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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Journal ArticleDOI
Passive Tracking of the Electrochemical Impedance of a Hybrid Electric Vehicle Battery and State of Charge Estimation through an Extended and Unscented Kalman Filter
TL;DR: In this paper, a passive method for battery impedance estimation in the time domain that involves the voltage and current profile induced by the battery through its ordinary operation without injecting a small excitation signal was presented.
Journal ArticleDOI
A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles
TL;DR: In this article, a nonlinear model for the battery cell and its observer can estimate the state of charge without the linearization technique commonly adopted by previous studies, and the proposed method has the following advantages: (1) the observability condition of the proposed nonlinear-model-based observer is derived regardless of the shape of the open circuit voltage curve, and (2) because the terminal voltage is contained in the state vector, the proposed model and observer are insensitive to sensor noise.
Proceedings ArticleDOI
A comparative study of state-of-charge estimation algorithms for lithium-ion batteries in wireless charging electric vehicles
TL;DR: In this paper, four model-based estimation algorithms, namely extended Kalman filter, unscented Kalman filters, sliding mode observer and nonlinear observer (NLO), are compared in terms of prediction accuracy, tracking ability to initial state of charge (SOC) error, and computation complexity.
Journal ArticleDOI
Impacts of power management on a PEMFC electric vehicle
TL;DR: Two power trains are deemed effective in providing continuous power for driving the scooter and the serial power train, although it uses an extra battery set, is shown to be more efficient than the parallel one.
Journal ArticleDOI
State of Charge Estimation for Lithium-Ion Battery Based on Improved Cubature Kalman Filter Algorithm
TL;DR: In this paper, an improved cubature Kalman filter (CKF) algorithm for estimating the state of charge of lithium-ion batteries is proposed, which implements the diagonalization decomposition of the covariance matrix and a strong tracking filter.
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.