scholarly journals Enhancing Stability and Robustness of State-of-Charge Estimation for Lithium-Ion Batteries by Using Improved Adaptive Kalman Filter Algorithms

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6284
Author(s):  
Fan Zhang ◽  
Lele Yin ◽  
Jianqiang Kang
Keyword(s):  
Kalman Filter ◽  
Adaptive Estimation ◽  
Lithium Ion ◽  
Operating Conditions ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Adaptive Kalman Filter ◽  
Initial Errors ◽  
Noise Covariance ◽  
Filter Algorithms

The traditional Kalman filter algorithms have disadvantages of poor stability (the program cannot converge or crash), robustness (sensitive to the initial errors) and accuracy, partially resulted from the fact that noise covariance matrices in the algorithms need to be set artificially. To overcome the above problems, some adaptive Kalman filter (AKF) algorithms are studied, but the problems still remain unsolved. In this study, two improved AKF algorithms, the improved Sage-Husa and innovation-based adaptive estimation (IAE) algorithms, are proposed. Under the different operating conditions, the estimation accuracy, filter stability, and robustness of the two proposed algorithms are analyzed. Results show that the state of charge (SOC) Max error based on the improved Sage-Husa and the improved IAE is less than 3% and 1.5%, respectively, while the Max errors of the original algorithms is larger than 16% and 4% The two proposed algorithms have higher filter stability than the traditional algorithms. In addition, analyses of the robustness of the two proposed algorithms are carried out by changing the initial parameters, proving that neither are sensitive to the initial errors.

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.

scholarly journals State of Charge Estimation for Lithium-Ion Battery by Using Dual Square Root Cubature Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Luping Chen ◽  
Liangjun Xu ◽  
Ruoyu Wang
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Square Root ◽  
Battery Management ◽  
Balance Performance ◽  
Soc Estimation ◽  
Cubature Kalman Filter

The state of charge (SOC) plays an important role in battery management systems (BMS). However, SOC cannot be measured directly and an accurate state estimation is difficult to obtain due to the nonlinear battery characteristics. In this paper, a method of SOC estimation with parameter updating by using the dual square root cubature Kalman filter (DSRCKF) is proposed. The proposed method has been validated experimentally and the results are compared with dual extended Kalman filter (DEKF) and dual square root unscented Kalman filter (DSRUKF) methods. Experimental results have shown that the proposed method has the most balance performance among them in terms of the SOC estimation accuracy, execution time, and convergence rate.

scholarly journals State-of-Charge Estimation of Lithium-Ion Battery Based on Extended Kalman Filter Algorithm

2020 ◽  
pp. 420-437
Author(s):  
Wu Xiaogang ◽  
Xuefeng Li ◽  
Nikolay I. Shurov ◽  
Alexander A. Shtang ◽  
Michael V. Yaroslavtsev ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Electric Vehicle ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
Operating Conditions ◽  
State Of Charge ◽  
Battery Management ◽  
Soc Estimation ◽  
Ternary Polymer

As the core component of electric vehicle, lithium-ion battery needs to adopt effective battery management method to prolong battery life and improve the reliability and safety. The accurate estimation of the battery SOC can be used to prevent the battery over charge and over discharge, reduce damage to the battery and improve battery performance, which plays a vital role in the battery management system. The study of battery SOC estimation mainly focused on the battery model construction and SOC estimation algorithm. Aiming at the problem that the state of charge (SOC) of electric vehicle is difficult to be accurately estimated under complex operating conditions, based on the parameter identification of the equivalent circuit of a ternary polymer lithium-ion battery, an Extended Kalman Filter (EKF) algorithm was used to estimate the SOC of the ternary polymer lithium-ion battery. Simulation and experimental results show that the estimation of SOC can be carried out by using the EKF algorithm under the conditions of China Passenger Car Condition (Chinacar) and new European driving cycle (NEDC) Compared with the coulomb counting method, the average error of SOC estimation can be realized is 1.042% and 1.138% respectively, the maximum error within 4%. Application of this algorithm to achieve SOC estimation has good robustness and convergence

scholarly journals A Variational Bayesian and Huber-Based Robust Square Root Cubature Kalman Filter for Lithium-Ion Battery State of Charge Estimation

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1717 ◽  
Author(s):  
Jing Hou ◽  
He He ◽  
Yan Yang ◽  
Tian Gao ◽  
Yifan Zhang
Keyword(s):  
Kalman Filter ◽  
Lithium Ion ◽  
Operating Conditions ◽  
State Of Charge ◽  
Constant Current ◽  
Square Root ◽  
Variational Bayesian ◽  
Soc Estimation ◽  
Cubature Kalman Filter ◽  
M Estimation

An accurate state of charge (SOC) estimation is vital for safe operation and efficient management of lithium-ion batteries. To improve the accuracy and robustness, an adaptive and robust square root cubature Kalman filter based on variational Bayesian approximation and Huber’s M-estimation (VB-HASRCKF) is proposed. The variational Bayesian (VB) approximation is used to improve the adaptivity by simultaneously estimating the measurement noise covariance and the SOC, while Huber’s M-estimation is employed to enhance the robustness with respect to the outliers in current and voltage measurements caused by adverse operating conditions. A constant-current discharge test and an urban dynamometer driving schedule (UDDS) test are performed to verify the effectiveness and superiority of the proposed algorithm by comparison with the square root cubature Kalman filter (SRCKF), the VB-based SRCKF, and the Huber-based SRCKF. The experimental results show that the proposed VB-HASRCKF algorithm outperforms the other three filters in terms of SOC estimation accuracy and robustness, with a little higher computation complexity.

scholarly journals State of Charge Estimation of a Lithium Ion Battery Based on Adaptive Kalman Filter Method for an Equivalent Circuit Model

2019 ◽  
Vol 9 (13) ◽  
pp. 2765 ◽  
Author(s):  
Xiao Ma ◽  
Danfeng Qiu ◽  
Qing Tao ◽  
Daiyin Zhu
Keyword(s):  
Kalman Filter ◽  
Equivalent Circuit ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Circuit Model ◽  
State Of Charge ◽  
Filter Method ◽  
Adaptive Kalman Filter ◽  
Soc Estimation ◽  
Kalman Filter Method

Due to its accuracy, simplicity, and other advantages, the Kalman filter method is one of the common algorithms to estimate the state-of-charge (SOC) of batteries. However, this method still has its shortcomings. The Kalman filter method is an algorithm designed for linear systems and requires precise mathematical models. Lithium-ion batteries are not linear systems, so the establishment of the battery equivalent circuit model (ECM) is necessary for SOC estimation. In this paper, an adaptive Kalman filter method and the battery Thevenin equivalent circuit are combined to estimate the SOC of an electric vehicle power battery dynamically. Firstly, the equivalent circuit model is studied, and the battery model suitable for SOC estimation is established. Then, the parameters of the corresponding battery charge and the discharge experimental detection model are designed. Finally, the adaptive Kalman filter method is applied to the model in the unknown interference noise environment and is also adopted to estimate the SOC of the battery online. The simulation results show that the proposed method can correct the SOC estimation error caused by the model error in real time. The estimation accuracy of the proposed method is higher than that of the Kalman filter method. The adaptive Kalman filter method also has a correction effect on the initial value error, which is suitable for online SOC estimation of power batteries. The experiment under the BBDST (Beijing Bus Dynamic Stress Test) working condition fully proves that the proposed SOC estimation algorithm can hold the satisfactory accuracy even in complex situations.

scholarly journals State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter

2021 ◽  
Vol 13 (9) ◽  
pp. 5046
Author(s):  
Jie Xing ◽  
Peng Wu
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Soc Estimation ◽  
Error Covariance ◽  
Adaptive Unscented Kalman Filter ◽  
Noise Statistics

State of charge (SOC) of the lithium-ion battery is an important parameter of the battery management system (BMS), which plays an important role in the safe operation of electric vehicles. When existing unknown or inaccurate noise statistics of the system, the traditional unscented Kalman filter (UKF) may fail to estimate SOC due to the non-positive error covariance of the state vector, and the SOC estimation accuracy is not high. Therefore, an improved adaptive unscented Kalman filter (IAUKF) algorithm is proposed to solve this problem. The IAUKF is composed of the improved unscented Kalman filter (IUKF) that is able to suppress the non-positive definiteness of error covariance and Sage–Husa adaptive filter. The IAUKF can improve the SOC estimation stability and can improve the SOC estimation accuracy by estimating and correcting the system noise statistics adaptively. The IAUKF is verified under the federal urban driving schedule test, and the SOC estimation results are compared with IUKF and UKF. The experimental results show that the IAUKF has higher estimation accuracy and stability, which verifies the effectiveness of the proposed method.

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.

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