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Outline
The state-of-health (SOH) of batteries is an important indicator for evaluating their performance. Aimed at the difficulty in accurately estimating the SOH of batteries, a novel SOH estimation method for lithium-ion batteries was proposed by combining the hunter-prey optimizer (HPO) algorithm and long short-term memory (LSTM) neural network. Through the analysis of influences of current and temperature on the incremental capacity (IC) curve, the peak value of the highest peak in the IC curve and the corresponding voltage, temperature and current were introduced as the model input, and the dynamic parameters of LSTM neural network were adjusted using the HPO algorithm. Finally, the battery charging and discharging data under the condition of peak cutting and valley filling in the environment of energy storage were used for experimental verification. Results indicate that the SOH estimation method for lithium-ion batteries based on HPO-LSTM neural network has higher estimation accuracy compared with the traditional LSTM network methods, and it also has good robustness under different network training amounts.
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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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