离子电池的剩余使用寿命预测使用时空多式联络注意网络
Sungho Suh1,2, Dhruv Aditya Mittal2, Hymalai Bello1,2
1German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
Heliyon
|September 12, 2024
概括
本研究引入了一种新的时空多式注意网络 (ST-MAN),用于预测离子电池的剩余使用寿命 (RUL). 通过考虑复杂的电池数据特征,ST-MAN方法提供了更高的准确性,提高了运行可靠性.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 离子电池对于电动汽车和能源存储至关重要,需要准确的剩余使用寿命 (RUL) 预测,以提高运营效率和降低成本.
- 当前的RUL预测方法往往过于简化了电池数据,忽视了温度,内部电阻和材料类型等关键因素,并与代周期预测作斗争.
- 现实世界电池生命周期预测由于复杂的操作动态和代预测的困难而存在挑战.
研究的目的:
- 为离子电池开发先进的RUL预测方案,克服现有方法的局限性.
- 通过结合复杂的时空依赖和经常被忽视的特征,提高电池RUL预测的准确性和可靠性.
- 提供一种可靠的方法来预测电池寿命,而无需事先了解寿命结束事件.
主要方法:
- 建议采用一种新的时空多式注意网络 (ST-MAN) 的两阶段RUL预测方案.
- ST-MAN架构旨在捕捉电池运行数据中的复杂的时空依赖关系.
- 该模型有效地整合了多式联运特征,包括温度,内部电阻和材料类型,这些特征在传统方法中经常被忽视.
主要成果:
- 与现有的卷积神经网络 (CNN) 和长短期记忆 (LSTM) 基于的方法相比,提议的ST-MAN方法实现了更高的RUL预测准确性.
- 该模型显示出较低的误差率,平均绝对误差 (MAE) 为0.0275和平均平方误差 (MSE) 为0.0014.
- 即使没有对生命终端事件 (EOL) 的事先了解,ST-MAN也始终超过了基线方法.
结论:
- 开发的ST-MAN在离子电池RUL预测方面取得了重大进展,解决了当前技术的关键局限性.
- 该方法能够捕捉复杂的数据依赖性,并集成多种功能,从而提高电池的运行可靠性和效率.
- 这种方法在依赖离子电池技术的各种行业中具有广泛的适用性,有望提高性能和减少维护.
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