The Variation of Oil Effective Bulk Modulus With Pressure in Hydraulic Systems

1994 ◽  
Vol 116 (1) ◽  
pp. 146-150 ◽  
Author(s):  
Yu Jinghong ◽  
Chen Zhaoneng ◽  
Lu Yuanzhang
Keyword(s):  
Experimental Investigation ◽  
Parameter Identification ◽  
Bulk Modulus ◽  
Hydraulic System ◽  
Theoretical Modeling ◽  
Model Parameters ◽  
Hydraulic Systems ◽  
Pressure Sensitive ◽  
Effective Bulk Modulus

The paper presents theoretical modeling and an experimental investigation of the variation of oil effective bulk modulus (βe) with pressure in hydraulic systems. A pressure sensitive model of βe and its several simplified forms have been derived. In addition, a method for parameter identification has been formulated. In an actual hydraulic system, values for βe at different load pressures were obtained, model parameters identified and modelling errors evaluated.

Effect of Feedback Control on the Bifurcation Stability of a Nonlinear Servohydraulic System: An Experimental Investigation

2003 ◽  
Author(s):  
Amit Shukla ◽  
David F. Thompson ◽  
Srinivas Kowta
Keyword(s):  
Experimental Investigation ◽  
Feedback Control ◽  
Hydraulic System ◽  
Control Structure ◽  
Linear Feedback ◽  
Hydraulic Systems ◽  
Order Model ◽  
Stability Behavior ◽  
Bifurcation Stability ◽  
System A

Servo-hydraulic systems are commonly used for motion and force control. In this work, an experimental investigation is performed to study the effect of feedback control on the bifurcation stability of a nonlinear servo-hydraulic system. A low-order model of the experimental test stand is developed, validated and analyzed. It is shown that the use of appropriate feedback control structure can improve the bifurcation stability of a nonlinear servo-hydraulic system. Parametric space investigation is conducted to study the bifurcation stability behavior of the system and stability boundaries are developed to demonstrate the effect of linear feedback on the nonlinear systems.

Experimental Investigation on Effective Bulk Modulus and Effective Volume in an External Gear Pump

2016 ◽  
Author(s):  
Shuichi Nakagawa ◽  
Takayoshi Ichiyanagi ◽  
Takao Nishiumi
Keyword(s):  
Bulk Modulus ◽  
Gear Pump ◽  
Hydraulic Systems ◽  
Hydraulic Oil ◽  
Positive Displacement ◽  
Effective Bulk Modulus ◽  
Source Impedance ◽  
Displacement Pump ◽  
External Gear Pump ◽  
Pressure Ripples

Pressure ripples generated by a positive displacement pump in a hydraulic system can lead to severe noise and vibration problems. The source impedance of a positive displacement pump has a considerable impact on the generation of pressure ripples. It is, therefore, important to be able to predict the source impedance in order to design quiet hydraulic systems. The source impedance of a positive displacement pump depends, amongst other things, on bulk modulus and volume. However, it is known that the mathematical model that takes into account the bulk modulus of hydraulic oil and the volume of a discharge room in the pump results in an estimated value of the source impedance that is greater than the measured value. In this study, the factors which affect the source impedance of an external gear pump for an agricultural tractor have been investigated. In particular, the effect of the following factors has been investigated experimentally: the effective bulk modulus as determined by the components of the pump: leakage in the pump: the specific volume ratio of entrained air to hydraulic oil: and the volume of the tooth space of the pump. In addition, the effect of volumetric change of the discharge room by pumping action has been investigated using CFD with moving mesh technique.

Parameter Identification of Hydraulic Systems Using Modified Coyote Optimization Algorithm

2020 ◽  
Author(s):  
Reza Mohammadi Asl ◽  
Heikki Handroos
Keyword(s):  
Artificial Intelligence ◽  
Parameter Identification ◽  
Optimization Algorithm ◽  
Traditional Method ◽  
Hydraulic System ◽  
Hydraulic Systems ◽  
Basic Algorithm ◽  
Control Engineering ◽  
Identification Method ◽  
Previous Condition

Abstract Parameter identification is one of most interesting fields for researchers in control engineering field. Different methods have been investigated in recent years. Methods can be divided into two main categories: mathematical based methods and artificial intelligence approaches. Mathematical approaches also known as traditional method which uses different formula to have an estimation of some missed parameters in practical systems, and on the other hand artificial intelligence methods uses different approaches for this purpose. This paper presents a new parameter identification method. A new modified evolutionary algorithm, which can be sorted as a approach of artificial intelligence, is presented and applied to a servo-hydraulic system as a parameter identification method. Coyote Optimization algorithm is chosen for this purpose. The presented modified algorithm is changed in a way that in each iteration, uses details from previous steps and have a better performance in comparison with the basic algorithm. The proposed algorithm tries to update each candidate based on its previous condition which has been missed in basic algorithm. The proposed intelligent method is used as parameter identification method and applied to servo-hydraulic system. The results for simulation are given. Results show the efficiency of the presented method. Based on results, it can be drawn that the proposed method can be supposed as reliable method for nonlinear systems.

scholarly journals Parameter Identification and State-of-Charge Estimation for Lithium-Ion Batteries Using Separated Time Scales and Extended Kalman Filter

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1054
Author(s):  
Kuo Yang ◽  
Yugui Tang ◽  
Zhen Zhang
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Soc Estimation ◽  
Battery Model

With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

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