State of Charge Estimation of Power Lithium-Ion Battery Based on Sliding Mode Observer

2013 ◽  
Vol 805-806 ◽  
pp. 1692-1699 ◽  
Author(s):  
Tie Zhou Wu ◽  
Ming Yue Wang ◽  
Qin Xiao
Keyword(s):  
Sliding Mode ◽  
Estimation Error ◽  
Lithium Ion ◽  
Value Theory ◽  
The State ◽  
Sliding Mode Observer ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Design Parameters ◽  
State Of Charge Estimation

The study of the application of the sliding mode observer method that estimates the state of charge. Based on the state space model of battery established on the model of improved EMF equivalent circuit, a sliding mode state observer is designed to help improve the jitter problem. Considering the nonlinear terms in the model for the analysis of the stability of the observer and the characteristics of the industry under its derivative, and using Lagrange mean value theory to guarantee the convergence conditions of the observer, the design parameters of the observer can thus be determined .Then, this thesis compares the simulation of this method under Matlab environment with the extended Kalman filter method. The results show that the method has higher estimation accuracy in the case of the same battery modeling errors. Therefore, the SOC estimation of the sliding mode observer can effectively reduce the state of charge estimation error introduced by the model error.

scholarly journals State-Of-Charge Estimation for Lithium-Ion Battery Using Improved DUKF Based on State-Parameter Separation

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4036 ◽  
Author(s):  
Yu ◽  
Xie ◽  
Sang ◽  
Yang ◽  
Huang
Keyword(s):  
Pulse Discharge ◽  
Lithium Ion ◽  
State Parameter ◽  
Open Loop ◽  
The State ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Loop Model ◽  
Parameter Separation ◽  
State Of Charge Estimation

State-of-charge estimation and on-line model modification of lithium-ion batteries are more urgently required because of the great impact of the model accuracy on the algorithm performance. This study aims to propose an improved DUKF based on the state-parameter separation. Its characteristics include: (1) State-Of-Charge (SoC) is treated as the only state variable to eliminate the strong correlation between state and parameters. (2) Two filters are ranked to run the parameter modification only when the state estimation has converged. First, the double polarization (DP) model of battery is established, and the parameters of the model are identified at both the pulse discharge and long discharge recovery under Hybrid Pulse Power Characterization (HPPC) test. Second, the implementation of the proposed algorithm is described. Third, combined with the identification results, the study elaborates that it is unreliable to use the predicted voltage error of closed-loop algorithm as the criterion to measure the accuracy of the model, while the output voltage obtained by the open-loop model with dynamic parameters can reflect the real situation. Finally, comparative experiments are designed under HPPC and DST conditions. Results show that the proposed state-parameter separated IAUKF-UKF has higher SoC estimation accuracy and better stability than traditional DUKF.

scholarly journals Estimating the State of Charge of Lithium-ion Battery based on Sliding Mode Observer

2016 ◽  
Vol 49 (11) ◽  
pp. 54-61 ◽  
Author(s):  
Yan Ma ◽  
Bingsi Li ◽  
Yongqiang Xie ◽  
Hong Chen
Keyword(s):  
Lithium Ion Battery ◽  
Sliding Mode ◽  
Lithium Ion ◽  
The State ◽  
Sliding Mode Observer ◽  
State Of Charge

scholarly journals A Novel Adaptive State of Charge Iterative Calculation Method by Using the Improved Unscented Kalman Filtering Algorithm for the Lithium Ion Battery Packs

2018 ◽  
Author(s):  
Shun-Li Wang ◽  
Carlos Fernandez ◽  
Chun-Mei Yu ◽  
Chuan-Yun Zou ◽  
James Coffie-Ken
Keyword(s):  
Lithium Ion Battery ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Battery Management ◽  
Iterative Calculation ◽  
Calculation Process ◽  
State Of Charge Estimation

The state of charge estimation is an important part of the battery management system, the estimation accuracy of which seriously affects the working performance of the lithium ion battery pack. The unscented Kalman filter algorithm has been developed and applied to the iterative calculation process. When it is used to estimate the SOC value, there is a rounding error in the numerical calculation. When the sigma point is sampled in the next round, an imaginary number appears, resulting in the estimation failure. In order to improve the estimation accuracy, an improved adaptive square root - unscented Kalman filter method is introduced which combines the QR decomposition in the calculation process. Meanwhile, an adaptive noise covariance matching method is implied. Experiments show that the proposed method can guarantee the semi-positive and numerical stability of the state covariance, and the estimation accuracy can reach the third-order precision. The error remains about 1.60% under the condition of drastic voltage and current changes. The conclusion of this experiment can provide a theoretical basis of the state of charge estimation in the battery management of the lithium ion battery pack.

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