Vehicle Supervisory Controller Dynamic Data Transmission Optimization
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Solution Overview
Problem
Current systems for distributed monitoring and control of vehicle systems struggle to identify performance issues outside anticipated operating conditions and optimize data transmission, leading to missed opportunities in understanding machine states and predicting problems, which can result in costly failures and delayed system improvements.
Innovation Solution
A method involving a supervisory controller that simulates vehicle systems using functional models, calibrates parameters with sensor data, predicts future states, detects events of interest, and transmits relevant data to a server for analysis, allowing for dynamic optimization of data communication and parameter monitoring across different networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If model-based monitoring and control systems use preset parameters and models for data transmission, then data transmission costs are reduced, but the system fails to identify performance problems outside anticipated operating conditions
Solution Approach 1:
The system dynamically adjusts monitoring parameters and data transmission strategies based on real-time system state predictions. The supervisory controller continuously updates which parameters to monitor and transmit based on predicted future states and detected events of interest, rather than using fixed preset parameters. This allows the system to adapt to unexpected operating conditions while optimizing data transmission costs.
Solution Approach 2:
The system changes monitoring and transmission parameters dynamically based on system state. The supervisory controller determines which parameters to monitor and transmit based on predicted future states and detected events, adjusting the set of monitored parameters in real-time. This enables the system to focus transmission resources on critical parameters that indicate performance problems or unexpected conditions.
2Loss of information
If telematics systems transmit all available sensor data to the server, then complete system state information is available for analysis, but data transmission costs and network bandwidth requirements increase significantly
Solution Approach 1:
The system extracts only the most relevant information for transmission to the server. The supervisory controller predicts future system states and detects events of interest, then transmits only data related to these predicted events and current system state characterizations. This extraction approach ensures complete information about critical system states is available at the server while minimizing unnecessary data transmission.
Solution Approach 2:
The system transmits a partial set of data that is sufficient for effective monitoring and analysis. Rather than transmitting all available sensor data, the supervisory controller selectively transmits data about detected events of interest and characterized system states. This partial action approach provides adequate information completeness for server analysis while significantly reducing transmission costs and bandwidth requirements.
3Reliability
If the system monitors and transmits detailed operational parameters in real-time, then early prediction of failures is enabled, but the complexity of data processing and storage requirements increase
Solution Approach 1:
The supervisory controller performs preliminary processing of sensor data on-vehicle by characterizing system states and predicting future states before transmission. This preliminary action includes detecting events of interest and filtering data to only transmit relevant information to the server. By performing these processing actions locally before transmission, the system enables failure prediction capabilities while reducing the data processing and storage complexity at the server.
4Ease of manufacture
If preset choices are made for parameters to monitor and data transmission methods, then system implementation is simplified, but opportunities to correctly understand machine state are missed due to data transmission limits
Solution Approach 1:
The system replaces static preset choices with dynamic selection of monitoring parameters and transmission methods. The supervisory controller continuously determines which parameters to monitor and how to transmit data based on predicted future states and detected events of interest. This dynamic approach maintains ease of implementation through automated decision-making while significantly improving machine state understanding accuracy by adapting to actual system conditions.
Data Source
AI summary
A distributed system for monitoring and control of a vehicle includes a supervisory controller with a first computer readable storage media for monitoring and storing a plurality of operational parameters regarding a physical system of the vehicle. The supervisory controller communicates with a server via two different communications networks. Method steps are provided for characterizing and predicting functional details of a system state of the physical system using the model parameters and at least one operational parameter of the physical system, and for using values obtained by the server regarding a plurality of different vehicles in order to improve the monitoring and control of the vehicle. A method is also provided to determine and report any operational parameters miss a corresponding performance target. A method is also provided for changing the storage or transmission of operational parameters based on their relative importance.