预测离子电池在热失控状态下释放热量的变异性,热量计数据很少或不存在
Karina Masalkovaitė1,2, Paul Gasper3, Donal P Finegan4
1Department of Materials Science and Engineering, Stanford University, Stanford, CA, 94305, USA.
Nature communications
|September 27, 2024
概括
这项研究使用转移学习来预测离子电池热失控热输出变异性从质量测量. 这加快了电池安全性特征,减少了广泛的热量计测试的需要.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 电池技术 电池技术
背景情况:
- 准确地描述离子电池的热失控变异性对于安全的电池设计至关重要.
- 对这种变性的实验性确定往往是昂贵和耗时的.
- 现有的方法在预测不同细胞类型的热失控行为方面缺乏效率.
研究的目的:
- 开发一种具有成本效益和快速的方法,用于估计离子电池中的热失控热输出可变性.
- 利用转移学习来预测使用最小实验数据的热失控行为.
- 为了加快电池系统的安全表征.
主要方法:
- 利用转移学习方法来预测热量输出的变化.
- 员工喷射的质量测量和细胞元数据作为输入特征.
- 从开放式访问的电池故障数据库中利用了139次热量测量测量.
- 对新型细胞类型的验证预测,采用0到5次热量计测量.
主要成果:
- 准确预测了热失控期间的热量输出分布,包括异常值.
- 在新离子电池类型的预测中获得了高可靠性.
- 成功预测了来自各个细胞组件 (正通风,细胞体,负通风) 的分量热喷射.
- 证明了使用低成本,快速测量的有效性,用于安全特性.
结论:
- 转移学习能够准确地预测热失控热变量的变化,使用最小的数据.
- 这种方法大大减少了与电池安全测试相关的时间和成本.
- 该方法加速了更安全的离子电池系统的开发.
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