电动汽车离子电池的电压故障诊断使用优化的图形神经网络
Jian Ouyang1, ZiHao Lin2, Liyazhou Hu3
1Industrial Training Center, Guangdong Polytechnic Normal University, Guangzhou, 510665, Guangdong, China.
Scientific reports
|July 27, 2025
概括
本研究介绍了一种优化的图形神经网络 (GNN),用于诊断离子电池电压故障. 通过准确地定位故障,GNN模型提高了电动汽车 (EV) 的安全性和可靠性.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
背景情况:
- 精确诊断离子电池的电压故障对于储能系统和电动汽车 (EV) 的安全性和可靠性至关重要.
- 现有的方法可能无法完全捕捉到电池系统中的复杂的相互依赖和物理合.
研究的目的:
- 提出一个优化的图形神经网络 (GNN) 模型,以改善离子电池的电压故障诊断.
- 通过利用电池拓和物理合来提高故障定位的准确性和有效性.
主要方法:
- 开发了一种优化的图形神经网络 (GNN) 模型,该模型学习电池拓,以提取电池之间的关系.
- 集成的物理合和测量纠与GNN的非线性处理能力用于故障定位.
- 在三个公共数据集上验证了该方法,与多个基线模型进行比较 (GraphConv, GCNConv, ChebConv, SGConv, CNN, DBN, LSTM, CNN-LSTM).
主要成果:
- 提议的优化GNN模型在精度,精度,回忆和F1分数方面显著超过了基线方法,用于电压故障定位.
- 与最佳基线相比,实现了4.31%的急剧故障和3.68%的渐进故障的最大精度改进.
- 在使用电池电压数据诊断电压故障方面表现出卓越的有效性和准确性.
结论:
- 优化的GNN方法为诊断离子电池电压故障提供了高度准确和稳定的方法.
- 这一进步对电动汽车和储能应用的发展和安全具有重要意义.
- 该模型学习电池拓和物理合的能力为未来的电池管理系统提供了有希望的方向.
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