| 摘要: | 在全球能源轉型與2050淨零碳排政策驅動下，電動車與儲能需求快速增長，鋰離子電池技術因其高能量密度與長循環壽命成為核心支撐。P公司自2006年成立以來，專注固態鋰陶瓷電池研發與量產，涵蓋材料、製程、設備及安全機制，並在車用市場中持續投資。塗佈環節作為電池整體性能關鍵，影響產品品質達80%。因此，本研究選擇P公司塗佈製程為標的，旨在釐清材料、製程與設備等多重因子對產品一致性及品質的貢獻來源。研究首先蒐集鋰離子電池發展與塗佈工藝相關文獻，並依據製程重點設定分析指標；進一步運用 Python 在 Jupyter 平台構建巢狀ANOVA與混合效應模型，對多個因子進行變異來源定量分析，並比較不同模型的適配效果；最終透過變異成分佔比，收斂出影響最大之關鍵因子，提出針對性改善建議。透過本研究，期望提升P公司塗佈製程的一致性與良率，降低製造資源浪費，並為未來固態鋰離子電池製程優化提供實質參考，進而推動電動車與儲能系統的普及與綠色轉型;Under the driving force of global energy transition and the 2050 net-zero carbon emission policy, demand for electric vehicles and energy storage has surged. Lithium-ion battery technology, with its high energy density and long cycle life, has become a core enabler. Since its founding in 2006, Company P has focused on the research, development, and mass production of solid-state lithium-ceramic batteries, covering materials, processes, equipment, and safety mechanisms, and continues to invest heavily in the automotive market. The coating stage is critical—accounting for up to 80 % of overall cell performance. Therefore, this study targets Company P’s coating process to clarify how multiple factors—such as materials, process parameters, and equipment—contribute to product consistency and quality. First, relevant literature on lithium-ion battery development and coating techniques was collected, and key process indicators were defined. Next, Python was used on the Jupyter platform to build nested ANOVA and mixed-effects models, quantitatively analyzing the sources of variation across multiple factors and comparing model fit. Finally, by examining the proportion of variance components, the most influential factors were identified, and targeted improvement recommendations were proposed. Through this research, we aim to enhance the consistency and yield of Company P’s coating process, reduce manufacturing waste, and provide practical guidance for future optimization of solid-state lithium-ion battery manufacturing processes—ultimately promoting the widespread adoption of electric vehicles and energy storage systems in the green transition |
