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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Proceedings ArticleDOI
Predictive Model Based Battery Constraints for Electric Motor Control within EV Powertrains
TL;DR: In this paper, a method of predicting the maximum power capability of a Li-Ion battery, to be used for electric motor control within automotive powertrains, is presented, which consists of a pack level state observer coupled with a predictive battery model.
Journal ArticleDOI
Vehicle Running State Estimation by Adaptive Soft-Sensing Algorithm
TL;DR: Under the typical working condition, AUKF soft-sensing algorithm has a good performance in robustness and is able to realize the effective estimation of vehicle running state more precisely than UKFSoft-Sensing algorithm.
Journal ArticleDOI
Design of sliding observers for Lipschitz nonlinear system using a new time-averaged Lyapunov function
TL;DR: The paper compares the LMIs for the two observer designs to demonstrate the design of the sliding mode observer using TAL can greatly enhance the scope of observer design for nonlinear systems.
Dissertation
Ensemble Kalman filter design for battery management systems - State and parameter estimation in li-ion battery cells
TL;DR: A novel model-based statistical filtering approach to the SOC estimation problem, where an Ensemble Kalman Filter is implemented to jointly estimate inner battery states and parameters and suggests that the accuracy of the EnKF is similar to that of the PF, but with a lower computational cost, and overall more accurate than the UKF.
Journal ArticleDOI
Thermal management for high-power photonic crystal light emitting diodes
TL;DR: The results show that the integrated structure can obtain a significant improvement in thermal management and achieve a reduction in temperature in the working status of CSM360.
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.