Influence of change in open circuit voltage on the state of charge estimation with an extended Kalman filter

2017 ◽  
Vol 12 ◽  
pp. 149-156 ◽  
Author(s):  
Christian Campestrini ◽  
Stephan Kosch ◽  
Andreas Jossen
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Open Circuit Voltage ◽  
Open Circuit ◽  
The State ◽  
State Of Charge ◽  
State Of Charge Estimation

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 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.

Lithium Iron Phosphate Power Battery State of Charge Estimation Based on the Extended Kalman Filter

2013 ◽  
Vol 336-338 ◽  
pp. 784-788
Author(s):  
Ming Li ◽  
Yang Jiang ◽  
Jian Zhong Zheng ◽  
Xiao Xiao Peng
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Lithium Iron Phosphate ◽  
Equivalent Circuit Model ◽  
Estimation Algorithm ◽  
Iron Phosphate ◽  
Open Circuit ◽  
The State ◽  
State Of Charge ◽  
Power Battery

In order to estimate the state of charge (SOC) of lithium iron phosphate (LiFePO4) power battery, the state space model that fit for kalman filter to estimate was established on the basis of PNGV equivalent circuit model. In the case that considering the influence factors such as power battery charge and discharge current, environmental temperature and battery state of health, an improved composite SOC estimation algorithm based on extended kalman filter (EKF) algorithm was proposed, this proposed algorithm integrated using EKF algorithm, improved Ah counting method and open circuit voltage method to estimate SOC. The simulation results show that the proposed algorithm can track the change of the power battery SOC effectively, verify the validity of the proposed algorithm.

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 Estimation for Battery State of Charge Based on Temperature Effect and Fractional Extended Kalman Filter

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5947
Author(s):  
Chengcheng Chang ◽  
Yanping Zheng ◽  
Yang Yu
Keyword(s):  
Kalman Filter ◽  
Equivalent Circuit ◽  
Extended Kalman Filter ◽  
Open Circuit Voltage ◽  
Equivalent Circuit Model ◽  
Open Circuit ◽  
Circuit Model ◽  
State Of Charge ◽  
First Order ◽  
Battery Capacity

The electric vehicle has become an important development direction of the automobile industry, and the lithium-ion power battery is the main energy source of electric vehicles. The accuracy of state of charge (SOC) estimation directly affects the performance of the vehicle. In this paper, the first order fractional equivalent circuit model of a lithium iron phosphate battery was established. Battery capacity tests with different charging and discharging rates and open circuit voltage tests were carried out under different ambient temperatures. The conversion coefficient of charging and discharging capacity and the simplified open circuit voltage model considering the hysteresis characteristics of the battery were proposed. The parameters of the first order fractional equivalent circuit model were identified by using a particle swarm optimization algorithm with dynamic inertia weight. Finally, the recursive formula of a fractional extended Kalman filter was derived, and the battery SOC was estimated under continuous Dynamic Stress Test (DST) conditions. The results show that the estimation method has high accuracy and strong robustness.

Sign in / Sign up

Export Citation Format

Share Document