The SOC Estimation for Power Battery Using KF Method which Parameters are Updated by Least Square Method

2014 ◽  
Vol 694 ◽  
pp. 67-72 ◽  
Author(s):  
Ying Zhou Pang ◽  
Xing Yong Zhang ◽  
Jian Tao Tian
Keyword(s):  
Kalman Filter ◽  
Least Square Method ◽  
Least Square ◽  
Constant Current ◽  
Counting Method ◽  
Adaptive Kalman Filter ◽  
Soc Estimation ◽  
Battery Model ◽  
Power Battery ◽  
Fixed Parameter

In this article we first introduced some methods for estimating battery’s SOC and their advantages and shortcomings respectively. With experimental data, we proved that parameters of battery model are time variant. So fixed parameter Kalman Filter (FPKF) will not be suitable, then we came up with a new algorithm named adaptive Kalman Filter (APKF),which associated two algorithms—Kalman Filter and Least Square method. Kalman Filer estimates SOC of battery, while Least Square method updates parameters used in Kalman Filter. Then we used battery’s discharging data to test whether this new algorithm took effect. The results produced by Ah-counting method was viewed as a reference because of constant current discharging situation. According to the estimating results, the results produced by APKF have much smaller deviation than that produced by fixed parameters Kalman Filter (FPKF).

The Experiments of Dual Kalman Filter in Lithium Battery SOC Estimation

2014 ◽  
Vol 494-495 ◽  
pp. 1509-1512 ◽  
Author(s):  
Jian Long Huang ◽  
Ying Nan Wang ◽  
Zhong Feng Wang ◽  
Feng Li Han ◽  
Li Gang Li
Keyword(s):  
Kalman Filter ◽  
Lithium Battery ◽  
Lithium Ion ◽  
Least Square Method ◽  
Least Square ◽  
Testing Laboratory ◽  
Soc Estimation ◽  
Battery Model ◽  
True State ◽  
Kalman Filter Algorithm

We use Thevenin battery model and Kalman filter algorithm to online estimate lithium-ion battery pack state of charge (SOC) in this paper. In order to improve the accuracy of the model we use least square method and Dual Kalman filter (DEKF) algorithms to identify the parameters of model. The battery model can reflect the true state of internal battery well. The principle of Kalman filter algorithm is introduced. The relevant battery testing laboratory is designed. The algorithm has better accuracy when online estimate SOC and adapt to the environment well from experimental results. Finally, the convergence and robustness of DEKF algorithm are verified. It solves the problems that initial estimates are not accuracy and cumulative error.

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.

The Analysis of Modeling of Dual Kalman Filter in Lithium Battery SOC Estimates

2014 ◽  
Vol 513-517 ◽  
pp. 4294-4297
Author(s):  
Jian Long Huang ◽  
Zhong Feng Wang ◽  
Kun Ya Guo ◽  
Xiao Tian Wang ◽  
Li Gang Li ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Lithium Ion ◽  
Least Square Method ◽  
The State ◽  
State Of Charge ◽  
Least Square ◽  
Battery Model ◽  
True State ◽  
Kalman Filter Algorithm ◽  
Filter Algorithms

In order to online estimate the state of charge (SOC) of lithium-ion battery pack in this paper. This article establishes a dual Kalman filter (DEKF) algorithms for it. We establish a battery model of state space expression based on Thevenin battery model and Kalman filter algorithms. We use least square method and DEKF algorithms to identify the parameters of battery model in order to improve the accuracy of battery model. It can make battery model reflect true state of internal battery well. It introduces the principle which is dual Kalman filter algorithm online estimate the state of charge. Finally, it verifies DEKF algorithm has better convergence and robustness which can effectively resolve the problems of initial estimates error and cumulative error issue.

The Research of Power Battery SOC Estimation Based on Adaptive Kalman Filter Algorithm

2013 ◽  
Vol 433-435 ◽  
pp. 754-759
Author(s):  
Wei Bo Yu ◽  
Ting Ting Yang ◽  
Cui Yuan Feng ◽  
Hong Jun Li
Keyword(s):  
Kalman Filter ◽  
Lithium Iron Phosphate ◽  
Pulse Discharge ◽  
Iron Phosphate ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Adaptive Kalman Filter ◽  
Soc Estimation ◽  
Power Battery ◽  
Kalman Filter Algorithm

Taking the lithium iron phosphate power battery as the research object, through analysis on characteristics of the battery, this paper chooses the improved second-order RC model as the model of battery whose complexity is moderate and it can better reflect the battery dynamic and static characteristics. Then by pulse discharge experiments and with improved recursive least squares algorithm to identify model parameters online, and puts forward up the adaptive kalman filtering algorithm to estimate battery SOC. The results show that the adaptive kalman filter algorithm can effectively improve battery SOC estimation precision.

scholarly journals Performance evaluation of GPS / BDS combined single-point positioning based on RTKLIB

2020 ◽  
Vol 165 ◽  
pp. 03009
Author(s):  
Li Yan-yi ◽  
Huang Jin ◽  
Tang Ming-xiu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Single Point ◽  
Least Square Method ◽  
Least Square ◽  
Filter Method ◽  
Single System ◽  
Positioning Error ◽  
Dual Mode ◽  
Kalman Filter Method

In order to evaluate the performance of GPS / BDS, RTKLIB, an open-source software of GNSS, is used in this paper. In this paper, the least square method, the weighted least square method and the extended Kalman filter method are respectively applied to BDS / GPS single system for data solution. Then, the BDS system and GPS system are used for fusion positioning and the positioning results of the two systems are compared with that of the single system. Through the comparison of experiments, on the premise of using the extended Kalman filter method for positioning, when the GPS signal is not good, BDS data is introduced for dual-mode positioning, the positioning error in e direction is reduced by 36.97%, the positioning error in U direction is reduced by 22.95%, and the spatial positioning error is reduced by 16.01%, which further reflects the advantages of dual-mode positioning in improving a system robustness and reducing the error.

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 Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Wenxian Duan ◽  
Chuanxue Song ◽  
Yuan Chen ◽  
Feng Xiao ◽  
Silun Peng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Life ◽  
Model Parameters ◽  
Simulation Results

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

The Adaptive Kalman Filter Based on Improved Variable Oblivion Factor Least Square Arithmetic

2011 ◽  
Vol 181-182 ◽  
pp. 124-129
Author(s):  
Yu Song
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Transfer Matrix ◽  
Least Square ◽  
Flood Forecast ◽  
Adaptive Kalman Filter ◽  
Dam Reservoir ◽  
State Transfer

Adopting improved variable oblivion factor least square arithmetic to real-time amend Kalman’s state transfer matrix, we put Maple Dam reservoir flood forecast real-time adjustment for example, then apply and compare with other ways. The result shows this arithmetic is preferable.

The State of Charge Estimation of Lithium Battery in Electric Vehicle Based on Extended Kalman Filter

2014 ◽  
Vol 953-954 ◽  
pp. 796-799
Author(s):  
Huan Huan Sun ◽  
Jun Bi ◽  
Sai Shao
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Extended Kalman Filter ◽  
Nonlinear Problems ◽  
Least Square Method ◽  
State Equation ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery State Of Charge

Accurate estimation of battery state of charge (SOC) is important to ensure operation of electric vehicle. Since a nonlinear feature exists in battery system and extended kalman filter algorithm performs well in solving nonlinear problems, the paper proposes an EKF-based method for estimating SOC. In order to obtain the accurate estimation of SOC, this paper is based on composite battery model that is a combination of three battery models. The parameters are identified using the least square method. Then a state equation and an output equation are identified. All experimental data are collected from operating EV in Beijing. The results of the experiment show  that the relative error of estimation of state of charge is reasonable, which proves this method has good estimation performance.

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.

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