A noise‐immune model identification method for lithium‐ion battery using two‐swarm cooperative particle swarm optimization algorithm based on adaptive dynamic sliding window

Summary Accurate and reliable model parameters are not only a prerequisite for model‐based estimation but also a significant part of battery operating characteristics. However, the measurement signal inevitably contains noise, which brings great challenges to model identification. This paper focuses...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:International journal of energy research Ročník 46; číslo 3; s. 3512 - 3528
Hlavní autori: Zhu, Yongjie, Chen, Jiajun, Mao, Ling, Zhao, Jinbin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Chichester, UK John Wiley & Sons, Inc 10.03.2022
Predmet:
Accuracy
Adaptive algorithms
battery management
dynamic sliding window
Feature extraction
Identification
Identification methods
Immunity
Lithium
Lithium-ion batteries
Mathematical models
model identification
Noise
noise corruption
Noise measurement
Optimization
Parameter identification
Parameters
Particle swarm optimization
Redundancy
Sliding
Slumping
state of charge
ISSN:0363-907X, 1099-114X
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Summary Accurate and reliable model parameters are not only a prerequisite for model‐based estimation but also a significant part of battery operating characteristics. However, the measurement signal inevitably contains noise, which brings great challenges to model identification. This paper focuses on the noise immunity performance of model identification based on two‐swarm cooperative particle swarm optimization. An adaptive dynamic sliding window based on the current rate criterion and the identification results feedback is designed to avoid data redundancy and improve the robustness of model identification. The model parameters are obtained using two‐swarm cooperative particle swarm optimization based on the adaptive dynamic sliding window. The proposed method effectively improves the accuracy and speed of parameter identification through optimization of data fragments and particle update rules. Compared with two existing parameter identification methods, simulation studies illustrate that the average mean square deviation of the proposed method is reduced by at least 35 dB. The proposed method is superior to existing parameter identification methods in noise immunity performance, parameter identification reliability, and state‐of‐charge estimation accuracy. By employing the proposed method, the maximum errors of state‐of‐charge estimation are limited within 1% under experimental verification. The experiment results verify that the proposed method has the potential to extract reliable model features online.
Bibliografia:Funding information
National Natural Science Foundation of China, Grant/Award Number: 51777120
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0363-907X
1099-114X
DOI:10.1002/er.7401