Heavy-Duty Vehicle Partial Deactivation Using Usage-Pattern Control
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Solution Overview
Problem
Existing 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 and geographical data to selectively deactivate vehicle subsystems, minimizing energy consumption and optimizing deactivation timing.
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 the energy efficiency is poor and deactivation accuracy is low
Solution Approach 1:
The system performs preliminary analysis of vehicle state, component states, and environmental conditions before making deactivation decisions. This allows the system to predict whether deactivation will be beneficial, avoiding unnecessary deactivations and reducing energy consumption while maintaining reasonable control complexity.
Solution Approach 2:
The system continuously monitors vehicle operational data, component states, and energy consumption patterns, using this feedback to dynamically adjust deactivation decisions. This feedback mechanism enables the system to learn from past behavior and optimize energy efficiency without requiring overly complex control procedures.
2Measurement precision
If comprehensive analysis of vehicle component states is performed to determine deactivation suitability, then deactivation accuracy improves, but energy consumption increases and cost increases
Solution Approach 1:
The system performs partial analysis by selectively evaluating only the most critical vehicle states and components relevant to deactivation decisions, rather than comprehensively analyzing all vehicle systems. This approach achieves sufficient deactivation accuracy while minimizing the energy and computational resources required.
Solution Approach 2:
The system applies different levels of analysis depth to different vehicle components based on their relevance to deactivation decisions. Critical components receive detailed analysis while less relevant components receive minimal or no analysis, optimizing the balance between accuracy and energy consumption.
3Use of energy by moving object
If frequent deactivation is performed to save energy, then energy consumption decreases, but component lifetime decreases due to increased wear and tear
Solution Approach 1:
The system cushions against excessive wear by analyzing component conditions and predicting wear patterns before making deactivation decisions. This allows the system to avoid deactivations that would cause excessive wear while still achieving energy savings through selective deactivation of appropriate components.
Solution Approach 2:
The system dynamically adjusts deactivation strategies based on real-time component states, operational history, and predicted usage patterns. This dynamic approach allows the system to optimize the balance between energy savings and component protection, deactivating components when safe to do so while maintaining them when wear risk is high.
Data Source
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AI summary
A computer system (900) comprising a processor device (902) is provided. The processor device (902) is configured to receive a deactivation request (20) to deactivate a heavy-duty vehicle (10). The processor device (902) is further configured to determine a controlled partial deactivation instruction (40) of at least one subsystem (14) of the vehicle (10), wherein the controlled partial deactivation instruction (40) is determined by an autonomous model (36) comprising a historical usage pattern (34) of the vehicle (10). The historical usage pattern (34) comprises information of deactivation events and activation events of the vehicle (10) that has historically occurred at reference locations (33). The processor device (902) is further configured to control the vehicle (10) to execute the controlled partial deactivation instruction (40) such that the vehicle (10) is at least partially deactivated either immediately, or after a delay, as determined by the controlled partial deactivation instruction (40).