Health Self-Learning System for Electrical Distribution in Automated Driving Vehicles
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Solution Overview
Problem
Automated driving vehicles require advanced diagnostics for their electrical distribution systems to detect potential failures before they occur, ensuring safety and reliability, especially in scenarios where human intervention is not immediate.
Innovation Solution
A health self-learning system that includes microcontrollers in vehicles to monitor operating characteristics of switches, transmit data to a remote server for analysis, and predict Remaining Useful Life (RUL) of MOSFETs, enabling early detection of failures and proactive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional diagnostic methods are used in automated driving vehicles, then device complexity is reduced, but reliability deteriorates due to inability to detect potential failures before they occur
Solution Approach 1:
The system performs preliminary diagnostics by continuously monitoring operating characteristics of electrical components (voltage, current, temperature) and predicting remaining useful life before actual failures occur. This allows the system to detect potential failures in advance, maintaining high reliability without requiring complex real-time intervention mechanisms.
Solution Approach 2:
The diagnostic system implements feedback loops where monitoring data from microcontrollers is continuously analyzed, predictions are generated and transmitted to remote servers, and results are used to adjust maintenance schedules and operational parameters. This feedback mechanism enables proactive failure detection while managing system complexity through automated decision-making.
2Reliability
If advanced diagnostic systems with continuous monitoring are implemented, then reliability improves through early failure detection, but device complexity increases
Solution Approach 1:
The system uses multi-functional microcontrollers that perform multiple roles: monitoring operating characteristics, predicting remaining useful life, transmitting data to remote servers, and receiving diagnostic results. By consolidating these functions into existing control units rather than adding separate dedicated components, the system achieves high reliability without proportionally increasing device complexity.
Solution Approach 2:
The patent introduces a remote server as an intermediary that handles complex data analysis and prediction algorithms externally. The vehicle's onboard systems transmit raw monitoring data to the remote server, which processes the information and returns diagnostic results. This distributes computational complexity from the vehicle to the cloud, maintaining safety and reliability while minimizing onboard device complexity.
3Productivity
If predictive maintenance is implemented using remote server analysis, then productivity improves through timely maintenance scheduling, but loss of information increases due to wireless data transmission requirements
Solution Approach 1:
The system creates digital copies of operating characteristic data from multiple vehicles and transmits them to remote servers for centralized analysis. These data copies enable predictive maintenance modeling without affecting the original vehicle operations. The remote server analyzes aggregated data patterns to generate maintenance predictions, improving productivity while managing information loss through redundant data collection from fleet-wide monitoring.
Data Source
AI summary
In at least one embodiment, an apparatus for adaptively performing diagnostics in a vehicle is provided. The apparatus includes at least one microcontroller and a communication controller. The at least one microcontroller is positioned in a vehicle and is configured to provide first information indicative of operating characteristics for at least one switch in the vehicle. The communication controller is configured to receive the first information from the at least one microcontroller and to wirelessly transmit the first information to a remote server. The communication controller is further configured to receive second information related to a remaining useful life (RUL) for the at least one switch from the remote server and to transmit the second information to the at least one microcontroller to determine when the at least one switch will exhibit a failure.