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145 results on '"Liu, Kailong"'

1. Data Science-Based Full-Lifespan Management of Lithium-Ion Battery

Author

Liu, Kailong, Wang, Yujie, and Lai, Xin

Subjects
Lithium-ion Battery, Battery Manufacturing Management, Battery Operation Management, Battery Recycling Management, Data Science, Artificial Intelligence, Open Access, bic Book Industry Communication::T Technology, engineering, agriculture::TG Mechanical engineering & materials::TGM Materials science, bic Book Industry Communication::U Computing & information technology::UN Databases, bic Book Industry Communication::T Technology, engineering, agriculture::TH Energy technology & engineering
Abstract

This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed. As battery full-lifespan (manufacturing, operation, and reutilization) management is a hot research topic in both energy and AI fields and none specific book has focused on systematically describing this particular from a data science perspective before, this book can attract the attention of academics, scientists, engineers, and practitioners. It is useful as a reference book for students and graduates working in related fields. Specifically, the audience could not only get the basics of battery manufacturing, operation, and reutilization but also the information of related data-science technologies. The step-by-step guidance, comprehensive introduction, and case studies to the topic make it accessible to audiences of different levels, from graduates to experienced engineers.

Published
2022
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23. Advanced charging strategies for Lithium-ion batteries in electric vehicles

Author

Liu, Kailong, Li, Kang, and Douglas, Roy

Abstract

This thesis is focused on the development of advanced charging strategies for Li-ion batteries in electric vehicles. A literature survey about battery charging terminology, infrastructure, objective, and termination condition is presented firstly, followed by the introduction of some traditional but popular charging approaches and their improvement measures. Then some intelligent technologies including battery coupled thermoelectric model, heuristic algorithms such as teaching-learning-based optimization (TLBO) and multi-objective biogeography-based optimization (M-BBO), and generalized predictive control (GPC) are described. These literature surveys and intelligent technologies form the basis to conduct novel battery charging strategies. Then in order to derive an optimal CCCV charging profile, a specific triple-objective function is proposed, where TLBO is used to optimize this triple-objective function. From a higher level perspective, in order to analyse the relation between conflicting charging objectives, a model-based strategy to optimize charging profiles and also balance charging speed, energy conversion and temperature variation is proposed. The corresponding Pareto fronts are obtained by using M-BBO approaches. In addition to the optimization of conflicting objectives by searching the suitable charging profiles, an attempt is made to apply the constrained GPC to maintain the battery internal temperature within a desirable level while achieving fast charging. Simulation results confirm the effectiveness of these proposed charging strategies. All the proposed battery charging strategies in this thesis can be easily extended to other battery types. The results presented in this thesis are not only novel in the methodology development, but also significant in practical applications to manipulate the charge current for battery thermal management, guarantee charging efficiency and prolong battery service lifetime.

Published
2018

33. Revisiting the thermal decomposition mechanism of MAPbI3.

Author

Yang, Weijie, Shi, Ruiyang, Lu, Huan, Liu, Kailong, Yan, Qingqi, Bai, Yang, Ding, Xunlei, Li, Hao, and Gao, Zhengyang

Abstract

The thermal stability of MAPbI3 poses a challenge for the industry. To overcome this limitation, a thorough investigation of MAPbI3 is necessary. In this work, thermal gravimetric analysis (TGA) and Fourier transform infrared (FTIR) spectroscopy were conducted to identify the thermal decomposition products of MAPbI3, which were found to be CH3I, NH3, and PbI2. In situ X-ray diffraction (XRD) measurements were then performed in the temperature range from 300 to 700 K, which revealed the significant decomposition of the (110), (220), and (310) surfaces of MAPbI3 between 550 and 600 K. Density functional theory (DFT) calculations demonstrated that the (220) surface exhibited the highest stability. Additionally, the transition states of thermal decomposition showed that the energy barrier for the decomposition of the (110) surface was 2.07 eV. Our combined experimental and theoretical results provide a better understanding of the thermal decomposition mechanism of MAPbI3, providing valuable theoretical support for the design of long-term stable devices. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Data Science-Based Full-Lifespan Management of Lithium-Ion Battery. Manufacturing, Operation and Reutilization.

Author

Liu, Kailong, Lai, Xin, and Wang, Yujie

Subjects
Databases, Energy technology & engineering, Materials science, Artificial Intelligence, Battery Manufacturing Management, Battery Operation Management, Battery Recycling Management, Data Science, Lithium-ion Battery, Open Access
Abstract

Summary: This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed. As battery full-lifespan (manufacturing, operation, and reutilization) management is a hot research topic in both energy and AI fields and none specific book has focused on systematically describing this particular from a data science perspective before, this book can attract the attention of academics, scientists, engineers, and practitioners. It is useful as a reference book for students and graduates working in related fields. Specifically, the audience could not only get the basics of battery manufacturing, operation, and reutilization but also the information of related data-science technologies. The step-by-step guidance, comprehensive introduction, and case studies to the topic make it accessible to audiences of different levels, from graduates to experienced engineers.

47. Evolutionary multi-objective optimisation for large-scale portfolio selection with both random and uncertain returns

Author

Liu, Weilong, Zhang, Yong, Liu, Kailong, Quinn, Barry, Yang, Xingyu, and Peng, Qiao

Subjects
Uncertain random variable, ddc:330, Evolutionary computations, Multi-objective optimisation, Large-scale investment, Portfolio optimisation
Abstract

With the advent of Big Data, managing large-scale portfolios of thousands of securities is one of the most challenging tasks in the asset management industry. This study uses an evolutionary multi objective technique to solve large-scale portfolio optimisation problems with both long-term listed and newly listed securities. The future returns of long-term listed securities are defined as random variables whose probability distributions are estimated based on sufficient historical data, while the returns of newly listed securities are defined as uncertain variables whose uncertainty distribution are estimated based on experts' knowledge. Our approach defines security returns as theoretically uncertain random variables and proposes a three-moment optimisation model with practical trading constraints. In this study, a framework for applying arbitrary multi-objective evolutionary algorithms to portfolio optimisation is established, and a novel evolutionary algorithm based on large-scale optimisation techniques is developed to solve the proposed model. The experimental results show that the proposed algorithm outperforms state-of-the-art evolutionary algorithms in large-scale portfolio optimisation.

Published
2023

48. Chapter 1 - Introduction

Author

Li, Bowen, Fernandez, Carlos, Yu, Chunmei, Liu, Donglei, Stroe, Daniel-Ioan, Li, Huan, Liu, Kailong, Xia, Lili, Yu, Peng, Wang, Shunli, Shi, Weihao, Ji, Weikang, Xu, Wenhua, Xiao, Xianyong, and Yang, Xiaoyong

Published
2023
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