Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach

By Rui Xiong, Hongwen He, Fengchun Sun, Kai Zhao. IEEE Transactions on Vehicular Technology, 2013

414 citations recorded by the source corpus.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/40db6cb467a21633e18508f380d2b4d6c2b230ff

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Citations in the corpus

State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H∞ Method (2017, methodology): “To overcome the drawback of the ordinary and extended KF requiring prior knowledge of model and measurement noise covariances, the adaptive KF is applied to improve the convergence and robustness [2,15].”
Design Methodology of LLC Resonant Converters for Electric Vehicle Battery Chargers (2014, neutral): “2 [26].”
State-of-the-Art and Energy Management System of Lithium-Ion Batteries in Electric Vehicle Applications: Issues and Recommendations (2018, methodology): “The SOH indicates how much the battery has been abused and if its performance has been degraded [87], [90].”
Industrial Applications of the Kalman Filter: A Review (2013, methodology): “To improve the performance of the KF for SoC estimation, an adaptive EKF can be designed, so as to estimate the covariance matrice [82].”
Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries (2019, methodology): “Table 2 Description of different EECM models [33,39,69,136,137,150,151].”
Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles (2018, background): “also can ensure an acceptable accuracy for the LiMn2O4 lithium battery [63].”
Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine (2016, methodology): “The absolute error ea in the literature [10], [17] is confined to ±2% error band and ±3% in [11] and [16].”
Estimation of State of Charge, Unknown Nonlinearities, and State of Health of a Lithium-Ion Battery Based on a Comprehensive Unobservable Model (2014, methodology): “EKF is another popular statistical tool to extract internal battery parameters such as SoC [8]–[10].”