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
Parameters identification of Thevenin model for lithium-ion batteries using self-adaptive Particle Swarm Optimization Differential Evolution algorithm to estimate state of charge
TL;DR: In this article, a self-adaptive particle swarm optimization differential evolution (SaPSODE) algorithm was proposed to identify the parameters of lithium-ion battery better, which is closely related to parameters of battery and system non-linearity.
Proceedings ArticleDOI
Online battery state-of-charge estimation based on sparse gaussian process regression
TL;DR: In this article, a new online method for state-of-charge (SoC) estimation of Li-ion batteries based on sparse Gaussian process regression (GPR) is presented.
Journal ArticleDOI
Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation
TL;DR: The equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method to prove the importance of considering capacity degradation in SOC estimation.
Journal ArticleDOI
A hybrid approach for UAV flight data estimation and prediction based on flight mode recognition
TL;DR: This paper proposes a hybrid approach of Gaussian Process-Unscented Kalman Filter (GP-UKF) based on Flight Mode Recognition (FMR) for UAV based on non-linearity, uncertainty, and noise involved in flight data.
Journal ArticleDOI
Remaining Discharge Time Prognostics of Lithium-Ion Batteries Using Dirichlet Process Mixture Model and Particle Filtering Method
TL;DR: A new approach using Dirichlet process mixture model (DPMM) and particle filtering method to predict remaining discharge time (RDT) of ongoing discharge processes of lithium-ion batteries is proposed and demonstrates that this approach improves accuracy of RDT prediction compared with benchmark PF-based method.
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.