Full
Outline
The state of health (SOH) estimation method for lithium batteries is crucial for battery management systems used in electric vehicles. At present, most of the SOH estimation methods often require the charging and discharging data during a long time, resulting in a poor real-time performance. To address this issue, an SOH estimation method based on random charging segments for feature extraction analysis is proposed. First, the complete constant current (CC) charging process is divided into several fixed-length data segments according to voltage intervals. Then, health indicators (HIs) are extracted for different voltage intervals. Finally, based on the characteristics of HIs, a back propagation neural network (BPNN) optimized by the particle swarm optimization (PSO) algorithm is trained, which can extract features from any CC charging segment to complete SOH estimation. NASA and Oxford datasets verify that this method has high flexibility and accuracy.
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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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