Design of Unscented Kalman Filter with Gated Recurrent Units-based Battery Model for SOC Estimation

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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Real-time SOC Estimation of the EKF and UKF Algorithms Under Different Working Conditions

Journal of Electrochemical Energy Conversion and Storage ◽

10.1115/1.4051254 ◽

2021 ◽

pp. 1-29

Author(s):

Xiongbin Peng ◽

Yuwu Li ◽

Wei Yang ◽

Akhil Garg

Keyword(s):

Kalman Filter ◽

Working Conditions ◽

Unscented Kalman Filter ◽

Stress Test ◽

Estimation Method ◽

Influential Factor ◽

Recursive Least Squares ◽

Maximum Deviation ◽

Soc Estimation ◽

Proposed Model

Abstract In the battery thermal management system (BMS), the state of charge (SOC) is a very influential factor, which can prevent overcharge and over-discharge of the lithium-ion battery (LIB). This paper proposed a battery modeling and online battery parameter identification method based on the Thevenin equivalent circuit model (ECM) and recursive least squares (RLS) algorithm. The proposed model proved to have high accuracy. The error between the ECM terminal voltage value and the actual value basically fluctuates between ±0.1V. The extended Kalman filter (EKF) algorithm and the unscented Kalman filter (UKF) algorithm were applied to estimate the SOC of the battery based on the proposed model. The SOC experimental results obtained under dynamic stress test (DST), federal urban driving schedule (FUDS), and US06 cycle conditions were analyzed. The maximum deviation of the SOC based on EKF was 1.4112%~2.5988%, and the maximum deviation of the SOC based on UKF was 0.3172%~0.3388%. The SOC estimation method based on UKF and RLS provides a smaller deviation and better adaptability in different working conditions, which makes it more implementable in a real-world automobile application.

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Battery state of charge estimation and online wheel slip ratio control for autonomous wheeled mobile robot

10.32920/ryerson.14668371.v1 ◽

2021 ◽

Author(s):

Maral Partovibakhsh

Keyword(s):

Kalman Filter ◽

Power Consumption ◽

Mobile Robot ◽

Unscented Kalman Filter ◽

Longitudinal Velocity ◽

Accurate Estimation ◽

Model Parameters ◽

Slip Ratio ◽

Soc Estimation ◽

Increasing Function

For autonomous mobile robots moving in unknown environment, accurate estimation of available power along with the robot power demand for each mission is paramount to successful completion of that mission. Regarding the power consumption, the control unit deals with two tasks simultaneously: 1) it has to monitor the power supply (batteries) state of charge (SoC) constantly. This leads to estimation of robot current available power. Besides, batteries are sensitive to deep discharge or overcharge. The battery SoC is an essential factor in power management of a mobile robot. Accurate estimation of the battery SoC can improve power management, optimize the performance, extend the lifetime, and prevent permanent damage to the batteries. 2) The dynamic characteristics of the terrain the robot traverse requires rapid online modifications in its behaviour. The power required for driving a wheel is an increasing function of its slip ratio. For a wheeled robot moving for driving a wheel is an increasing function of its slip ratio. For a wheeled robot moving on different terrains, slip of the wheels should be checked and compensated for to keep the robot moving with less power consumption. To reduce the power consumption, the target robot moving with less power consumption. To reduce the power consumption, the target of the control system is to keep the slip ratio of the driving wheels around the desired value of the control system is to keep the slip ratio of the driving wheels around the desired value. To fulfill the above mentioned tasks, in this thesis, to increase model validity of lithium-ion battery in various charge/discharge scenarios during the mobile robot operation, the battery capacity fade and internal resistance change are modeled by adding them as state variables to a state space model. Using the output measured data, adaptive unscented Kalman Filter (AUKF) is employed for online model parameters identification of the equivalent circuit model at each sampling time. Subsequently, based on the updated model parameters, SoC estimation is conducted using AUKF. The effectiveness of the proposed method is verified through experiments under different power duties in the lab environment through experiments under different power duties in the lab environment. Better results are obtained both in battery model parameters estimation and the battery SoC estimation in comparison with other Kalman filter extensions. Furthermore, for effective control of the slip ratio, a model-based approach to estimating the longitudinal velocity of the mobile robot is presented. The AUKF is developed to estimate the vehicle longitudinal velocity and the wheel angular velocity using measurements from wheel encoders. Based on the estimated slip ratio, a sliding mode controller is designed for slip control of the uncertain nonlinear dynamical system in the presence of model uncertainties, parameter variations, and disturbances. Experiments are carried out in real time on a four-wheel mobile robot to verify the effectiveness of the estimation algorithm and the controller. It is shown that the controller is able to control the slip ratio of the mobile robot on different terrains while adaptive concept of AUKF leads to better results than the unscented Kalman filter in estimating the vehicle velocity which is difficult to measure in actual practice.

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State of Charge Estimation for Lithium-Ion Battery by Using Dual Square Root Cubature Kalman Filter

Mathematical Problems in Engineering ◽

10.1155/2017/5489356 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Luping Chen ◽

Liangjun Xu ◽

Ruoyu Wang

Keyword(s):

Kalman Filter ◽

Unscented Kalman Filter ◽

Lithium Ion ◽

State Of Charge ◽

Estimation Accuracy ◽

Square Root ◽

Battery Management ◽

Balance Performance ◽

Soc Estimation ◽

Cubature Kalman Filter

The state of charge (SOC) plays an important role in battery management systems (BMS). However, SOC cannot be measured directly and an accurate state estimation is difficult to obtain due to the nonlinear battery characteristics. In this paper, a method of SOC estimation with parameter updating by using the dual square root cubature Kalman filter (DSRCKF) is proposed. The proposed method has been validated experimentally and the results are compared with dual extended Kalman filter (DEKF) and dual square root unscented Kalman filter (DSRUKF) methods. Experimental results have shown that the proposed method has the most balance performance among them in terms of the SOC estimation accuracy, execution time, and convergence rate.

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State of charge estimation by using extended Kalman filter based on improved open circuit voltage model

International Journal of Applied Power Engineering (IJAPE) ◽

10.11591/ijape.v10.i1.pp1-10 ◽

2021 ◽

Vol 10 (1) ◽

pp. 1

Author(s):

Maamar Souaihia ◽

Bachir Belmadani ◽

Rachid Taleb ◽

Kamel Tounsi

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Equivalent Circuit Model ◽

Open Circuit ◽

State Of Charge ◽

Soc Estimation ◽

Battery Model ◽

Filter Approach ◽

State Of Charge Estimation ◽

Good Agreement

This paper focuses on the state of charge estimation (SOC) for battery Li-ion. By modeling a battery based on the equivalent circuit model, the extended Kalman filter approach can be applied to estimate the battery SOC. An electrical battery model is developed in Matlab, Where the structure of the model is detailed by equations and blocks. The battery model has been validated from the experiment results. The comparison shows a good agreement in predicting the voltage, SOC estimation and the model performs better in SOC estimation.

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Online SoC Estimation of Lithium-Ion Batteries Using a New Sigma Points Kalman Filter

Applied Sciences ◽

10.3390/app112411797 ◽

2021 ◽

Vol 11 (24) ◽

pp. 11797

Author(s):

Dongdong Ge ◽

Zhendong Zhang ◽

Xiangdong Kong ◽

Zhiping Wan

Keyword(s):

Kalman Filter ◽

Lithium Ion Batteries ◽

Unscented Kalman Filter ◽

Stress Test ◽

Estimation Method ◽

Lithium Ion ◽

Operating Conditions ◽

Recursive Least Squares ◽

Primary Concern ◽

Soc Estimation

The accurate state of charge (SoC) online estimation for lithium-ion batteries is a primary concern for predicting the remaining range in electric vehicles. The Sigma points Kalman Filter is an emerging SoC filtering technology. Firstly, the charge and discharge tests of the battery were carried out using the interval static method to obtain the accurate calibration of the SoC-OCV (open circuit voltage) relationship curve. Secondly, the recursive least squares method (RLS) was combined with the dynamic stress test (DST) to identify the parameters of the second-order equivalent circuit model (ECM) and establish a non-linear state-space model of the lithium-ion battery. Thirdly, based on proportional correction sampling and symmetric sampling Sigma points, an SoC estimation method combining unscented transformation and Stirling interpolation center difference was designed. Finally, a semi-physical simulation platform was built. The Federal Urban Driving Schedule and US06 Highway Driving Schedule operating conditions were used to verify the effectiveness of the proposed estimation method in the presence of initial SoC errors and compare with the EKF (extended Kalman filter), UKF (unscented Kalman filter) and CDKF (central difference Kalman filter) algorithms. The results showed that the new algorithm could ensure an SoC error within 2% under the two working conditions and quickly converge to the reference value when the initial SoC value was inaccurate, effectively improving the initial error correction ability.

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General Model and SOC Estimation of Battery

International Journal of Circuits, Systems and Signal Processing ◽

10.46300/9106.2021.15.17 ◽

2021 ◽

Vol 15 ◽

pp. 155-163

Author(s):

Li Meng ◽

Haipeng Guo ◽

Xiaowei Zhao

Keyword(s):

Kalman Filter ◽

General Model ◽

Unscented Kalman Filter ◽

Measurement Data ◽

Work Conditions ◽

Filter Method ◽

Soc Estimation ◽

The Difference ◽

Kalman Filter Method ◽

Set Up

Monitoring the battery state is of great importance for the safety and normal of the systems which are powered by batteries. SOC (State of Charge) is one of the most important state parameters of battery. SOC cannot be measured directly. The Kalman filter algorithm is one of the techniques often applied to estimate SOC value. An accurate model is necessary for this algorithm. In this paper, a general SOC model is set up. It takes into account not only the difference between discharging and charging work conditions, but also the influence of the working atmosphere, such as temperature and discharging rate. Then based on this general model, unscented Kalman filter method is used to predict the SOC value. It can avoid the error which is caused by ignoring high-order terms, which is a shortcoming exist in the extended Kalman filter method. The simulation experiments prove the approach can get satisfactory results even when the measurement data is mixed with noise or the initial SOC value is not accurate.

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