A novel combined estimation method of online full‐parameter identification and adaptive unscented particle filter for Li ‐ion batteries SOC based on fractional‐order modeling

2021 ◽  
Author(s):  
Lei Chen ◽  
Shunli Wang ◽  
Hong Jiang ◽  
Carlos Fernandez ◽  
Xin Xiong
Keyword(s):  
Parameter Identification ◽  
Particle Filter ◽  
Fractional Order ◽  
Estimation Method ◽  
Li Ion Batteries ◽  
Unscented Particle Filter ◽  
Combined Estimation ◽  
Li Ion

scholarly journals Unscented Particle Filter for SOC Estimation Algorithm Based on a Dynamic Parameter Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Fang Liu ◽  
Jie Ma ◽  
Weixing Su
Keyword(s):  
Genetic Algorithm ◽  
Parameter Identification ◽  
Particle Filter ◽  
Electric Vehicles ◽  
Estimation Method ◽  
Operating Conditions ◽  
Model Parameters ◽  
Improved Genetic Algorithm ◽  
Soc Estimation ◽  
Unscented Particle Filter

In order to solve the problem that the model-based State of Charge (SOC) estimation method is too dependent on the model parameters in the SOC estimation of electric vehicles, an improved genetic algorithm is proposed in this paper. The method has the advantages of being able to quickly determine the search range, reducing the probability of falling into local optimum, and having high recognition accuracy. Then we can realize online dynamic identification of power battery model parameters and improve the accuracy of model parameter identification. In addition, considering the complex application environment and operating conditions of electric vehicles, an SOC estimation method based on improved genetic algorithm and unscented particle filter (improved GA-UPF) is proposed. And we compare the improved GA-UPF algorithm with the least square unscented particle filter (LS-UPF) and improved GA unscented Kalman filter (improved GA-UKF) algorithm. The comparison results show that the improved GA-UPF algorithm proposed in this paper has higher estimation accuracy and better stability. It also reflects the practicability and accuracy of the improved GA parameter identification algorithm proposed in this paper.

scholarly journals A Comparison of Time-Domain Implementation Methods for Fractional-Order Battery Impedance Models

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4415
Author(s):  
Brian Ospina Agudelo ◽  
Walter Zamboni ◽  
Eric Monmasson
Keyword(s):  
Fractional Order ◽  
Time Domain ◽  
Li Ion Batteries ◽  
Simulation Framework ◽  
Frequency Range ◽  
Interesting Approach ◽  
Li Ion ◽  
Impedance Parameters ◽  
Impedance Models ◽  
Made In

This paper is a comparative study of the multiple RC, Oustaloup and Grünwald–Letnikov approaches for time domain implementations of fractional-order battery models. The comparisons are made in terms of accuracy, computational burden and suitability for the identification of impedance parameters from time-domain measurements. The study was performed in a simulation framework and focused on a set of ZARC elements, representing the middle frequency range of Li-ion batteries’ impedance. It was found that the multiple RC approach offers the best accuracy–complexity compromise, making it the most interesting approach for real-time battery simulation applications. As for applications requiring the identification of impedance parameters, the Oustaloup approach offers the best compromise between the goodness of the obtained frequency response and the accuracy–complexity requirements.

Discharge capacity estimation for Li-ion batteries based on particle filter under multi-operating conditions

Energy ◽  
2015 ◽  
Vol 86 ◽  
pp. 638-648 ◽  
Author(s):  
Junfu Li ◽  
Lixin Wang ◽  
Chao Lyu ◽  
Liqiang Zhang ◽  
Han Wang
Keyword(s):  
Particle Filter ◽  
Discharge Capacity ◽  
Operating Conditions ◽  
Li Ion Batteries ◽  
Capacity Estimation ◽  
Li Ion

On-line estimation of state-of-charge of Li-ion batteries in electric vehicle using the resampling particle filter

2014 ◽  
Vol 32 ◽  
pp. 207-217 ◽  
Author(s):  
Sai Shao ◽  
Jun Bi ◽  
Fan Yang ◽  
Wei Guan
Keyword(s):  
Particle Filter ◽  
Electric Vehicle ◽  
State Of Charge ◽  
Li Ion Batteries ◽  
On Line ◽  
Li Ion

Parameter Identification of Commercial Li-ion Batteries with Marine Predator Algorithm

2021 ◽  
Author(s):  
Shahenda M. Abdelhafiz ◽  
A. M. AbdelAty ◽  
M. E. Fouda ◽  
A. G. Radwan
Keyword(s):  
Parameter Identification ◽  
Li Ion Batteries ◽  
Marine Predator ◽  
Li Ion

scholarly journals Iterative Truncated Unscented Particle Filter

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 214
Author(s):  
Yanbo Wang ◽  
Fasheng Wang ◽  
Jianjun He ◽  
Fuming Sun
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
State Estimation ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Process Models ◽  
Filter Method ◽  
Proposal Distribution ◽  
Truncation Method ◽  
Unscented Particle Filter

The particle filter method is a basic tool for inference on nonlinear partially observed Markov process models. Recently, it has been applied to solve constrained nonlinear filtering problems. Incorporating constraints could improve the state estimation performance compared to unconstrained state estimation. This paper introduces an iterative truncated unscented particle filter, which provides a state estimation method with inequality constraints. In this method, the proposal distribution is generated by an iterative unscented Kalman filter that is supplemented with a designed truncation method to satisfy the constraints. The detailed iterative unscented Kalman filter and truncation method is provided and incorporated into the particle filter framework. Experimental results show that the proposed algorithm is superior to other similar algorithms.

Adaptive model parameter identification for large capacity Li-ion batteries on separated time scales

2016 ◽  
Vol 184 ◽  
pp. 119-131 ◽  
Author(s):  
Haifeng Dai ◽  
Tianjiao Xu ◽  
Letao Zhu ◽  
Xuezhe Wei ◽  
Zechang Sun
Keyword(s):  
Parameter Identification ◽  
Time Scales ◽  
Adaptive Model ◽  
Li Ion Batteries ◽  
Large Capacity ◽  
Model Parameter ◽  
Li Ion ◽  
Model Parameter Identification

scholarly journals Improved state of charge estimation for Li-ion batteries using fractional order extended Kalman filter

2019 ◽  
Vol 435 ◽  
pp. 226710 ◽  
Author(s):  
Kodjo S.R. Mawonou ◽  
Akram Eddahech ◽  
Didier Dumur ◽  
Dominique Beauvois ◽  
Emmanuel Godoy
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Fractional Order ◽  
State Of Charge ◽  
Li Ion Batteries ◽  
Li Ion ◽  
State Of Charge Estimation

A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation

2014 ◽  
Vol 123 ◽  
pp. 263-272 ◽  
Author(s):  
Xingtao Liu ◽  
Zonghai Chen ◽  
Chenbin Zhang ◽  
Ji Wu
Keyword(s):  
Particle Filter ◽  
State Of Charge ◽  
Li Ion Batteries ◽  
Li Ion ◽  
State Of Charge Estimation
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