Accurate and stable state estimation is critical for battery management. Although dual Kalman filtering can jointly estimate states and parameters, the strong coupling between filters may cause divergence under large initialization errors or model mismatch. This paper proposes a Stability Guaranteed Dual Kalman Filtering (SG-DKF) method. A Lyapunov-based analysis yields a sufficient stability condition, leading to an adaptive dead-zone rule that suspends parameter updates when the innovation exceeds a stability bound. Applied to an electrochemical battery model, SG-DKF achieves accuracy comparable to a dual EKF and reduces state of charge RMSE by over 45% under large initial state errors.
From Electrochemical Energy Storage to Next-Generation Intelligent Battery Technologies for Electric Vehicles: A Survey
Badji Mokhtar University（巴吉·莫赫塔尔大学）；USTHB（阿尔及利亚科学技术大学）；University of Windsor（温莎大学）；RMIT University（皇家墨尔本理工大学）；Central Michigan University（中密歇根大学）；Chadli Bendjedid University
Optimizing Experiments for Accurate Battery Circuit Parameters Estimation: Reduction and Adjustment of Frequency Set Used in Electrochemical Impedance Spectroscopy
Optimizing Parameter Estimation for Electrochemical Battery Model: A Comparative Analysis of Operating Profiles on Computational Efficiency and Accuracy
Institute for Materials Research (IMO-imomec), Hasselt University（哈瑟尔特大学材料研究所）；Institute of Physical Chemistry, RWTH Aachen University（亚琛工业大学物理化学研究所）
Physics-guided residual Kalman learning for state-of-charge estimation of lithium iron phosphate batteries
VITO（维托研究所）；EnergyVille（能源村）；Institute for Materials Research (IUMAT), Hasselt University（哈瑟尔特大学材料研究所）；College of Tianyin Lake Science and Technology Innovation, Nanjing Institute of Technology（南京工程学院天目湖学院）；School of Vehicles and Intelligent Transportation, Fuyao University of Science and Technology（福耀科技大学车辆与智能交通学院）；IUMAT, IMEC（IMEC-哈瑟尔特大学材料研究所）
Cross-modal topology decodes battery faults from sparse voltage snapshots
College of Mechanical and Vehicle Engineering, Chongqing University（重庆大学机械与车辆工程学院）；Center for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronics Systems (CARL), RWTH Aachen University（亚琛工业大学电化学与电力电子系统老化、可靠性与寿命预测中心）；Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University（亚琛工业大学电力电子与电气驱动研究所）；Department of Energy Technology, Aalborg University（奥尔堡大学能源技术系）；Department of Energy Science and Engineering, Stanford University（斯坦福大学能源科学与工程系）
GAETS: A Graph Autoencoder Time Series Approach Towards Battery Parameter Estimation
ML Collective（机器学习联盟）；National Institute of Science Education and Research, Bhubaneswar（布巴内斯瓦尔国家科学教育与研究学院）；Homi Bhabha National Institute, Mumbai（孟买霍米·巴巴国立研究所）；M S Ramaiah Institute of Technology（M S Ramaiah理工学院）
锂离子电池正在推动正在进行的交通电气化革命。锂离子电池具有较高的能量密度和良好的电化学性能，使其成为电动汽车的首选能源。精确估计电池参数（充电容量、电压等）对于估计电动汽车的可用里程至关重要。基于图的估计技术使我们能够理解支撑它们的变量依赖关系，以改进估计。本文将图神经网络用于电池参数估计，提出了一种独特的图自编码时间序列估计方法。已知电池测量中的变量在感兴趣的变量内以某种相关性彼此具有潜在关系。我们使用基于非线性版本NOTEARS的图形自动编码器，因为这允许我们在学习结构时执行梯度下降（而不是将其视为组合优化问题）。所提出的架构优于最先进的图形时间序列（GTS）架构的电池参数估计。我们称之为GAETS（Graph AutoEncoder Time Series）。
