Course Design and Implementation of Fault Diagnosis for New Energy Vehicle Power Batteries based on Deep Learning

Abstract

This paper focuses on the design and implementation of the course on fault diagnosis of power batteries for new energy vehicles, exploring the process of integrating deep learning technology with power battery fault diagnosis and transforming it into teaching content. It plans the teaching module of the diagnostic model, introduces the teaching methods and case implementation paths, and analyzes the teaching effect evaluation and the direction of course optimization. It clarifies the course positioning goals, enabling students to master the principles, fault modes, and model applications, and cultivate problem-solving abilities; it builds a content system centered on data, algorithms, etc., integrating knowledge from multiple disciplines; it designs a theory-practice balanced plan, plans progressive cases and projects, and builds a skill chain; it proposes diversified teaching assessment and resource construction ideas, establishes an assessment system, and discusses the construction of a hybrid experimental platform. The innovative features of the course lie in focusing on technology integration, emphasizing engineering practice, solving industrial problems, filling teaching gaps, providing a knowledge system, and delivering compound talents to support industrial development.