Automotive E/E Architecture Co-Design Using Pareto Optimization
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Solution Overview
Problem
Automotive electrical/electronic (E/E) architectures are constrained by network parameters, leading to inefficiencies and increased design and debugging costs due to a disconnect between model implementation and actual architecture, necessitating a method for co-designing efficient E/E architectures.
Innovation Solution
A method utilizing Pareto optimization to design new E/E architectures by generating prospective software controllers and exploring Ethernet network parameters, such as packet priority and buffer sizes, to create a Pareto front of optimal solutions for control objectives like rise time and quadratic cost, thereby automating the co-design of control systems and network architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional E/E architecture design methods are used, then design flexibility is maintained, but design time and debugging costs increase significantly
Solution Approach 1:
The patent replaces manual, iterative mechanical design processes with an automated computer-based optimization system. The control system automatically generates and evaluates multiple E/E architecture configurations using mathematical models and optimization algorithms, eliminating the need for manual trial-and-error design iterations and significantly reducing design time while maintaining optimal performance.
Solution Approach 2:
The system systematically varies key design parameters such as sampling rates, feedback gain values, packet priorities, and buffer sizes to explore the design space. By automatically adjusting these parameters and evaluating their impact on control performance and network utilization, the system identifies optimal parameter combinations without requiring manual intervention for each configuration change.
2Reliability
If control system design is separated from network architecture design, then each component can be optimized independently, but the overall system performance deteriorates due to parameter mismatches
Solution Approach 1:
The patent merges the control system design process with the network architecture design process into a unified co-optimization framework. The system simultaneously optimizes controller parameters (sampling rates, feedback gains) and network parameters (packet priorities, buffer sizes) based on a shared performance model, ensuring that both subsystems are designed to work together harmoniously rather than being developed in isolation.
Solution Approach 2:
The system employs feedback mechanisms where the performance impact of control parameters on network utilization is continuously evaluated, and this information is fed back into the optimization process. The quadratic cost function incorporates both control performance metrics and network utilization metrics, creating a feedback loop that guides the simultaneous optimization of both control and network parameters toward a common optimal solution.
Data Source
AI summary
A method for designing electrical/electric architectures includes receiving a system model of an automotive system. The system model includes an initial electrical/electronic architecture. The method further includes receiving control objectives for automotive system and designing, using Pareto optimization, a new electrical/electronic architecture based on the control objectives and the system model.