Transfer learning for state of charge estimation across batteries and chemistries: a lightweight, physics-guided LSTM with regime-aware temporal attention and staged adaptation

已完结 10 由懒洋洋发布于 2026/8/8 17:40:47

DOI:10.1007/s11581-025-06846-5
作者：Ebubekir Bu?ra ?zarslan,?Senem Kursun
文献类型：期刊论文
补充材料：只需要正文

Springer

Abstract：A cross-battery, cross-chemistry framework for state-of-charge (SOC) estimation under domain shift is proposed. A compact sequence encoder, composed of two stacked LSTM layers with a single-

应助信息

21小时前

li75

0d1726e8930e11f195bd34735aa3bc31.pdf 已采纳

21小时前

Book学术机器人

2026/8/8 17:40:48 未找到该文献，机器人已退出，请等待人工下载

2026/8/8 17:40:47 Book学术AI机器人收到请求，开始寻找文献

2026/8/8 17:40:47 已向机器人发送请求

21小时前

懒洋洋发布求助

上一个求助:其它数据库Naturally Fractured Carbonate Reservoir Characterization: A Case Study of a Mature High-Pour Point Oil Field in Hungary 下一个求助:其它数据库State of charge estimation for lithium-ion batteries based on cross-domain transfer learning with feedback mechanism