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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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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Journal ArticleDOI
A Novel Method for Estimating State-of-Charge in Power Batteries for Electric Vehicles
TL;DR: A second-order charge–discharge resistor–capacitor model that can accurately simulate external characteristics of the battery and identify them online is described and an improved adaptive unscented Kalman filter algorithm based on Sage–Husa is introduced to estimate SOC.
Journal ArticleDOI
High precision estimation of inertial rotation via the extended Kalman filter
TL;DR: In this paper, the extended Kalman filter (EKF) was used for the estimation of the inertial rotation rate of the atomic spin gyroscope, and the root-mean-squared errors were shown to be at least 103 times smaller than those obtained by the steady-state estimation method under the same response time.
Journal ArticleDOI
A method for remaining discharge time prediction of lithium‐ion batteries under dynamic uncertainty
Journal ArticleDOI
Experimental Data-Driven Parameter Identification and State of Charge Estimation for a Li-Ion Battery Equivalent Circuit Model
Hui Pang,Fengqi Zhang +1 more
TL;DR: In this paper, an experimental data-driven parameter identification scheme and an adaptive extended Kalman filter (AEKF)-based state of charge estimation algorithm for a Li-Ion battery equivalent circuit model in EV applications were proposed.
Proceedings ArticleDOI
On the analytic accuracy of battery SOC, capacity and resistance estimation
TL;DR: The Cramer-Rao bounds for battery state of charge, capacity and resistance estimation are derived and it is found that for current inputs that satisfy certain patterns, loss of accuracy in combined estimation can be avoided.
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.