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.

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 Estimation of lithium-ion battery state-of-charge using an extended kalman filter

2021 ◽  
Vol 10 (4) ◽  
pp. 1759-1768
Author(s):  
Mouhssine Lagraoui ◽  
Ali Nejmi ◽  
Hassan Rayhane ◽  
Abderrahim Taouni
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
The State ◽  
Operating Conditions ◽  
State Of Charge ◽  
Ohmic Loss ◽  
Battery Management System ◽  
Transfer Resistance

The main goal of a battery management system (BMS) is to estimate parameters descriptive of the battery pack operating conditions in real-time. One of the most critical aspects of BMS systems is estimating the battery's state of charge (SOC). However, in the case of a lithium-ion battery, it is not easy to provide an accurate estimate of the state of charge. In the present paper we propose a mechanism based on an extended kalman filter (EKF) to improve the state-of-charge estimation accuracy on lithium-ion cells. The paper covers the cell modeling and the system parameters identification requirements, the experimental tests, and results analysis. We first established a mathematical model representing the dynamics of a cell. We adopted a model that comprehends terms that describe the dynamic parameters like SOC, open-circuit voltage, transfer resistance, ohmic loss, diffusion capacitance, and resistance. Then, we performed the appropriate battery discharge tests to identify the parameters of the model. Finally, the EKF filter applied to the cell test data has shown high precision in SOC estimation, even in a noisy system.

Research of MH-Ni Battery State of Charge Estimation Based on Grey System Neural Network Model

2011 ◽  
Vol 66-68 ◽  
pp. 583-587 ◽  
Author(s):  
Jian Xiong Long
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Pulse Current ◽  
The State ◽  
State Of Charge ◽  
Current Response ◽  
Grey System ◽  
Battery State Of Charge ◽  
State Of Charge Estimation

In order to effectively achieve MH-Ni battery state of charge estimation, grey system neural network model is put forward to predict battery state of charge by using the parameters of battery pulse current response signal as input for grey system neural network. The state of charge is as the network output and the response parameters of the battery pulse current as the input. The results show that its prediction accuracy of the state of charge can be achieved to requirements of the electric vehicles in applications by this method to predict the state of charge.

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