新的机器学习模型有望实现更快、更准确的电池状态预测 · 2篇论文

报道来源 [3]

1. arXiv cs.LG TIER_1 English(EN) · Gihyun Lee, Thorben Menne, Simon Olma, Jakob Hilgert, Sangyoung Park · 2026-06-19 04:00

   用于自回归预测电池内部状态的神经代理架构的比较研究

   arXiv:2606.20053v1 Announce Type: new Abstract: The Doyle-Fuller-Newman (DFN) model resolves internal electrochemical states in lithium-ion batteries with high fidelity. However, the numerical solution of its governing equations is computationally prohibitive for real-time deploy…

2. arXiv cs.LG TIER_1 English(EN) · Sangyoung Park · 2026-06-18 10:27

   用于自回归预测电池内部状态的神经代理架构的比较研究

   The Doyle-Fuller-Newman (DFN) model resolves internal electrochemical states in lithium-ion batteries with high fidelity. However, the numerical solution of its governing equations is computationally prohibitive for real-time deployment, limiting scalability from individual cells…

3. arXiv cs.AI TIER_1 English(EN) · Junting Wen, Dan Li, Qihao Quan, Xiwen Wang, Hang Yang, Zhaohong Meng, Zigui Jiang, Changlin Yang, Tianle Liu, Diego Mu\~noz-Carpintero, Jian Lou · 2026-06-16 04:00

   面向电池系统的通过时间对比表示学习实现的自主端到端SOH预测服务

   arXiv:2606.16434v1 Announce Type: cross Abstract: Accurate state of health (SOH) estimation is a critical diagnostic service for lithium-ion battery management. However, reliance on labor-intensive manual feature engineering and opaque black-box models hinders scalable industrial…