A simplified fractional order modeling and parameter identification for lithium-ion batteries

2021 ◽  
pp. 1-10
Author(s):  
Zheng Liu ◽  
Yuan Qiu ◽  
Jin Feng ◽  
Shaohang Chen ◽  
Chunshan Yang
Keyword(s):  
Lithium Ion Batteries ◽  
Fractional Order ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Parameters Estimation ◽  
Recursive Least Squares ◽  
Noise Compensation ◽  
Reliable Model ◽  
New Energy ◽  
The One

Abstract With the widespread development of new energy, the study of power lithium-ion batteries (LIBs) has broad prospects and great academic significance. The model and parameters are two essential prerequisites for LIBs state estimation which is used to provide guarantee for the secure and convenient handling of LIBs. To obtain the reliable model and parameters, a simplified fractional order equivalent circuit model (FO-ECM) with high precision is presented in this paper. The dynamic external electrical characteristic of LIBs is represented by the one-order FO-ECM and then the FO-ECM parameters are identified by the combination of Grunwald-Letnikov (G-L) definition based factional order numerical calculation and noise compensation based forgetting factor recursive least squares (FFRLS) method. The simplified FO-ECM can better characterize the nonlinear dynamic behaviors of LIBs, and the G-L definition based FO-FFRLS algorithm can maintain good accuracy in the parameters estimation process. The results show that the simplified FO-ECM can improve the modeling precision and parameters identification performance compared with the common integer order ECM at the different test cycles.

scholarly journals Online Parameter Identification of the Lithium-Ion Battery with Refined Instrumental Variable Estimation

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
An Wen ◽  
Jinhao Meng ◽  
Jichang Peng ◽  
Lei Cai ◽  
Qian Xiao
Keyword(s):  
Instrumental Variable ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Parameters Estimation ◽  
Measurement Noise ◽  
Recursive Least Squares ◽  
Ar Model ◽  
Noise Model ◽  
Li Ion Battery ◽  
Li Ion

Refined Instrumental Variable (RIV) estimation is applied to online identify the parameters of the Equivalent Circuit Model (ECM) for Lithium-ion (Li-ion) battery in this paper, which enables accurate parameters estimation with the measurement noise. Since the traditional Recursive Least Squares (RLS) estimation is extremely sensitive to the noise, the parameters in the ECM may fail to converge to their true values under the measurement noise. The RIV estimation is implemented in a bootstrap form, which alternates between the estimation in the system model and the noise model. The Box-Jenkins model of the Li-ion battery transformed from the two RC ECM is selected as the transfer function model for the RIV estimation in this paper. The errors of the two RC ECM are independently generated by the residual of high-order Auto Regressive (AR) model estimation. With the benefit of a series of auxiliary models, the data filtering technology can prefilter the measurement and increase the robustness of the parameters against the noise. Reasonable parameters are possible to be obtained regardless of the noise in the measurement by RIV. Simulation and experimental tests on a LiFePO4 battery validate the efficiency of RIV for parameter online identification compared with traditional RLS.

Parameter identification of fractional‐order model with transfer learning for aging lithium‐ion batteries

2021 ◽  
Author(s):  
Dongxu Guo ◽  
Geng Yang ◽  
Xuebing Han ◽  
Xuning Feng ◽  
Languang Lu ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Lithium Ion Batteries ◽  
Transfer Learning ◽  
Fractional Order ◽  
Lithium Ion ◽  
Order Model ◽  
Fractional Order Model

A nonlinear fractional-order H∞ observer for SOC estimation of battery pack of electric vehicles

2021 ◽  
pp. 095440702199434
Author(s):  
Wei Yue ◽  
Cong-zhi Liu ◽  
Liang Li ◽  
Xiang Chen ◽  
Fahad Muhammad
Keyword(s):  
Fractional Order ◽  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Open Circuit ◽  
Design Criterion ◽  
Observer Design ◽  
Nonlinear Observer ◽  
Battery Pack ◽  
Soc Estimation

This work is focused on designing a fractional-order [Formula: see text] observer and applying it into the state of charge (SOC) estimation for lithium-ion battery pack system. Firstly, a fractional order equivalent circuit model based on the fractional capacitor is established and identified. Secondly, the SOC estimation method based on the fractional-order [Formula: see text] observer is proposed. The nonlinear intrinsic relationship between the open-circuit voltage and SOC is described as a polynomial function, and its Lipschitz proposition has been discussed. Then, the nonlinear observer design criterion is established based on the Lyapunov method. Finally, the effectiveness of the proposed method is verified with high accuracy and robustness by the experiment results.

scholarly journals Adaptive State-of-Charge Estimation for Lithium-Ion Batteries by Considering Capacity Degradation

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 122
Author(s):  
Peipei Xu ◽  
Junqiu Li ◽  
Chao Sun ◽  
Guodong Yang ◽  
Fengchun Sun
Keyword(s):  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Maximum Error ◽  
State Of Charge ◽  
Recursive Least Squares ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Soc Estimation ◽  
Capacity Degradation ◽  
Battery Capacity

The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is commonly used to estimate SOC; however, this method relies on the precise estimation of the battery’s model parameters and capacity. Furthermore, the actual capacity and battery parameters change in real time with the aging of the batteries. Therefore, to eliminate the influence of above-mentioned factors on SOC estimation, the main contributions of this paper are as follows: (1) the equivalent circuit model (ECM) is presented, and the parameter identification of ECM is performed by using the forgetting-factor recursive-least-squares (FFRLS) method; (2) the sensitivity of battery SOC estimation to capacity degradation is analyzed to prove the importance of considering capacity degradation in SOC estimation; and (3) the capacity degradation model is proposed to perform the battery capacity prediction online. Furthermore, an online adaptive SOC estimator based on capacity degradation is proposed to improve the robustness of the AEKF algorithm. Experimental results show that the maximum error of SOC estimation is less than 1.3%.

A unified modeling framework for lithium-ion batteries: An artificial neural network based thermal coupled equivalent circuit model approach

Energy ◽  
2017 ◽  
Vol 138 ◽  
pp. 118-132 ◽  
Author(s):  
Qian-Kun Wang ◽  
Yi-Jun He ◽  
Jia-Ni Shen ◽  
Zi-Feng Ma ◽  
Guo-Bin Zhong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Lithium Ion Batteries ◽  
Equivalent Circuit ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Circuit Model ◽  
Modeling Framework ◽  
Unified Modeling ◽  
Model Approach

SOH estimation method for lithium-ion batteries based on nonlinear autoregressive neural network model with exogenous input

2021 ◽  
pp. 1-28
Author(s):  
Yanbo Che ◽  
Yibin Cai ◽  
Hongfeng Li ◽  
Yushu Liu ◽  
Mingda Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Equivalent Circuit ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Circuit Model ◽  
Variable Order ◽  
Nonlinear Correlation ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

Abstract The working state of lithium-ion batteries must be estimated accurately and efficiently in the battery management system. Building a model is the most prevalent way of predicting the battery's working state. Based on the variable order equivalent circuit model, this paper examines the attenuation curve of battery capacity with the number of cycles. It identifies the order of the equivalent circuit model using Bayesian Information Criterion (BIC). Based on the correlation between capacity and resistance, the paper concludes that there is a nonlinear correlation between model parameters and state of health (SOH). The nonlinear autoregressive neural network with exogenous input (NARX) is used to fit the nonlinear correlation for capacity regeneration. Then, the self-adaptive weight particle swarm optimization (SWPSO) method is suggested to train the neural network. Finally, single-battery and multi-battery tests are planned to validate the accuracy of the SWPSO-NARX estimate of SOH. The experimental findings indicate that the SOH estimate effect is significant.

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