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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Posted Content
eXogenous Kalman Filter for Lithium-Ion Batteries State-of-Charge Estimation in Electric Vehicles
TL;DR: In this paper, a two-stage nonlinear estimator called the eXogenous Kalman filter (XKF) is proposed for state-of-charge estimation of rechargeable batteries in electric vehicles.
Journal ArticleDOI
Scientometric research and critical analysis of battery state-of-charge estimation
TL;DR: In this article , the authors used an advanced search method to analyse the literature in the field of battery SoC estimation from 2004 to 2020 in the Web of Science (WoS) database.
Journal ArticleDOI
State of charge monitoring of Li-ion batteries for electric vehicles using GP filtering
TL;DR: Results show that Bayes’ filtering techniques increase the predictability of the SOC under uncertainty about the effect of environmental conditions on the SOC.
Journal ArticleDOI
State estimation of lithium polymer battery based on Kalman filter
TL;DR: In this article, an improved adaptive unscented Kalman filter is created to adjust the fixed window in the adaptive algorithm, which can promote the accuracy of state of charge (SOC) estimation.
Journal ArticleDOI
State of charge and state of power estimation for power battery in HEV based on optimized particle filtering
Xiaoyan Niu,Guosheng Feng +1 more
TL;DR: An improved particle filter algorithm based on particle swarm optimization (PSO) is proposed, aiming at the uncertainty of system noise in traditional particle filter (PF) algorithm, which has higher accuracy and is applicable to the dynamic estimation of the actual driving cycles of hybrid electric vehicles.
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.