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市场调查报告书
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1892681

车辆预测性维护市场机会、成长驱动因素、产业趋势分析及2025-2034年预测

Predictive Maintenance for Vehicles Market Opportunity, Growth Drivers, Industry Trend Analysis, and Forecast 2025 - 2034
出版日期: | 出版商: Global Market Insights Inc. | 英文 206 Pages | 商品交期: 2-3个工作天内

价格
简介目录

2024 年全球车辆预测性维护市场价值为 46.6 亿美元，预计到 2034 年将以 17.5% 的复合年增长率增长至 233.9 亿美元。

车辆预测性维修市场 - IMG1

汽车和车队生态系统的快速数位化正在改变车辆的监控、维护和保养方式。预测性维护解决方案利用远端资讯处理、物联网感测器、车载诊断、人工智慧/机器学习分析和云端运算，实现车辆健康状况的即时监控、早期故障检测以及对引擎、电池、煞车、轮胎和电力电子设备的剩余使用寿命 (RUL) 预测。随着车辆向软体定义架构演进，数据驱动的维护正在取代商用车队、乘用车和电动车中传统的被动式和定期保养。新冠疫情加速了远端诊断、空中升级和数位化车队健康平台的普及。供应链中断以及最大限度地延长车辆正常运行时间和使用寿命的需求进一步推高了相关需求。人工智慧模型分析远端资讯处理、故障码、振动、温度和历史维修资料，从而预测故障的发生，使车队营运商和原始设备製造商 (OEM) 能够减少停机时间、优化维护计划并确保安全。

市场范围
起始年份 2024
预测年份 2025-2034
起始值 46.6亿美元
预测值 233.9亿美元
复合年增长率 17.5%

2024年，乘用车细分市场占据74%的市场份额，预计到2034年将以17%的复合年增长率成长。该细分市场之所以占据领先地位，主要得益于全球乘用车保有量的庞大、互联汽车技术的广泛应用，以及消费者对可靠性、安全性和更低维护成本日益增长的需求。现代乘用车越来越多地配备远端资讯处理控制单元、人工智慧诊断工具和车载感测器，用于监测引擎、电池和煞车系统的健康状况，从而推动了预测性维护的普及。

2024年，硬体部分占据了45%的市场份额，预计到2034年将以16.8%的复合年增长率成长。硬件，包括感测器、远端资讯处理设备、OBD-II网关和物联网模组，对于收集引擎性能、煞车系统、电池健康状况、振动和温度等即时资料至关重要。这些数据是人工智慧和机器学习模型准确预测故障的基础。乘用车和商用车都高度依赖可靠的硬体来确保持续监控并防止非计划性停机。

美国车辆预测性维护市场占86%的市场份额，预计2024年市场规模将达到14.6亿美元。美国市场受益于先进的互联车队生态系统、广泛的远端资讯处理技术应用以及人工智慧驱动的分析。包括物流、最后一公里配送、叫车和租赁业者在内的商业车队高度依赖预测性维护平台。投资于云端分析、即时诊断和基于人工智慧的维护解决方案的公司已将预测性维护打造成为交通运输产业的核心营运工具。

目录

第一章：方法论

第二章：执行概要

第三章：行业洞察

  • 产业生态系分析
    • 供应商格局
    • 利润率分析
    • 成本结构
    • 每个阶段的价值增加
    • 影响价值链的因素
    • 中断
  • 产业影响因素
    • 成长驱动因素
      • 车辆复杂性和感测器化程度不断提高
      • 监管机构对安全和排放合规性施加压力
      • 维护和维修成本不断上涨
      • 扩展远端资讯处理和 5G 连接
    • 产业陷阱与挑战
      • 高昂的整合成本和传统车队的限制
      • 对OEM诊断资料的存取权限受限
    • 市场机会
      • 电动汽车电池的预测性维护
      • 以车队为中心的AI诊断平台
      • 车辆零件的数位孪生建模
  • 成长潜力分析
  • 监管环境
    • 北美洲
    • 欧洲
    • 亚太地区
    • 洛杉矶
    • MEA
  • 波特的分析
  • PESTEL 分析
  • 技术与创新格局
    • 当前技术趋势
    • 新兴技术
  • 专利分析
  • 价格趋势
    • 按地区
    • 按组件
  • 成本細項分析
  • 永续性和环境影响分析
    • 永续实践
    • 减少废弃物策略
    • 生产中的能源效率
    • 环保倡议
  • 碳足迹考量
  • 数位转型经济学与总拥有成本分析
    • 总拥有成本 (TCO) 框架
    • 投资报酬率计算方法
    • 数位转型成熟度模型
  • 技术整合和平台互通性标准
    • 资料交换标准和协议
    • API及整合框架
    • 云端平台集成
    • 企业系统集成
    • 互通性挑战与解决方案
  • 新兴用例
    • 飞行器的预测性维护
    • 自动驾驶车辆车队管理
    • 共享出行与微出行
    • 最后一公里配送机器人
  • 监理演变
    • 美国国家公路交通安全管理局自动驾驶汽车分步计画的影响
    • 全球标准协调
    • 网路安全监理收紧
    • 资料主权要求
  • 商业模式创新
    • 预测性维护即服务
    • 按使用量付费和基于结果的定价
    • 数据货币化策略
    • 生态系平台模型
  • 投资热点
    • 电动汽车电池健康监测
    • 自动驾驶车辆感知器诊断
    • 非公路及工程机械
    • 新兴市场基础设施

第四章：竞争格局

  • 介绍
  • 公司市占率分析
  • 主要市场参与者的竞争分析
  • 竞争定位矩阵
  • 战略展望矩阵
  • 关键进展
    • 併购
    • 合作伙伴关係与合作
    • 新产品发布
    • 扩张计划和资金

第五章：市场估价与预测：依车辆类型划分，2021-2034年

  • 搭乘用车
    • 掀背车
    • 轿车
    • SUV
  • 商用车辆
    • 轻型商用车（LCV）
    • 中型商用车（MCV）
    • 重型商用车（HCV）

第六章：市场估计与预测：依技术划分，2021-2034年

  • 物联网与车载资讯服务
  • 人工智慧（AI）
  • 机器学习（ML）
  • 边缘运算
  • 云端运算
  • 巨量资料分析
  • 其他的

第七章：市场估计与预测：依组件划分，2021-2034年

  • 硬体
  • 软体
  • 服务

第八章：市场估算与预测：依维护类型划分，2021-2034年

  • 基于状态的维护
  • 预测性诊断
  • 远端监控
  • 即时故障检测
  • 其他的

第九章：市场估算与预测：依最终用途划分，2021-2034年

  • 原始设备製造商
  • 售后市场

第十章：市场估算与预测：依部署模式划分，2021-2034年

  • 本地部署
  • 基于云端的
  • 杂交种

第十一章：市场估计与预测：按地区划分，2021-2034年

  • 北美洲
    • 我们
    • 加拿大
  • 欧洲
    • 德国
    • 英国
    • 法国
    • 义大利
    • 西班牙
    • 俄罗斯
    • 北欧
  • 亚太地区
    • 中国
    • 印度
    • 日本
    • 澳洲
    • 韩国
    • 菲律宾
    • 印尼
  • 拉丁美洲
    • 巴西
    • 墨西哥
    • 阿根廷
  • MEA
    • 南非
    • 沙乌地阿拉伯
    • 阿联酋

第十二章：公司简介

  • OEMs & Tier-1 Suppliers
    • BMW
    • Bosch
    • Continental
    • Mercedes-Benz
    • Volkswagen
    • Volvo
    • ZF Friedrichshafen
  • Telematics & Technology Leaders
    • Geotab
    • IBM
    • Microsoft
    • PTC
    • Samsara
    • Siemens
    • Solera
    • Thermo King
    • Trimble
    • Verizon Connect
  • 区域玩家
    • Amerit Fleet Solutions
    • Bendix Commercial Vehicle Systems
    • Fleetworthy Solutions
    • PrePass Safety Alliance
  • 新兴参与者
    • Adesso
    • CDK Global
    • Simply Fleet
简介目录
Product Code: 15385

The Global Predictive Maintenance for Vehicles Market was valued at USD 4.66 billion in 2024 and is estimated to grow at a CAGR of 17.5% to reach USD 23.39 billion by 2034.

Predictive Maintenance for Vehicles Market - IMG1

The rapid digitalization of the automotive and fleet ecosystem is transforming how vehicles are monitored, maintained, and serviced. Predictive maintenance solutions leverage telematics, IoT sensors, onboard diagnostics, AI/ML analytics, and cloud computing to enable real-time vehicle health monitoring, early fault detection, and remaining-useful-life (RUL) predictions for engines, batteries, brakes, tires, and power electronics. As vehicles evolve toward software-defined architectures, data-driven maintenance is replacing traditional reactive and scheduled servicing across commercial fleets, passenger vehicles, and EVs. The COVID-19 pandemic accelerated the adoption of remote diagnostics, over-the-air updates, and digital fleet-health platforms. Supply chain disruptions and the need to maximize uptime and vehicle lifespan further increased demand. AI models analyze telematics, fault codes, vibration, temperature, and historical repair data to forecast failures before they occur, allowing fleet operators and OEMs to reduce downtime, optimize maintenance schedules, and ensure safety.

Market Scope
Start Year2024
Forecast Year2025-2034
Start Value$4.66 Billion
Forecast Value$23.39 Billion
CAGR17.5%

The passenger vehicle segment held a 74% share in 2024 and is expected to grow at a CAGR of 17% through 2034. This segment leads due to the sheer size of the global passenger vehicle fleet, widespread adoption of connected-car technologies, and growing consumer demand for reliability, safety, and lower maintenance costs. Modern passenger vehicles are increasingly equipped with telematics control units, AI-powered diagnostic tools, and onboard sensors to monitor engine, battery, and braking system health, boosting the adoption of predictive maintenance.

The hardware segment held a 45% share in 2024 and is projected to grow at a CAGR of 16.8% through 2034. Hardware, including sensors, telematics devices, OBD-II gateways, and IoT modules, is essential for collecting real-time data on engine performance, braking systems, battery health, vibration, and temperature. These inputs form the foundation for AI and machine learning models to forecast failures accurately. Both passenger and commercial vehicles rely heavily on robust hardware to ensure continuous monitoring and prevent unplanned downtime.

U.S. Predictive Maintenance for Vehicles Market held 86% share, generating USD 1.46 billion in 2024. The U.S. market benefits from advanced connected-fleet ecosystems, widespread telematics adoption, and AI-driven analytics. Commercial fleets, including logistics, last-mile delivery, ride-hailing, and rental operators, rely heavily on predictive maintenance platforms. Companies investing in cloud analytics, real-time diagnostics, and AI-based maintenance solutions have made predictive maintenance a central operational tool in the transportation industry.

Major players in the Global Predictive Maintenance for Vehicles Market include Bosch, Continental, GE, Geotab, IBM, Microsoft, PTC, Samsara, Siemens, and Trimble. Companies in the Predictive Maintenance for Vehicles Market are expanding their footprint by investing in advanced AI and machine learning models to enhance predictive accuracy for vehicle components. Strategic partnerships with OEMs, fleet operators, and telematics providers help increase solution adoption and long-term service contracts. Cloud integration and real-time analytics platforms are being developed to improve remote diagnostics and over-the-air updates. Firms are also focusing on robust hardware development, including IoT sensors, telematics modules, and OBD-II devices, to ensure reliable data capture in harsh automotive environments.

Table of Contents

Chapter 1 Methodology

  • 1.1 Market scope and definition
  • 1.2 Research design
    • 1.2.1 Research approach
    • 1.2.2 Data collection methods
  • 1.3 Data mining sources
    • 1.3.1 Global
    • 1.3.2 Regional/Country
  • 1.4 Base estimates and calculations
    • 1.4.1 Base year calculation
    • 1.4.2 Key trends for market estimation
  • 1.5 Primary research and validation
    • 1.5.1 Primary sources
  • 1.6 Forecast model
  • 1.7 Research assumptions and limitations

Chapter 2 Executive Summary

  • 2.1 Industry 3600 synopsis, 2021 - 2034
  • 2.2 Key market trends
    • 2.2.1 Regional
    • 2.2.2 Vehicle
    • 2.2.3 Technology
    • 2.2.4 Component
    • 2.2.5 Maintenance
    • 2.2.6 Deployment mode
    • 2.2.7 End Use
  • 2.3 TAM Analysis, 2026-2034
  • 2.4 CXO perspectives: Strategic imperatives
    • 2.4.1 Executive decision points
    • 2.4.2 Critical success factors
  • 2.5 Future outlook and strategic recommendations

Chapter 3 Industry Insights

  • 3.1 Industry ecosystem analysis
    • 3.1.1 Supplier landscape
    • 3.1.2 Profit margin analysis
    • 3.1.3 Cost structure
    • 3.1.4 Value addition at each stage
    • 3.1.5 Factor affecting the value chain
    • 3.1.6 Disruptions
  • 3.2 Industry impact forces
    • 3.2.1 Growth drivers
      • 3.2.1.1 Increasing vehicle complexity & sensorization
      • 3.2.1.2 Regulatory pressure for safety & emissions compliance
      • 3.2.1.3 Rising maintenance & repair costs
      • 3.2.1.4 Expansion of telematics & 5G connectivity
    • 3.2.2 Industry pitfalls and challenges
      • 3.2.2.1 High integration costs & legacy fleet limitations
      • 3.2.2.2 Restricted access to OEM diagnostic data
    • 3.2.3 Market opportunities
      • 3.2.3.1 Predictive maintenance for EV batteries
      • 3.2.3.2 Fleet-centric AI diagnostic platforms
      • 3.2.3.3 Digital twin modeling for vehicle components
  • 3.3 Growth potential analysis
  • 3.4 Regulatory landscape
    • 3.4.1 North America
    • 3.4.2 Europe
    • 3.4.3 Asia Pacific
    • 3.4.4 LA
    • 3.4.5 MEA
  • 3.5 Porter's analysis
  • 3.6 PESTEL analysis
  • 3.7 Technology and Innovation landscape
    • 3.7.1 Current technological trends
    • 3.7.2 Emerging technologies
  • 3.8 Patent analysis
  • 3.9 Price trends
    • 3.9.1 By region
    • 3.9.2 By component
  • 3.10 Cost breakdown analysis
  • 3.11 Sustainability and environmental impact analysis
    • 3.11.1 Sustainable practices
    • 3.11.2 Waste reduction strategies
    • 3.11.3 Energy efficiency in production
    • 3.11.4 Eco-friendly initiatives
  • 3.12 Carbon footprint considerations
  • 3.13 Digital transformation economics & TCO analysis
    • 3.13.1 Total cost of ownership (TCO) framework
    • 3.13.2 ROI calculation methodologies
    • 3.13.3 Digital transformation maturity model
  • 3.14 Technology integration & platform interoperability standards
    • 3.14.1 Data exchange standards & protocols
    • 3.14.2 API & integration frameworks
    • 3.14.3 Cloud platform integration
    • 3.14.4 Enterprise system integration
    • 3.14.5 Interoperability challenges & solutions
  • 3.15 Emerging use cases
    • 3.15.1 Predictive maintenance for flying vehicles
    • 3.15.2 Autonomous vehicle fleet management
    • 3.15.3 Shared mobility & micromobility
    • 3.15.4 Last-mile delivery robots
  • 3.16 Regulatory evolution
    • 3.16.1 NHTSA AV step program impact
    • 3.16.2 Global harmonization of standards
    • 3.16.3 Cybersecurity regulation tightening
    • 3.16.4 Data sovereignty requirements
  • 3.17 Business model innovation
    • 3.17.1 Predictive maintenance-as-a-service
    • 3.17.2 Pay-per-use & outcome-based pricing
    • 3.17.3 Data monetization strategies
    • 3.17.4 Ecosystem platform models
  • 3.18 Investment hotspots
    • 3.18.1 Ev battery health monitoring
    • 3.18.2 Autonomous vehicle sensor diagnostics
    • 3.18.3 Off-highway & construction equipment
    • 3.18.4 Emerging markets infrastructure

Chapter 4 Competitive Landscape, 2024

  • 4.1 Introduction
  • 4.2 Company market share analysis
    • 4.2.1 North America
    • 4.2.2 Europe
    • 4.2.3 Asia Pacific
    • 4.2.4 Latin America
    • 4.2.5 MEA
  • 4.3 Competitive analysis of major market players
  • 4.4 Competitive positioning matrix
  • 4.5 Strategic outlook matrix
  • 4.6 Key developments
    • 4.6.1 Mergers & acquisitions
    • 4.6.2 Partnerships & collaborations
    • 4.6.3 New Product Launches
    • 4.6.4 Expansion Plans and funding

Chapter 5 Market Estimates & Forecast, By Vehicle, 2021 - 2034 ($Bn, Units)

  • 5.1 Key trends
  • 5.2 Passenger vehicles
    • 5.2.1 Hatchbacks
    • 5.2.2 Sedans
    • 5.2.3 SUV
  • 5.3 Commercial vehicles
    • 5.3.1 Light commercial vehicles (LCV)
    • 5.3.2 Medium commercial vehicles (MCV)
    • 5.3.3 Heavy commercial vehicles (HCV)

Chapter 6 Market Estimates & Forecast, By Technology, 2021 - 2034 ($Bn, Units)

  • 6.1 Key trends
  • 6.2 IoT & telematics
  • 6.3 Artificial intelligence (AI)
  • 6.4 Machine learning (ML)
  • 6.5 Edge computing
  • 6.6 Cloud computing
  • 6.7 Big data analytics
  • 6.8 Others

Chapter 7 Market Estimates & Forecast, By Component, 2021 - 2034 ($Bn, Units)

  • 7.1 Key trends
  • 7.2 Hardware
  • 7.3 Software
  • 7.4 Services

Chapter 8 Market Estimates & Forecast, By Maintenance, 2021 - 2034 ($Bn, Units)

  • 8.1 Key trends
  • 8.2 Condition-based maintenance
  • 8.3 Predictive diagnostics
  • 8.4 Remote monitoring
  • 8.5 Real-time fault detection
  • 8.6 Others

Chapter 9 Market Estimates & Forecast, By End Use, 2021 - 2034 ($Bn, Units)

  • 9.1 Key trends
  • 9.2 OEMs
  • 9.3 Aftermarket

Chapter 10 Market Estimates & Forecast, By Deployment mode, 2021 - 2034 ($Bn, Units)

  • 10.1 Key trends
  • 10.2 On-Premise
  • 10.3 Cloud-Based
  • 10.4 Hybrid

Chapter 11 Market Estimates & Forecast, By Region, 2021 - 2034 ($Bn, Units)

  • 11.1 Key trends
  • 11.2 North America
    • 11.2.1 US
    • 11.2.2 Canada
  • 11.3 Europe
    • 11.3.1 Germany
    • 11.3.2 UK
    • 11.3.3 France
    • 11.3.4 Italy
    • 11.3.5 Spain
    • 11.3.6 Russia
    • 11.3.7 Nordics
  • 11.4 Asia Pacific
    • 11.4.1 China
    • 11.4.2 India
    • 11.4.3 Japan
    • 11.4.4 Australia
    • 11.4.5 South Korea
    • 11.4.6 Philippines
    • 11.4.7 Indonesia
  • 11.5 Latin America
    • 11.5.1 Brazil
    • 11.5.2 Mexico
    • 11.5.3 Argentina
  • 11.6 MEA
    • 11.6.1 South Africa
    • 11.6.2 Saudi Arabia
    • 11.6.3 UAE

Chapter 12 Company Profiles

  • 12.1 OEMs & Tier-1 Suppliers
    • 12.1.1 BMW
    • 12.1.2 Bosch
    • 12.1.3 Continental
    • 12.1.4 Mercedes-Benz
    • 12.1.5 Volkswagen
    • 12.1.6 Volvo
    • 12.1.7 ZF Friedrichshafen
  • 12.2 Telematics & Technology Leaders
    • 12.2.1 Geotab
    • 12.2.2 IBM
    • 12.2.3 Microsoft
    • 12.2.4 PTC
    • 12.2.5 Samsara
    • 12.2.6 Siemens
    • 12.2.7 Solera
    • 12.2.8 Thermo King
    • 12.2.9 Trimble
    • 12.2.10 Verizon Connect
  • 12.3 Regional Players
    • 12.3.1 Amerit Fleet Solutions
    • 12.3.2 Bendix Commercial Vehicle Systems
    • 12.3.3 Fleetworthy Solutions
    • 12.3.4 PrePass Safety Alliance
  • 12.4 Emerging Players
    • 12.4.1 Adesso
    • 12.4.2 CDK Global
    • 12.4.3 Simply Fleet