黄晓智1  张华明2  黄艺航1  许志杰1

（1.广东工业大学机电工程学院，广东 广州 510006

2.广东工业大学先进制造学院，广东 揭阳 522000）

摘要：蓄电池剩余使用寿命预测对能源的安全性和可持续发展至关重要。该文提出一种蓄电池剩余使用寿命的预测方法，利用蓄电池的历史运行数据和充放电周期，构建Bi-LSTM-Dropout网络模型。利用Bi-LSTM提取时间序列中蓄电池长期依赖的特征，采用Dropout优化算法降低Bi-LSTM网络模型的复杂度，提高模型的泛化能力。实验结果表明，该方法在测试集上的准确率达96.2%，实现了蓄电池剩余使用寿命的精确预测。

关键词：蓄电池；剩余使用寿命预测；Bi-LSTM；Dropout优化算法

中图分类号：TM912           文献标志码：A           文章编号：1674-2605(2024)01-0007-06

DOI：10.3969/j.issn.1674-2605.2024.01.007

Prediction Method of Battery Remaining Useful Life                   Based on Bi-LSTM-Dropout

HUANG Xiaozhi1  ZHANG Huaming2  HUANG Yihang1  XU Zhijie1

(1.School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China

2.School of Advanced Manufacturing Guangdong University of Technology, Jieyang 522000, China)

Abstract: The prediction of the remaining useful life of battery is crucial for the safety and sustainable development of energy. This article proposes a prediction method for the remaining useful life of battery, using historical operating data and charging and discharging cycles of battery to construct a Bi-LSTM-Dropout network model. Using Bi-LSTM to extract long-term dependent features of battery in time series, using Dropout optimization algorithm to reduce the complexity of Bi LSTM network model and improve its generalization ability. The experimental results show that the accuracy of this method on the test set reaches 96.2%, achieving accurate prediction of the remaining useful life of the battery.

Keywords: battery; remaining useful life; Bi-LSTM; dropout optimization algorithm