scholarly journals Universal Adaptive Stabilizer Based Optimization for Li-Ion Battery Model Parameters Estimation: An Experimental Study

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 49546-49562 ◽  
Author(s):  
Hafiz M. Usman ◽  
Shayok Mukhopadhyay ◽  
Habibur Rehman
Keyword(s):  
Experimental Study ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Li Ion Battery ◽  
Battery Model ◽  
Li Ion ◽  
Adaptive Stabilizer

UAS based Li-ion battery model parameters estimation

2017 ◽  
Vol 66 ◽  
pp. 126-145 ◽  
Author(s):  
D. Ali ◽  
S. Mukhopadhyay ◽  
H. Rehman ◽  
A. Khurram
Keyword(s):  
Parameters Estimation ◽  
Model Parameters ◽  
Li Ion Battery ◽  
Battery Model ◽  
Li Ion

A GPU-accelerated parallel Jaya algorithm for efficiently estimating Li-ion battery model parameters

2018 ◽  
Vol 65 ◽  
pp. 12-20 ◽  
Author(s):  
Long Wang ◽  
Zijun Zhang ◽  
Chao Huang ◽  
Kwok Leung Tsui
Keyword(s):  
Model Parameters ◽  
Jaya Algorithm ◽  
Li Ion Battery ◽  
Battery Model ◽  
Li Ion

A Separated-Frequency Parameters Identification Method of Li-Ion Battery Model for Electric Vehicles

2014 ◽  
Vol 672-674 ◽  
pp. 727-730 ◽  
Author(s):  
Da Zhong Mu ◽  
Zhi Hong Dong ◽  
Ming Fei Wang
Keyword(s):  
Electric Vehicles ◽  
Main Idea ◽  
Open Circuit ◽  
Current Data ◽  
Model Parameters ◽  
Wavelet Domain ◽  
Li Ion Battery ◽  
Battery Model ◽  
Identification Method ◽  
Li Ion

This paper proposed a separated-frequency identification method of Li-ion battery model for Electric Vehicles (EVs). The main idea is to decompose the measured terminal voltage and current data in wavelet domain, and then the weighting least squares (LS) algorithm is used to extract the model parameters. Since the signal energy of open circuit voltage (OCV) mainly distributes in the low frequency band, the identifiable wavelet-domain battery model can be approximately obtained by neglecting the high frequency wavelet decomposition coefficients. Furthermore, based on the Akaike’s information criterion, we study the optimum decomposition order of the wavelet-domain battery model.

Identification of Parameters in Li-Ion Battery Model by Least Squares Method with Variable Forgetting Factor

2019 ◽  
Vol 17 (07) ◽  
pp. 1950027
Author(s):  
Xiong Wei ◽  
Mo Yimin ◽  
Zhang Feng
Keyword(s):  
Least Squares ◽  
Electric Vehicle ◽  
Least Squares Method ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Li Ion Battery ◽  
Forgetting Factor ◽  
Battery Model ◽  
Li Ion ◽  
Variable Forgetting Factor

The inaccuracy of the battery model of an electric vehicle will seriously affect the safe operation of the electric vehicle. This paper aims to design a better identification method for Li-ion battery model parameters to improve the accuracy of the model. A least squares method was developed with variable forgetting factor (VFF) to identify the parameters of a second-order resistor-recapacitor (RC) model of Li-ion battery. After using the identified parameters, the battery model can reliably and accurately track the variability of the actual working state of the energy storage system. Results at different values of the forgetting factor were analyzed to determine the principle for selecting the value of the forgetting factor, and disclose the impacts of the factor values on model accuracy. Finally, the proposed identification algorithm was tested through comparison between results of the model simulation and experimental data. This method provides an important basis for subsequent development of accurate state-of-charge (SOC) and state-of-health (SOH) estimation algorithms.

Wavelet-Based Identification Method of Li-Ion Battery Model for Electric Vehicles

2012 ◽  
Vol 608-609 ◽  
pp. 1529-1532
Author(s):  
Da Zhong Mu ◽  
Jiu Chun Jiang
Keyword(s):  
Electric Vehicles ◽  
Noise Removal ◽  
Recursive Least Squares ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Battery Management System ◽  
Li Ion Battery ◽  
Battery Model ◽  
Identification Method ◽  
Li Ion

Online parameters identification is one of the major functions of model-based battery management system (BMS), which can be used to monitor the working status of battery, such as state of charge (SOC) and state of health (SOH). This paper proposed a wavelet-based identification method of Li-ion battery model for Electric Vehicles (EVs). The main idea is to decompose the measured terminal voltage and current data at multiple scales, and then recursive least squares (RLS) algorithm is used to extract the model parameters at a suitable scale. The proposed method is shown to have good robustness to measured noise and thus enhances the estimation accuracy by taking advantage of the noise removal ability and signal approximation properties of wavelet decomposition.

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