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 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.

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.

Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries

2016 ◽  
Vol 183 ◽  
pp. 513-525 ◽  
Author(s):  
Fangdan Zheng ◽  
Yinjiao Xing ◽  
Jiuchun Jiang ◽  
Bingxiang Sun ◽  
Jonghoon Kim ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
State Of Charge ◽  
Online Estimation

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

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
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.

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 A Nonlinear-Model-Based Observer for a State-of-Charge Estimation of a Lithium-Ion Battery in Electric Vehicles

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3383 ◽  
Author(s):  
Woo-Yong Kim ◽  
Pyeong-Yeon Lee ◽  
Jonghoon Kim ◽  
Kyung-Soo Kim
Keyword(s):  
Lithium Ion Battery ◽  
Nonlinear Model ◽  
Open Circuit Voltage ◽  
Lithium Ion ◽  
Open Circuit ◽  
The State ◽  
State Of Charge ◽  
Battery Cell ◽  
Model Based ◽  
State Of Charge Estimation

This paper presents a nonlinear-model-based observer for the state of charge estimation of a lithium-ion battery cell that always exhibits a nonlinear relationship between the state of charge and the open-circuit voltage. The proposed nonlinear model for the battery cell and its observer can estimate the state of charge without the linearization technique commonly adopted by previous studies. The proposed method has the following advantages: (1) The observability condition of the proposed nonlinear-model-based observer is derived regardless of the shape of the open circuit voltage curve, and (2) because the terminal voltage is contained in the state vector, the proposed model and its observer are insensitive to sensor noise. A series of experiments using an INR 18650 25R battery cell are performed, and it is shown that the proposed method produces convincing results for the state of charge estimation compared to conventional SOC estimation methods.

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