Journal ArticleDOI
Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles
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TLDR
In this article, an adaptive unscented Kalman filtering method was proposed to estimate the state of charge of a lithium-ion battery for battery electric vehicles, where the adaptive adjustment of the noise covariances in the state-of-charge estimation process was implemented by an idea of covariance matching.About:
This article is published in Energy.The article was published on 2011-05-01. It has received 476 citations till now. The article focuses on the topics: Extended Kalman filter & Unscented transform.read more
Citations
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Journal ArticleDOI
A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations
TL;DR: In this article, a comprehensive review of the battery state of charge estimation and its management system for the sustainable future electric vehicles (EVs) applications is presented, which can guarantee a reliable and safe operation and assess the battery SOC.
Journal ArticleDOI
Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles
TL;DR: In this paper, the methods for monitoring the battery state of charge, capacity, impedance parameters, available power, state of health, and remaining useful life are reviewed with the focus on elaboration of their strengths and weaknesses for the use in on-line BMS applications.
Journal ArticleDOI
State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures
TL;DR: In this article, the unscented Kalman filtering (UKF) was applied to tune the model parameters at each sampling step to cope with various uncertainties arising from the operation environment, cell-to-cell variation, and modeling inaccuracy.
Journal ArticleDOI
Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles
TL;DR: The review presents the key feedback factors that are indispensable for accurate estimation of battery SoC, and presents the possible recommendations for the development of next generation of smart SoC estimation and battery management systems for electric vehicles and battery energy storage system.
Journal ArticleDOI
Combined State of Charge and State of Health estimation over lithium-ion battery cell cycle lifespan for electric vehicles
TL;DR: In this paper, a combined state of charge (SOC) and SOH (State Of Health) estimation method over the lifespan of a lithium-ion battery is proposed, where the SOH is estimated in real-time and the capacity and internal ohmic resistance are updated offline.
References
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Proceedings ArticleDOI
New extension of the Kalman filter to nonlinear systems
Simon Julier,Jeffrey Uhlmann +1 more
TL;DR: It is argued that the ease of implementation and more accurate estimation features of the new filter recommend its use over the EKF in virtually all applications.
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 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
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 1. Background
TL;DR: In this paper, an extended Kalman filter (EKF) was proposed to estimate the battery state of charge, power fade, capacity fade, and instantaneous available power of a hybrid-electric-vehicle battery pack.
Sigma-point kalman filters for probabilistic inference in dynamic state-space models
TL;DR: This work has consistently shown that there are large performance benefits to be gained by applying Sigma-Point Kalman filters to areas where EKFs have been used as the de facto standard in the past, as well as in new areas where the use of the EKF is impossible.