scholarly journals A Hybrid Method for SOC Estimation of Power Battery

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.

scholarly journals Estimation accuracy of covariance matrices when their eigenvalues are almost duplicated

2018 ◽  
Vol 7 ◽  
Author(s):  
Kantaro Shimomura ◽  
Kazushi Ikeda
Keyword(s):  
Covariance Matrix ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Essential Information ◽  
The Matrix ◽  
The Difference ◽  
Processing Techniques ◽  
Monotonic Property ◽  
Signal Processing Techniques ◽  
Special Case

The covariance matrix of signals is one of the most essential information in multivariate analysis and other signal processing techniques. The estimation accuracy of a covariance matrix is degraded when some eigenvalues of the matrix are almost duplicated. Although the degradation is theoretically analyzed in the asymptotic case of infinite variables and observations, the degradation in finite cases are still open. This paper tackles the problem using the Bayesian approach, where the learning coefficient represents the generalization error. The learning coefficient is derived in a special case, i.e., the covariance matrix is spiked (all eigenvalues take the same value except one) and a shrinkage estimation method is employed. Our theoretical analysis shows a non-monotonic property that the learning coefficient increases as the difference of eigenvalues increases until a critical point and then decreases from the point and converged to the distinct case. The result is validated by numerical experiments.

scholarly journals A Novel ULA-Difference-Coarray-Based DOA Estimation Method for General Coherent Signals

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhang Chen ◽  
Hao Wu ◽  
Yongxiang Liu
Keyword(s):  
Good Accuracy ◽  
Linear Array ◽  
Estimation Method ◽  
Doa Estimation ◽  
Estimation Accuracy ◽  
Coherent Signals ◽  
Low Snr ◽  
Sample Covariance ◽  
Lower Complexity ◽  
The Difference

In this article, a difference-coarray-based direction of arrival (DOA) method is introduced, which utilizes the uniform linear array (ULA) in a novel fashion to address the problem of DOA estimation for coherent signals. Inspired by the coarray-based estimators employed in cases of sparse arrays, we convert the sample covariance matrix of the observed signals into the difference coarray domain and process the signals using a spatial smoothing technique. The proposed method exhibits good accuracy and robustness in both the uncorrelated and coherent cases. Numerical simulations verify that the ULA difference coarray- (UDC-) based method can achieve good DOA estimation accuracy even when the SNR is very low. In addition, the UDC-based method is insensitive to the number of snapshots. Under extremely challenging conditions, the proposed UDC-ES-DOA method is preferred because of its outstanding robustness, while the UDC-MUSIC method is suitable for most moderate cases of lower complexity. Due to its demonstrated advantages, the proposed method is a promising and competitive solution for practical DOA estimation, especially for low-SNR or snapshot-limited applications.

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

2013 ◽  
Vol 712-715 ◽  
pp. 1956-1959 ◽  
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.

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 Review on the Battery Model and SOC Estimation Method

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1685
Author(s):  
Wenlu Zhou ◽  
Yanping Zheng ◽  
Zhengjun Pan ◽  
Qiang Lu
Keyword(s):  
Estimation Method ◽  
Estimation Algorithm ◽  
Management Control ◽  
Research Progress ◽  
Estimation Algorithms ◽  
Soc Estimation ◽  
Advantages And Disadvantages ◽  
Battery Model ◽  
Efficient Management ◽  
Power Battery

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.

scholarly journals Application Error Analysis of SOC Estimation of Pure Electric Vehicles Based on Kalman Signal Big Data Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhaona Lu ◽  
Junlong Wang ◽  
Chuanxing Wang ◽  
Guoqing Li
Keyword(s):  
Electric Vehicles ◽  
Time Integration ◽  
Estimation Method ◽  
Open Circuit ◽  
Battery Pack ◽  
Estimation Accuracy ◽  
Battery Management System ◽  
Battery Management ◽  
Current Time ◽  
Power Battery

The state of charge estimation of a pure electric vehicle power battery pack is one of the important contents of the battery management system. Improving the estimation accuracy of the battery pack’s SOC is conducive to giving full play to its performance and preventing overcharge and discharge of a single battery. At present, the open-circuit voltage ampere-hour integral method is traditionally used to estimate the SOC value of the battery pack; however, this estimation method is not accurate enough to correct the initial value of SOC and cannot solve the problem of current time integration error between this correction and the next correction. As for the battery performance and characteristics of electric vehicles, it is pointed out that the size of the model value will affect the estimation accuracy of the Kalman signal value. Based on the analysis of the factors to be referred to in the calculation and estimation of SOC by Kalman for pure electric vehicles, the scheme is improved considering the change of battery model value, and the Kalman scheme is proposed. The feasibility and accuracy of the scheme are proved by several battery simulation experiments.

scholarly journals A Time-Efficient and Accurate Open Circuit Voltage Estimation Method for Lithium-Ion Batteries

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1803 ◽  
Author(s):  
Yingjie Chen ◽  
Geng Yang ◽  
Xu Liu ◽  
Zhichao He
Keyword(s):  
Lithium Ion Batteries ◽  
Open Circuit Voltage ◽  
Estimation Method ◽  
Nonlinear Function ◽  
Lithium Ion ◽  
Open Circuit ◽  
Constant Current ◽  
Estimation Accuracy ◽  
Battery Modeling ◽  
The Difference

The open circuit voltage (OCV) of lithium-ion batteries is widely used in battery modeling, state estimation, and management. However, OCV is a function of state of charge (SOC) and battery temperature (Tbat) and is very hard to estimate in terms of time efficiency and accuracy. This is because two problems arise in normal operations: (1) Tbat changes with the current (I), which makes it very hard to obtain the data required to estimate OCV—terminal voltage (U) data of different I under the same Tbat; (2) the difference between U and OCV is a complex nonlinear function of I and is very difficult to accurately calculate. Therefore, existing methods have to design special experiments to avoid these problems, which are very time consuming. The proposed method consists of a designed test and a data processing algorithm. The test is mainly constant current tests (CCTs) of large I, which is time-efficient in obtaining data. The algorithm solves the two problems and estimates OCV accurately using the test data. Experimental results and analyses showed that experimental time was reduced and estimation accuracy was adequate.

scholarly journals Terrain Feature Estimation Method for a Lower Limb Exoskeleton Using Kinematic Analysis and Center of Pressure

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4418 ◽  
Author(s):  
Myounghoon Shim ◽  
Jong In Han ◽  
Ho Seon Choi ◽  
Seong Min Ha ◽  
Jung-Hoon Kim ◽  
...  
Keyword(s):  
Lower Limb ◽  
Kinematic Analysis ◽  
Center Of Pressure ◽  
Contact Point ◽  
Estimation Method ◽  
Least Square Method ◽  
Least Square ◽  
Estimation Accuracy ◽  
Lower Limb Exoskeleton ◽  
Contact Points

While controlling a lower limb exoskeleton providing walking assistance to wearers, the walking terrain is an important factor that should be considered for meeting performance and safety requirements. Therefore, we developed a method to estimate the slope and elevation using the contact points between the limb exoskeleton and ground. We used the center of pressure as a contact point on the ground and calculated the location of the contact points on the walking terrain based on kinematic analysis of the exoskeleton. Then, a set of contact points collected from each step during walking was modeled as the plane that represents the surface of the walking terrain through the least-square method. Finally, by comparing the normal vectors of the modeled planes for each step, features of the walking terrain were estimated. We analyzed the estimation accuracy of the proposed method through experiments on level ground, stairs, and a ramp. Classification using the estimated features showed recognition accuracy higher than 95% for all experimental motions. The proposed method approximately analyzed the movement of the exoskeleton on various terrains even though no prior information on the walking terrain was provided. The method can enable exoskeleton systems to actively assist walking in various environments.

scholarly journals Highway Reliability Management Model Based on Depth Recursion for Transportation Demand

CONVERTER ◽  
2021 ◽  
pp. 470-481
Author(s):  
Guozhen Sang
Keyword(s):  
Transition Probability ◽  
Estimation Method ◽  
Sampling Technique ◽  
Reliability Estimation ◽  
Estimation Accuracy ◽  
Optimal Test ◽  
Transportation Demand ◽  
Reliability Management ◽  
Model Based ◽  
The Difference

An effective estimation method for the highway reliability management according to the Zhukov usage model based on the recursive test is put forward. This method makes use of the important sampling technique to ensure that under the conditions of the unbiased reliability estimation, the depth recursion is used to measure the difference between the operation profile and the distribution of the zero variance sampling, to correct the test profile by adjusting the transition probability between all the states through the heuristic iterative process. It has proved theoretically that the reliability of the estimation using the modified test profile test is unbiased estimate with the variance of 0. Finally, the heuristic iterative algorithm for the generation of the optimal test profile of the highway reliability estimation is given. The simulation results show that the method put forward in this paper can significantly reduce the variance of the estimate compared with the Newton algorithm, and can increase the speed of the recursive test while improving the estimation accuracy at the same time. The research done in this paper can effectively meet the requirements of the transportation industry in the tertiary industry.

scholarly journals A Battery SOC Estimation Method Based on AFFRLS-EKF

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5698
Author(s):  
Ming Li ◽  
Yingjie Zhang ◽  
Zuolei Hu ◽  
Ying Zhang ◽  
Jing Zhang
Keyword(s):  
Least Squares ◽  
Lithium Ion Battery ◽  
Estimation Method ◽  
Lithium Ion ◽  
Recursive Least Squares ◽  
Estimation Accuracy ◽  
Forgetting Factor ◽  
Battery Charge ◽  
Estimation Strategy ◽  
Soc Estimation

The lithium-ion battery is the key power source of a hybrid vehicle. Accurate real-time state of charge (SOC) acquisition is the basis of the safe operation of vehicles. In actual conditions, the lithium-ion battery is a complex dynamic system, and it is tough to model it accurately, which leads to the estimation deviation of the battery SOC. Recursive least squares (RLS) algorithm with fixed forgetting factor is widely used in parameter identification, but it lacks sufficient robustness and accuracy when battery charge and discharge conditions change suddenly. In this paper, we proposed an adaptive forgetting factor regression least-squares–extended Kalman filter (AFFRLS–EKF) SOC estimation strategy by designing the forgetting factor of least squares algorithm to improve the accuracy of SOC estimation under the change of battery charge and discharge conditions. The simulation results show that the SOC estimation strategy of the AFFRLS–EKF based on accurate modeling can effectively improve the estimation accuracy of SOC.

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