coefficient of determination [R2>0.8], providing technical support for the full-life-cycle health management of power batteries.
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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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