新能源汽车绝缘故障预警与主动服务研究

汽车电器 ›› 2026, Vol. 1 ›› Issue (3) : 17-19.

新能源

新能源汽车绝缘故障预警与主动服务研究

余天天

作者信息 +

Research on Insulation Fault Early Warning and Active Service for New Energy Vehicles

Yu Tiantian

Author information +

文章历史 +

摘要

高压系统绝缘故障是威胁新能源汽车安全与用户体验的核心问题。传统被动式售后模式难以应对其突发性与复杂性，亟需向数据驱动的主动服务转型。文章聚焦于绝缘故障预警这一高价值场景，系统构建了集数据采集、实时分析、智能决策与闭环处置于一体的售后大数据平台。创新性地采用规则与机器学习融合的预警模型，实际应用表明，该模型预警准确率达 92.5%，故障发现时间平均提前 14 天。本文详细阐述平台数据层、计算层、分析层与应用层架构，并重点研究融合刚性规则与机器学习算法的预警模型构建路径；通过系统论述从预警发现、远程诊断到决策推送的业务闭环，验证该平台在实现故障早期识别、精准干预及流程优化方面的有效性与实践价值，为整车企业构建智能售后体系提供关键方法论与实施范例。

Abstract

High-voltage system insulation faults represent a critical issue threatening the safety and user experience of new energy vehicles (NEVs). The traditional passive after-sales model struggles to address their suddenness and complexity, necessitating a transition towards a data-driven active service paradigm. This paper focuses on the high-value scenario of insulation fault early warning, systematically constructing an integrated after-sales big data platform encompassing data collection, real-time analysis, intelligent decision-making, and closed-loop management. The paper elaborates on the platform's layered architecture, including the data, computing, analytics, and application layers, and specifically investigates the development path of an early warning model that integrates deterministic rules with machine learning algorithms. By detailing the entire business closed-loop process from warning discovery and remote diagnosis to decision-making and push-notification, the study validates the platform's effectiveness and practical value in achieving early fault identification, precise intervention, and process optimization. It provides a key methodology and implementation case for automotive manufacturers building intelligent after-sales systems.

导出引用

余天天. 新能源汽车绝缘故障预警与主动服务研究[J]. 汽车电器. 2026, 1(3): 17-19

Yu Tiantian. Research on Insulation Fault Early Warning and Active Service for New Energy Vehicles[J]. AUTO ELECTRIC PARTS. 2026, 1(3): 17-19

中图分类号： U469.72

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