你作为母语为英语的电子信息专业的教授如何将下列这段话用英文表述。在信息化战争的时代锂离子电池在军用无人机和各类先进武器设备中应用很广泛但是由于锂电池在使用过程中会受到高温高压、隔膜老化、存储使用不当等问题这会造成电池质量和性能的不断退化从而造成武器在使用过程中出现不可预知的故障这也会带来不可估量的人力物力财力的损失。因此对锂电池进行剩余寿命预测是十分重要的。考虑到军用武器使用的锂电池寿命循环周期比

In the era of information warfare, lithium-ion batteries are widely used in military drones and various advanced weapons and equipment. However, due to problems such as high temperature and pressure, membrane aging, and improper storage and use during the use of lithium batteries, the quality and performance of the batteries will continuously degrade, causing unpredictable failures during the use of weapons, resulting in incalculable losses of manpower, material resources, and financial resources. Therefore, predicting the remaining life of lithium batteries is extremely important. Considering that the cycle period of lithium batteries used in military weapons is relatively long, it is difficult to obtain a large amount of performance degradation data. Therefore, this paper needs to solve the problem of small sample data of lithium batteries and predict the RUL of lithium batteries under this condition. In view of the problem of low accuracy of lithium battery remaining life prediction under small sample conditions, this paper improves the LSTM neural network and mainly studies the following contents:

Firstly, this paper selects the NASA B05 and CACLE CS2_35 lithium battery aging datasets as the lithium battery public dataset, analyzes the battery characteristics in these two datasets, and takes the 5 extracted indirect health factors as the input of the prediction model. The correlation degree with battery capacity is analyzed based on the gray correlation method to verify the effectiveness of the selected indirect health factors.

Secondly, according to the extracted indirect health factors, the curve relationship between them and lithium battery capacity is drawn to obtain prior knowledge. Then, this monotonicity prior knowledge is added to the performance function of the LSTM neural network in mathematical form, and the coefficient of the penalty factor is set. Then, the lithium battery RUL prediction model with fusion constraints is established, called the MC-LSTM model. Experimental simulation results show that compared with the prediction effect of the standard LSTM, the MC-LSTM algorithm model proposed in this paper has higher prediction accuracy.

Thirdly, in order to further optimize the prediction accuracy of the network, this paper improves the MC-LSTM to the bi-directional constrained network, namely Bi-MC-LSTM. Because the hyperparameters of the Bi-MC-LSTM neural network are more, and the artificial setting will affect the model accuracy, the WOA optimization algorithm is selected to optimize the network hyperparameters, and the optimized hyperparameters are re-assigned to the network to construct the WOA-Bi-MC-LSTM prediction model. Finally, through experimental verification, the model proposed in this paper is closer to the actual capacity of lithium batteries, and has higher prediction accuracy and smaller errors compared with other algorithms.

Finally, a lithium battery remaining life prediction evaluation software system is designed. The software can realize functions such as feature extraction and pre-processing of lithium battery data, and can also display the algorithm models and RUL prediction results proposed in this paper. Users can predict the remaining life of lithium batteries based on their own data, and the system will provide corresponding suggestions according to the prediction results.

Keywords: lithium-ion battery; small sample; LSTM neural network; prior knowledge; monotonicity constraint; Bi-LSTM neural network; WOA optimization algorithm.