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Journal ArticleDOI

Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation

Gregory L. Plett
- 12 Aug 2004 - 
- Vol. 134, Iss: 2, pp 277-292
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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.

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Citations
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Journal ArticleDOI

A new method of modeling and state of charge estimation of the battery

TL;DR: In this article, a fractional order model based on the PNGV (Partnership for a New Generation of Vehicle) model is proposed after analyzing the impedance characteristics of the lithium battery and compared with the integer order model.
Journal ArticleDOI

Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery

TL;DR: In this article, the state estimation and capacity prediction methods are coupled to improve the estimation accuracy at different temperatures among the lifetime of battery, considering the influence of temperature and degradation, the data-driven algorithm namely least squares support vector machine is implemented to predict the available capacity.
Proceedings ArticleDOI

Li-ion battery parameter estimation for state of charge

TL;DR: In this paper, an onboard adaptive algorithm is developed to estimate six electrical parameters for Li-ion batteries and provide a reliable battery state of charge (SOC) based on one of the estimated battery parameters, i.e., open circuit voltage (OCV).
Journal ArticleDOI

Model-based Dynamic Power Assessment of Lithium-Ion Batteries Considering Different Operating Conditions

TL;DR: This paper is concerned with model-based dynamic peak-power evaluation for LiNMC and LiFePO4 batteries under different operating conditions and the robustness of the peak- power estimation approach against varying battery temperatures and aging levels is investigated.
Journal ArticleDOI

State of charge estimation of a lithium ion cell based on a temperature dependent and electrolyte enhanced single particle model

TL;DR: The accuracy of model-based state of charge estimation depends on the accuracy of the underlying model, including temperature effects that greatly influence cell dynamics as discussed by the authors, which is a critical information to system engineers and end users of consumer electronics to electric vehicles.
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

Simon Haykin
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.
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