Optimal sensor placement and model parameter identification for distributed- parameter structures

2014 ◽  
pp. 1051-1056
Author(s):  
T Yin
Keyword(s):  
Parameter Identification ◽  
Sensor Placement ◽  
Distributed Parameter ◽  
Optimal Sensor Placement ◽  
Model Parameter ◽  
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.

scholarly journals Model-Based Condition Monitoring of a Vanadium Redox Flow Battery

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 3005 ◽  
Author(s):  
Meng ◽  
Xiong ◽  
Lim
Keyword(s):  
Parameter Identification ◽  
Real Time ◽  
Vanadium Redox Flow Battery ◽  
Redox Flow Battery ◽  
Flow Battery ◽  
Model Parameter ◽  
Model Based ◽  
Model Parameter Identification ◽  
Vanadium Redox ◽  
Capacity Decay

The safe, efficient and durable utilization of a vanadium redox flow battery (VRB) requires accurate monitoring of its state of charge (SOC) and capacity decay. This paper focuses on the unbiased model parameter identification and model-based monitoring of both the SOC and capacity decay of a VRB. Specifically, a first-order resistor-capacitance (RC) model was used to simulate the dynamics of the VRB. A recursive total least squares (RTLS) method was exploited to attenuate the impact of external disturbances and accurately track the change of model parameters in realtime. The RTLS-based identification method was further integrated with an H-infinity filter (HIF)-based state estimator to monitor the SOC and capacity decay of the VRB in real-time. Experiments were carried out to validate the proposed method. The results suggested that the proposed method can achieve unbiased model parameter identification when unexpected noises corrupt the current and voltage measurements. SOC and capacity decay can also be estimated accurately in real-time without requiring additional open-circuit cells.

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