摘 要
随着新能源汽车的快速发展，其故障检测技术已成为保障车辆安全性和可靠性的重要研究领域。本研究旨在探索适用于新能源汽车的先进故障检测技术，并提出一种基于多源数据融合的智能化检测方法。通过整合车载传感器数据、电池管理系统信息以及电机运行参数，构建了以深度学习算法为核心的故障诊断模型，实现了对关键部件如动力电池、驱动电机及电控系统的精准监测与异常识别。实验结果表明，该方法能够有效提升故障检测的准确率和实时性，相较于传统方法具有更高的灵敏度和适应性。研究创新性地引入了数据驱动的预测性维护理念，为新能源汽车的全生命周期管理提供了技术支持。此外，本研究还验证了多源数据融合在复杂工况下的可行性，为后续技术优化奠定了理论基础。最终结论显示，所提出的故障检测技术可显著降低车辆故障风险，为新能源汽车的安全运行提供可靠保障。
关键词：新能源汽车；故障检测技术；多源数据融合；深度学习算法；预测性维护
Abstract
With the rapid development of new energy vehicles (NEVs), fault detection technology has become a critical research area for ensuring vehicle safety and reliability. This study aims to explore advanced fault detection techniques tailored for NEVs and proposes an intelligent detection method based on multi-source data fusion. By integrating data from on-board sensors, battery management systems, and motor operation parameters, a fault diagnosis model centered on deep learning algorithms was constructed, enabling precise monitoring and anomaly identification of key components such as power batteries, drive motors, and electronic control systems. Experimental results demonstrate that this approach effectively enhances the accuracy and real-time performance of fault detection, exhibiting higher sensitivity and adaptability compared to traditional methods. Innovatively, the study incorporates a data-driven predictive maintenance concept, providing technical support for the full lifecycle management of NEVs. Additionally, the feasibility of multi-source data fusion under complex operating conditions was validated, laying a theoretical foundation for future technological optimization. The final conclusion indicates that the proposed fault detection technique significantly reduces vehicle fault risks, offering a reliable guarantee for the safe operation of NEVs.
Keywords: New Energy Vehicle；Fault Detection Technology；Multi-Source Data Fusion；Deep Learning Algorithm；Predictive Maintenance
目 录
引言 1
一、新能源汽车故障检测技术概述 1
（一）新能源汽车故障特点分析 1
（二）故障检测技术的基本原理 2
（三）当前技术应用现状与挑战 2
二、关键部件故障检测技术研究 2
（一）电池管理系统故障检测方法 3
（二）电机驱动系统故障诊断技术 3
（三）充电系统异常检测与分析 3
三、数据驱动的故障检测技术应用 4
（一）大数据分析在故障检测中的作用 4
（二）人工智能算法的应用实践 4
（三）实时监测系统的开发与优化 5
四、故障检测技术的集成与未来方向 5
（一）综合诊断平台的设计与实现 5
（二）新技术融合的潜力与前景 5
（三）标准化与行业发展趋势 6
结 论 6
致 谢 8
参考文献 9
随着新能源汽车的快速发展，其故障检测技术已成为保障车辆安全性和可靠性的重要研究领域。本研究旨在探索适用于新能源汽车的先进故障检测技术，并提出一种基于多源数据融合的智能化检测方法。通过整合车载传感器数据、电池管理系统信息以及电机运行参数，构建了以深度学习算法为核心的故障诊断模型，实现了对关键部件如动力电池、驱动电机及电控系统的精准监测与异常识别。实验结果表明，该方法能够有效提升故障检测的准确率和实时性，相较于传统方法具有更高的灵敏度和适应性。研究创新性地引入了数据驱动的预测性维护理念，为新能源汽车的全生命周期管理提供了技术支持。此外，本研究还验证了多源数据融合在复杂工况下的可行性，为后续技术优化奠定了理论基础。最终结论显示，所提出的故障检测技术可显著降低车辆故障风险，为新能源汽车的安全运行提供可靠保障。
关键词：新能源汽车；故障检测技术；多源数据融合；深度学习算法；预测性维护
Abstract
With the rapid development of new energy vehicles (NEVs), fault detection technology has become a critical research area for ensuring vehicle safety and reliability. This study aims to explore advanced fault detection techniques tailored for NEVs and proposes an intelligent detection method based on multi-source data fusion. By integrating data from on-board sensors, battery management systems, and motor operation parameters, a fault diagnosis model centered on deep learning algorithms was constructed, enabling precise monitoring and anomaly identification of key components such as power batteries, drive motors, and electronic control systems. Experimental results demonstrate that this approach effectively enhances the accuracy and real-time performance of fault detection, exhibiting higher sensitivity and adaptability compared to traditional methods. Innovatively, the study incorporates a data-driven predictive maintenance concept, providing technical support for the full lifecycle management of NEVs. Additionally, the feasibility of multi-source data fusion under complex operating conditions was validated, laying a theoretical foundation for future technological optimization. The final conclusion indicates that the proposed fault detection technique significantly reduces vehicle fault risks, offering a reliable guarantee for the safe operation of NEVs.
Keywords: New Energy Vehicle；Fault Detection Technology；Multi-Source Data Fusion；Deep Learning Algorithm；Predictive Maintenance
目 录
引言 1
一、新能源汽车故障检测技术概述 1
（一）新能源汽车故障特点分析 1
（二）故障检测技术的基本原理 2
（三）当前技术应用现状与挑战 2
二、关键部件故障检测技术研究 2
（一）电池管理系统故障检测方法 3
（二）电机驱动系统故障诊断技术 3
（三）充电系统异常检测与分析 3
三、数据驱动的故障检测技术应用 4
（一）大数据分析在故障检测中的作用 4
（二）人工智能算法的应用实践 4
（三）实时监测系统的开发与优化 5
四、故障检测技术的集成与未来方向 5
（一）综合诊断平台的设计与实现 5
（二）新技术融合的潜力与前景 5
（三）标准化与行业发展趋势 6
结 论 6
致 谢 8
参考文献 9
