Full
Outline
The parameter identification accuracy of a lithium battery model is of significance for the development of a battery management system. To improve the accuracy and efficiency of parameter identification, a multi-step parameter identification method for the electrochemical-thermal coupling model of lithium battery is proposed in combination with the Arrhenius law, which uses the whale optimization algorithm improved by oppositional learning strategy and Levy mutation. In this method, constant-current data at 15 ℃, 25 ℃ and 35 ℃ are adopted for parameter identification, and constant-current and dynamic working conditions are used to verify the identification results. Simulation and experimental results show that the mean absolute percentage error of parameters is reduced by 7.2%, the maximum root mean square error of voltage predicted by the model is 26 mV and that of temperature is 0.4 ℃, indicating that the multi-step parameter identification method using the whale optimization algorithm based on oppositional learning strategy and Levy mutation can efficiently identify parameters.
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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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