SOC Estimation of Ternary Lithium Battery Based on Interpolation Method and Online Parameter Identification

2020 ◽  
pp. 36-44
Author(s):  
Dawei Wang ◽  
Ying Yang ◽  
Weiguang Zhao ◽  
Tianyang Yu ◽  
Dongni Zhang
Keyword(s):  
Parameter Identification ◽  
Lithium Battery ◽  
Interpolation Method ◽  
Soc Estimation

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.

Online State of Charge Estimation for Battery of Electric Vehicle Using Sigma-Points Kalman Filters

2013 ◽  
Vol 427-429 ◽  
pp. 824-829
Author(s):  
Li Cun Fang ◽  
Gang Xu ◽  
Tian Li Li ◽  
Ke Min Zhu
Keyword(s):  
Electric Vehicle ◽  
Kalman Filters ◽  
Hybrid Electric Vehicle ◽  
Lithium Battery ◽  
Cell Model ◽  
Operating Conditions ◽  
State Of Charge ◽  
Battery Cell ◽  
Soc Estimation ◽  
Simulation And Experiment

An accurate state-of-charge (SOC) estimation of the hybrid electric vehicle (HEV) and electric vehicle (EV) battery pack is a difficult task to be performed online in a vehicle because of the noisy and low accurate measurements and the wide operating conditions in which the vehicle battery can operate. A Sigma-points Kalman Filters (SPKF) algorithm based on an improved Lithium battery cell model to estimate the SOC of a Lithium battery cell is proposed in this paper. The simulation and experiment results show the effectiveness and ease of implementation of the proposed technique.

Modified dual extended Kalman filters for SOC estimation and online parameter identification of lithium-ion battery via modified gray wolf optimizer

2021 ◽  
pp. 095440702110466
Author(s):  
Kangfeng Qian ◽  
Xintian Liu ◽  
Yiquan Wang ◽  
Xueguang Yu ◽  
Bixiong Huang
Keyword(s):  
Parameter Identification ◽  
Lithium Ion Battery ◽  
Kalman Filters ◽  
Lithium Ion ◽  
Gray Wolf ◽  
Extended Kalman Filters ◽  
Noise Covariance Matrix ◽  
Soc Estimation ◽  
Gray Wolf Optimizer ◽  
Noise Covariance

In order to achieve accurate state of charge (SOC) estimation of Lithium-Ion Battery, A method that dual Extended Kalman filters (DEKF) optimized by PSO-based Gray Wolf optimizer (MGWO) is proposed. A second-order equivalent circuit model with two resistor-capacitor branches is applied. The battery parameters are determined by battery test. Dual Extended Kalman filters are divided into state filter and parameter filter. Parameter filter is applied to adjust battery parameters online, state filter is applied to SOC estimation. Meanwhile, MGWO is applied to optimize the noise covariance matrix to improve the state estimation accuracy of SOC which reduces the linearization error from EKF. The results shows that the accuracy of algorithm is improved by adding online parameter identification and the optimization of the noise covariance matrix, meanwhile, the proposed method can adapt to the initial error well.

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