A novel online model parameters identification method with anti‐interference characteristics for lithium‐ion batteries

2021 ◽  
Vol 45 (6) ◽  
pp. 9502-9517
Author(s):  
Heng Miao ◽  
Jiajun Chen ◽  
Ling Mao ◽  
Keqing Qu ◽  
Jinbin Zhao ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Parameters Identification ◽  
Model Parameters ◽  
Identification Method

scholarly journals Electrochemical Model-Based Investigation of Thick LiFePO4 Electrode Design Parameters

Modelling ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 259-287
Author(s):  
Robert Franke-Lang ◽  
Julia Kowal
Keyword(s):  
Lithium Ion Batteries ◽  
Porous Electrode ◽  
Lithium Ion ◽  
Size Composition ◽  
Design Parameters ◽  
Model Parameters ◽  
Electrode Structure ◽  
Transport Channel ◽  
Scale Particle ◽  
Electrochemical Model

The electrification of the powertrain requires enhanced performance of lithium-ion batteries, mainly in terms of energy and power density. They can be improved by optimising the positive electrode, i.e., by changing their size, composition or morphology. Thick electrodes increase the gravimetric energy density but generally have an inefficient performance. This work presents a 2D modelling approach for better understanding the design parameters of a thick LiFePO4 electrode based on the P2D model and discusses it with common literature values. With a superior macrostructure providing a vertical transport channel for lithium ions, a simple approach could be developed to find the best electrode structure in terms of macro- and microstructure for currents up to 4C. The thicker the electrode, the more important are the direct and valid transport paths within the entire porous electrode structure. On a smaller scale, particle size, binder content, porosity and tortuosity were identified as very impactful parameters, and they can all be attributed to the microstructure. Both in modelling and electrode optimisation of lithium-ion batteries, knowledge of the real microstructure is essential as the cross-validation of a cellular and lamellar freeze-casted electrode has shown. A procedure was presented that uses the parametric study when few model parameters are known.

scholarly journals An All-Region State-of-Charge Estimator Based on Global Particle Swarm Optimization and Improved Extended Kalman Filter for Lithium-Ion Batteries

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 321 ◽  
Author(s):  
Xin Lai ◽  
Wei Yi ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Lithium Ion Batteries ◽  
Extended Kalman Filter ◽  
Particle Swarm ◽  
Lithium Ion ◽  
State Of Charge ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Soc Estimation

In this paper, a novel model parameter identification method and a state-of-charge (SOC) estimator for lithium-ion batteries (LIBs) are proposed to improve the global accuracy of SOC estimation in the all SOC range (0–100%). Firstly, a subregion optimization method based on particle swarm optimization is developed to find the optimal model parameters of LIBs in each subregion, and the optimal number of subregions is investigated from the perspective of accuracy and computation time. Then, to solve the problem of a low accuracy of SOC estimation caused by large model error in the low SOC range, an improved extended Kalman filter (IEKF) algorithm with variable noise covariance is proposed. Finally, the effectiveness of the proposed methods are verified by experiments on two kinds of batteries under three working cycles, and case studies show that the proposed IEKF has better accuracy and robustness than the traditional extended Kalman filter (EKF) in the all SOC range.

scholarly journals A Real-Time Joint Estimator for Model Parameters and State of Charge of Lithium-Ion Batteries in Electric Vehicles

Energies ◽  
2015 ◽  
Vol 8 (8) ◽  
pp. 8594-8612 ◽  
Author(s):  
Jianping Gao ◽  
Yongzhi Zhang ◽  
Hongwen He
Keyword(s):  
Lithium Ion Batteries ◽  
Real Time ◽  
Electric Vehicles ◽  
Lithium Ion ◽  
State Of Charge ◽  
Model Parameters

scholarly journals Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Zheng Liu ◽  
Xuanju Dang ◽  
Hanxu Sun
Keyword(s):  
Moving Average ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
State Of Charge ◽  
Parameters Identification ◽  
Estimation Methods ◽  
Model Parameters ◽  
Battery Management System ◽  
H Infinity ◽  
Soc Estimation

The state of charge (SOC) estimation is one of the most important features in battery management system (BMS) for electric vehicles (EVs). In this article, a novel equivalent-circuit model (ECM) with an extra noise sequence is proposed to reduce the adverse effect of model error. Model parameters identification method with variable forgetting factor recursive extended least squares (VFFRELS), which combines a constructed incremental autoregressive and moving average (IARMA) model with differential measurement variables, is presented to obtain the ECM parameters. The independent open circuit voltage (OCV) estimator with error compensation factors is designed to reduce the OCV error of OCV fitting model. Based on the IARMA battery model analysis and the parameters identification, an SOC estimator by adaptive H-infinity filter (AHIF) is formulated. The adaptive strategy of the AHIF improves the numerical stability and robust performance by synchronous adjusting noise covariance and restricted factor. The results of experiment and simulation have verified that the proposed approach has superior advantage of parameters identification and SOC estimation to other estimation methods.

State of charge estimation of lithium-ion batteries based on an improved parameter identification method

Energy ◽  
2015 ◽  
Vol 90 ◽  
pp. 1426-1434 ◽  
Author(s):  
Bizhong Xia ◽  
Chaoren Chen ◽  
Yong Tian ◽  
Mingwang Wang ◽  
Wei Sun ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
State Of Charge ◽  
Identification Method ◽  
State Of Charge Estimation
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