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
A Variational Bayesian and Huber-Based Robust Square Root Cubature Kalman Filter for Lithium-Ion Battery State of Charge Estimation
TL;DR: In this article, an adaptive and robust square root cubature Kalman filter based on variational Bayesian approximation and Huber's M-estimation (VB-HASRCKF) is proposed.
Proceedings ArticleDOI
UAS Operators Safety and Reliability Survey: Emerging Technologies towards the Certification of Autonomous UAS
Matthew Osborne,Jennifer Lantair,Zain Shafiq,Xingyu Zhao,Valentin Robu,David Flynn,John Perry +6 more
TL;DR: The integration of emerging technologies and methods as candidate solutions to the respondents reported challenges, such as; Integrated Vehicle Health Management, Formal Methods, Simulations and Fault Tolerant Control, are identified.
Journal ArticleDOI
Design and management of lithium-ion batteries: A perspective from modeling, simulation, and optimization
Book ChapterDOI
Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management
Xingyu Zhao,Matthew Osborne,Jennifer Lantair,Valentin Robu,David Flynn,Xiaowei Huang,Michael J. Fisher,Fabio Papacchini,Angelo Ferrando +8 more
TL;DR: In this paper, the authors model an unmanned aerial vehicle (UAV) inspection mission on a wind farm and via probabilistic model checking in PRISM show how the battery features may affect the verification results significantly in practical cases.
Proceedings ArticleDOI
Simultaneous State and Parameter Estimation of Li-Ion Battery With One State Hysteresis Model Using Augmented Unscented Kalman Filter
TL;DR: In this article, an online technique for simultaneous state and parameter estimation of an electric or hybrid vehicle battery using an augmented unscented Kalman filter (AUKF) is proposed.
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.