Heavy-Duty Vehicle Partial Deactivation Using Usage-Based Timing
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Solution Overview
Problem
Current vehicle deactivation control systems in heavy-duty vehicles lack versatility and accuracy, often leading to unnecessary energy consumption and excessive deactivation, which can increase wear and tear on components and reduce energy efficiency.
Innovation Solution
A computer system using an autonomous model that determines controlled partial deactivation instructions based on historical usage patterns of heavy-duty vehicles, including geographical data, to selectively deactivate vehicle subsystems either immediately or after a delay, minimizing energy consumption and optimizing component usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a fixed timer is used to determine vehicle deactivation, then the control procedure is simple, but energy consumption is not optimized and deactivation accuracy is poor
Solution Approach 1:
The system changes the parameter of deactivation timing from a fixed timer value to a dynamic value based on historical usage patterns and predicted vehicle activation needs. The autonomous model adjusts deactivation timing parameters based on learned patterns, transforming the control from static to adaptive.
Solution Approach 2:
The autonomous model learns from historical deactivation and activation events to automatically determine optimal deactivation timing without requiring complex real-time analysis. The system serves itself by using its own historical data to improve future decisions.
2Measurement precision
If comprehensive vehicle component state analysis is performed to determine deactivation suitability, then deactivation accuracy improves, but energy consumption increases and cost rises
Solution Approach 1:
The system performs preliminary learning during periods when the vehicle is active, accumulating historical usage pattern data. This preliminary action prepares the autonomous model to make accurate deactivation decisions without requiring complex real-time analysis at the moment of deactivation, thus reducing instantaneous energy consumption.
Solution Approach 2:
The autonomous model uses feedback from historical deactivation and activation events to continuously improve its predictions. By learning from past outcomes, the system achieves high accuracy in determining deactivation suitability without requiring energy-intensive real-time component state analysis.
3Use of energy by moving object
If frequent vehicle deactivation occurs to save energy, then energy consumption decreases, but component lifetime is reduced due to increased wear and tear
Solution Approach 1:
The system dynamically adjusts deactivation decisions based on learned patterns of vehicle usage. Rather than following a fixed schedule or simple timer, the autonomous model adapts deactivation timing to actual vehicle needs, activating deactivation only when historically likely to be appropriate, thus balancing energy savings with component protection.
Solution Approach 2:
The controlled partial deactivation instruction selectively deactivates only certain subsystems rather than the entire vehicle system. This partial action approach saves energy in non-critical subsystems while keeping critical systems active, thereby reducing overall energy consumption without excessively impacting component lifetime.
4Speed
If the vehicle is deactivated immediately upon request, then responsiveness is high, but unnecessary deactivations occur reducing energy efficiency
Solution Approach 1:
The autonomous model performs preliminary analysis of historical usage patterns before executing deactivation. This preliminary action allows the system to predict whether immediate deactivation is appropriate, maintaining high responsiveness when conditions favor deactivation while avoiding unnecessary deactivations that would reduce energy efficiency.
Data Source
AI summary
A computer system comprising a processor device is provided. The processor device is configured to receive a deactivation request to deactivate a heavy-duty vehicle. The processor device is further configured to determine a controlled partial deactivation instruction of at least one subsystem of the vehicle, wherein the controlled partial deactivation instruction is determined by an autonomous model comprising a historical usage pattern of the vehicle. The historical usage pattern comprises information of deactivation events and activation events of the vehicle that has historically occurred at reference locations. The processor device is further configured to control the vehicle to execute the controlled partial deactivation instruction such that the vehicle is at least partially deactivated either immediately, or after a delay, as determined by the controlled partial deactivation instruction.