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.

scholarly journals A State of Charge Estimation Method of Lithium-Ion Battery Based on Fused Open Circuit Voltage Curve

2020 ◽  
Vol 10 (4) ◽  
pp. 1264
Author(s):  
Yipeng Wang ◽  
Lin Zhao ◽  
Jianhua Cheng ◽  
Junfeng Zhou ◽  
Shuo Wang
Keyword(s):  
Polarization Effect ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Model Parameters ◽  
Fusion Method ◽  
Test Results ◽  
Soc Estimation ◽  
Current Test

The open circuit voltage (OCV) and model parameters are critical reference variables for a lithium-ion battery management system estimating the state of charge (SOC) accurately. However, the polarization effect reduces the accuracy of the OCV test, and the model parameters coupled to the polarization voltage increase the non-linearity of the cell model, all challenging SOC estimation. This paper presents an OCV curve fusion method based on the incremental and low-current test. Fusing the incremental test results without polarization effect and the low current test results with non-linear characteristics of electrodes, the fusion method improves the OCV curve’s accuracy. In addition, we design a state observer with model parameters and SOC, and the unscented Kalman filter (UKF) method is employed for co-estimation of SOC and model parameters to eliminate the drift noise effects. The SOC estimation root mean square error (RMSE) of the proposed method achieves 0.99% and 1.67% in the pulse constant current test and dynamic discharge test, respectively. Experimental results and comparisons with other methods highlight the SOC estimation accuracy and robustness of the proposed method.

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

Electronics ◽  
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%.

scholarly journals An All-Region State-of-Charge Estimator Based on Global Particle Swarm Optimization and Improved Extended Kalman Filter for Lithium-Ion Batteries

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 321 ◽  
Author(s):  
Xin Lai ◽  
Wei Yi ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Extended Kalman Filter ◽  
Particle Swarm ◽  
Lithium Ion ◽  
State Of Charge ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Soc Estimation

In this paper, a novel model parameter identification method and a state-of-charge (SOC) estimator for lithium-ion batteries (LIBs) are proposed to improve the global accuracy of SOC estimation in the all SOC range (0–100%). Firstly, a subregion optimization method based on particle swarm optimization is developed to find the optimal model parameters of LIBs in each subregion, and the optimal number of subregions is investigated from the perspective of accuracy and computation time. Then, to solve the problem of a low accuracy of SOC estimation caused by large model error in the low SOC range, an improved extended Kalman filter (IEKF) algorithm with variable noise covariance is proposed. Finally, the effectiveness of the proposed methods are verified by experiments on two kinds of batteries under three working cycles, and case studies show that the proposed IEKF has better accuracy and robustness than the traditional extended Kalman filter (EKF) in the all SOC range.

SOC Estimation of Lithium-Ion Battery Based on Kalman Filter Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 1852-1855 ◽  
Author(s):  
Ding Xuan Yu ◽  
Yan Xia Gao
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
First Order ◽  
Soc Estimation ◽  
Simulation And Experiment ◽  
Kalman Filter Algorithm ◽  
Model Curve

This paper presents Extended Kalman-filter (EKF) algorithm which is based on a first-order Lithium-ion batteries model. Curve fitting According to the OCV(open circuit voltage),SOC(state of charge) parameters measured in experiments, descript status equation and observation equation of Lithium-ion battery in detail , so that it can accurately demonstrates the characteristics of the Lithium-ion battery. Simulation and experiment results show the feasibility and effectiveness of the algorithm.

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

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.

scholarly journals An Open-Circuit-Voltage Model of Lithium-Ion Batteries for Effective Incremental Capacity Analysis

2013 ◽  
Author(s):  
Caihao Weng ◽  
Jing Sun ◽  
Huei Peng
Keyword(s):  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Parametric Model ◽  
Open Circuit ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Cell Behavior ◽  
Capacity Analysis ◽  
Soc Estimation ◽  
Incremental Capacity

Open-Circuit-Voltage (OCV) is an essential part of battery models for state-of-charge (SOC) estimation. In this paper, we propose a new parametric OCV model, which considers the staging phenomenon during the lithium intercalation/deintercalation process. Results show that the new parametric model improves SOC estimation accuracy compared to other existing OCV models. Moreover, the model is shown to be suitable and effective for battery state-of-health monitoring. In particular, the new OCV model can be used for incremental capacity analysis (ICA), which reveals important information on the cell behavior associated with its electrochemical properties and aging status.

scholarly journals Adaptive Square-Root Unscented Kalman Filter-Based State-of-Charge Estimation for Lithium-Ion Batteries with Model Parameter Online Identification

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4968
Author(s):  
Quan Ouyang ◽  
Rui Ma ◽  
Zhaoxiang Wu ◽  
Guotuan Xu ◽  
Zhisheng Wang
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Square Root ◽  
Model Parameter ◽  
Soc Estimation ◽  
Online Identification

The state-of-charge (SOC) is a fundamental indicator representing the remaining capacity of lithium-ion batteries, which plays an important role in the battery’s optimized operation. In this paper, the model-based SOC estimation strategy is studied for batteries. However, the battery’s model parameters need to be extracted through cumbersome prior experiments. To remedy such deficiency, a recursive least squares (RLS) algorithm is utilized for model parameter online identification, and an adaptive square-root unscented Kalman filter (SRUKF) is designed to estimate the battery’s SOC. As demonstrated in extensive experimental results, the designed adaptive SRUKF combined with RLS-based model identification is a promising SOC estimation approach. Compared with other commonly used Kalman filter-based methods, the proposed algorithm has higher precision in the SOC estimation.

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