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 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.

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 Quadrature Kalman filter–based state of charge estimation for lithium-ion battery

2020 ◽  
Vol 12 (7) ◽  
pp. 168781402094269
Author(s):  
Mengtao Huang ◽  
Chao Wang ◽  
Bao Liu ◽  
Fan Wang ◽  
Jingting Wang
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Extended Kalman Filter ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Quadrature Rule ◽  
State Of Charge ◽  
Gauss Hermite Quadrature ◽  
State Of Charge Estimation ◽  
Unscented Filter

This article presents an approach to lithium-ion battery state of charge estimation based on the quadrature Kalman filter. Among the existing state of charge estimation approaches, the extended Kalman filter–based state of charge and unscented filter–based state of charge algorithms are influenced by the linearization or the solution of sigma points. The proposed quadrature Kalman filter–based state of charge algorithm avoids these problems. Specifically, the battery system equations are built based on the second-order resistance–capacitance equivalent circuit model, and the parameters are identified according to the hybrid pulse power characterization discharging test. Then, the quadrature points and corresponding weights are defined by the Gauss–Hermite quadrature rule, and the Kronecker tensor product is adopted to solve the points of multivariate. In addition, the stability of quadrature Kalman filter–based state of charge is verified. Finally, the simulation is carried out under the discharging and urban dynamometer driving schedule condition, which demonstrates that the quadrature Kalman filter–based state of charge algorithm has a better performance compared with extended Kalman filter–based state of charge and unscented filter–based state of charge.

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 A parameter adaptive method for state of charge estimation of lithium-ion batteries with an improved extended Kalman filter

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shichun Yang ◽  
Sida Zhou ◽  
Yang Hua ◽  
Xinan Zhou ◽  
Xinhua Liu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Extended Kalman Filter ◽  
Mathematical Proof ◽  
Stress Test ◽  
Robustness Analysis ◽  
Equivalent Circuit Model ◽  
Operating Conditions ◽  
State Of Charge ◽  
Soc Estimation

AbstractAn accurate state of charge (SOC) estimation in battery management systems (BMS) is of crucial importance to guarantee the safe and effective operation of automotive batteries. However, the BMS consistently suffers from inaccuracy of SOC estimation. Herein, we propose a SOC estimation approach with both high accuracy and robustness based on an improved extended Kalman filter (IEKF). An equivalent circuit model is established, and the simulated annealing-particle swarm optimization (SA-PSO) algorithm is used for offline parameter identification. Furthermore, improvements have been made with noise adaptation, a fading filter and a linear-nonlinear filtering based on the traditional EKF method, and rigorous mathematical proof has been carried out accordingly. To deal with model mismatch, online parameter identification is achieved by a dual Kalman filter. Finally, various experiments are performed to validate the proposed IEKF. Experimental results show that the IEKF algorithm can reduce the error to 2.94% under dynamic stress test conditions, and robustness analysis is verified with noise interference, hence demonstrating its practicability for extending to state estimation of battery packs applied in real-world operating conditions.

Battery SOC Estimation Based on Multi-Model Adaptive Kalman Filter

2011 ◽  
Vol 403-408 ◽  
pp. 2211-2215 ◽  
Author(s):  
Ke Xin Wei ◽  
Qiao Yan Chen
Keyword(s):  
Kalman Filter ◽  
State Of Charge ◽  
Model Parameters ◽  
Adaptive Kalman Filter ◽  
Single Model ◽  
Soc Estimation ◽  
Battery Model ◽  
Kalman Filter Algorithm ◽  
Estimation Precision ◽  
State Of Charge Estimation

This paper introduces multi-model adaptive kalman filter estimation algorithm.Based on the battery thevenin model,the multi-model adaptive kalman filter is applied to the battery SOC(state of charge) estimation, which solute the battery SOC estimation in conditions that the battery model parameters change caused by temperature changing. Simulation results show that compared to the single model kalman filter algorithm, Multi-Model adaptive kalman filter algorithm improves the estimation precision and reliability greatly.

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.

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