技術發展軌跡的分析框架-鋰離子電池電解質之案例研究

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研究生：	李明達 Ming-Ta Lee
論文名稱：	技術發展軌跡的分析框架-鋰離子電池電解質之案例研究 Framework for analyzing technological development trajectory – a case study of lithium-ion battery electrolytes
指導教授：	蘇威年 Wei-Nien Su
口試委員:	林瑞珠 Jui-Chu Lin 范建得 Chien-Te Fan 張宏展 Hong-Chan Chang 楊思源 Sih-Yuan Yang 蘇威年 Wei-Nien Su
學位類別：	博士 Doctor
系所名稱：	應用科技學院 - 應用科技研究所 Graduate Institute of Applied Science and Technology
論文出版年：	2021
畢業學年度：	109
語文別：	英文
論文頁數：	185
中文關鍵詞：	鋰離子電池、電解質、社會網路分析、專利分析、文字探勘分析
外文關鍵詞：	Lithium-ion battery, Electrolyte, Social network analysis, Patent analysis, Text mining analysis
相關次數：	點閱：384 下載：0
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查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

可充電式鋰離子電池是一種具有前途的能量存儲裝置，由於它們比其他的電池技術具有更長的壽命以及更高的能量和功率密度而應用於電動汽車中。在電池的元件組成中，電解質是用於控制電池性能的主要成分之一。為了研發適合於電動汽車中的電解質系統，許多以專利數據為核心的分析方法，例如基於專利計量的方法、基於專利引用的社交網路分析等以用於了解一段時間內的技術發展狀況。然而，對於能發表最新研究成果的學術期刊中的研究論文的分析較少。而且，所提及的個別分析方法都不能提供討論發展狀況的完整全貌。因此，本研究的目的是提供一種系統的分析方法，支持研究人員能從深入且全面的角度能夠清楚地了解鋰離子電池及電解質技術的發展狀況。

本研究所提供的系統性分析方法包含社會網路分析、具有指標的專利分析、以及文字探勘分析。此外，分析的資料同時包含專利及學術期刊資料庫以了解更廣闊的技術發展狀況。本研究的分析由社會網路分析開始，以找出在全世界中主要鋰離子電池技術開發者及其相對關係。而社會網路分析包含國家、機構和學術期刊的分析的三種態樣。基於國家的社會網路顯示出美國、中國、台灣、日本、韓國、新加坡、法國、意大利、瑞典、以及德國之10個主要國家在技術發展方面有著密切的聯繫。這些國家中的機構扮演著知識產生者或知識吸收者的角色。由學術期刊為分析目標的社會網路是特別用於探討知識流動的現象。具有高引用的學術期刊所組成的網路具有六個技術群聚，且無論電池和電解質類型如何，它們都表現出廣泛的知識移轉過程。這個現象表明不同類型技術的緊密交織關係。另外，由知識轉移路徑亦可以詳細描述技術演進過程。

本研究的第二部分是具有指標的專利分析，以重點探討專利權人的技術發展策略。宇部、三菱化學、松下電器、索尼、LG化學、以及三星SDI的六個專利權人是位於基於專利權人的社會網路圖的中心位置。此外，此六個專利權人擁有超過 50% 之所有專利權人所擁有的已核准專利數量，因此，此六個專利權人具有足夠的分析代表性。專利指標包含”修正”的Ernst指標，向前引用計量分析，技術功效矩陣，以用於比較六個專利權人的專利活動程度。由修正的Ernst指標表明專利權人對於電解質的發展具有不同的創新策略。而由向前引用計量分析及技術功效矩陣分析的結果顯示出使用混合鋰鹽及混合有機溶劑與特別添加劑之化合物是提高鋰離子電池性能的發展趨勢。

研究的最後一部分是利用文字探勘分析以深入探討六個專利權人的鋰鹽、有機溶劑和添加劑的技術內容。藉由文字探勘分析以繪製語義網絡圖以識別六個專利權人在技術發展策略的異同。分析的結果表明此些專利權人使用幾乎相同的混合鋰鹽及混合有機溶劑的系統。在混合鋰鹽的系統中，LiPF6、LiBF4和LiClO4等是廣泛使用的無機鋰鹽，而LiN(CF3SO2)2、LiN(C2F5SO2)2、LiCF3SO3等則是常用的有機鋰鹽。對於混合有機溶劑系統，乙烯碳酸酯、丙烯碳酸酯、伸乙烯碳酸酯等碳酸酯是最主要使用的液態有機溶劑。另外，酯類、醚類、酸酐類、乙酸酯類亦是此些專利權人所常使用的有機溶劑類別。這些技術語意詞在語義網絡圖中的節點尺寸大於其他節點，這代表這些技術語意詞在申請專利範圍中出現的次數較為頻繁。而此，此些技術語意詞彼此鏈結強度高，其表示此些鋰鹽化合物及有機溶劑種類通常一起在電解質中使用。最值得注意的部分是添加劑的開發，主要有五種添加劑技術：(1)氟代烷基碳酸酯，(2)苯類化合物，(3)內酯類化合物，(4)硫屬化合物，和(5)氨基甲酸烷基酯類化合物。此些添加劑化合物的技術語意詞在語義網絡圖中的節點尺寸較小，或與主要碳酸酯有機溶劑種類鏈結較弱，而很容易識別於網路中。除了鋰鹽、有機溶劑和添加劑之外，主要專利權人亦有開發聚合物電解質以做為另一種替代選擇。這些聚合物化合物包括乙二醇類，例如聚乙二醇、聚丙二醇、或其共聚物，以及聚偏二氟乙烯、聚丙烯腈、聚醯亞胺、聚醯胺，此些為較廣泛使用聚合物。

從上述的分析結果而言，本研究通過提出的分析框架：先以社會網路分析發現技術的整體發展狀況，再以具有指標的專利分析探討技術發展趨勢，最後以文字探勘分析深入挖掘技術內容，因而可以獲得對技術發展狀況的完整概覽。我們相信所屬技術研究人員可以通過這種獨特的分析方法來有效地設計研發計劃以促進可充電式鋰離子電池的發展。更特別的是，我們認為該方法能夠適用於許多其他技術領域的分析。

Rechargeable lithium-ion batteries (LIBs) are promising energy storages and now adopted in electric vehicles (EVs) because of their longer lifespan than other technologies along with higher energy and power densities. Besides, the electrolyte is one of the major components governing the performance of LIBs. To develop the appropriate electrolyte system, many kinds of analysis methods focusing on patent data, such as patent count-based method, social network analysis (SNA) with patent citation, are used to understand the development status in the period of time. However, less analysis is considered about the research papers in journals, which regularly publish the latest research findings. Besides, none of the mentioned individual approaches can provide a complete picture in discussing development status. Therefore, the purpose of the research is to provide a systematic analysis method for supporting researchers to clearly understand the development status of LIBs and electrolytes technologies from an in-depth and comprehensive perspective.

The proposed analysis methods include SNA, patent analysis with indicator, and text mining analysis. In addition, the patent and journal article database are both considered to discover a broad view of development status. The analysis starts with the SNA method to figure out the major technology developers and their relationship. Three kinds of social networks, including country-, institution-, and research paper-based analyses, have been applied. Country-based network shows that major countries, United States of America, China, Taiwan, Japan, Korea, Singapore, France, Italy, Sweden, and Germany have a close relationship for technology development. The institutions of those countries play the roles, i.e. knowledge producers or absorbers. Research paper-based social network is used to discuss the knowledge flow in particular. That network consisting of high-citation articles is grouped into six technology clusters. They exhibit a broad range of knowledge transfer processes regardless of the battery and electrolyte types, indicating the closely interwoven relationship behind them. The knowledge transfer path also can describe the technology evolution in detail.

Next, patent analysis with indicators is used to explore the development strategies of patent assignees in particular. The six patent assignees, Ube, Mitsubishi Chem., Panasonic, Sony, LG Chem., and Samsung SDI, locate at the central positions in the patent assignee-based social network map. Besides, these top six patent assignees owned more than 50% of total issued patents and are thought to be representative enough for the analysis. Several indicators, “modified” Ernst indicators, forward-citation counts, and technology-function matrix are used to compare the patent activities of these patent assignees. The “modified” Ernst indicators are applied to reflect different innovation strategies among these patent assignees. Forward-citation analysis and technology-function matrix show that using mixed lithium salts & organic solvents with novel additives compounds are the developing trends to improve the performance of LIBs.

The last part of the work applies text mining analysis to explore in-depth technical content of lithium salts, organic solvents, and additives without manual reading. The semantic network maps are drawn to identify the similarities and dissimilarities of top patent assignees’ development strategies. The findings demonstrate that patent assignees used almost the same lithium salts and organic solvents. LiPF6, LiBF4, and LiClO4 etc. are the widely used as inorganic salts, and LiN(CF3SO2)2, LiN(C2F5SO2)2, LiCF3SO3 etc. are organic salts. For mixed solvents system, ethylene carbonate, propylene carbonate, vinylene carbonate, and other carbonate are the most common examples. In addition, there have been some other solvents used by leading companies in the preparation of liquid electrolytes for LIBs such as ester, ether, acid anhydride, acetate, and their chain, cyclic derivatives. These technical terms are in large size, i.e. appearance frequently in the claim scope, and have strong connections, i.e. commonly used together. The most noteworthy part is the development of additives. There are main five kinds of additive technologies: (1) fluoro-group alkyl carbonates, (2) benzene derivatives, (3) lactone derivatives, (4) sulfur compound derivatives, and (5) alkyl carbamate, that mainly considered by the six patent assignees. Besides, polymer electrolyte is another choice that considers by those patent assignees. These polymer compounds include glycol, polyethylene glycol, polypropylene glycol, or copolymers, polyvinylidene fluoride, polyacrylonitrile, polyimide, polyamide, etc. that are widely used.

A complete picture of technological development information can be thus obtained through the analysis framework: discovering the overview development status by SNA, analyzing development trends by bibliometric analysis, and exploring in-depth technical content by text mining analysis. Furthermore, an objective development trend can be discovered by this work. It is believed that the researchers can effectively design their R&D plans to facilitate LIBs development by this unique approach. Besides, this method can also be applicable to many other fields of technology.

Contents
中文摘要 I
Abstract IV
Acknowledgments VII
List of Figures XI
List of Tables XIV
Chapter 1. Introduction 1
1.1 Electric vehicles and lithium-ion batteries 1
1.2 Analyzing technique for discovering the development status 7
1.3 Comments on existing analytical methods 12
Chapter 2.The data/information analysis techniques 15
2.1 Bibliometrics analysis with indicators 15
2.2 Text mining analysis techniques 18
2.2.1 Text mining approaches 18
2.2.2 Visualization techniques 24
Chapter 3. Research methodology 29
3.1 Patents and journal publications data collection 33
3.1.1 Extraction of patent database 34
3.1.2 Extraction of journal articles 37
3.2 Social network analysis (SNA) 41
3.2.1 Citation matrix 41
3.2.2 The social network analysis tool 54
3.3 Patent analysis with indicators 58
3.3.1 RGR and RDGR as a combined indicator 58
3.3.2 RPP and RPA as a combined indicator 59
3.3.3 Citation indicators 60
3.3.4 Technology-Function matrix 61
3.4 Text mining analysis 62
Chapter 4. SNA & network maps of journal publications database 65
4.1 Country-based social network 65
4.2 Institutions-based social network 69
4.2.1 Social network structure & “small-world” effect 69
4.2.2 Centrality analysis based on individual institutions 72
4.3 Research paper-based social network 77
4.3.1 Social network map – development time landscape 77
4.3.2 Centrality analysis based on individual research papers 80
4.3.3 Social network map –technology cluster and tracing knowledge flow 83
Chapter 5. Patent analysis of patent database 87
5.1 Patent assignees-based social network & structure properties 87
5.2 RGR & RDGR combination indicator 91
5.3 RPP & RPA combination indicator 94
5.4 The forward-citation information 96
5.4 Technology-Function Matrix 104
Chapter 6. Text mining analysis of claim scope 107
6.1 Semantic network analysis 107
6.2 Semantic network of claim scope of six major patent assignees 110
6.3 Comparison sub-group semantic network of six major patent assignees 112
6.3.1 Semantic network structure of the sub-group - organic solvents & additives 112
6.3.2 Semantic network structure of the sub-group - lithium salts 120
6.3.3 Semantic network structure of the sub-group - polymers 126
6.4 Multi-dimensional scaling (MDS) 131
Chapter 7. Conclusion and future outlook 153
7.1 Conclusion 153
7.2 Future outlook 157
Reference 159

List of Figures XI
Figure 1-1 Schematic representation of the main component of a lithium-ion battery 2
Figure 1-2 The SEI layer on graphite 4
Figure 1-3 Configuration of a Li/S cell with a dual-layer structural sulfur cathode 5
Figure 1-4 Schematic configuration of a Li/air cell with a gas diffusion cathode 6
Figure 1-5 TCL for Li-ion batteries based on the published number of all patents (1995–2018) 8
Figure 1-6 The number of patent families per country of origin 9
Figure 1-7 Patent citation network of assignee organizations 10
Figure 1-8 Country-Institute social network map 11
Figure 2-1 Patent portfolio on the level of technology fields 16
Figure 2-2 Patent Retrieval and Analysis Platform architecture 19
Figure 2-3 Architecture of TrendPerceptor 20
Figure 2-4 An overview of the PTCM techniques 21
Figure 2-5 The architecture of SIPMS for supporting invention 22
Figure 2-6 Proposed technological strategy planning model 23
Figure 2-7 A patent map based on a semantic network 24
Figure 2-8 Patent network in overall network level 25
Figure 2-9 Topical map in the field of surfactants and similar products 27
Figure 2-10 An example of bibliometric map from VOSviewer software 28
Figure 3-1 Research flow chart 32
Figure 3-2 Patents & journal publications database analysis timeline 33
Figure 4-1 Social network map of major ten countries – local position and their relationships (The node size proportional to institutions counts in the country. The number on link between two nodes shows citation counts. The link thickness is proportional to citation counts) 67
Figure 4-2 Social network map of the top 48 institutions-local position and their relationships (The size of the node is proportional to its publication counts of each institutions and nodes from the same country have the same color) 71
Figure 4-3 Centrality analysis of 48 institutions characterized by the degree of knowledge flow and Eigenvector centrality (X-axis is degree of knowledge, and Y-axis is Eigenvector centrality. Nodes from the same country have the same color) 76
Figure 4-4 Social network evolution through (a) 2011, (b) 2012, (c) 2013, (d) 2014, (e) 2015 years (Each node means one journal article) 79
Figure 4-5 (a) Technology cluster distribution; (b) knowledge transfer routes within an example node, PB4 (Each node means one journal article) 85
Figure 5-1 Social network map of the top 34 patent assignees-local position and their relationships (The size of the node is proportional to its patent counts of each patent assignee and nodes from the same country have the same color) 89
Figure 5-2 RGR & RDGR of six selected companies in the fields of LIB’s electrolyte technologies: (a) H01M10/0567 (additives), (b) H01M10/0568 (lithium salts), and (c) H01M10/0569 (solvents) (The dark color bar = RGR, the light color bar = RDGR) 93
Figure 5-3 RPA & RPP analysis of six companies based on three technological fields: (a) H01M10/0567 (additives), (b) H01M10/0568 (lithium salts), and (c) H01M10/0569 (solvents). For the purpose of clarity, only their RPA values were noted for easier comparison (Each pattern represents on patent assignee) 95
Figure 5-4 Top 10 patents with high forward-citation counts (The number means the forward-citation counts of each patent) 96
Figure 5-5 Bubble chart of technology-function matrix (The size of the bubble is proportional to its publication counts. The percentage pie chart of in the bubble represents the occupation of the six patent assignee) 105
Figure 6-1 An example of a semantic network map (Each node represents one semantic word) 108
Figure 6-2 Semantic network map after coloring each node according to its technology type (Each node according to its technology type: lithium salts is blue, organic solvents & additives is green, electrode materials is red, and polymer is purple. The size of the node is proportional to its frequency counts in all claim scope) 109
Figure 6-3 Organic solvents & additives technology of six selected patent assignees: (a) Ube, (b) Mitsubishi Chem., (c) Sony, (d) Panasonic, (e) LG Chem., (f) Samsung SDI (Each node represents one semantic word. The size of the node is proportional to its frequency counts in all claim scope. The link thickness is proportional to frequency counts of two nodes in pair) 118
Figure 6-4 Lithium salts technology sub-group of six selected patent assignees: (a) Ube, (b) Mitsubishi Chem., (c) Sony, (d) Panasonic, (e) LG Chem., (f) Samsung SDI (Each node represents one semantic word. The size of the node is proportional to its frequency counts in all claim scope. The link thickness is proportional to frequency counts of two nodes in pair) 124
Figure 6-5 Polymers technology sub-group of four selected patent assignees: (a) Sony, (b) Panasonic, (c) LG Chem., (d) Samsung SDI (Each node represents one semantic word. The size of the node is proportional to its frequency counts in all claim scope. The link thickness is proportional to frequency counts of two nodes in pair) 129
Figure 6-6 MDS (a) lithium salts, (b) organic solvents & additives (The bubble size of a patent assignee is proportional to its frequency counts in all claim scope. The number next to bubble means frequency counts. The relative positions of multiple bubble points are configured by the number of words of specific technology) 133

List of Tables XIV
Table 1-1 The common methods to explore the development status 13
Table 2-1 Ranking of patent portfolio strength in the chemical industry 17
Table 3-1 Node symbol and patent assignees 35
Table 3-2 Node symbol and institutions 37
Table 3-3 Node symbols, titles, and sources of journal publications 42
Table 3-4 Main structure features of a social network 54
Table 3-5 Features and comparison of six models of social network 57
Table 4-1 Centrality analysis of major 10 countries 68
Table 4-2 Comparison of institution-based social network features of this study and a random network 70
Table 4-3 Top 10 institutions with the highest centrality values 73
Table 4-4 Top 10 research papers with the highest Eigenvector centrality 82
Table 5-1 Comparison of patent assignee-based social network features of this study and a random network 88
Table 5-2 Patent count of top 6 patent assignees 90
Table 5-3 Patent information about top 10 powerful patents 98
Table 5-4 Information of representative patents 106
Table 6-1 The node numbers of the six patent assignees in the semantic network map before & after filtering 111
Table 6-2 The distribution percentage of technological terms of the six major patent assignees 111
Table 6-3 The major dissimilarities in the sub-group “organic solvents & additives” of six selected patent assignees 119
Table 6-4 The major dissimilarities in the sub-group “lithium salts” of six selected patent assignees 125
Table 6-5 The major dissimilarities in the sub-group “polymers” technologies of four selected patent assignees 130
Table 6-6 The semantic terms and frequency count in the semantic network of Ube’s claim 134
Table 6-7 The semantic terms and frequency count in the semantic network of Mitsubishi Chem.’s claim 137
Table 6-8 The semantic terms and frequency count in the semantic network of Sony’s claim 140
Table 6-9 The semantic terms and frequency count in the semantic network of Panasonic’s claim 143
Table 6-10 The semantic terms and frequency count in the semantic network of LG Chem.’s claim 145
Table 6-11 The semantic terms and frequency count in the semantic network of Samsung SDI’s claim 149
                                

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