基于充电网络的早期预警方法,为充电电动汽车离子电池的热失控充电
Yuan-Ming Cheng1, De-Xin Gao2, Feng-Ming Zhao1
1Department of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, 266061, China.
这项研究引入了电动汽车 (EV) 离子电池充电安全的早期预警系统. 该方法使用长短期记忆-时间卷积网络 (LSTM-TCN) 模型来预测充电温度并防止热失控.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 电动汽车 (EV) 对可持续运输至关重要,但充电安全,特别是离子电池,仍然是一个重大挑战.
- 电动汽车行业的发展受到电池充电过程中电池热稳定性的担忧所阻碍.
- 了解充电参数和电池温度之间的复杂关系对于确保安全至关重要.
研究的目的:
- 开发用于电动汽车离子电池充电的先进预警系统.
- 提高电动汽车充电基础设施的安全性和可靠性.
- 为了减轻充电过程中发生热失控事件的风险.
主要方法:
- 从电动汽车充电网络收集的充电数据.
- 开发并实施长期短期记忆-时间卷积网络 (LSTM-TCN) 模型来预测电池充电温度.
- 使用滑动窗方法分析预测残留物,以确定热失控的预警值.
主要成果:
- 与其他模型相比,LSTM-TCN模型在预测电动汽车充电温度方面表现出卓越的准确性.
- 开发的系统有效地监控剩余变化,提供及时警告.
- 该方法可以准确快速检测潜在的充电异常.
结论:
- 提出的基于LSTM-TCN的早期预警系统显著提高了电动汽车离子电池充电的安全性.
- 这种方法为防止热失控事件提供了可靠的解决方案.
- 这些发现有助于更广泛地采用和安全的电动汽车.
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