Onboard Vehicle Digital Twin for Real-Time Control Adaptation
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Solution Overview
Problem
Conventional vehicle component management systems rely on preexisting datasets derived from predefined test conditions, failing to emulate real-world scenarios, leading to overdesign and conservative control strategies that inefficiently utilize resources.
Innovation Solution
A digital twin system onboard a vehicle executes physics-based simulations of vehicle components, modifying control strategies in real-time based on simulations and real-world data to optimize energy consumption and operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If preexisting datasets from predefined test conditions are used for component management, then control strategies can be established, but the system cannot emulate all real-world scenarios leading to overdesign and conservative control
Solution Approach 1:
The system performs preliminary actions by using physics-based simulations to predict component behavior under various conditions before actual operation occurs. The digital twin simulates thermal and operational characteristics in advance, allowing the control strategy to be optimized based on predicted rather than just historical data, thereby improving both reliability and real-world adaptability
Solution Approach 2:
The system implements feedback by continuously comparing simulated digital twin data with actual vehicle operating data. This feedback loop allows the control strategy to be dynamically adjusted based on real-world performance, enabling the system to adapt to scenarios not covered by predefined tests while maintaining reliability through validated simulation models
2Reliability
If large factors of safety are applied to handle unexpected scenarios, then component reliability is improved, but component design becomes overconservative and resource utilization decreases
Solution Approach 1:
The system changes parameters by using physics-based simulations to dynamically determine thermal and operational parameters based on actual component conditions and environmental factors. This replaces static safety factors with dynamic, condition-based parameter adjustment, allowing components to operate closer to their true limits while maintaining reliability through continuous simulation validation
Solution Approach 2:
The system applies dynamics by transitioning from static safety factors to dynamic control strategies that continuously adapt to changing operating conditions. The digital twin simulates real-time thermal and operational states, enabling the control system to adjust parameters dynamically based on actual component stress and environmental conditions, thereby improving resource utilization while maintaining reliability
3Use of energy by moving object
If physics-based simulations are executed onboard the vehicle, then control strategies can be optimized for energy consumption, but computational resources and system complexity increase
Solution Approach 1:
The system uses copying by creating a virtual digital twin that replicates the physical vehicle's thermal and operational characteristics. This copy allows physics-based simulations to be executed onboard without modifying the actual vehicle hardware, enabling energy optimization through simulation while containing complexity within the virtual model rather than physical systems
Solution Approach 2:
The system applies universality by designing the digital twin to serve multiple functions: thermal management simulation, component wear prediction, control strategy optimization, and real-time diagnostics. This multi-functionality justifies the added system complexity by providing comprehensive vehicle management capabilities that optimize energy consumption across multiple operational aspects simultaneously
Data Source
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AI summary
A method includes executing, by an Electronic Control Unit (ECU) of a vehicle, a physics-based simulation of a first simulated component of a plurality of simulated components of a digital twin including a plurality of corresponding simulated components, the first simulated component corresponding to a first physical component of a plurality of physical components of the vehicle. The method further includes modifying, by the ECU, a control strategy for operation of the vehicle to modify an operation of the vehicle based on the simulation of the first simulated component. The method further includes executing, by the ECU, the modified control strategy to control the first physical component. The third aspect of the disclosure may seek to improve operational efficiency for the vehicle.