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Outline
Accurate state of health (SOH) estimation is crucial for the safe operation of lithium-ion batteries. However, the complex cycling conditions and random initial charging state of lithium-ion batteries make the inputs of most existing methods unable to be satisfied. To address this problem, we propose an end-to-end SOH estimation method based on relaxation voltage. This method directly establishes the nonlinear relationship between relaxation voltage and SOH using a Bayesian optimization-assisted deep feedforward neural network (BO-DNN), eliminating the laborious feature extraction process. The performance of the estimation method was verified on three different battery aging datasets. The results show that the model achieves a root mean square error (RMSE) of 0.011 0 on the dataset used for model building. A transfer learning model developed by fine-tuning the basic model only relies on less than 3.5% training data, and it achieves an RMSE of less than 0.016 3 on the datasets used for model validation.
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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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