摘 要
随着全球能源危机和环境污染问题日益严峻，汽车混合动力系统因其高效节能和低排放特性成为研究热点。本研究旨在探讨混合动力系统的保养与故障排除技术，以提升其运行可靠性和使用寿命。通过文献分析、实验测试及案例研究相结合的方法，本文系统梳理了混合动力系统的关键部件保养策略，并提出了一种基于数据驱动的故障诊断算法。研究结果表明，优化的保养方案可显著降低系统损耗并延长关键部件寿命，而所提出的故障诊断算法在准确性与实时性方面均优于传统方法。本研究的创新点在于将机器学习技术引入混合动力系统故障预测，实现了从被动维修到主动预防的转变。此外，研究还为行业提供了具有实际操作价值的技术指南，对推动混合动力汽车的普及和维护技术的发展具有重要意义。
关键词：混合动力系统；故障诊断算法；保养策略；数据驱动；机器学习技术
Abstract
As global energy crises and environmental pollution become increasingly severe, hybrid vehicle power systems have garnered significant attention due to their high energy efficiency, cost savings, and low emissions. This study focuses on the maintenance and troubleshooting techniques of hybrid power systems to enhance their operational reliability and service life. By integrating literature analysis, experimental testing, and case studies, this paper systematically examines the maintenance strategies for key components of hybrid power systems and proposes a data-driven fault diagnosis algorithm. The results indicate that an optimized maintenance plan can substantially reduce system wear and extend the lifespan of critical components, while the proposed fault diagnosis algorithm demonstrates superior performance in both accuracy and real-time capability compared to traditional methods. The innovation of this research lies in the integration of machine learning techniques into fault prediction for hybrid power systems, facilitating a transition from reactive repairs to proactive prevention. Additionally, this study provides technically actionable guidelines for the industry, contributing significantly to the promotion of hybrid electric vehicles and the advancement of maintenance technologies.
Keywords: Hybrid Power System；Fault Diagnosis Algorithm；Maintenance Strategy；Data Driven；Machine Learning Technology
目 录
引言 1
一、混合动力系统保养技术分析 1
（一）保养技术基础理论 1
（二）关键部件保养策略 2
（三）保养周期优化研究 2
二、故障诊断方法与技术研究 2
（一）常见故障类型分析 3
（二）故障诊断技术应用 3
（三）数据驱动诊断方法 3
三、系统性能评估与维护策略 4
（一）性能评估指标体系 4
（二）维护策略设计原则 4
（三）预防性维护方案 5
四、实际案例与技术改进探讨 5
（一）典型案例分析研究 5
（二）技术改进方向探索 5
（三）未来发展趋势预测 6
结 论 6
致 谢 8
参考文献 9
随着全球能源危机和环境污染问题日益严峻，汽车混合动力系统因其高效节能和低排放特性成为研究热点。本研究旨在探讨混合动力系统的保养与故障排除技术，以提升其运行可靠性和使用寿命。通过文献分析、实验测试及案例研究相结合的方法，本文系统梳理了混合动力系统的关键部件保养策略，并提出了一种基于数据驱动的故障诊断算法。研究结果表明，优化的保养方案可显著降低系统损耗并延长关键部件寿命，而所提出的故障诊断算法在准确性与实时性方面均优于传统方法。本研究的创新点在于将机器学习技术引入混合动力系统故障预测，实现了从被动维修到主动预防的转变。此外，研究还为行业提供了具有实际操作价值的技术指南，对推动混合动力汽车的普及和维护技术的发展具有重要意义。
关键词：混合动力系统；故障诊断算法；保养策略；数据驱动；机器学习技术
Abstract
As global energy crises and environmental pollution become increasingly severe, hybrid vehicle power systems have garnered significant attention due to their high energy efficiency, cost savings, and low emissions. This study focuses on the maintenance and troubleshooting techniques of hybrid power systems to enhance their operational reliability and service life. By integrating literature analysis, experimental testing, and case studies, this paper systematically examines the maintenance strategies for key components of hybrid power systems and proposes a data-driven fault diagnosis algorithm. The results indicate that an optimized maintenance plan can substantially reduce system wear and extend the lifespan of critical components, while the proposed fault diagnosis algorithm demonstrates superior performance in both accuracy and real-time capability compared to traditional methods. The innovation of this research lies in the integration of machine learning techniques into fault prediction for hybrid power systems, facilitating a transition from reactive repairs to proactive prevention. Additionally, this study provides technically actionable guidelines for the industry, contributing significantly to the promotion of hybrid electric vehicles and the advancement of maintenance technologies.
Keywords: Hybrid Power System；Fault Diagnosis Algorithm；Maintenance Strategy；Data Driven；Machine Learning Technology
目 录
引言 1
一、混合动力系统保养技术分析 1
（一）保养技术基础理论 1
（二）关键部件保养策略 2
（三）保养周期优化研究 2
二、故障诊断方法与技术研究 2
（一）常见故障类型分析 3
（二）故障诊断技术应用 3
（三）数据驱动诊断方法 3
三、系统性能评估与维护策略 4
（一）性能评估指标体系 4
（二）维护策略设计原则 4
（三）预防性维护方案 5
四、实际案例与技术改进探讨 5
（一）典型案例分析研究 5
（二）技术改进方向探索 5
（三）未来发展趋势预测 6
结 论 6
致 谢 8
参考文献 9
