A Brief Review of Battery Model Parameter Identification Methods

2021 ◽  
Author(s):  
Ivan Lopez-Granados ◽  
Jose M. Sosa ◽  
Gerardo Vazquez ◽  
Adolfo R. Lopez ◽  
Diego Langarica
Keyword(s):  
Parameter Identification ◽  
Identification Methods ◽  
Model Parameter ◽  
Battery Model ◽  
Model Parameter Identification

Real-Time Battery Model Identification Using a Two Time-Scaled Approach

2013 ◽  
Author(s):  
Yiran Hu ◽  
Yue-Yun Wang
Keyword(s):  
Parameter Identification ◽  
Least Squares ◽  
State Estimation ◽  
Real Time ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Model Parameter ◽  
Battery Model ◽  
Model Parameter Identification

Battery state estimation (BSE) is one of the most important design aspects of an electrified propulsion system. It includes important functions such as state-of-charge estimation which is essentially for the energy management system. A successful and practical approach to battery state estimation is via real time battery model parameter identification. In this approach, a low-order control-oriented model is used to approximate the battery dynamics. Then a recursive least squares is used to identify the model parameters in real time. Despite its good properties, this approach can fail to identify the optimal model parameters if the underlying system contains time constants that are very far apart in terms of time-scale. Unfortunately this is the case for typical lithium-ion batteries especially at lower temperatures. In this paper, a modified battery model parameter identification method is proposed where the slower and faster battery dynamics are identified separately. The battery impedance information is used to guide how to separate the slower and faster dynamics, though not used specifically in the identification algorithm. This modified algorithm is still based on least squares and can be implemented in real time using recursive least squares. Laboratory data is used to demonstrate the validity of this method.

Active Excitation for Battery Model Parameter Identification

2014 ◽  
Author(s):  
Yonghua Li ◽  
Hai Yu
Keyword(s):  
Parameter Identification ◽  
Real World ◽  
Input Signal ◽  
Identification Accuracy ◽  
Model Parameters ◽  
Model Parameter ◽  
Battery Model ◽  
Battery Power ◽  
Reference Parameter ◽  
Model Parameter Identification

In this paper an active excitation approach to battery model parameter identification is discussed. Based on begin-of-life battery model, it is possible to establish a reference parameter table (either fixed, or adaptively learned), and based on such reference parameter table, as well as by analysing battery input signal, active excitation request may be generated. Active excitation is achieved based on maintaining overall torque level with regard to drive input, while adjusting both engine and battery power output (and input). Both conditions for active excitation request, as well as active excitation generation approaches, are presented in detail. Simulation examples using production electrified vehicle battery model parameters and real world drive cycles demonstrate that the proposed approach indeed improves battery model parameter identification accuracy.

Novel supercapacitor model parameter identification methods

2017 ◽  
Author(s):  
Xiao Chang ◽  
Da Lei ◽  
Shifeng Zhang ◽  
Shengwen Li ◽  
Yunlei Yang
Keyword(s):  
Parameter Identification ◽  
Identification Methods ◽  
Model Parameter ◽  
Model Parameter Identification

scholarly journals Constitutive model parameter identification via full-field calibration

2019 ◽  
Vol 651 ◽  
pp. 012070
Author(s):  
C Ilg ◽  
K Witowski ◽  
D Koch ◽  
P Roehl Suanno ◽  
A Haufe
Keyword(s):  
Constitutive Model ◽  
Parameter Identification ◽  
Full Field ◽  
Model Parameter ◽  
Field Calibration ◽  
Model Parameter Identification

Particle swarm optimization based load model parameter identification

2010 ◽  
Author(s):  
Young-Gon Kim ◽  
Hwachang Song ◽  
Hong Rae Kim ◽  
Byongjun Lee
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Model Parameter ◽  
Load Model ◽  
Model Parameter Identification

Carbon Brake Friction Model Parameter Identification Using Genetic Algorithms

1995 ◽  
Author(s):  
Roger C. von Doenhoff ◽  
Robert J. Streifel ◽  
Robert J. Marks
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Surface Temperature ◽  
Parameter Identification ◽  
Friction Model ◽  
Carbon Surface ◽  
Model Parameters ◽  
Model Parameter ◽  
Brake Pressure ◽  
Model Parameter Identification

Abstract A model of the friction characteristics of carbon brakes is proposed to aid in the understanding of the causes of brake vibration. The model parameters are determined by a genetic algorithm in an attempt to identify differences in friction properties between brake applications during which vibration occurs and those during which there is no vibration. The model computes the brake torque as a function of wheelspeed, brake pressure, and the carbon surface temperature. The surface temperature is computed using a five node temperature model. The genetic algorithm chooses the model parameters to minimize the error between the model output and the torque measured during a dynamometer test. The basics of genetic algorithms and results of the model parameter identification process are presented.

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