scholarly journals A Novel Variable Forgetting Factor Recursive Least Square Algorithm to Improve the Anti-Interference Ability of Battery Model Parameters Identification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 61548-61557 ◽  
Author(s):  
Qiang Song ◽  
Yuxuan Mi ◽  
Wuxuan Lai
Keyword(s):  
Parameters Identification ◽  
Least Square ◽  
Model Parameters ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Battery Model ◽  
Variable Forgetting Factor ◽  
Recursive Least Square Algorithm

scholarly journals Online Parameter Identification of Lithium-Ion Batteries Using a Novel Multiple Forgetting Factor Recursive Least Square Algorithm

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3180 ◽  
Author(s):  
Bizhong Xia ◽  
Rui Huang ◽  
Zizhou Lao ◽  
Ruifeng Zhang ◽  
Yongzhi Lai ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Lithium Ion ◽  
Least Square ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Temperature State

The model parameters of the lithium-ion battery are of great importance to model-based battery state estimation methods. The fact that parameters change in different rates with operation temperature, state of charge (SOC), state of health (SOH) and other factors calls for an online parameter identification algorithm that can track different dynamic characters of the parameters. In this paper, a novel multiple forgetting factor recursive least square (MFFRLS) algorithm was proposed. Forgetting factors were assigned to each parameter, allowing the algorithm to capture the different dynamics of the parameters. Particle swarm optimization (PSO) was utilized to determine the optimal forgetting factors. A state of the art SOC estimator, known as the unscented Kalman filter (UKF), was combined with the online parameter identification to create an accurate estimation of SOC. The effectiveness of the proposed method was verified through a driving cycle under constant temperature and three different driving cycles under varied temperature. The single forgetting factor recursive least square (SFFRLS)-UKF and UKF with fixed parameter were also tested for comparison. The proposed MFFRLS-UKF method obtained an accurate estimation of SOC especially when the battery was running in an environment of changing temperature.

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.

Identification of Parameters in Li-Ion Battery Model by Least Squares Method with Variable Forgetting Factor

2019 ◽  
Vol 17 (07) ◽  
pp. 1950027
Author(s):  
Xiong Wei ◽  
Mo Yimin ◽  
Zhang Feng
Keyword(s):  
Least Squares ◽  
Electric Vehicle ◽  
Least Squares Method ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Li Ion Battery ◽  
Forgetting Factor ◽  
Battery Model ◽  
Li Ion ◽  
Variable Forgetting Factor

The inaccuracy of the battery model of an electric vehicle will seriously affect the safe operation of the electric vehicle. This paper aims to design a better identification method for Li-ion battery model parameters to improve the accuracy of the model. A least squares method was developed with variable forgetting factor (VFF) to identify the parameters of a second-order resistor-recapacitor (RC) model of Li-ion battery. After using the identified parameters, the battery model can reliably and accurately track the variability of the actual working state of the energy storage system. Results at different values of the forgetting factor were analyzed to determine the principle for selecting the value of the forgetting factor, and disclose the impacts of the factor values on model accuracy. Finally, the proposed identification algorithm was tested through comparison between results of the model simulation and experimental data. This method provides an important basis for subsequent development of accurate state-of-charge (SOC) and state-of-health (SOH) estimation algorithms.

scholarly journals Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2242 ◽  
Author(s):  
Xiangdong Sun ◽  
Jingrun Ji ◽  
Biying Ren ◽  
Chenxue Xie ◽  
Dan Yan
Keyword(s):  
Lithium Ion Battery ◽  
Equivalent Circuit ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Circuit Model ◽  
Least Square ◽  
Model Parameters ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Online Identification

With the popularity of electric vehicles, lithium-ion batteries as a power source are an important part of electric vehicles, and online identification of equivalent circuit model parameters of a lithium-ion battery has gradually become a focus of research. A second-order RC equivalent circuit model of a lithium-ion battery cell is modeled and analyzed in this paper. An adaptive expression of the variable forgetting factor is constructed. An adaptive forgetting factor recursive least square (AFFRLS) method for online identification of equivalent circuit model parameters is proposed. The equivalent circuit model parameters are identified online on the basis of the dynamic stress testing (DST) experiment. The online voltage prediction of the lithium-ion battery is carried out by using the identified circuit parameters. Taking the measurable actual terminal voltage of a single battery cell as a reference, by comparing the predicted battery terminal voltage with the actual measured terminal voltage, it is shown that the proposed AFFRLS algorithm is superior to the existing forgetting factor recursive least square (FFRLS) and variable forgetting factor recursive least square (VFFRLS) algorithms in accuracy and rapidity, which proves the feasibility and correctness of the proposed parameter identification algorithm.

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