Visualizing, Analyzing and Optimizing Automotive Architecture Models using Sirius

Maximilian Hammer, Ralph Maschotta
and Armin Zimmermann
Systems and Software Engineering Group
Technische Universität Ilmenau
Ilmenau, Germany,
http://www.tu-ilmenau.de/sse/
Model-Driven Application Development for
Evaluation and Optimization of Automotive
E/E-Architectures
SiriusCon 2022
1 Maximilian Hammer, SiriusCon 2022
Visualizing, analyzing and optimizing
automotive architecture models
using Sirius

SiriusCon 2022
2 Maximilian Hammer, SiriusCon 2022
Outline
1. Introduction
• Rationale
• Scenario
2. Toolchain & Workflow
3. Implementation Details
• Architecture Visualization
• System Analysis
4. Performance Evaluation
5. Conclusion

3
• Formerly: automotive development dominated by mechanical enhancements
• Digitalization  new sorts of requirements:
• Safety features
(e.g. driver assistance systems like proximity warning, …)
• Comfort features
(e.g. onboard entertainment systems, rain sensing wipers, …)
• Pro-environmental measures
Introduction
Rationale
Maximilian Hammer, SiriusCon 2022

4
• Modern cars = complex cyber-physical systems (Electric/Electronic systems, short E/E)
• Sensors, Actuators
• Electronic Control Units (ECUs)
• Network switches, bus systems
• … and complexity continuously increases
 Demand for suitable evaluation and optimization methods
Introduction
Rationale
Source: Vector Informatik GmbH, online

5
• Model-based approaches to handle increasing complexity
• Major challenge: development of flexible, consistent and integrated toolchains
• Research project of Technical University of Ilmenau:
• development of a workflow and integrated toolchain for
• Model-driven analysis, evaluation, optimization of automotive E/E-architectures
Introduction
Rationale

6
• This paper presents:
• Central application developed within the project
• Conceptual design and development approach for integrated tools in this context
• Goals:
• Improve reusability and interoperability of applications
• Simplifying integrated and extendable toolchain development
• Toolchains based on uniform metamodels
Introduction
Rationale

7
• Central design problem: Deployment Problem
• Find „good“ mapping between logical and physical architecture
• Potentially great impact on efficiency and cost-effectiveness
• Various possible optimization measures like:
• Number of ECUs, overall cable length (cost-effectiveness)
• Mean power consumption (especially important for e-Mobility)
• Communication load balancing
 Paper use case: Find optimal communication paths based on existing architecture
Introduction
Scenario

8
• Toolchain based on PREEvision by Vector Informatik GmbH
• Model-based automotive E/E engineering
• Proprietary, widely used in the industry
• Presented application based on:
• Eclipse Modeling Framework (EMF)
 Open-source modeling environment
• Obeo Sirius Project
 Open-source framework for developing graphical model editors
Toolchain & Workflow

9
Toolchain & Workflow

10
• First step: type-dependant data reduction
(metamodel contains over 4000 metaclasses)
• Filter model elements regarding types actually required for visualization
• Logical Layer:
• only metaclass LogicalFunction
• Physical layer:
• subtypes of DetailedElectricElectronics (e.g. ECU or ActiveStar)
• metaclass BusSystem
Implementation Details
Architecture Visualization

11

12

• Next step: context-specific selection
• Determine elements within the context that should be visualized/analyzed
• User defines „anchor function“, i.e. context element
• Is of type LogicalFunction
• Application queries model‘s structure to determine required elements
• Selects LogicalFunctions directly connected to the context function
• Selects ECUs that run these functions
13

14

15
• Visualization of intermediate elements on all possible communication paths
required
 Depth-first search (DFS) algorithm
• Traverses model‘s physical architecture (electronic components and interconnecting bus
systems)
• Stores traversed elements in adjacency lists (also avoids circles)
• If path between source and target is found  intermediate elements saved
• After termination: retrieved elements stored in graph-like structure and visualized

16

17
System Analysis

18
• Four test models:
• testModel_complete: PREEvision demo model, contains complete E/E architecture
• testModel_compPackages: filtered based on relevant component clusters (e.g. components for
engine control are not relevant for control of windows)
• testModel_context: filtered further based on observation context (i.e. relevant functionalities
within the component cluster)
• referenceModel_useCase: excerpt of real-life automotive model
Performance Evaluation

19
• Single bus system = complete graph 𝐾𝐾𝑛𝑛
• 𝐸𝐸𝐾𝐾𝑛𝑛
=
𝑛𝑛∗(𝑛𝑛−1)
2
 let 𝑛𝑛 = 20 results in 190 edges
Topology Traversal and Build-Up
1436
4
2
44991
1
10
100
1000
10000
100000
testModel_complete
testModel_compPackages
testModel_context
referenceModel_useCase
Figure 1:
execution times
for traversal in ms
Table 1: number of electronic components (EC) and
bus systems (BS) per test model

20
Shortest-Path analysis
0,212 0,204 0,195
1,7
0
0,2
0,4
0,6
0,8
1
1,2
1,4
1,6
1,8
testModel_complete
testModel_compPackages
testModel_context
referenceModel_useCase
Figure 2:
execution times for
path analysis in ms
Table 2: number of vertices (V), edges (E) and mapped bus systems
(BSm) contained in the application-internal graph per model

21
• Model-driven analysis, evaluation & optimization of automotive E/E-architectures
• Capabilities:
• Visualization of different architectural layers & mappings
• Editing via graphical model editor
• Architecture analysis & optimization (example: shortest communication paths)
• Easily extendable by introducing certain weighting functions
 Potential to enable more sophisticated analyses & optimizations
Conclusion

22
• Computational complexity mainly depends on:
• Size of the model
• Complexity of topology (especially bus systems)
 Additional bus systems can increase complexity drastically
• Mainly affects Depth-first-search traversal of topology
• Less effect on path analysis due to efficient data preprocessing
Conclusion

23
Thank you for your attention!
Contact:
Maximilian.Hammer@tu-ilmenau.de
Related paper: Maschotta et al.:
„Model-Driven Aspect-Specific Systems
Engineering in the Automotive Domain“
Systems and Software Engineering Group
Technische Universität Ilmenau,
http://www.tu-ilmenau.de/sse/
Model-Driven Application Development for
Evaluation and Optimization of Automotive
E/E-Architectures

Visualizing, Analyzing and Optimizing Automotive Architecture Models using Sirius

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