Model-based fault detection of a battery system in a hybrid electric vehicle
暂无分享,去创建一个
[1] Branko Ristic,et al. Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .
[2] Petros G. Voulgaris,et al. On optimal ℓ∞ to ℓ∞ filtering , 1995, Autom..
[3] Dan Simon,et al. Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .
[4] Valerie H. Johnson,et al. Battery performance models in ADVISOR , 2002 .
[5] Keizo Yamada,et al. Battery condition monitoring (BCM) technologies about lead–acid batteries , 2006 .
[6] S. Haykin. Kalman Filtering and Neural Networks , 2001 .
[7] and Charles K. Taft Reswick,et al. Introduction to Dynamic Systems , 1967 .
[8] David A. Stone,et al. Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles , 2005, IEEE Transactions on Vehicular Technology.
[9] J. Voelcker. Lithium Batteries Take To The Road , 2007, IEEE Spectrum.
[10] Gregory L. Plett,et al. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification , 2004 .
[11] Gregory L. Plett,et al. Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1: Introduction and state estimation , 2006 .
[12] Thiagalingam Kirubarajan,et al. Estimation with Applications to Tracking and Navigation , 2001 .
[13] Rolf Isermann,et al. Model-based fault-detection and diagnosis - status and applications , 2004, Annu. Rev. Control..
[14] Józef Korbicz. Artificial neural networks in fault diagnosis of dynamical systems , 2010 .
[15] Tony Markel,et al. ADVISOR: A SYSTEMS ANALYSIS TOOL FOR ADVANCED VEHICLE MODELING , 2002 .
[16] Mohinder S. Grewal,et al. Kalman Filtering: Theory and Practice Using MATLAB , 2001 .
[17] S.M.T. Bathaee,et al. Predicting state of charge of lead-acid batteries for hybrid electric vehicles by extended Kalman filter , 2008 .
[18] Hugh F. Durrant-Whyte,et al. A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..
[19] Gregory L. Plett,et al. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 1. Background , 2004 .
[20] T. Başar,et al. A New Approach to Linear Filtering and Prediction Problems , 2001 .
[21] Saeid Habibi,et al. The Smooth Variable Structure Filter , 2007, Proceedings of the IEEE.
[22] Saeid Habibi,et al. The Variable Structure Filter , 2002 .
[23] S. Andrew Gadsden,et al. A new form of the smooth variable structure filter with a covariance derivation , 2010, 49th IEEE Conference on Decision and Control (CDC).
[24] Arthur Gelb,et al. Applied Optimal Estimation , 1974 .
[25] L. A. Mcgee,et al. Discovery of the Kalman filter as a practical tool for aerospace and industry , 1985 .
[26] Hui He,et al. An intelligent approach for engine fault diagnosis based on wavelet pre-processing neural network model , 2010, The 2010 IEEE International Conference on Information and Automation.
[27] D. Simon. Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .
[28] Saeid R. Habibi,et al. Fault detection and diagnosis of an electrohydrostatic actuator using a novel interacting multiple model approach , 2011, Proceedings of the 2011 American Control Conference.
[29] R. E. Kalman,et al. New Results in Linear Filtering and Prediction Theory , 1961 .
[30] Jin Jiang,et al. An interacting multiple-model based fault detection, diagnosis and fault-tolerant control approach , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).