CAN Bus Signal Assignment Using Virtual ECU Simulation
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Solution Overview
Problem
The manual analysis of electrical signals in a vehicle's CAN bus is time-consuming due to the lack of a unified standard, leading to inefficiencies when retrofitting or replacing powertrains, as specific electronic control units are omitted, causing errors without simulating the removed ECUs.
Innovation Solution
A method using machine learning techniques to generate and assign electrical signals by comparing them with reference signals and network states, eliminating the need for manual programming and ensuring error-free operation by identifying and simulating necessary signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis is used to assign electrical signals in CAN bus, then signal assignment can be performed, but the process is very time-consuming due to the large number of unknown messages
Solution Approach 1:
The patent creates virtual copies of ECUs and their signal structures to simulate the behavior of removed control units. By generating virtual message copies with expected signal patterns, the system can automatically match unknown CAN bus messages against these templates, dramatically reducing manual analysis time while maintaining assignment accuracy.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated electronic signal processing and comparison algorithms. The system uses computational methods to automatically correlate unknown messages with expected signal patterns, substituting human analysts with machine-based pattern recognition and assignment algorithms.
2Device complexity
If removed ECUs are not simulated, then the system operates with fewer components, but errors occur due to missing data from omitted control units
Solution Approach 1:
The patent introduces virtual ECU intermediaries that mediate between the physical ECUs and the network. These virtual representations act as placeholders that receive, process, and forward signals according to the original ECU behavior patterns, ensuring that data expectations are met even though the physical ECUs are removed.
Solution Approach 2:
The patent creates functional copies of removed ECUs in virtual form. These virtual ECUs replicate the signal production and consumption patterns of the original control units, allowing the network to operate as if the physical ECUs were still present, thereby maintaining reliability without the physical hardware.
3Ease of manufacture
If a unified standard exists for signal assignment, then the process becomes standardized, but vehicle-specific customizations are lost
Solution Approach 1:
The patent implements a dynamic signal assignment system that adapts to different vehicle models and configurations. The virtual ECU templates and correlation algorithms can be customized for specific vehicle types while following a standardized overall process framework, allowing both standardization and vehicle-specific adaptations.
Solution Approach 2:
The patent applies local quality by allowing vehicle-specific signal patterns and ECU configurations at the local level while maintaining a standardized overall assignment process. Each vehicle model can have customized virtual ECU templates that reflect its specific requirements, while the general methodology remains consistent across different applications.
Data Source
AI summary
A method in which electrical signals are assigned in a network includes generating first signals from data sources; generating second signals from the data sources; assigning the second signals by comparing with first signals and/or with differences of the second signals in data traces for the second signal based on network states; if a number of unassigned second signals after assigning is zero, terminating assigning the second signals; if the number is greater than zero after assigning, generating further first and second signals with modified network states and/or output conditions and assigning by comparing the further second signals with the first signals and/or with differences of the further second signals in data traces for the second signal due to network states.