合数据驱动和强化学习用于电池中的材料开发和设备管理
Zihao He1,2, Zijun Wang1, Yueyao Dong1
1State Key Laboratory of Space Power-Sources, School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, China.
Advanced materials (Deerfield Beach, Fla.)
|February 6, 2026
概括
机器学习和强化学习通过统一预测建模和自适应优化来加速电池创新. 这种结合的方法可以在整个生命周期中提高电池材料的发现,安全性和性能.
科学领域:
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
- 储能 储能 储能 储能 储能 储能
背景情况:
- 传统的电池开发依赖于经验方法和基于物理的模型,这些模型对于复杂的下一代系统来说是不够的.
- 对高能耗,安全和耐用电池的需求需要先进的材料发现和设备管理策略.
研究的目的:
- 引入一个结合的范式,整合数据驱动的机器学习和强化学习 (RL) 进行电池创新.
- 为电池材料开发和设备管理建立闭环框架.
主要方法:
- 使用数据驱动的方法通过多源数据挖掘快速选电池材料 (阴极,阳极,电解质).
- 使用RL代理来代优化合成条件,接口属性和充电协议.
- 开发用于预测,探索,验证,数据洞察和战略优化的闭环框架.
主要成果:
- 加快发现先进的电池材料和提高设备性能.
- 通过对操作参数的自适应性优化,提高了电池的安全性和耐用性.
- 证明了自主,高吞吐量电池创新的潜力.
结论:
- 数据驱动机器学习和RL之间的协同作用为下一代电池技术提供了强大的途径.
- 解决数据处理,特征工程和模型构建方面的挑战对于工业部署至关重要.
- 这种综合方法为自主和高效的电池开发和管理提供了基础.
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