Journal ArticleDOI
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation
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TLDR
In this article, extended Kalman filtering (EKF) is used to estimate battery state-of-charge, power fade, capacity fade, and instantaneous available power for hybrid-electric-vehicle battery packs.About:
This article is published in Journal of Power Sources.The article was published on 2004-08-12. It has received 1587 citations till now. The article focuses on the topics: Battery pack & State of charge.read more
Citations
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Proceedings ArticleDOI
Simplified Extended Kalman Filter Observer for SOC Estimation of Commercial Power-Oriented LFP Lithium Battery Cells
TL;DR: In this paper, the extended Kalman filter (EKF) algorithm was combined with a two-RC-block equivalent circuit and the traditional coulomb counting method, and the model converged to within 4% of the true state of charge (SOC).
Journal ArticleDOI
An Adaptive Gain Nonlinear Observer for State of Charge Estimation of Lithium-Ion Batteries in Electric Vehicles
TL;DR: In this article, an adaptive gain nonlinear observer (AGNO) was proposed for the estimation of the state of charge (SOC) of lithium-ion batteries in electric vehicles.
Proceedings ArticleDOI
Experimental modeling and analysis of lithium-ion battery temperature dependence
TL;DR: In this paper, a 36V/1100mAh Li-ion battery cell was tested at temperature between −30°C and +50°C, and its main parameters were measured, including discharge capacity, the charge and discharge resistance, and the opencircuit voltage.
Proceedings ArticleDOI
A new parameter estimation algorithm for an electrical analogue battery model
TL;DR: In this paper, a new parameter estimation algorithm for a well-recognized electrical analogue battery model is described, which makes use of the limited bandwidth of the battery model by reconciling the approximation with the limitation of the simulation bandwidth.
Journal ArticleDOI
Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter
Kuo Yang,Yugui Tang,Zhen Zhang +2 more
TL;DR: An improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries that can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance.
References
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Book ChapterDOI
A New Approach to Linear Filtering and Prediction Problems
TL;DR: In this paper, the clssical filleting and prediclion problem is re-examined using the Bode-Shannon representation of random processes and the?stat-tran-sition? method of analysis of dynamic systems.
Book
Kalman Filtering and Neural Networks
TL;DR: This book takes a nontraditional nonlinear approach and reflects the fact that most practical applications are nonlinear.
Journal ArticleDOI
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification
TL;DR: In this article, an extended Kalman filter (EKF) was used to estimate the battery state of charge, power fade, capacity fade, and instantaneous available power of a hybrid electric vehicle battery pack.
Journal ArticleDOI
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 1. Background
TL;DR: In this paper, an extended Kalman filter (EKF) was proposed to estimate the battery state of charge, power fade, capacity fade, and instantaneous available power of a hybrid-electric-vehicle battery pack.