Parameter identification of electrical and thermal model of a lithium polymer battery using particle swarm optimization

In recent years, the demand for lightweight, high-endurance batteries has driven a significant shift in battery cell chemistry, particularly toward Lithium-ion (Li-ion) types. These chemistries offer advantages such as higher volumetric and gravimetric energy densities, making them suitable for appl...

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Main Author: Tade Hrutuja Sanju
Other Authors: Yun Yang
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/180713
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1807132024-10-25T15:45:29Z Parameter identification of electrical and thermal model of a lithium polymer battery using particle swarm optimization Tade Hrutuja Sanju Yun Yang School of Electrical and Electronic Engineering yun.yang@ntu.edu.sg Engineering Lithium polymer battery (LiP) Li-ion batteries Parameter identification Particle swarm optimization (PSO) Battery management system (BMS) Battery electrical model Battery thermal model Dual time window method Optimization algorithm Equivalent circuit model (ECM) Real-time monitoring In recent years, the demand for lightweight, high-endurance batteries has driven a significant shift in battery cell chemistry, particularly toward Lithium-ion (Li-ion) types. These chemistries offer advantages such as higher volumetric and gravimetric energy densities, making them suitable for applications requiring lower weight and higher energy storage. Lithium Polymer (LiP) batteries is a type of Li-ion technology. LiP batteries differ from other Li-ion technologies primarily due to their use of a solid or gel-like polymer electrolyte instead of the liquid electrolyte found in typical Li-ion batteries. This structural change allows LiP batteries to be lighter and more flexible, making them suitable for compact, portable devices. However, these benefits come with the challenge of modelling the complex behaviours of Li-ion batteries. The accurate representation of their electrical and thermal dynamics is crucial for performance prediction and management, but traditional identification methods often fail to capture their behaviour with precision, leading to inaccurate models.To overcome the limitations of conventional methods, this project focuses on using the Particle Swarm Optimization (PSO) algorithm for parameter estimation in LiP battery models. PSO, an evolutionary algorithm inspired by the social behaviour of bird flocking, excels in optimization problems and offers a robust solution to parameter identification. By iteratively adjusting and refining the parameters using real-world experimental data, PSO helps to significantly enhance the accuracy of electrical and thermal model. A pulse discharge test and thermal tests are conducted to obtain experimental data, which is then used as input for the PSO algorithm. The test data is compared against the simulated curves generated by the model, ensuring that the identified parameters align with the actual battery behaviour. The results indicate a high correlation between the experimental data and the responses predicted by the model, validating the effectiveness of PSO in improving parameter estimation. This approach not only addresses the shortcomings of traditional methods but also provides a pathway for the development of a more reliable digital twin for LiP batteries. By utilizing PSO, this project offers a promising solution for enhancing the precision of battery models, with potential applications in the optimization of battery performance in fields such as electric vehicles, drones, and portable electronics. This work contributes to the broader goal of advancing battery technology and addressing the growing demand for efficient energy storage solutions. Master's degree 2024-10-21T23:30:05Z 2024-10-21T23:30:05Z 2024 Thesis-Master by Coursework Tade Hrutuja Sanju (2024). Parameter identification of electrical and thermal model of a lithium polymer battery using particle swarm optimization. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/180713 https://hdl.handle.net/10356/180713 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Lithium polymer battery (LiP)
Li-ion batteries
Parameter identification
Particle swarm optimization (PSO)
Battery management system (BMS)
Battery electrical model
Battery thermal model
Dual time window method
Optimization algorithm
Equivalent circuit model (ECM)
Real-time monitoring
spellingShingle Engineering
Lithium polymer battery (LiP)
Li-ion batteries
Parameter identification
Particle swarm optimization (PSO)
Battery management system (BMS)
Battery electrical model
Battery thermal model
Dual time window method
Optimization algorithm
Equivalent circuit model (ECM)
Real-time monitoring
Tade Hrutuja Sanju
Parameter identification of electrical and thermal model of a lithium polymer battery using particle swarm optimization
description In recent years, the demand for lightweight, high-endurance batteries has driven a significant shift in battery cell chemistry, particularly toward Lithium-ion (Li-ion) types. These chemistries offer advantages such as higher volumetric and gravimetric energy densities, making them suitable for applications requiring lower weight and higher energy storage. Lithium Polymer (LiP) batteries is a type of Li-ion technology. LiP batteries differ from other Li-ion technologies primarily due to their use of a solid or gel-like polymer electrolyte instead of the liquid electrolyte found in typical Li-ion batteries. This structural change allows LiP batteries to be lighter and more flexible, making them suitable for compact, portable devices. However, these benefits come with the challenge of modelling the complex behaviours of Li-ion batteries. The accurate representation of their electrical and thermal dynamics is crucial for performance prediction and management, but traditional identification methods often fail to capture their behaviour with precision, leading to inaccurate models.To overcome the limitations of conventional methods, this project focuses on using the Particle Swarm Optimization (PSO) algorithm for parameter estimation in LiP battery models. PSO, an evolutionary algorithm inspired by the social behaviour of bird flocking, excels in optimization problems and offers a robust solution to parameter identification. By iteratively adjusting and refining the parameters using real-world experimental data, PSO helps to significantly enhance the accuracy of electrical and thermal model. A pulse discharge test and thermal tests are conducted to obtain experimental data, which is then used as input for the PSO algorithm. The test data is compared against the simulated curves generated by the model, ensuring that the identified parameters align with the actual battery behaviour. The results indicate a high correlation between the experimental data and the responses predicted by the model, validating the effectiveness of PSO in improving parameter estimation. This approach not only addresses the shortcomings of traditional methods but also provides a pathway for the development of a more reliable digital twin for LiP batteries. By utilizing PSO, this project offers a promising solution for enhancing the precision of battery models, with potential applications in the optimization of battery performance in fields such as electric vehicles, drones, and portable electronics. This work contributes to the broader goal of advancing battery technology and addressing the growing demand for efficient energy storage solutions.
author2 Yun Yang
author_facet Yun Yang
Tade Hrutuja Sanju
format Thesis-Master by Coursework
author Tade Hrutuja Sanju
author_sort Tade Hrutuja Sanju
title Parameter identification of electrical and thermal model of a lithium polymer battery using particle swarm optimization
title_short Parameter identification of electrical and thermal model of a lithium polymer battery using particle swarm optimization
title_full Parameter identification of electrical and thermal model of a lithium polymer battery using particle swarm optimization
title_fullStr Parameter identification of electrical and thermal model of a lithium polymer battery using particle swarm optimization
title_full_unstemmed Parameter identification of electrical and thermal model of a lithium polymer battery using particle swarm optimization
title_sort parameter identification of electrical and thermal model of a lithium polymer battery using particle swarm optimization
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/180713
_version_ 1814777813848293376