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13.30 Alessandro Fauda - VHIT.pdf

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1、DIGITAL ECOSYSTEM FOR THERMAL MANAGEMENT SYSTEMSAlessandro Fauda VHIT/INNVHIT|Thermal Management Expo-Europe|5/12/2024 2024 VHIT-All rights reserved,also regarding any disposal,exploitation,reproduction,editing,distribution,as well as in the event of applications for industrial property rights.Digit

2、al Ecosystem for Thermal Management SystemsCompany history and area of activity2Electric Oil pump for eAxlecooling&lubricationDigital TwinVHIT|Thermal Management Expo-Europe|5/12/2024 2024 VHIT-All rights reserved,also regarding any disposal,exploitation,reproduction,editing,distribution,as well as

3、in the event of applications for industrial property rights.Digital Twins in the Automotive IndustryApplications to Full Vehicle and Components3Full vehicle applicationsSingle component applicationsTest Integration and Compatibility of components before building a prototypeRoute PlanningEnhance over

4、all Vehicle EfficiencyOptimize Maintenance Scheduleidentifying components close to faultEstimation of internal signals that cantbe sensed,or Virtual Sensors to replace physical onesTest different component Designbefore building a prototypeReal-time Monitoring of performance and Fault DetectionDigita

5、l Twin:Virtual representation of a physical asset or processFocus on Digital Twin application at component level,rather than at vehicle levelVHIT|Thermal Management Expo-Europe|5/12/2024 2024 VHIT-All rights reserved,also regarding any disposal,exploitation,reproduction,editing,distribution,as well

6、as in the event of applications for industrial property rights.Digital Twins in the Automotive IndustryReal use case:Virtual Sensors4VCUPump+DTMotor of eAxleVCUTPQPumpMotor of eAxlePhysicalsensorsExample:Lubrication Oil circuitVirtual sensorsDigital Twin elimination of physical sensors with technica

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本文介绍了数字生态系统在热管理系统中的应用,特别是通过数字孪生技术优化电动轴(eAxle)的冷却和润滑。关键点如下: 1. **数字孪生应用**:数字孪生用于全车和零部件的测试、路线规划、提高车辆效率、优化维护计划,并通过虚拟传感器取代物理传感器。 2. **虚拟传感器优势**:通过数字孪生消除物理传感器,减少组件和复杂性,具有技术和经济优势。 3. **案例研究:eLOP**:通过多物理场建模和实验数据验证,开发并测试了虚拟传感器,使用多项式回归、高斯过程回归和神经网络进行训练。 4. **性能指标**:虚拟传感器的预测精度通过均方根误差(RMSE)进行评估,结果显示算法性能符合预期。 5. **部署和应用**:虚拟传感器可在边缘或云端部署,提供即时响应,且成本较低,而云端部署则允许远程控制和精细调整。 6. **结论**:数字孪生可应用于零部件级别,能准确预测健康状态和使用寿命,具有替代物理传感器的技术和经济优势。 核心数据引用:文中提到,通过4个输入信号,模型生成了多个输出,4414个数据点(DoE)用于训练和测试,332个测试点用于验证仿真模型。虚拟传感器的性能通过RMSE进行量化,显示了其在预测流量、油温和油压方面的准确性。
"虚拟传感器如何优化冷却? "数字化双胞胎在汽车行业的妙用? "边缘计算与云计算,哪种更适合实时监控?"
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