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:Â
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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Journal ArticleDOI
A data-based model for driving distance estimation of battery electric logistics vehicles
TL;DR: A practical and effective data-based modeling method, regression analysis, is used to establish the data- based model of driving distance estimation of battery electric logistics vehicles, and verification results confirm the feasibility of the model and show that the model errors are small.
Journal ArticleDOI
A Method for Interval Prediction of Satellite Battery State of Health Based on Sample Entropy
TL;DR: A method for interval prediction of the satellite battery state of health (SOH) based on SampEn was proposed, which adopts a neural network model based on lower upper bound estimation (LUBE) and a modified comprehensive indicator function, the coverage width-based criterion (CWC), was constructed.
Journal ArticleDOI
A skewed unscented Kalman filter
Javad Rezaie,Jo Eidsvik +1 more
TL;DR: The traditional unscented Kalman filter is modified to capture the third-order moment (skewness) of the state vector, and results confirm that the method is better than, or at least as good as, the unscenting Kalman filters.
Journal ArticleDOI
Maximum pulse current estimation for high accuracy power capability prediction of a Li-Ion battery
TL;DR: It can be certainly mentioned that this work sufficiently provides an outstanding solution related to the available maximum pulse current estimation of a Li-Ion battery to be operated within the safety discharging/charging range.
Journal ArticleDOI
Online Parameter Identification of Lithium-Ion Batteries Using a Novel Multiple Forgetting Factor Recursive Least Square Algorithm
Bizhong Xia,Rui Huang,Zizhou Lao,Ruifeng Zhang,Yongzhi Lai,Weiwei Zheng,Huawen Wang,Wei Wang,Mingwang Wang +8 more
TL;DR: In this paper, a multiple forgetting factor recursive least square (MFFRLS) algorithm was proposed to estimate the state of charge (SOC), state of health (SOH), and other factors of the lithium-ion battery.
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.