摘 要
随着全球能源危机和环境污染问题的加剧，电动汽车作为绿色交通的重要发展方向，其核心部件电池管理系统（BMS）的性能优化与故障诊断成为研究热点。本研究旨在针对电动汽车电池管理系统中潜在故障的早期识别与预警展开深入探讨，以提升系统运行的安全性和可靠性。通过结合数据驱动与模型驱动的方法，提出了一种基于多源传感器信息融合的故障诊断框架，利用机器学习算法对电池运行状态进行实时监测，并通过引入深度学习技术实现对复杂故障模式的精准识别。同时，设计了一套多层次预警机制，能够根据不同故障类型及严重程度提供分级响应策略。实验结果表明，该方法在故障检测准确率和预警时效性方面均优于传统方法，显著降低了误报率和漏报率。本研究的主要创新点在于将深度学习与物理模型相结合，有效解决了电池系统故障特征提取困难的问题，为电动汽车电池管理系统的智能化升级提供了理论支持和技术参考。研究成果可为实际工程应用中的故障诊断与健康管理提供重要指导，具有较高的实用价值和推广前景。
关键词：电动汽车；电池管理系统；故障诊断；深度学习；多源信息融合
Abstract
With the intensification of global energy crises and environmental pollution, electric vehicles (EVs), as a key direction for green transportation, have drawn significant attention, particularly in optimizing the performance and diagnosing faults in their core component, the battery management system (BMS). This study focuses on the early identification and warning of potential faults within EV BMS to enhance the safety and reliability of system operations. By integrating data-driven and model-driven approaches, a fault diagnosis fr amework based on multi-source sensor information fusion is proposed. Real-time monitoring of battery operational states is achieved through machine learning algorithms, while deep learning techniques are incorporated to accurately identify complex fault patterns. Additionally, a multi-level early warning mechanism is designed to provide tiered response strategies according to different fault types and severity levels. Experimental results demonstrate that this method outperforms traditional approaches in terms of fault detection accuracy and warning timeliness, significantly reducing both false alarm and missed detection rates. The primary innovation of this research lies in the combination of deep learning and physical modeling, effectively addressing the challenge of extracting fault features in battery systems and providing theoretical support and technical references for the intelligent upgrade of EV BMS. The findings offer critical guidance for fault diagnosis and health management in practical engineering applications, showcasing substantial practical value and broad application prospects.
Keywords: Electric Vehicle；Battery Management System；Fault Diagnosis；Deep Learning；Multi-Source Information Fusion
目 录
摘 要 I
Abstract II
一、绪论 1
（一）电动汽车电池管理系统研究背景与意义 1
（二）故障诊断与预警技术的研究现状分析 1
（三）本文研究方法与技术路线设计 2
二、电池管理系统故障特征分析 2
（一）电池系统常见故障类型及其影响 2
（二）故障特征提取与数据处理方法 3
（三）基于实验的故障特征验证与评估 3
三、故障诊断算法研究与优化 4
（一）故障诊断算法的基本原理与分类 4
（二）数据驱动型故障诊断算法设计 5
（三）混合模型在故障诊断中的应用研究 5
四、预警机制构建与性能评估 6
（一）预警机制的设计原则与框架 6
（二）基于实时数据的预警模型开发 6
（三）预警系统的测试与性能改进 7
结 论 7
致 谢 9
参考文献 10
