Research on State of Charge Estimation for Power Battery of Electric Vehicle

2013 ◽  
Vol 336-338 ◽  
pp. 799-803
Author(s):  
Chang Fu Zong ◽  
Hai Ou Xiang ◽  
Lei He ◽  
Dong Xue Chen
Keyword(s):  
Estimation Method ◽  
Open Circuit ◽  
State Of Charge ◽  
Method Of Least Squares ◽  
Soc Estimation ◽  
Battery Model ◽  
Power Battery ◽  
Internal Parameters ◽  
Battery State Of Charge ◽  
State Of Charge Estimation

An optimized battery state of charge (SOC) estimation method has been proposed in this paper. The method is based on extended Kalman filter (EKF) and combines Ah counting method and open-circuit voltage (OCV) method. According to the current excitation-response of a battery, the internal parameters of the battery model were identified by the method of least squares. Then the proposed estimation method is verified by experiments. The results show that the estimation method can reduce the cumulative error caused by long discharge and it can estimate the battery SOC effectively and accurately.

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.

Improved Method for State of Charge Estimation of Lithium Iron Phosphate Power Batteries

2013 ◽  
Vol 712-715 ◽  
pp. 1956-1959 ◽  
Author(s):  
Ming Li ◽  
Yang Jiang ◽  
Jian Zhong Zheng ◽  
Xiao Xiao Peng
Keyword(s):  
Lithium Iron Phosphate ◽  
Estimation Method ◽  
Iron Phosphate ◽  
Open Circuit ◽  
State Of Charge ◽  
Counting Method ◽  
Initial State ◽  
Soc Estimation ◽  
Power Battery ◽  
Environment Temperature

To resolve the problems that the initial state of charge (SOC) and the available capacity of batteries are difficult to estimate when using the Ah counting method, in this paper An improved SOC estimation method was proposed that combined with the open circuit voltage (OCV) method and Ah counting method based on the analysis and consideration of the battery available capacity variation caused by charge and discharge current, environment temperature and battery state of health (SOH). The precision of the proposed method was validated by using Federal Urban Driving Schedule (FUDS) test of a Lithium iron phosphate (LiFePO4) power battery. The SOC estimate error using the proposed method relative to a discharge test was better than the Ah counting 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.

Simulation Study on Battery State of Charge Estimation Using Kalman Filter

2016 ◽  
Vol 20 (6) ◽  
pp. 861-866 ◽  
Author(s):  
Furqan Asghar ◽  
◽  
Muhammad Talha ◽  
Sung Ho Kim ◽  
In-Ho Ra ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Estimation Method ◽  
Open Circuit ◽  
The State ◽  
Operating Conditions ◽  
State Of Charge ◽  
Estimation Algorithms ◽  
Battery State Of Charge ◽  
And Performance ◽  
State Of Charge Estimation

Low power dissipation and maximum battery run-time are crucial in portable electronics and EV’s. Battery characteristics and performance varied at different operating conditions. By using accurate, efficient circuit and battery models, designers can predict and optimize battery runtime, current state of charge (SOC) and circuit performance. A great factor in determining the stability of battery system lies within the state of charge estimation. Failing to predict SOC will cause overcharge or over discharge which potentially will bring permanent damage to the battery cells. Open circuit voltage (OCV) has been widely used to estimate the state of charge in estimation algorithms. This paper proposed an accurate and comprehensive battery state of charge (SOC) estimation method by using the Kalman filter. First, Kalman filter for Li-ion battery state of charge estimation was mathematically designed. Then Electrical battery model is being implemented with Kalman filter in matlab Simulink to estimate the exact battery state of charge using estimated battery open circuit voltages. The proposed model shows that system is estimating battery state of charge more accurately than commonly used methods which can help to improve battery performance and lifetime.

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.

Power battery state of charge estimation based on extended Kalman filter

2019 ◽  
Vol 11 (1) ◽  
pp. 014302 ◽  
Author(s):  
Qi Wang ◽  
Xiaoyi Feng ◽  
Bo Zhang ◽  
Tian Gao ◽  
Yan Yang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
State Of Charge ◽  
Power Battery ◽  
Battery State Of Charge ◽  
State Of Charge Estimation

SOC Estimation Strategy and its Accuracy Analysis

2014 ◽  
Vol 953-954 ◽  
pp. 790-795
Author(s):  
Yuan Bin Yu ◽  
Zhou Cai ◽  
Kai Peng ◽  
Wen Qiang Lv
Keyword(s):  
Hybrid Vehicles ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Battery System ◽  
Soc Estimation ◽  
Verification Algorithm ◽  
Line Identification ◽  
Battery State Of Charge ◽  
Kalman Filter Algorithm ◽  
State Of Charge Estimation

Accurate battery state of charge is the prerequisite and precondition for optimal control of hybrid vehicles. This article will be based on the established dynamic model of battery, estimate the battery state of charge in real time. Firstly, analysis the application limitations of Kalman filtering algorithm estimates battery state of charge. Secondly, for some uncertain parameters contained in the model of battery system, paper proposes a parameter line identification extended Kalman filter algorithm to estimate the battery state of charge. Finally, experimental verification algorithm dynamic conditions in the battery state of charge estimation accuracy and effectiveness.

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