Journal ArticleDOI
State of charge estimation for electric vehicle batteries using unscented kalman filtering
TLDR
A model to simulate battery terminal voltage as a function of state of charge under dynamic loading conditions is developed, tailored on-line in order to estimate uncertainty arising from unit-to-unit variations and loading condition changes.About:
This article is published in Microelectronics Reliability.The article was published on 2013-06-01. It has received 295 citations till now. The article focuses on the topics: State of charge & Electric vehicle.read more
Citations
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Proceedings ArticleDOI
Prognostics of Lithium ion battery using functional principal component analysis
Jian Guo,Zhaojun Li +1 more
TL;DR: This paper proposes a method for prognostics of lithium ion batteries based on the functional principal component analysis, which represents and modeled through eigenfunctions, which are further approximated and estimated using a combination of B-Splines.
Proceedings ArticleDOI
An optimal nonlinear observer for state-of-charge estimation of lithium-ion batteries
TL;DR: Experimental results indicate that the proposed ONLO can accurately estimate the battery SOC with a mean absolute error of 1.8% and a maximum error of less than 6.5%, which are both lower than that of the unscented Kalman filter (UKF).
Journal ArticleDOI
A Critical Study of Using the Peukert Equation and Its Generalizations for Determining the Remaining Capacity of Lithium-Ion Batteries
TL;DR: In this article, the classic Peukert equation is applied in two ranges of discharge currents, the first range isis the battery released capacity and the second range is from the inflection point of experimental curve to the highest currents used in the experiments.
Journal Article
eXogenous Kalman Filter for State-of-Charge Estimation in Lithium-ion Batteries
TL;DR: This paper presents State-of-Charge (SoC) estimation of lithium-ion batteries using eXogenous Kalman filter (XKF), which has linear process equations and a nonlinear output voltage equation.
Journal ArticleDOI
State-of-charge Estimation for Lithium-ion Batteries Using a Multi-state Closed-loop Observer
TL;DR: In this article, a model-based estimation method that applies closed-loop state observer theory and a comprehensive battery model is proposed to predict the open-circuit voltage (OCV) of a battery based on the battery state-space model.
References
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Proceedings ArticleDOI
The unscented Kalman filter for nonlinear estimation
Eric A. Wan,R. van der Merwe +1 more
TL;DR: The unscented Kalman filter (UKF) as discussed by the authors was proposed by Julier and Uhlman (1997) for nonlinear control problems, including nonlinear system identification, training of neural networks, and dual estimation.
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 3. State and parameter estimation
TL;DR: 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.
Journal ArticleDOI
Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries
TL;DR: In this paper, a smart estimation method based on coulomb counting is proposed to improve the estimation accuracy for state-of-charge (SOC) estimation of lithium-ion batteries with high charging and discharging efficiencies.
Journal ArticleDOI
State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model
TL;DR: An adaptive Kalman filter algorithm that can greatly improve the dependence of the traditional filter algorithm on the battery model is employed and is evaluated by experiments with federal urban driving schedules, showing that the proposed SOC estimation using AEKF is more accurate and reliable than that using EKF.