scholarly journals Parameter identification strategy for online detection of faults in smart structures for energy harvesting and sensing

2020 ◽  
Vol 28 ◽  
pp. 2104-2109
Author(s):  
Claudio Maruccio ◽  
Pasquale Montegiglio ◽  
Adnan Kefal
Keyword(s):  
Energy Harvesting ◽  
Parameter Identification ◽  
Smart Structures ◽  
Online Detection ◽  
Identification Strategy

Optimal parameter identification strategy applied to lithium‐ion battery model

2021 ◽  
Author(s):  
Seydali Ferahtia ◽  
Ali Djeroui ◽  
Hegazy Rezk ◽  
Aissa Chouder ◽  
Azeddine Houari ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Lithium Ion Battery ◽  
Optimal Parameter ◽  
Lithium Ion ◽  
Battery Model ◽  
Identification Strategy

scholarly journals Novel Application of Fast Simulated Annealing Method in Brushless Motor Drive (BLMD) Dynamical Parameter Identification for Electric Vehicle Propulsion

2021 ◽  
Author(s):  
Richard A. Guinee
Keyword(s):  
Simulated Annealing ◽  
Parameter Identification ◽  
Electric Vehicle ◽  
Weight Ratio ◽  
Torque Control ◽  
Function Evaluation ◽  
Dynamical Parameter ◽  
Penalty Cost ◽  
Brushless Motor ◽  
Identification Strategy

Permanent magnet brushless motor drives (BLMD) are extensively used in electric vehicle (EV) propulsion systems because of their high power and torque to weight ratio, virtually maintenance free operation with precision control of torque, speed and position. An accurate dynamical parameter identification strategy is an essential feature in the adaptive control of such BLMD-EV systems where sensorless current feedback is employed for reliable torque control, with multi-modal penalty cost surfaces, in EV high performance tracking and target ranging. Application of the classical Powell Conjugate Direction optimization method is first discussed and its inaccuracy in dynamical parameter identification is illustrated for multimodal cost surfaces. This is used for comparison with the more accurate Fast Simulated Annealing/Diffusion (FSD) method, presented here, in terms of the returned parameter estimates. Details of the FSD development and application to the BLMD parameter estimation problem based on the minimum quantized parameter step sizes from noise considerations are provided. The accuracy of global parameter convergence estimates returned, cost function evaluation and the algorithm run time are presented. Validation of the FSD identification strategy is provided by excellent correlation of BLMD model simulation trace coherence with experimental test data at the optimal estimates and from cost surface simulation.

A new electrochemical impedance spectroscopy model of a high-power lithium-ion battery

RSC Advances ◽  
2014 ◽  
Vol 4 (57) ◽  
pp. 29988-29998 ◽  
Author(s):  
J. G. Zhu ◽  
Z. C. Sun ◽  
X. Z. Wei ◽  
H. F. Dai
Keyword(s):  
Electrochemical Impedance Spectroscopy ◽  
Impedance Spectroscopy ◽  
Parameter Identification ◽  
Lithium Ion Battery ◽  
High Power ◽  
Electrochemical Impedance ◽  
Lithium Ion ◽  
Identification Strategy

A new electrochemical impedance spectroscopy model and a preliminary parameter identification strategy are proposed in this paper.

Parameter Identification of a Damage Model for the Lifetime Prediction of Adhesively Bonded Joints

2015 ◽  
Vol 784 ◽  
pp. 300-307 ◽  
Author(s):  
Ulrich Kroll ◽  
Anton Matzenmiller
Keyword(s):  
Parameter Identification ◽  
Model Prediction ◽  
Damage Model ◽  
Fatigue Tests ◽  
Model Parameters ◽  
Bonded Joints ◽  
Adhesively Bonded ◽  
Adhesively Bonded Joints ◽  
Creep Fatigue ◽  
Identification Strategy

A damage model for thin adhesively bonded joints is presented, which predicts the time to creep-fatigue failure of the joint subjected to combined static and cyclic sustained loadings with constant or variable amplitudes. The influences of particular model parameters on the predicted lifetime are elaborated suggesting the proposed stepwise parameter identification strategy by means of creep and Wöhler fatigue tests until rupture. The parameters are identified and computationally optimized. As a conclusion, the model prediction is verified and validated.

scholarly journals Modelling and parameter identification of electromechanical systems for energy harvesting and sensing

2019 ◽  
Vol 121 ◽  
pp. 890-912 ◽  
Author(s):  
Adnan Kefal ◽  
Claudio Maruccio ◽  
Giuseppe Quaranta ◽  
Erkan Oterkus
Keyword(s):  
Energy Harvesting ◽  
Parameter Identification ◽  
Electromechanical Systems

Predictive Modeling for Rollover Warning of Heavy Vehicles

2002 ◽  
Author(s):  
Dongyoon Hyun ◽  
Reza Langari
Keyword(s):  
Parameter Identification ◽  
Predictive Model ◽  
Degrees Of Freedom ◽  
Load Transfer ◽  
Practical Implementation ◽  
Identification Algorithm ◽  
Heavy Vehicles ◽  
Vehicle Simulation ◽  
Threat Index ◽  
Identification Strategy

A predictive model to determine the rollover threat of heavy vehicles is proposed. The purpose of this model is to predict the rollover threat sufficiently in advance of the actual event so as to enable the driver to react accordingly. The Load Transfer Ratio (LTR) is used as a rollover threat index in this model. In order to facilitate a practical implementation of the proposed approach, a predictive model is established using a simple roll-plane model of the vehicle in conjunction with an online parameter identification strategy. This is to address the inherent issues involved in assessing certain key parameters in the proposed predictive model. The proposed predictive model and the associated parameter-identification algorithm are verified using a 14 degrees-of-freedom vehicle simulation model previously developed as a part of a broader study on heavy vehicles undertaken at Texas A&M University and supported by Texas Transportation Institute (TTI).

Battery parameter identification strategy based on modified coot optimization algorithm

2022 ◽  
Vol 46 ◽  
pp. 103848
Author(s):  
Essam H. Houssein ◽  
Fatma A. Hashim ◽  
Seydali Ferahtia ◽  
Hegazy Rezk
Keyword(s):  
Parameter Identification ◽  
Optimization Algorithm ◽  
Identification Strategy
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