A novel transformer-embedded lithium-ion battery model for joint estimation of state-of-charge and state-of-health  

作　　者：Shang-Yu Zhao Kai Ou Xing-Xing Gu Zhi-Min Dan Jiu-Jun Zhang Ya-Xiong Wang 

机构地区：[1]School of Mechanical Engineering and Automation,Fuzhou University,Fuzhou 350108,China [2]Chongqing Key Laboratory of Catalysis and New Environmental Materials,College of Environment and Resources,Chongqing Technology and Business University,Chongqing 400067,China [3]Contemporary Amperex Technology Co,Limited(CATL),Ningde 352100,China [4]College of Materials Science and Engineering,Fuzhou University,Fuzhou 350108,China

出　　处：《Rare Metals》2024年第11期5637-5651,共15页稀有金属（英文版）

基　　金：financially supported by the Science and Technology Major Project of Fujian Province of China(No.2022HZ028018);the National Natural Science Foundation of China(No.51907030)。

摘　　要：The state-of-charge(SOC)and state-of-health(SOH)of lithium-ion batteries affect their operating performance and safety.The coupled SOC and SOH are difficult to estimate adaptively in multi-temperatures and aging.This paper proposes a novel transformer-embedded lithium-ion battery model for joint estimation of state-ofcharge and state-of-health.The battery model is formulated across temperatures and aging,which provides accurate feedback for unscented Kalman filter-based SOC estimation and aging information.The open-circuit voltages(OCVs)are corrected globally by the temporal convolutional network with accurate OCVs in time-sliding windows.Arrhenius equation is combined with estimated SOH for temperature-aging migration.A novel transformer model is introduced,which integrates multiscale attention with the transformer's encoder to incorporate SOC-voltage differential derived from battery model.This model simultaneously extracts local aging information from various sequences and aging channels using a self-attention and depth-separate convolution.By leveraging multi-head attention,the model establishes information dependency relationships across different aging levels,enabling rapid and precise SOH estimation.Specifically,the root mean square error for SOC and SOH under conditions of 15℃dynamic stress test and 25℃constant current cycling was less than 0.9%and 0.8%,respectively.Notably,the proposed method exhibits excellent adaptability to varying temperature and aging conditions,accurately estimating SOC and SOH.

关 键 词：State-of-charge(SOC) State-of-health(SOH) Global correction Temperature Aging migration TRANSFORMER Multiscale attention 

分 类 号：TM912[电气工程—电力电子与电力传动]