Algebraic Driver Steering Model Parameter Identification

2022 ◽  
Author(s):  
Zejiang Wang ◽  
Xingyu Zhou ◽  
Heran Shen ◽  
Junmin Wang
Keyword(s):  
Parameter Identification ◽  
Driving Simulator ◽  
Measurement Data ◽  
Algebraic Method ◽  
Recursive Least Squares ◽  
Superior Performance ◽  
Model Parameters ◽  
Driver Assistance Systems ◽  
Short Period ◽  
Dynamics And Control

Abstract Modeling driver steering behavior plays an ever-important role in nowadays automotive dynamics and control applications. Especially, understanding individuals' steering characteristics enables the advanced driver assistance systems (ADAS) to adapt to particular drivers, which provides enhanced protection while mitigating human-machine conflict. Driver-adaptive ADAS requires identifying the parameters inside a driver steering model in real-time to account for driving characteristics variations caused by weather, lighting, road, or driver physiological conditions. Usually, Recursive Least Squares (RLS) and Kalman Filter (KF) are employed to update the driver steering model parameters online. However, because of their asymptotical nature, the convergence speed of the identified parameters could be slow. In contrast, this paper adopts a purely algebraic perspective to identify parameters of a driver steering model, which can achieve parameter identification within a short period. To demonstrate the effectiveness of the proposed method, we first apply synthetic driver steering data from simulation to show its superior performance over an RLS identifier in identifying constant model parameters, including feedback steering gain, feedforward steering gain, preview time, and first-order neuromuscular lag. Then, we utilize real measurement data from human subject driving simulator experiments to illustrate how the time-varying feedback and feedforward steering gains can be updated online via the algebraic method.

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.

Model Parameter Identification and Simulation of Ship Power System Based on Recursive Least Squares Method

2012 ◽  
Vol 220-223 ◽  
pp. 482-486 ◽  
Author(s):  
Jin Hui Hu ◽  
Da Bin Hu ◽  
Jian Bo Xiao
Keyword(s):  
Mathematical Model ◽  
Power Systems ◽  
Parameter Identification ◽  
Least Squares ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Design Parameters ◽  
Model Parameters ◽  
Model Parameter ◽  
Model Parameter Identification

According to the lack of the part of the equipment design parameters of a certain type of ship power systems, the algorithm of recursive least squares for model parameter identification is studied. The mathematical model of the propulsion motor is established. The model parameters are calculated and simulated based on parameter identification method of recursive least squares. The simulation results show that a more precise mathematical model can be simple and easily obtained by using of the method.

Parameter Identification from Normal and Pathological Middle Ears Using a Tailored Parameter Identification Algorithm

2021 ◽  
Author(s):  
Benjamin Sackmann ◽  
Peter Eberhard ◽  
Michael Lauxmann
Keyword(s):  
Parameter Identification ◽  
Middle Ear ◽  
Measurement Data ◽  
High Sensitivity ◽  
Element Model ◽  
Great Sensitivity ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Good Representation ◽  
Adaptive Parameter

Abstract Current clinical practice is often unable to identify the causes of conductive hearing loss in the middle ear with sufficient certainty without exploratory surgery. Besides the large uncertainties due to interindividual variances, only partially understood cause-effect principles are a major reason for the hesitant use of objective methods such as wideband tympanometry in diagnosis, despite their high sensitivity to pathological changes. For a better understanding of objective metrics of the middle ear, this study presents a model that can be used to reproduce characteristic changes in metrics of the middle ear by altering local physical model parameters linked to the anatomical causes of a pathology. A finite-element model is therefore fitted with an adaptive parameter identification algorithm to results of a temporal bone study with stepwise and systematically prepared pathologies. The fitted model is able to reproduce well the measured quantities reflectance, impedance, umbo and stapes transfer function for normal ears and ears with otosclerosis, malleus fixation and disarticulation. In addition to a good representation of the characteristic influences of the pathologies in the measured quantities, a clear assignment of identified model parameters and pathologies consistent with previous studies is achieved. The identification results highlight the importance of the local stiffness and damping values in the middle ear for correct mapping of pathological characteristics, and address the challenges of limited measurement data and wide parameter ranges from literature. The great sensitivity of the model with respect to pathologies indicates a high potential for application in model-based diagnosis.

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.

scholarly journals Model parameter identification from measurement data for dynamic torque calibration – Measurement results and validation

ACTA IMEKO ◽  
2016 ◽  
Vol 5 (3) ◽  
pp. 55 ◽  
Author(s):  
Leonard Klaus
Keyword(s):  
Parameter Identification ◽  
Transfer Functions ◽  
Dynamic Properties ◽  
Measurement Data ◽  
Model Parameters ◽  
Dynamic Calibration ◽  
Measuring Device ◽  
Model Parameter ◽  
Calibration Measurement ◽  
Model Parameter Identification

<p><span lang="EN-US">The dynamic calibration of torque transducers requires the </span><span lang="EN-GB">modelling</span><span lang="EN-US"> of the measuring device and of the transducer under test. The transducer's dynamic properties are described by means of model parameters, which are going to be identified from measurement data. To be able to do so, two transfer functions are calculated. In this paper, the transfer functions and the procedure for the model parameter identification are presented. Results of a parameter identification of a torque transducer are also given, and the validity of the identified parameters is </span><span lang="EN-GB">analysed</span><span lang="EN-US"> by comparing the results with independent measurements. The successful parameter identification is a prerequisite for a model-based dynamic calibration of torque transducers.</span></p>

scholarly journals Online Identification of Lithium-ion Battery Model Parameters with Initial Value Uncertainty and Measurement Noise

2021 ◽  
Author(s):  
Xinghao Du ◽  
Jinhao Meng ◽  
Kailong Liu ◽  
Yingmin Zhang ◽  
Shunli Wang ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Least Squares ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Initial Value ◽  
Online Identification ◽  
Noise Disturbances ◽  
The Cost

Abstract Online parameter identification is essential for the accuracy of the battery Equivalent Circuit Model (ECM). The traditional Recursive Least Squares (RLS) method is easily biased with the noise disturbances from sensors, which degrades the modeling accuracy in practice. Meanwhile, the Recursive Total Least Squares (RTLS) method can deal with the noise interferences, but the parameter slowly converges to the reference with initial value uncertainty. To alleviate the above issues, this paper proposes a co-estimation framework utilizing the advantages of RLS and RTLS for a higher parameter identification performance of the battery ECM. RLS converges quickly by updating the parameters along the gradient of the cost function. RTLS is applied to attenuate the noise effect once the parameters have converged. Both simulation and experimental results prove that the proposed method has good accuracy, fast convergence rate, and also robustness against noise corruption.

Online parameter identification methods for oscillatory systems: Estimation of changes in stiffness properties

2018 ◽  
Vol 25 (4) ◽  
pp. 725-738
Author(s):  
Christoph Ludwig ◽  
Oliver Junge ◽  
Utz Wever
Keyword(s):  
Parameter Identification ◽  
Measurement Data ◽  
Superior Performance ◽  
Rotating Shaft ◽  
Oscillatory Systems ◽  
Rotating Systems ◽  
Oscillating Systems ◽  
Stiffness Properties ◽  
Spring System ◽  
Mass Spring

Model based real-time parameter identification in oscillating systems is a topic of ongoing interest, especially in the context of fault diagnosis during the operation of the system. At the core is a sufficiently small model which is successively calibrated by measurement data. For smooth data such as temperatures, Kalman-based filtering works well. However, for highly oscillatory data from, for example, rotating systems which are often additionally disturbed by harmonic excitations, these methods are prone to failure. In this paper we present an identification method that is able to detect changes in the stiffness properties of the system characterized by a single fault parameter based on frequency data. Its superior performance is demonstrated by a mass–spring system as well as a rotating shaft.

scholarly journals State-of-charge Estimation of Lithium-ion Battery Based Online Parameter Identification

2020 ◽  
Vol 194 ◽  
pp. 02023
Author(s):  
Juqiang Feng ◽  
Long Wu ◽  
Kaifeng Huang ◽  
Xing Zhang ◽  
Jun Lu
Keyword(s):  
Parameter Identification ◽  
Lithium Ion ◽  
State Of Charge ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
System Stability ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Soc Estimation ◽  
Online Identification

Accurately estimating the state of charge (SOC) of lithium-ion is very important to improving the dynamic performance and energy utilization efficiency. In order to reduce the influence of model parameters and system coloured noise on SOC estimation accuracy, this paper proposes the SOC estimation based on online identification. Based on the mixed simplified electrochemical model, the forgetting factor recursive least squares (FFRLS) method was used to identify the parameters online, and the SOC estimation was carried out in combination with Unscented Kalman Filter (UKF). Finally, the accuracy and feasibility of the method are verified by Federal Urban Driving Schedule (FUDS), the online identification and SOC estimation are carried out. The experimental results show that the SOC estimation of online parameter identification is more accurate, the system stability is faster and the error is smaller.

scholarly journals High-Accuracy Parameter Identification Method for Equivalent-Circuit Models of Lithium-Ion Batteries Based on the Stochastic Theory Response Reconstruction

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 834
Author(s):  
Fazheng Wen ◽  
Bin Duan ◽  
Chenghui Zhang ◽  
Rui Zhu ◽  
Yunlong Shang ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Equivalent Circuit ◽  
Stochastic Theory ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Battery Voltage ◽  
Model Parameter ◽  
Noise Interference ◽  
Model Parameter Identification ◽  
Response Reconstruction

The precision of battery modeling is usually determined by the identification of model parameters, which is dependent on the measured outside characteristic data of batteries. However, there is a lot of noise because of the environment noise and measurement error, leading to poor estimation accuracy of model parameters. This paper proposes a stochastic theory response reconstruction (STRR) method to reconstruct the measured battery voltage data, which can eliminate the noise interference and ensure high-precision model parameter identification. The relationship between the battery voltage and current is established based on the the second-order equivalent circuit model (ECM) by the convolution theorem, and the impulse function is calculated by the correlation function between the measured voltage and current. Then, the battery voltage is reconstructed and used to identify model parameters with the recursive least squares (RLS) algorithm. All data for model parameter identification is produced through the pseudo random binarysequence (PRBS) excitation signal. Finally, the Urban Dynamometer Driving Schedule (UDDS) and Federal Urban Driving Schedule (FUDS) tests are conducted to validate the performance of the proposed method. Experimental results show that when compared with the traditional solution using low-pass filter, the proposed method can eliminate the noise interference more effectively and has higher identification accuracy.

scholarly journals Modeling of German Low Voltage Cables with Ground Return Path

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1265 ◽  
Author(s):  
Johanna Geis-Schroer ◽  
Sebastian Hubschneider ◽  
Lukas Held ◽  
Frederik Gielnik ◽  
Michael Armbruster ◽  
...  
Keyword(s):  
Low Voltage ◽  
Measurement Data ◽  
Analytical Calculation ◽  
Laboratory Measurement ◽  
Model Parameters ◽  
Calculation Methods ◽  
Modeling Approach ◽  
Measurement Results ◽  
Simulation Results ◽  
Return Path

In this contribution, measurement data of phase, neutral, and ground currents from real low voltage (LV) feeders in Germany is presented and analyzed. The data obtained is used to review and evaluate common modeling approaches for LV systems. An alternative modeling approach for detailed cable and ground modeling, which allows for the consideration of typical German LV earthing conditions and asymmetrical cable design, is proposed. Further, analytical calculation methods for model parameters are described and compared to laboratory measurement results of real LV cables. The models are then evaluated in terms of parameter sensitivity and parameter relevance, focusing on the influence of conventionally performed simplifications, such as neglecting house junction cables, shunt admittances, or temperature dependencies. By comparing measurement data from a real LV feeder to simulation results, the proposed modeling approach is validated.

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