数据驱动的电池特性和预测:最近的进展,挑战和前景
Shanling Ji1, Jianxiong Zhu1,2, Yaxin Yang1
1School of Mechanical Engineering, Southeast University, Nanjing, Jiangsu, 211189, China.
Small methods
|January 12, 2024
概括
数据驱动的人工智能增强了电池特征和预后,以实现更安全的操作. 本综述强调了先进的方法和可解释学习在加速下一代电池开发中的作用.
科学领域:
- 材料科学 材料科学 材料科学
- 电化学 电化学 电化学
- 人工智能的人工智能
背景情况:
- 电池的表征和预后对于理解电化学机制和确保运行安全至关重要.
- 数据驱动的人工智能 (AI) 系统为电池分析提供了先进的功能.
- 最近的进展主要集中在将AI与各种表征技术相结合.
研究的目的:
- 为电池表征和预后提供数据驱动方法的全面审查.
- 突出基于物理的可解释学习在加速能源设备开发中的作用.
- 讨论该领域的挑战和未来前景.
主要方法:
- 总结了信息图像表征和阻抗光谱学的最新进展.
- 审查高通量选方法,以揭示电化学机制.
- 对比各种基于物理的模拟策略,以预测电池的情况.
主要成果:
- 数据驱动的人工智能显著提高了对电池性能进行表征和预测的能力.
- 基于物理的可解释学习是从大型电池数据集中解锁洞察力的关键.
- 先进的方法提高了电化学透明度和电池模型中的概括性.
结论:
- 数据驱动的方法,特别是基于物理的可解释学习,对于下一代电池的开发至关重要.
- 通过这些先进的方法,可以提高电化学透明度和概括性.
- 本综述为未来电池技术研究提供了洞察力.
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