Vehicle Parameter Set Selection for Usage-Specific Configuration
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Solution Overview
Problem
Existing vehicle configuration settings are not optimized for specific operating conditions, leading to reduced efficiency and increased wear on components, which is particularly problematic for enterprises operating large fleets of vehicles.
Innovation Solution
A configuration management system that selectively provides parameter sets to vehicles based on predicted or sensed conditions of usage, allowing for supervised or autonomous selection of optimal settings for different sub-fleets or individual vehicles, using a combination of hardware processing circuits and machine-readable instructions to manage and transmit parameter sets over wireless networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single set of configuration settings is used for all vehicles, then device complexity is reduced and ease of operation is improved, but vehicle efficiency and component durability deteriorate due to suboptimal operation under varying conditions
Solution Approach 1:
The system dynamically assigns configuration settings to vehicles based on real-time or predicted usage conditions. Instead of static configuration, the system adapts parameters such as routing preferences, operational modes, and resource allocation dynamically, allowing vehicles to operate optimally under varying conditions while maintaining manageable complexity through automated decision-making algorithms.
Solution Approach 2:
The system changes operational parameters of vehicles based on predicted or sensed usage conditions. Different configuration sets with optimized parameter values are assigned according to specific conditions (e.g., delivery routes, load types, time of day), enabling vehicles to adapt their behavior without physical modifications, thus improving efficiency while keeping the base vehicle design simple.
2Reliability
If configuration settings are optimized for specific usage conditions, then vehicle efficiency and component durability are improved, but device complexity increases due to multiple parameter sets and prediction systems
Solution Approach 1:
The system performs preliminary analysis of usage conditions and pre-assigns optimal configuration settings before vehicles operate. By predicting future usage patterns or analyzing historical data, the system prepares appropriate parameter sets in advance, ensuring vehicles are configured for durability and efficiency from the start of each operation cycle, rather than reacting to conditions after damage occurs.
Solution Approach 2:
The system implements feedback mechanisms where operational data from vehicles is continuously collected and analyzed. This feedback loop allows the system to learn from actual usage patterns, refine predictions, and adjust configuration assignments to maximize component durability. The feedback-driven approach automates the complexity of managing multiple parameter sets, making the system self-optimizing rather than manually complex.
3Adaptability or versatility
If multiple parameter sets are provided for different conditions, then adaptability and vehicle efficiency are improved, but ease of operation deteriorates due to difficulty in selecting appropriate settings
Solution Approach 1:
The system enables vehicles to effectively select their own optimal configuration settings through automated decision-making based on predicted or sensed conditions. Rather than requiring human operators to manually choose from multiple parameter sets, the system self-determines the appropriate configuration and applies it automatically, maintaining high adaptability while preserving ease of operation through automation.
Solution Approach 2:
The system introduces an intermediary layer (the configuration management system) that sits between the multiple available parameter sets and the vehicle operations. This intermediary automatically matches vehicles with appropriate configurations based on usage conditions, shielding operators from the complexity of multiple settings while maintaining full adaptability. The intermediary translates complex condition-parameter mappings into simple automated decisions.
Data Source
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AI summary
In some examples, a controller determines a target condition of usage of a vehicle, and selects a parameter set from among a plurality of parameter sets based on the determined target condition of usage of the vehicle, the plurality of parameter sets corresponding to different conditions of usage of the vehicle, where each parameter set of the plurality of parameter sets includes one or more parameters that control adjustment of one or more respective adjustable elements of the vehicle. The controller transmits, to the vehicle, the selected parameter set to control a setting of the one or more adjustable elements of the vehicle.