Semantic Embedding Models for Technical Documentation Analysis in Battery Management Systems Education

The increasing adoption of electric vehicles (EVs) has highlighted the critical role of battery management systems (BMS) in ensuring safety, efficiency, and longevity of energy storage components, emphasizing the need for effective educational tools to train future engineers and technicians. Traditional BMS training relies heavily on static manuals, unstructured technical documents, and lecture-based instruction, which limits learners' ability to quickly extract, relate, and apply complex technical information. To address this challenge, this paper proposes a Semantic Embedding Model for Technical Documentation Analysis in Battery Management Systems Education (SEM-BMS), which leverages advanced natural language processing (NLP) techniques, including domain-specific embeddings and semantic similarity analysis, to automatically extract key concepts, relationships, and procedures from diverse BMS documentation. SEM-BMS organizes the extracted knowledge into structured, interactive knowledge maps, enabling adaptive learning, efficient information retrieval, and enhanced comprehension of intricate battery operation concepts. Experimental evaluation on a curated dataset of BMS manuals, technical reports, and instructional notes demonstrated that SEM-BMS achieved a 92 % accuracy in concept extraction, improved knowledge retrieval efficiency by 36 %, and increased student learning outcomes by 29 % compared to conventional keyword-based and lecture-driven methods. These results confirm that semantic embedding-based analysis significantly enhances both accessibility and quality of BMS education. In conclusion, SEMBMS provides a scalable, intelligent, and interactive framework for structuring unstructured technical content, bridging the gap between theoretical knowledge and practical understanding, and fostering a more effective learning environment for battery management systems education.

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