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In order to accurately estimate the State of Health (SOH) of lithium-ion batteries, this paper proposes an advanced SOH estimation method that integrates Strategic Optimization Algorithm (SOA) with Memory-Enhanced Long Short-Term Memory (MELSTM) neural network. Firstly, a Variational AutoEncoder (VAE) is utilized to process raw data, reducing redundant information and extracting health indicators, thereby achieving a precise representation of battery degradation information. Subsequently, a hybrid model combining SOA and MELSTM is proposed to estimate SOH of lithium-ion batteries. Finally, effectiveness of the proposed method is validated using 2 public datasets for lithium-ion battery aging, namely CACLE and NASA. Experimental results demonstrate that the proposed method improves RMSE indicators by over 30% compared with conventional LSTM algorithm, offering new insights and solutions for accurate SOH estimation of lithium-ion battery.
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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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