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題名 從動力電池專利看中國電動車的後進發展策略
Examining China's Latecomer Development Strategy in the Electric Vehicle Industry through Power Battery Patents作者 劉奕
Liu, Yi貢獻者 李浩仲<br>李文傑
Li, Hao-Chung<br>Lee, Wen-Chieh
劉奕
Liu, Yi關鍵詞 動力電池
電動車電池
專利文本分析
技術進步率
專利引用網絡
Electric vehicle battery
Patent text analysis
Technological improvement rate
Patent Citation Network日期 2025 上傳時間 1-Sep-2025 15:46:30 (UTC+8) 摘要 隨著全球暖化的問題日益嚴重，各國紛紛制定淨零碳排目標，並陸續推動禁售燃油車政策與規劃，進而加速電動車產業的發展。然而，電動車仍面臨價格高昂與續航力不足等挑戰，又電動車電池約佔電動車整車成本的30～40%，故動力電池為未來發展的關鍵核心技術之一。本研究以動力電池專利為研究對象，運用專利資料去探討其技術發展趨勢，進一步解析不同技術領域的發展潛力與路徑。研究方法包括詞頻與共現詞分析，從專利文本中萃取核心技術詞彙與研發趨勢，並依據Triulz等人（2020）所提的模型，計算各技術子領域的技術進步率，以評估其未來潛力，並以Park和Magee（2017）中的主路徑分析方法，建構技術知識發展路徑。資料來源涵蓋美國與中國專利局，自1990年至2022年共計數十萬筆專利資料，並將動力電池依產業鏈區分為上游（極板原物料、電解質、隔離膜）、中游（極板製造、電池芯組裝）與下游（電池模組組裝、電池管理系統、熱管理系統）技術子領域。研究結果顯示中國聚焦於製程優化與性能提升，上中游的技術布局集中在極板原材料與製造，發展重點包含複合材料、奈米結構材料及正極材料回收技術；下游則以電池管理系統為核心，發展智慧化監控與無線充電等應用，亦顯示其技術導向實作與應用能力。全球產業發展方面，上游的極板材料技術已趨於成熟，研發重點逐步轉向固態電解質，其具備高能量密度與高電壓穩定性等潛力，技術進步率逐年提升；中游領域則延續極板與塗佈材料創新，強調上中游整合；下游則處於相對成熟階段，電池管理系統架構穩定，並朝向無線充電技術與智慧化管理系統等應用面向積極拓展。
In response to climate change, global net-zero targets and bans on fuel-powered vehicles are accelerating the electric vehicle (EV) industry. Despite this momentum, EVs still face challenges such as high costs and limited range. As the battery accounts for 30–40% of an EV’s cost, power batteries have become a core technology for future advancement. This study analyzes power battery patents to explore technological development trends and future trajectories across different domains. Using patent data from the USPTO and CNIPA (1990–2022), we classify technologies into upstream (e.g., electrode materials, electrolytes, separators), midstream (e.g., electrode manufacturing, cell assembly), and downstream (e.g., module assembly, battery management, thermal systems). Methods include term frequency and co-word analysis to identify key R&D trends, Triulz et al.’s (2020) model to calculate technological improvement rates, and main path analysis by Park and Magee (2017) to map knowledge flows. Findings indicate that China's upstream and midstream efforts prioritize process optimization and performance enhancement, with a particular emphasis on composite and nanostructured electrode materials and electrode recycling. Downstream efforts center on intelligent battery management and wireless charging, demonstrating strong integration and application capacity. Globally, upstream technologies are maturing, with growing interest in solid-state electrolytes. Midstream innovation stresses material advancement and vertical integration, while downstream technologies are relatively mature and shifting toward smart, wireless applications.參考文獻 中文文獻 李旭弘（2023）。七層開箱電動車動力電池系統。綠學院，取自 https://greenimpact.cc/Articles/detail?cid=1&id=486。 林匯凱（2021）。電動車的心臟－電池產業鏈中的主要玩家有哪些？臺灣新創資訊平台，取自https://findit.org.tw/Res/1866。 英文文獻 Callon, M., Courtial, J.-P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co‑word analysis. Social Science Information, 22(2), 191–235. Farmer, J. D., & Lafond, F. (2016). How predictable is technological progress? Research Policy, 45(3), 647–665. Feng, S., & Magee, C. L. (2020). Technological development of key domains in electric vehicles: Improvement rates, technology trajectories and key assignees. Applied Energy, 260, 114264. Golembiewski, B., Vom Stein, N., Sick, N., & Wiemhöfer, H. D. (2015). Identifying trends in battery technologies with regard to electric mobility: evidence from patenting activities along and across the battery value chain. Journal of Cleaner Production, 87, 800-810. Gong, H., & Hansen, T. (2023). The rise of China’s new energy vehicle lithium-ion battery industry: The coevolution of battery technological innovation systems and policies. Environmental Innovation and Societal Transitions, 46, 100689. Hummon, N. P., & Doreian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social Networks, 11(1), 39–63. Kelly, B., Papanikolaou, D., Seru, A., & Taddy, M. (2020). Measuring technological innovation over the long run. Working paper, 25266. Koh, H., & Magee, C. L. (2006). A functional approach for studying technological progress: Application to information technology. Technological Forecasting & Social Change, 73(9), 1061–1083. Lee, S., Lee, S., Seol, H., & Park, Y. (2008). Using patent information for designing new product and technology: Keyword-based technology roadmapping. R&D Management, 38(2), 169–188. Li, Y.-R., Wang, L.-H., & Hong, C.-F. (2009). Extracting the significant-rare keywords for patent analysis. Expert Systems with Applications, 36(3, 1), 5200–5204. Li, W., Yang, M., & Sandu, S. (2018). Electric vehicles in China: A review of current policies. Energy & Environment, 0(0), 1–13. Martinelli, A., & Nomaler, Ö. (2014). Measuring knowledge persistence: A genetic approach to patent citation networks. Journal of Evolutionary Economics, 24(3), 623–652. Park, H., & Magee, C. L. (2017). Tracing technological development trajectories: A genetic knowledge persistence-based main path approach. PloS one, 12(1), e0170895. Singh, A., Triulzi, G., & Magee, C. L. (2023). Technological improvement rate predictions for all technologies: Use of patent data and an extended domain description. Research Policy, 50, 104249 Tian, J., Wang, P., & Zhu, D. (2024). Overview of Chinese new energy vehicle industry and policy development. Green Energy and Resources, 2, 100075. Triulzi, G., Alstott, J., & Magee, C. L. (2020). Estimating technology performance improvement rates by mining patent data. Technological Forecasting and Social Change, 158, 120100. Tseng, Yuen‑Hsien; Lin, Chi‑Jen; Lin, Yu‑I. (2007). Text mining techniques for patent analysis. Information Processing & Management, 43(5), 1216–1247. Yoon, B., & Park, Y. (2004). A text‐mining‐based patent network: Analytical tool for high‐technology trend. Journal of High Technology Management Research, 15(1), 37–50. 2024 EV and ESS Battery Sales Volume by Makers, Retrieved February 25 2025, from https://www.sneresearch.com/en/insight/release_view/381/page/0 描述 碩士
國立政治大學
經濟學系
112258037資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112258037 資料類型 thesis dc.contributor.advisor 李浩仲<br>李文傑 zh_TW dc.contributor.advisor Li, Hao-Chung<br>Lee, Wen-Chieh en_US dc.contributor.author (Authors) 劉奕 zh_TW dc.contributor.author (Authors) Liu, Yi en_US dc.creator (作者) 劉奕 zh_TW dc.creator (作者) Liu, Yi en_US dc.date (日期) 2025 en_US dc.date.accessioned 1-Sep-2025 15:46:30 (UTC+8) - dc.date.available 1-Sep-2025 15:46:30 (UTC+8) - dc.date.issued (上傳時間) 1-Sep-2025 15:46:30 (UTC+8) - dc.identifier (Other Identifiers) G0112258037 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/159206 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 經濟學系 zh_TW dc.description (描述) 112258037 zh_TW dc.description.abstract (摘要) 隨著全球暖化的問題日益嚴重，各國紛紛制定淨零碳排目標，並陸續推動禁售燃油車政策與規劃，進而加速電動車產業的發展。然而，電動車仍面臨價格高昂與續航力不足等挑戰，又電動車電池約佔電動車整車成本的30～40%，故動力電池為未來發展的關鍵核心技術之一。本研究以動力電池專利為研究對象，運用專利資料去探討其技術發展趨勢，進一步解析不同技術領域的發展潛力與路徑。研究方法包括詞頻與共現詞分析，從專利文本中萃取核心技術詞彙與研發趨勢，並依據Triulz等人（2020）所提的模型，計算各技術子領域的技術進步率，以評估其未來潛力，並以Park和Magee（2017）中的主路徑分析方法，建構技術知識發展路徑。資料來源涵蓋美國與中國專利局，自1990年至2022年共計數十萬筆專利資料，並將動力電池依產業鏈區分為上游（極板原物料、電解質、隔離膜）、中游（極板製造、電池芯組裝）與下游（電池模組組裝、電池管理系統、熱管理系統）技術子領域。研究結果顯示中國聚焦於製程優化與性能提升，上中游的技術布局集中在極板原材料與製造，發展重點包含複合材料、奈米結構材料及正極材料回收技術；下游則以電池管理系統為核心，發展智慧化監控與無線充電等應用，亦顯示其技術導向實作與應用能力。全球產業發展方面，上游的極板材料技術已趨於成熟，研發重點逐步轉向固態電解質，其具備高能量密度與高電壓穩定性等潛力，技術進步率逐年提升；中游領域則延續極板與塗佈材料創新，強調上中游整合；下游則處於相對成熟階段，電池管理系統架構穩定，並朝向無線充電技術與智慧化管理系統等應用面向積極拓展。 zh_TW dc.description.abstract (摘要) In response to climate change, global net-zero targets and bans on fuel-powered vehicles are accelerating the electric vehicle (EV) industry. Despite this momentum, EVs still face challenges such as high costs and limited range. As the battery accounts for 30–40% of an EV’s cost, power batteries have become a core technology for future advancement. This study analyzes power battery patents to explore technological development trends and future trajectories across different domains. Using patent data from the USPTO and CNIPA (1990–2022), we classify technologies into upstream (e.g., electrode materials, electrolytes, separators), midstream (e.g., electrode manufacturing, cell assembly), and downstream (e.g., module assembly, battery management, thermal systems). Methods include term frequency and co-word analysis to identify key R&D trends, Triulz et al.’s (2020) model to calculate technological improvement rates, and main path analysis by Park and Magee (2017) to map knowledge flows. Findings indicate that China's upstream and midstream efforts prioritize process optimization and performance enhancement, with a particular emphasis on composite and nanostructured electrode materials and electrode recycling. Downstream efforts center on intelligent battery management and wireless charging, demonstrating strong integration and application capacity. Globally, upstream technologies are maturing, with growing interest in solid-state electrolytes. Midstream innovation stresses material advancement and vertical integration, while downstream technologies are relatively mature and shifting toward smart, wireless applications. en_US dc.description.tableofcontents 第一章 緒論 1 第一節 研究背景 1 第二節 電動車動力電池銷售現況 2 第三節 中國的產業政策與崛起 3 第四節 專利價值重要性 5 第二章 文獻回顧 7 第一節 電動車電池產業鏈 7 第二節 文本探勘分析 8 第三節 技術進步率 9 第四節 專利引用主路徑分析 10 第三章 研究方法 11 第一節 專利文本探勘與關鍵詞分析 11 第二節 技術進步率估計 12 第三節 專利引用主路徑分析 14 第四章 資料 17 第一節 資料來源 17 第二節 電池產業鏈專利與專利引用建構 17 第三節 文本分析與文字處理 21 第五章 實證結果 23 第一節 資料敘述統計 23 第二節 中國與他國各子領域關鍵字對比 26 一、上游極板原材料 26 二、上游電解質 28 三、上游隔離膜 30 四、中游極板製造 32 五、中游電池芯組裝 34 六、下游電池模組組裝 36 七、下游電池管理系統 38 八、下游熱管理系統 40 第三節 技術進步率的估計 42 第四節 主路徑分析與高持續性專利 47 一、美國專利局上游極板原材料 47 二、美國專利局中游極板製造 49 三、美國專利局下游電池管理系統 50 四、中國專利局上游極板原材料 51 五、中國專利局中游極板製造 52 六、中國專利局下游電池管理系統 53 第六章 結論 54 參考文獻 56 zh_TW dc.format.extent 2995983 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112258037 en_US dc.subject (關鍵詞) 動力電池 zh_TW dc.subject (關鍵詞) 電動車電池 zh_TW dc.subject (關鍵詞) 專利文本分析 zh_TW dc.subject (關鍵詞) 技術進步率 zh_TW dc.subject (關鍵詞) 專利引用網絡 zh_TW dc.subject (關鍵詞) Electric vehicle battery en_US dc.subject (關鍵詞) Patent text analysis en_US dc.subject (關鍵詞) Technological improvement rate en_US dc.subject (關鍵詞) Patent Citation Network en_US dc.title (題名) 從動力電池專利看中國電動車的後進發展策略 zh_TW dc.title (題名) Examining China's Latecomer Development Strategy in the Electric Vehicle Industry through Power Battery Patents en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 中文文獻 李旭弘（2023）。七層開箱電動車動力電池系統。綠學院，取自 https://greenimpact.cc/Articles/detail?cid=1&id=486。 林匯凱（2021）。電動車的心臟－電池產業鏈中的主要玩家有哪些？臺灣新創資訊平台，取自https://findit.org.tw/Res/1866。 英文文獻 Callon, M., Courtial, J.-P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co‑word analysis. Social Science Information, 22(2), 191–235. Farmer, J. D., & Lafond, F. (2016). How predictable is technological progress? Research Policy, 45(3), 647–665. Feng, S., & Magee, C. L. (2020). Technological development of key domains in electric vehicles: Improvement rates, technology trajectories and key assignees. Applied Energy, 260, 114264. Golembiewski, B., Vom Stein, N., Sick, N., & Wiemhöfer, H. D. (2015). Identifying trends in battery technologies with regard to electric mobility: evidence from patenting activities along and across the battery value chain. Journal of Cleaner Production, 87, 800-810. Gong, H., & Hansen, T. (2023). The rise of China’s new energy vehicle lithium-ion battery industry: The coevolution of battery technological innovation systems and policies. Environmental Innovation and Societal Transitions, 46, 100689. Hummon, N. P., & Doreian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social Networks, 11(1), 39–63. Kelly, B., Papanikolaou, D., Seru, A., & Taddy, M. (2020). Measuring technological innovation over the long run. Working paper, 25266. Koh, H., & Magee, C. L. (2006). A functional approach for studying technological progress: Application to information technology. Technological Forecasting & Social Change, 73(9), 1061–1083. Lee, S., Lee, S., Seol, H., & Park, Y. (2008). Using patent information for designing new product and technology: Keyword-based technology roadmapping. R&D Management, 38(2), 169–188. Li, Y.-R., Wang, L.-H., & Hong, C.-F. (2009). Extracting the significant-rare keywords for patent analysis. Expert Systems with Applications, 36(3, 1), 5200–5204. Li, W., Yang, M., & Sandu, S. (2018). Electric vehicles in China: A review of current policies. Energy & Environment, 0(0), 1–13. Martinelli, A., & Nomaler, Ö. (2014). Measuring knowledge persistence: A genetic approach to patent citation networks. Journal of Evolutionary Economics, 24(3), 623–652. Park, H., & Magee, C. L. (2017). Tracing technological development trajectories: A genetic knowledge persistence-based main path approach. PloS one, 12(1), e0170895. Singh, A., Triulzi, G., & Magee, C. L. (2023). Technological improvement rate predictions for all technologies: Use of patent data and an extended domain description. Research Policy, 50, 104249 Tian, J., Wang, P., & Zhu, D. (2024). Overview of Chinese new energy vehicle industry and policy development. Green Energy and Resources, 2, 100075. Triulzi, G., Alstott, J., & Magee, C. L. (2020). Estimating technology performance improvement rates by mining patent data. Technological Forecasting and Social Change, 158, 120100. Tseng, Yuen‑Hsien; Lin, Chi‑Jen; Lin, Yu‑I. (2007). Text mining techniques for patent analysis. Information Processing & Management, 43(5), 1216–1247. Yoon, B., & Park, Y. (2004). A text‐mining‐based patent network: Analytical tool for high‐technology trend. Journal of High Technology Management Research, 15(1), 37–50. 2024 EV and ESS Battery Sales Volume by Makers, Retrieved February 25 2025, from https://www.sneresearch.com/en/insight/release_view/381/page/0 zh_TW
