航空锂电池寿命预测方法研究

Research on life prediction method of aviation lithium battery

随着航空机电系统的发展,对电池的检测与寿命预测成为地勤人员重要的工作之一。针对小子样条件下锂电池寿命预测困难的问题,研究了GM(1:1)模型、灰色Verhulst模型、神经网络模型在解决该问题时的优势与缺陷,通过对现有模型的分析,提出了一种灰色Verhulst-神经网络模型,弥补了灰色模型中长期预测精度低的缺陷,降低了神经网络对样本量的要求。以某型航空设备装备的锂电池为例,研究并比较以上方法的预测效果,结果显示灰色Verhulst-神经网络模型预测精度为0.7%,远低于其他模型,说明模型精度较高,证明了所提方法的可行性与有效性。

With the development of aviation electromechanical system, the detection and life prediction of battery become one of the important work of ground crew. In view of the difficulty of lithium battery life prediction under the condition of small sample, the advantages and disadvantages of GM(1∶1) model, grey Verhulst model and neural network model in solving the problem are studied. Through the analysis of existing models, a grey Verhulst neural network model is proposed, which makes up for the defect of low prediction accuracy in the medium and long term of grey model, and reduces the influence of neural network on the sample size requirement. Taking the lithium battery of a certain type of aviation equipment as an example.The results show that the prediction accuracy of grey Verhulst neural network model is 0.7%, which is far lower than that of other models. It shows that the model has high accuracy, which proves the feasibility and effectiveness of the proposed method.