Review on the Battery Model and SOC Estimation Method

The accuracy of the power battery model and SOC estimation directly affects the vehicle energy management control strategy and the performance of the electric vehicle, which is of great significance to the efficient management of the battery and the improvement of the reliability of the vehicle. Based on the research of domestic and foreign battery models and the previous results of SOC estimation, this paper classifies power battery models into electrochemical mechanism models, equivalent circuit models and data-driven models. This paper analyzes the advantages and disadvantages of various battery models and current research progress. According to the choice of battery model, the previous research results of the power battery SOC estimation method are divided into three categories: the direct measurement method not based on battery model, the estimation method using black box battery model, and the battery model SOC estimation method based on state space. This paper will summarize and analyze the principles, applicable scenarios and research progress of the three categories of estimation algorithms aiming to provide references for future in-depth research. Finally, in view of the shortcomings of the battery model and estimation algorithm of the existing method, the future improvement direction is proposed.

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Research on State of Charge Estimation for Power Battery of Electric Vehicle

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.336-338.799 ◽

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.

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SOC Estimation Algorithm for Seismic Vibrator Power Battery with Considering Temperature Compensation

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/677/3/032099 ◽

2019 ◽

Vol 677 ◽

pp. 032099

Author(s):

Qin Li ◽

Xueying Yang ◽

Gang Li ◽

Lei Ma ◽

Yufeng Huang

Keyword(s):

Temperature Compensation ◽

Estimation Algorithm ◽

Soc Estimation ◽

Power Battery ◽

Seismic Vibrator

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Improved Method for State of Charge Estimation of Lithium Iron Phosphate Power Batteries

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.712-715.1956 ◽

2013 ◽

Vol 712-715 ◽

pp. 1956-1959 ◽

Cited By ~ 3

Author(s):

Ming Li ◽

Yang Jiang ◽

Jian Zhong Zheng ◽

Xiao Xiao Peng

Keyword(s):

Lithium Iron Phosphate ◽

Estimation Method ◽

Iron Phosphate ◽

Open Circuit ◽

State Of Charge ◽

Counting Method ◽

Initial State ◽

Soc Estimation ◽

Power Battery ◽

Environment Temperature

To resolve the problems that the initial state of charge (SOC) and the available capacity of batteries are difficult to estimate when using the Ah counting method, in this paper An improved SOC estimation method was proposed that combined with the open circuit voltage (OCV) method and Ah counting method based on the analysis and consideration of the battery available capacity variation caused by charge and discharge current, environment temperature and battery state of health (SOH). The precision of the proposed method was validated by using Federal Urban Driving Schedule (FUDS) test of a Lithium iron phosphate (LiFePO4) power battery. The SOC estimate error using the proposed method relative to a discharge test was better than the Ah counting method.

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The online monitoring system software design and the SOC estimation algorithm research for power battery

Proceedings of 2013 IEEE International Conference on Vehicular Electronics and Safety ◽

10.1109/icves.2013.6619609 ◽

2013 ◽

Cited By ~ 2

Author(s):

Zhiping Wang ◽

Jinchao Xu ◽

Tun Wang

Keyword(s):

Monitoring System ◽

Software Design ◽

Online Monitoring ◽

Estimation Algorithm ◽

System Software ◽

Soc Estimation ◽

Power Battery ◽

Online Monitoring System

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Fast DOA Estimation Based on the Transform Domain Weighted Noise Subspace Fitting Algorithm for Generalized Sparse Array

International Journal of Antennas and Propagation ◽

10.1155/2021/9952575 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Chunxi Liu ◽

Zhikun Chen ◽

Dongliang Peng

Keyword(s):

Estimation Method ◽

Estimation Algorithm ◽

Doa Estimation ◽

Transform Domain ◽

Music Algorithm ◽

Sparse Array ◽

Estimation Algorithms ◽

Noise Subspace ◽

Fitting Algorithm ◽

Array Aperture

Compared with uniform arrays, a generalized sparse array (GSA) can obtain larger array aperture because of its larger element spacing, which improves the accuracy of DOA estimation. At present, most DOA estimation algorithms are only suitable for the uniform arrays, while a few DOA estimate algorithms that can be applied to the GSA are unsatisfactory in terms of computational speed and accuracy. To compensate this deficiency, an improved DOA estimation algorithm which can be applied to the GSA is proposed in this paper. First, the received signal model of the GSA is established. Then, a fast DOA estimation method is derived by combining the weighted noise subspace algorithm (WNSF) with the concept of “transform domain” (TD). Theoretical analysis and simulation results show that compared with the traditional multiple signal classification (MUSIC) algorithm and the traditional WNSF algorithm, the proposed algorithm has higher accuracy and lower computational complexity.

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A Review of Research on the State-of-Charge Estimation Methods for Electric Vehicle Battery

Recent Patents on Mechanical Engineering ◽

10.2174/2212797613666191219153911 ◽

2019 ◽

Vol 13 ◽

Author(s):

Mingyue Zhang ◽

Xiaobin Fan ◽

Jing Gan ◽

Zeng Song ◽

Bin Zhao

Keyword(s):

Control Theory ◽

Electric Vehicle ◽

Estimation Method ◽

Estimation Algorithm ◽

The State ◽

State Of Charge ◽

Estimation Methods ◽

Advantages And Disadvantages ◽

Electric Vehicle Battery ◽

State Of Charge Estimation

Background: Battery technology has been one of the bottlenecks in electric cars. Whether it is in theory or in practice, the research on battery management is extremely important, especially for battery state-of-charge estimation. In fact, the battery has a strong time change and non-linear properties, which are extremely complex systems. Therefore, accurate estimating the state of charge is a challenging thing. Objective: The study aims to report the latest progress in the studies of the state-of-charge estimation methods for electric vehicle battery. Methods: This paper reviews various representative patents and papers related to the state of charge estimation methods for electric vehicle battery. According to their theoretical and experimental characteristics, the estimation methods were classified into three groups: the traditional estimation algorithm based on the battery experiment, the estimation algorithm based on modern control theory and other estimation algorithm based on the innovative ideas, especially focusing on the algorithms based on control theory. Results: The advantages and disadvantages, current and future developments of the state-of-charge estimation methods are finally provided and discussed. Conclusion: Each kind of state of charge estimation method has its own characteristics, suitable for different occasions. At present, algorithms based on control theory, especially intelligent algorithms, are the focus of research in this field. The future development direction is to establish rich database, improve hardware technology, put up with more perfect battery model, and give full play to the advantages of each algorithm.

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A Hybrid Method for SOC Estimation of Power Battery

Journal of Control Science and Engineering ◽

10.1155/2021/6758679 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Jiliang Yi ◽

Xuechun Zhou ◽

Jin Zhang ◽

Zhongqi Li

Keyword(s):

Hybrid Method ◽

Estimation Method ◽

Threshold Value ◽

Least Square ◽

Convergence Time ◽

Equivalent Model ◽

Estimation Accuracy ◽

Soc Estimation ◽

Power Battery ◽

The Difference

The accuracy of battery state of charge (SOC) is crucial for solving the problems such as overcharge, overdischarge, and mileage anxiety of electric vehicle power battery. In this study, an SOC estimation method using a hybrid method (HM) based on threshold switching is proposed, which combines the advantages of the extended Kalman filter (EKF) and the ampere hour integration (AHI) to improve the estimation accuracy and convergence speed. First, the parameters of the second-order RC equivalent model are identified using the least square. Then, the equation of EKF for updating the state variable is reconstructed by using the identified parameters to solve the problem of multiple iterations caused by the uncertainty of the initial value. Finally, the difference between the estimated voltage and the sampling voltage is used as the threshold value for switching between the AHI and the EKF to estimate the SOC of the battery. Simulation results show that the estimated SOC error of the proposed algorithm is less than 1.6% and the convergence time is within 70 s. Experiments under different SOC initial values are carried out to prove the advantages of the proposed method.

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