An improved adaptive unscented kalman filtering for state of charge online estimation of lithium-ion battery

Shuzhi Zhang; Xu Guo; Xiongwen Zhang

doi:10.1016/j.est.2020.101980

ScienceGate Book Chapters

JOURNAL ARTICLE

An improved adaptive unscented kalman filtering for state of charge online estimation of lithium-ion battery

Shuzhi Zhang Xu Guo Xiongwen Zhang

Year: 2020 Journal: Journal of Energy Storage Vol: 32 Pages: 101980-101980 Publisher: Elsevier BV

DOI: 10.1016/j.est.2020.101980

Get Full-Text PDF Get Analytical Report

Keywords:

Cholesky decomposition Robustness (evolution) State of charge Covariance matrix Computer science Kalman filter Covariance Singular value decomposition Algorithm Extended Kalman filter Control theory (sociology) Battery (electricity) Mathematics Eigenvalues and eigenvectors Artificial intelligence Power (physics) Statistics

Metrics

130

Cited By

8.66

FWCI (Field Weighted Citation Impact)

Refs

0.98

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Advanced Battery Technologies Research

Physical Sciences → Engineering → Automotive Engineering

Electric Vehicles and Infrastructure

Physical Sciences → Engineering → Electrical and Electronic Engineering

Control Systems and Identification

Physical Sciences → Engineering → Control and Systems Engineering

An improved adaptive unscented kalman filtering for state of charge online estimation of lithium-ion battery

Metrics

Citation History

Topics

Related Documents

Lithium battery charge state estimation based on improved Unscented Kalman filtering

Lithium battery charge state estimation based on improved Unscented Kalman filtering

Unscented Kalman Filtering Approach for Online Estimation of State-of-Charge of Lithium-Ion Battery

State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter

State-of-Charge (SOC) Estimation of Lithium-Ion Battery Based on Unscented Kalman Filtering