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一种深度学习方法,以优化离子电池的剩余使用寿命预测

Mahrukh Iftikhar¹, Muhammad Shoaib¹, Ayesha Altaf²

¹Department of Computer Science, University of Engineering and Technology, Lahore, 54890, Pakistan.

Scientific reports

|October 29, 2024

概括

对离子 (Li-ion) 电池的准确剩余使用寿命 (RUL) 预测得到了AccuCell Prodigy深度学习模型的增强. 这种先进的方法提高了电动汽车和电子产品中的电池安全性和性能.

科学领域:

电池技术电池技术
人工智能的人工智能
预测性维护是指预测性维护.

背景情况:

准确的剩余使用寿命 (RUL) 预测对于离子 (Li-ion) 电池的性能和电动汽车和消费电子产品的安全至关重要.
现有的RUL预测方法正在使用深度学习来改进,以提高准确性和效率.

研究的目的:

推出AccuCell Prodigy,这是一个新的深度学习模型,旨在增强离子电池中的RUL预测.
证明模型在减少预测错误和提高效率方面的能力.

主要方法:

开发了一个深度学习模型,AccuCell Prodigy,集成自动编码器和长短期记忆 (LSTM) 层.
准备了电池操作特征的数据集,并将其分为80%用于培训和20%用于测试.
该模型经过训练和测试,在三个离子电池中使用了22种电流变化.

主要成果:

阿库塞尔Prodigy实现了0.1305%的平均平方误差,平均绝对误差为2.484%,根平均平方误差为3.613%.
该模型显示高的R平方值为0.9849,表明强大的预测性能.
与现有方法相比,观察到预测错误的显著减少.

结论:

在预测离子电池RUL方面,AccuCell Prodigy显示出强度和高准确性.
该模型有可能显著提升电池健康管理系统.
这种深度学习方法为电池RUL预测提供了更高的精度和效率.

关键词:

这是一个AccuCell神童.深度学习是一种深度学习.能源效率能源效率是指能源的使用效率.离子电池是一种离子电池.剩余的使用寿命.

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