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Outline
The state of health (SOH) of lead-acid batteries is an important indicator for their reliable operation. A prediction model based on convolutional neural networks (CNN) optimized by the whale optimization algorithm (WOA) was proposed to solve the problem of low prediction accuracy of SOH of forklift batteries. First, CNN was taken as the basic model for the WOA-CNN model. Then, to further optimize the performance of the CNN model, the WOA algorithm was introduced to optimize the weight and bias of CNN by searching the optimal solution in space. To verify the validity of the WOA-CNN method, experiments were conducted using a real data set of forklift batteries. Experimental results show that the WOA-CNN model reduced the mean square error, root mean square error, mean absolute error and mean absolute percentage error in predicting the SOH of forklift batteries by 29.4%, 19.7%, 41.5% and 19.5% respectively compared with the unoptimized CNN neural network, and it reduced these errors by 42.2%, 31.1%, 64.7% and 54.8% respectively compared with the traditional BP neural network, exhibiting smaller prediction errors, higher accuracy and higher stability. The WOA-CNN prediction model provides strong support for the battery health management of electric forklifts, thus extending the battery life and improving the forklift reliability.
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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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