持续的物理信息学习加速了电池机制的脱.
Shanling Ji1,2,3,4,5,6, Jun Yuan2,3,4,5,6, Bojing Zhang2,3,4,5,6
1School of Mechanical Engineering, Southeast University, Nanjing, 211189, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|October 27, 2025
概括
一个新的基于物理的电池建模网络 (PIBMN) 准确地预测电池在各种条件下的性能. 这种多功能框架增强了电池管理和制造流程.
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
- 计算科学 计算科学
背景情况:
- 准确的电池行为预测至关重要,但受到各种材料和架构的挑战.
- 传统的预测方法对于新电池技术缺乏通用性.
研究的目的:
- 开发一种新的基于物理的电池建模网络 (PIBMN),以实现广泛的应用.
- 为了使不同电池格式和化学成分的持续参数适应和高准确度预测.
主要方法:
- 将数据驱动的学习与物理前置集成到神经网络框架中.
- 开发一个模型,确保动态响应的非线性表达性和数值稳定性.
- 在不同的负载配置文件下捕捉快速/慢速动态的实现.
主要成果:
- PIBMN展示了内部电化学状态的高保真性,可解释的表示.
- 该模型有效地捕获了商业和实验室细胞的动态反应.
- PIBMN使复杂的动力学和实时终端电压跟踪的脱成为可能.
结论:
- PIBMN为电池预测和诊断提供了一个多功能和可扩展的框架.
- 该模型支持在线质量控制和自适应电池管理.
- PIBMN促进了下一代电池制造的基于数据的优化.
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