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
Robust Adaptive Sliding-Mode Observer Using RBF Neural Network for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles
TL;DR: A robust sliding-mode observer (RSMO) for state-of-charge (SOC) estimation of a lithium-polymer battery (LiPB) in electric vehicles (EVs) is presented and a radial basis function (RBF) neural network is employed to adaptively learn an upper bound of system uncertainty.
Journal ArticleDOI
Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends
TL;DR: A multilayer design architecture for advanced battery management, which consists of three progressive layers, which aims at providing a comprehensive understanding of battery, and the application layer ensures a safe and efficient battery system through sufficient management.
Journal ArticleDOI
Comparative Research on RC Equivalent Circuit Models for Lithium-Ion Batteries of Electric Vehicles
TL;DR: In this article, the authors compared the performance of the first-order RC model and second-order resistor-capacitor (RC) model in real-time battery management systems.
Journal ArticleDOI
Review of the Remaining Useful Life Prognostics of Vehicle Lithium-Ion Batteries Using Data-Driven Methodologies
Lifeng Wu,Xiaohui Fu,Yong Guan +2 more
TL;DR: In this article, the authors analyzed the problems of vehicle lithium-ion batteries in practical applications and identified the problems that need to be solved in the future to ensure the safety, stability, and long lifetime of electric vehicles.
Journal ArticleDOI
Bearing Degradation Evaluation Using Recurrence Quantification Analysis and Kalman Filter
TL;DR: An integrated approach, which combines recurrence quantification analysis (RQA) with the Kalman filter, for bearing degradation evaluation is presented, which can predict occurrence of the bearing failure 50 min in advance.
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.