Comparison of State-of-Charge (SOC) estimation performance based on three popular methods: Coulomb counting, open circuit voltage, and Kalman filter

2017 ◽  
Author(s):  
Ghufron Fathoni ◽  
Sigit Agung Widayat ◽  
Paris Ali Topan ◽  
Abdul Jalil ◽  
Adha Imam Cahyadi ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Open Circuit Voltage ◽  
Open Circuit ◽  
State Of Charge ◽  
Estimation Performance ◽  
Soc Estimation

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.

Battery Management for Small Hydroponic Systems and Cultivation Experiments

2021 ◽  
Vol 11 (3) ◽  
pp. 21-36
Author(s):  
Satoru Yamaguchi ◽  
Takuya Motosugi ◽  
Yoshihiko Takahashi
Keyword(s):  
Open Circuit Voltage ◽  
Low Cost ◽  
Open Circuit ◽  
State Of Charge ◽  
Battery Management ◽  
Charge Capacity ◽  
Hydroponic System ◽  
Soc Estimation ◽  
Hydroponic Systems ◽  
Electronic Load

A small hydroponic system that can use sustainable energy such as solar power has been developed. However, the amount of power generated is not constant, and in the case of unstable weather, enough power cannot be obtained. Therefore, it is necessary to store the generated energy in a battery. In order to design low-cost charging equipment, it is necessary to use a smaller battery and to estimate the remaining charge capacity (state of charge: SOC) accurately. To provide an accurate SOC estimation for such systems, a fusion of CI (current integral) and OCV (open circuit voltage) methods is proposed. When using this method, it is necessary to frequently disconnect the electronic load. In these experiments, the optimum disconnection duration, the effects on plants of frequent battery disconnection, and cutting off of the lighting were investigated.

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 Electronically Isolated Measuring Circuit for Multicell Traction Battery Modules Identifying State of Charge (SOC) Values

2019 ◽  
Vol 25 (6) ◽  
pp. 22-27 ◽  
Author(s):  
Michal Pipiska ◽  
Michal Frivaldsky ◽  
Matus Danko ◽  
Jozef Sedo
Keyword(s):  
Electronic Circuit ◽  
Open Circuit Voltage ◽  
Open Circuit ◽  
Measuring Circuit ◽  
State Of Charge ◽  
Voltage Measurement ◽  
Soc Estimation ◽  
In Series ◽  
Battery Module ◽  
Very High

Paper focuses on the topic related to State of Charge (SOC) estimation of the battery modules. The design issues of the electronic circuit suited for very accurate voltage and current measurement used for evaluation of the SOC parameters of battery module (NexSys 12 V) are presented. The SOC evaluation is not described here, but the principles are based on the open-circuit voltage measurement in combination with the coulomb counting method. Both methods are considered within the practical design of the measuring circuit of traction lead-acid batteries connected in series (3 cells). The circuit proposal is verified by the simulation model and consequently by the experimental measurement. Mutual comparisons are done for each battery within the module. The results show a very high accuracy between the simulation and measurement, while the relative tolerance varies from - 7 % to 1 % within wide measuring ranges.

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 a Composite Lithium-Based Battery Model Based on an Improved Extended Kalman Filter Algorithm

Inventions ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 66 ◽  
Author(s):  
Ning Ding ◽  
Krishnamachar Prasad ◽  
Tek Tjing Lie ◽  
Jinhui Cui
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Open Circuit ◽  
State Of Charge ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Battery Model ◽  
Operation Conditions ◽  
Battery State Of Charge

The battery State of Charge (SoC) estimation is one of the basic and significant functions for Battery Management System (BMS) in Electric Vehicles (EVs). The SoC is the key to interoperability of various modules and cannot be measured directly. An improved Extended Kalman Filter (iEKF) algorithm based on a composite battery model is proposed in this paper. The approach of the iEKF combines the open-circuit voltage (OCV) method, coulomb counting (Ah) method and EKF algorithm. The mathematical model of the iEKF is built and four groups of experiments are conducted based on LiFePO4 battery for offline parameter identification of the model. The iEKF is verified by real battery data. The simulation results with the proposed iEKF algorithm under both static and dynamic operation conditions show a considerable accuracy of SoC estimation.

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