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

Least Squares ◽

State Estimation ◽

Real Time ◽

Recursive Least Squares ◽

Model Parameters ◽

Identification Algorithm ◽

Model Parameter ◽

Battery Model ◽

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.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.220-223.482 ◽

2012 ◽

Vol 220-223 ◽

pp. 482-486 ◽

Cited By ~ 1

Author(s):

Jin Hui Hu ◽

Da Bin Hu ◽

Jian Bo Xiao

Keyword(s):

Mathematical Model ◽

Power Systems ◽

Least Squares ◽

Least Squares Method ◽

Recursive Least Squares ◽

Design Parameters ◽

Model Parameters ◽

Model Parameter ◽

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.

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

Electronics ◽

10.3390/electronics8080834 ◽

2019 ◽

Vol 8 (8) ◽

pp. 834

Author(s):

Fazheng Wen ◽

Bin Duan ◽

Chenghui Zhang ◽

Rui Zhu ◽

Yunlong Shang ◽

...

Keyword(s):

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.

Volume 2: Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing ◽

Active Excitation for Battery Model Parameter Identification

10.1115/dscc2014-6059 ◽

2014 ◽

Author(s):

Yonghua Li ◽

Hai Yu

Keyword(s):

Real World ◽

Input Signal ◽

Identification Accuracy ◽

Model Parameters ◽

Model Parameter ◽

Battery Model ◽

Battery Power ◽

Reference Parameter ◽

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.

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

Energies ◽

10.3390/en14041054 ◽

2021 ◽

Vol 14 (4) ◽

pp. 1054

Author(s):

Kuo Yang ◽

Yugui Tang ◽

Zhen Zhang

Keyword(s):

Kalman Filter ◽

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.

Volume 3A: 15th Biennial Conference on Mechanical Vibration and Noise — Vibration of Nonlinear, Random, and Time-Varying Systems ◽

Carbon Brake Friction Model Parameter Identification Using Genetic Algorithms

10.1115/detc1995-0373 ◽

1995 ◽

Author(s):

Roger C. von Doenhoff ◽

Robert J. Streifel ◽

Robert J. Marks

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Surface Temperature ◽

Friction Model ◽

Carbon Surface ◽

Model Parameters ◽

Model Parameter ◽

Brake Pressure ◽

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.

Model-Based Condition Monitoring of a Vanadium Redox Flow Battery

Energies ◽

10.3390/en12153005 ◽

2019 ◽

Vol 12 (15) ◽

pp. 3005 ◽

Cited By ~ 1

Author(s):

Meng ◽

Xiong ◽

Lim

Keyword(s):

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.

Payload Parameter Identification of a Flexible Space Manipulator System via Complex Eigenvalue Estimation

International Journal of Aerospace Engineering ◽

10.1155/2020/5142925 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17 ◽

Cited By ~ 2

Author(s):

Zhiyu Ni ◽

Shunan Wu ◽

Yewei Zhang ◽

Zhigang Wu

Keyword(s):

Least Squares ◽

State Space Model ◽

Recursive Least Squares ◽

Mode Shapes ◽

Identification Algorithm ◽

Complex Eigenvalue ◽

Nonlinear Dynamic Model ◽

Space Manipulator ◽

Affine Projection

Manipulator systems are widely used in payload capture and movement in the ground/space operation due to their dexterous manipulation capability. In this study, a method for identifying the payload parameters of a flexible space manipulator using the estimated system of complex eigenvalue matrix is proposed. The original nonlinear dynamic model of the manipulator is linearized at a selected working point. Subsequently, the system state-space model and corresponding complex eigenvalue parameters are determined by the observer/Kalman filter identification algorithm using the torque input signal of the motor and the vibration output signals of the link. Therefore, the inertia parameters of the payload, that is, the mass and the moment of inertia, can be derived from the identified complex eigenvalue system and mode shapes by solving a least-squares problem. In numerical simulations, the proposed parameter identification method is implemented and compared with the classical recursive least-squares and affine projection sign algorithms. Numerical results demonstrate that the proposed method can effectively estimate the payload parameters with satisfactory accuracy.

A UAV MODEL PARAMETER IDENTIFICATION METHOD: A SIMULATION STUDY

International Journal of Information Acquisition ◽

10.1142/s0219878909001977 ◽

2009 ◽

Vol 06 (04) ◽

pp. 225-238 ◽

Cited By ~ 6

Author(s):

K. S. HATAMLEH ◽

O. MA ◽

R. PAZ

Keyword(s):

Control Strategies ◽

Least Square ◽

Model Parameters ◽

Dynamics Modeling ◽

Recursive Least Square ◽

Nonlinear Dynamical ◽

Model Parameter ◽

Identification Method ◽

Dynamics modeling of Unmanned Aerial Vehicles (UAVs) is an essential step for design and evaluation of an UAV system. Many advanced control strategies for nonlinear dynamical or robotic systems which are applicable to UAVs depend upon known dynamics models. The accuracy of a model depends not only on the mathematical formulae or computational algorithm of the model but also on the values of model parameters. Many model parameters are very difficult to measure for a given UAV. This paper presents the results of a simulation based study of an in-flight model parameter identification method. Assuming the motion state of a flying UAV is directly or indirectly measureable, the method can identify the unknown inertia parameters of the UAV. Using the recursive least-square technique, the method is capable of updating the model parameters of the UAV while the vehicle is in flight. A scheme of estimating an upper bound of the identification error in terms of the input data errors (or sensor errors) is also discussed.

2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS) ◽

Real-Time Identification of Supercapacitor RC Model Parameters Using Recursive Least Squares Method

10.1109/icecocs50124.2020.9314439 ◽

2020 ◽

Author(s):

Bououchma Zoubida ◽

Sabor Jalal

Keyword(s):

Least Squares ◽

Real Time ◽

Least Squares Method ◽

Recursive Least Squares ◽

Model Parameters ◽

Recursive Least Squares Method ◽

Real Time Identification

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

ACTA IMEKO ◽

10.21014/acta_imeko.v5i3.318 ◽

2016 ◽

Vol 5 (3) ◽

pp. 55 ◽

Cited By ~ 2

Author(s):

Leonard Klaus

Keyword(s):

Transfer Functions ◽

Dynamic Properties ◽

Measurement Data ◽

Model Parameters ◽

Dynamic Calibration ◽

Measuring Device ◽

Model Parameter ◽

Calibration Measurement ◽

The dynamic calibration of torque transducers requires the modelling 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 analysed by comparing the results with independent measurements. The successful parameter identification is a prerequisite for a model-based dynamic calibration of torque transducers.