scholarly journals Parameter identification and state-of-charge prediction of decommissioned lithium batteries

2021 ◽  
Vol 267 ◽  
pp. 01017
Author(s):  
Qian Shi ◽  
Junkai Li ◽  
Qi Qin ◽  
Chijian Zhang
Keyword(s):  
Parameter Identification ◽  
Covariance Matrix ◽  
Optimization Algorithm ◽  
Lithium Batteries ◽  
Equivalent Circuit Model ◽  
Optimal Solution ◽  
Least Square Method ◽  
State Of Charge ◽  
Least Square ◽  
Recursive Least Square

Aiming at the problem that different temperatures and working modes affect the parameter identification and state of charge (SOC) estimation of decommissioned lithium batteries, a new method based on the second-order RC equivalent circuit model combined with the recursive least square method (RLS) is proposed to introduce the forgetting factor, and combined with the extended Kalman filter algorithm (EKF) to realize the method of online parameter identification of decommissioned lithium batteries and the optimal estimation of SOC. In order to solve the problem of obtaining the optimal solution of the error covariance matrix and the measurement noise covariance matrix in EKF, the particle swarm optimization algorithm (PSO) is used to optimize online to further improve the SOC prediction accuracy. The results show that the joint optimization algorithm can accurately identify the parameters and SOC values of retired lithium batteries, which is helpful to realize the echelon utilization of retired lithium batteries.

scholarly journals Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Wenxian Duan ◽  
Chuanxue Song ◽  
Yuan Chen ◽  
Feng Xiao ◽  
Silun Peng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Life ◽  
Model Parameters ◽  
Simulation Results

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

scholarly journals Lithium battery parameter identification and SOC estimation based on dual-polarized model

2021 ◽  
Vol 300 ◽  
pp. 01013
Author(s):  
Hui Xia ◽  
Changlei Li ◽  
Yusang Xu ◽  
Xuehong Liu
Keyword(s):  
Lithium Battery ◽  
Estimation Error ◽  
Equivalent Circuit Model ◽  
Least Square Method ◽  
Base Station ◽  
Least Square ◽  
Model Parameters ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Recursive Least Square Method

An equivalent circuit model of dual polarization (DP) of lithium battery was established according to the application characteristics of lithium battery under the standby condition of 5G base station. On the basis of the model, recursive least square method with forgetting factor (RLS) was used to identify the model parameters. Finally, the Unscented Kalman filtering (UKF) was used to estimate the SOC of lithium battery in real time with the identified model parameters. The simulation and experimental results showed that the combined estimation using recursive least square method with forgetting factor (RLS) and UKF could greatly improve the estimation accuracy of lithium battery SOC, reduce the estimation error, and further verify the accuracy and effectiveness of the whole modeling.

The Method Research of Parameter Identification of Li-Ion Battery Base on Least Square Method

2012 ◽  
Vol 490-495 ◽  
pp. 3854-3858
Author(s):  
Jin Feng ◽  
Yong Ling He ◽  
Ran Bao
Keyword(s):  
Parameter Identification ◽  
Equivalent Circuit ◽  
Equivalent Circuit Model ◽  
Least Square Method ◽  
Least Square ◽  
Voltage Response ◽  
Li Ion Battery ◽  
Model Parameter ◽  
Battery Model ◽  
Li Ion

This paper described the theory of parameter identification of Li-ion battery base on least square method. The result of voltage response by HPPC test is discussed. The model parameter is identified through least square method. The battery model is built with Matlab/Simulink and the identified parameter is verified. The result indicated that the least square method is suitable for parameter identification of Li-ion battery equivalent circuit model.

Modal Parameter Identification of Time-Varying Systems Using the Time-Varying Multivariate Autoregressive Model

2005 ◽  
Author(s):  
Lilan Liu ◽  
Hongzhao Liu ◽  
Ziying Wu ◽  
Daning Yuan ◽  
Pengfei Li
Keyword(s):  
Parameter Identification ◽  
Linear Time ◽  
Damping Ratio ◽  
Least Square Method ◽  
Least Square ◽  
Time Varying ◽  
Recursive Least Square ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Multivariate Autoregressive

A new time-varying multivariate autoregressive (TVMAR) model method for modal parameter identification of linear time-varying (TV) systems with multi-output is introduced. Besides, a modified recursive least square method based on the traditional one is presented to determine the coefficient matrices of the TVMAR model. In the proposed method, multi-dimensional nonstationary response signals of the vibrating system can be processed simultaneously. Not only the TV modal frequency and damping ratio of the system, but also the changing behavior of the mode shape in the course of vibration are identified by the proposed procedure. Numerical simulations, in which a three-degree-of-freedom system with TV stiffness is respectively subjected to impulse excitation and white noise excitation, are presented. The validity and accuracy of the method are demonstrated by the good simulation results.

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