Abstract
Abstract
As the global transition to electric vehicles (EVs) accelerates, predicting the industrial breakout nodes of next-generation energy storage technologies has become a critical challenge for engineering R&D and strategic planning. Traditional technology forecasting primarily relies on lagging indicators such as regional patent filings or publication volumes, which fail to capture the early consensus forming across global engineering communities. To address this limitation, this paper proposes a data-driven technology forecasting framework named Cross-lingual Temporal BERTopic Fusion (CTBF). By curating an initial corpus of 1.2 million raw records into a final analytical dataset of 412,583 unique EV energy storage documents (1995–2025), we utilize advanced deep learning (Orthogonal Procrustes Alignment and Temporal Convolutional Networks) to extract the temporal adoption lag of core battery technologies across global literature. Our framework successfully maps the 30-year evolutionary Sankey flow of EV batteries, clearly illustrating the industry's paradigm shift from fundamental electrochemistry (e.g., cell materials) to system-level engineering (e.g., thermal management and BMS). Furthermore, ablation studies demonstrate that incorporating this global engineering intelligence reduces the Mean Absolute Percentage Error (MAPE) in breakout prediction by 22.4%. Ultimately, the model issues high-confidence early warnings for the industrialization of "Solid-state Interface Impedance Optimization" and "DRL-based V2G Microgrids" in 2026–2028. This research provides a proactive, data-driven intelligence tool for EV battery engineering and industrial layout.