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

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.

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

E3S Web of Conferences ◽

10.1051/e3sconf/202019402023 ◽

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.

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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 ◽

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.

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Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter

Mathematics ◽

10.3390/math9151733 ◽

2021 ◽

Vol 9 (15) ◽

pp. 1733

Author(s):

Hao Wang ◽

Yanping Zheng ◽

Yang Yu

Keyword(s):

Kalman Filter ◽

Least Squares ◽

Unscented Kalman Filter ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Forgetting Factor ◽

Soc Estimation ◽

Online Identification ◽

Adaptive Forgetting Factor ◽

Adaptive Forgetting

In order to improve the estimation accuracy of the battery state of charge (SOC) based on the equivalent circuit model, a lithium-ion battery SOC estimation method based on adaptive forgetting factor least squares and unscented Kalman filtering is proposed. The Thevenin equivalent circuit model of the battery is established. Through the simulated annealing optimization algorithm, the forgetting factor is adaptively changed in real-time according to the model demand, and the SOC estimation is realized by combining the least-squares online identification of the adaptive forgetting factor and the unscented Kalman filter. The results show that the terminal voltage error identified by the adaptive forgetting factor least-squares online identification is extremely small; that is, the model parameter identification accuracy is high, and the joint algorithm with the unscented Kalman filter can also achieve a high-precision estimation of SOC.

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Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation

Electronics ◽

10.3390/electronics10020122 ◽

2021 ◽

Vol 10 (2) ◽

pp. 122

Author(s):

Peipei Xu ◽

Junqiu Li ◽

Chao Sun ◽

Guodong Yang ◽

Fengchun Sun

Keyword(s):

Equivalent Circuit Model ◽

Lithium Ion ◽

Maximum Error ◽

State Of Charge ◽

Recursive Least Squares ◽

Accurate Estimation ◽

Model Parameters ◽

Soc Estimation ◽

Capacity Degradation ◽

Battery Capacity

The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this paper are as follows: (1) the equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method; (2) the sensitivity of battery SOC estimation to capacity degradation is analyzed to prove the importance of considering capacity degradation in SOC estimation; and (3) the capacity degradation model is proposed to perform the battery capacity prediction online. Furthermore, an online adaptive SOC estimator based on capacity degradation is proposed to improve the robustness of the AEKF algorithm. Experimental results show that the maximum error of SOC estimation is less than 1.3%.

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Online Identification of Thevenin Equivalent Circuit Model Parameters and Estimation State of Charge of Lithium-Ion Batteries

2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) ◽

10.1109/eeeic.2018.8493924 ◽

2018 ◽

Cited By ~ 11

Author(s):

Edoardo Locorotondo ◽

Luca Pugi ◽

Lorenzo Berzi ◽

Marco Pierini ◽

Giovanni Lutzemberger

Keyword(s):

Lithium Ion Batteries ◽

Equivalent Circuit ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Circuit Model ◽

State Of Charge ◽

Model Parameters ◽

Online Identification ◽

Thevenin Equivalent

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A Simplified Fractional Order Equivalent Circuit Model and Adaptive Online Parameter Identification Method for Lithium-Ion Batteries

Mathematical Problems in Engineering ◽

10.1155/2019/6019236 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Jianlin Wang ◽

Le Zhang ◽

Dan Xu ◽

Peng Zhang ◽

Gairu Zhang

Keyword(s):

Parameter Identification ◽

Least Squares ◽

Equivalent Circuit ◽

Fractional Order ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

Circuit Model ◽

Stability And Convergence ◽

Battery Management System ◽

Identification Method

In order to improve the battery management system performance and enhance the adaptability of the system, a fractional order equivalent circuit model of lithium-ion battery based on electrochemical test was established. The parameters of the fractional order equivalent circuit model are identified by the least squares parameter identification method. The least squares parameter identification method needs to rely on the harsh test conditions of the laboratory, and the parameter identification result is static; it cannot adapt to the characteristics of the lithium battery under dynamic conditions. Taking into account the dynamic changes of lithium batteries, a parameter adaptive online estimation algorithm for fractional equivalent circuit model is proposed. Based on the theory of fractional order calculus and indirect Lyapunov method, the stability and convergence of the estimator are analyzed. Finally, simulation experiments show that this method can continuously estimate the parameters of the fractional order equivalent circuit under UDDS conditions.

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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 ◽

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.

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Microclimate control of a greenhouse by adaptive Generalized Linear Quadratic strategy

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v11.i1.pp377-385 ◽

2018 ◽

Vol 11 (1) ◽

pp. 377 ◽

Cited By ~ 1

Author(s):

Mohamed Essahafi ◽

Mustapha Ait Lafkih

Keyword(s):

Optimal Control ◽

Least Squares ◽

Recursive Least Squares ◽

Linear Quadratic ◽

Linear Quadratic Gaussian ◽

Advanced Technique ◽

Online Identification ◽

State Models ◽

The Cost ◽

And Control

<p>To highlight the conceptual aspects related to the implementation of techniques optimal control in the form state, we present in this paper, the identification and control of the temperature and humidity of the air inside a greenhouse. Using respectively an online identification based on the recursive least squares with forgotten Factor method and the multivariable adaptive linear quadratic Gaussian approach which the advanced technique (LQG) is presented. The design of this controller parameters is based on state models identified directly from measured greenhouse data. hence the performances of the controller developed are illustrated by different tests and simulations on identified models of a greenhouse. Discussions on the results obtained are then processed in the paper to show the effectiveness of the controller in terms of stability and optimization of the cost of control.</p>

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

Volume 3: Nonlinear Estimation and Control; Optimization and Optimal Control; Piezoelectric Actuation and Nanoscale Control; Robotics and Manipulators; Sensing; System Identification (Estimation for Automotive Applications, Modeling, Therapeutic Control in Bio-Systems); Variable Structure/Sliding-Mode Control; Vehicles and Human Robotics; Vehicle Dynamics and Control; Vehicle Path Planning and Collision Avoidance; Vibrational and Mechanical Systems; Wind Energy Systems and Control ◽

10.1115/dscc2013-3776 ◽

2013 ◽

Cited By ~ 1

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.

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