Predictive Tire And Brake Cooling Using Digital Twin Control
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Solution Overview
Problem
Existing vehicle tire and brake systems face thermal management challenges, particularly during high ambient temperatures and aggressive driving conditions, leading to increased risk of tire blowouts and reduced braking performance due to overheating.
Innovation Solution
A proactive cooling system using digital twin simulation and IoT sensors to predict future temperature thresholds, activating a venturi effect-based air cooling system to maintain tire/brake temperatures below critical levels, utilizing real-time data from various parameters to dynamically adjust cooling intensity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If proactive cooling is activated based on predicted future temperature, then tire/brake temperature is maintained within safe limits, but system complexity increases due to digital twin simulation and sensor integration
Solution Approach 1:
The system performs preliminary cooling actions by predicting future temperature thresholds using digital twin simulation before the actual overheating occurs. The machine learning model anticipates temperature violations and triggers cooling proactively, preventing thermal issues before they arise rather than reacting after problems occur.
Solution Approach 2:
A digital twin (virtual copy) of the physical wheel assembly is created to simulate and predict temperature behavior. This virtual model replicates the thermal characteristics of the actual system, allowing safe prediction and analysis without affecting the physical component, thereby managing complexity through simulation rather than direct physical intervention.
2Temperature
If continuous cooling is applied to maintain temperature, then thermal management is improved, but energy consumption increases
Solution Approach 1:
Instead of continuous cooling, the system uses periodic cooling activated only when the digital twin predicts future temperature threshold violations. The cooling system operates intermittently based on predicted thermal conditions, reducing unnecessary energy consumption while maintaining effective temperature control through targeted cooling cycles.
Solution Approach 2:
The cooling system activates autonomously based on predictions from the digital twin without requiring continuous external control. The system serves itself by using sensor data and machine learning models to automatically determine when cooling is needed, optimizing energy usage through intelligent, condition-based activation rather than continuous operation.
3Measurement precision
If real-time sensor data is collected and processed, then prediction accuracy is improved, but data processing requirements and computational load increase
Solution Approach 1:
The system collects comprehensive sensor data from multiple sources (temperature, ambient conditions, vehicle operation parameters) to ensure high prediction accuracy. By gathering more data than minimally required and processing it through the digital twin simulation, the system achieves superior temperature prediction accuracy that enables more reliable proactive cooling decisions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively maintains tire/brake temperatures within safe limits, reducing the risk of failures and enhancing vehicle safety and performance by anticipating and proactively cooling, while minimizing drag and avoiding the use of consumables.
Implementation Method 1
activating a venturi effect-based air cooling system
Data Source
AI summary
Aspects of this disclosure include a method for proactive cooling, a vehicle, and a computer program product for a proactive cooling system. One embodiment of the method may comprise creating a machine learning model of a physical object, and instrumenting the physical object with sensors to generate real-time data about the physical object and its operational environment. The method may further comprise generating, with the machine learning model, a predicted future temperature for the physical object using the real-time data about the physical object and its operational environment, and in response to the predicted future temperature exceeding a threshold temperature within a threshold time period, proactively cooling the physical object.