The automotive industry is characterized by an incredible diversity of products, each tailored to meet specific customer needs, market requirements, and regulatory constraints. With increasing vehicle complexity, the use of feature models and variants has become a key enabler of managing this diversity within a Model-Based Systems Engineering (MBSE) framework. Feature models allow engineers to efficiently represent and manage the variability in product lines, making it easier to design, validate, and produce a wide range of vehicle configurations while ensuring consistency and quality.
The Role of Feature Models in MBSE
Feature models are a fundamental concept in Software Product Line Engineering (SPLE) and are increasingly being integrated into MBSE for system design. In the context of automotive engineering, feature models help represent the various functions, configurations, and components of a vehicle as features. These models specify commonalities and differences between different product variants in a hierarchical manner, making it easy to map and understand complex system architectures.
For example, a modern vehicle can be configured with different powertrains (e.g., internal combustion engine, hybrid, electric), varying levels of Advanced Driver Assistance Systems (ADAS), infotainment systems, and comfort features. A feature model can represent these options, showing which features are mandatory, optional, or mutually exclusive. This model helps to manage product variability effectively, ensuring that all possible configurations comply with safety, regulatory, and customer requirements.
Variants Management for Automotive Systems
Variant management refers to the ability to handle different versions of a system by managing its variability. In the automotive industry, where customization is key, managing variants is crucial. Variants can exist across different system levels, such as hardware, software, and system configurations. MBSE tools support variant management by integrating feature models into the system design process, allowing engineers to create and verify different product configurations from a shared model.
For instance, consider a vehicle model that offers the following feature combinations:
- Powertrain: Gasoline, Diesel, Hybrid, Electric
- ADAS: Basic, Intermediate, Full (with features such as lane-keeping assist, automatic emergency braking, etc.)
- Infotainment: Standard, Premium (including advanced navigation and connectivity features)
These combinations can result in a wide array of vehicle configurations. By using feature models, engineers can define the dependencies between different features (e.g., a hybrid powertrain might require a specific braking system due to energy recovery) and ensure that only valid combinations are produced.
Example: Configuring an Autonomous Vehicle Platform
Consider the development of a modular autonomous vehicle platform. Feature models allow for the customization of various aspects of the vehicle, such as the level of autonomy, sensor configuration, and powertrain type. A high-level feature model for this platform might include the following:
- Autonomy Level:
- L2 (Partial Automation)
- L3 (Conditional Automation)
- L4 (High Automation)
- L5 (Full Automation)
- Sensor Suite:
- LiDAR (Optional for L2, Required for L3 and above)
- Radar (Standard for all levels)
- Cameras (Number of cameras increases with higher autonomy levels)
- Powertrain:
- Internal Combustion Engine (ICE)
- Hybrid Electric Vehicle (HEV)
- Battery Electric Vehicle (BEV)
With these features and their constraints defined in a feature model, engineers can generate and evaluate different configurations of the autonomous vehicle platform. For example, a vehicle with Level 5 autonomy would automatically require the most advanced sensor suite, which includes multiple LiDARs, radars, and cameras. Meanwhile, a Level 2 vehicle would have a more basic sensor configuration. This ensures that the design is aligned with regulatory standards, safety requirements, and customer expectations for each level of autonomy.
Benefits of Feature Models and Variants in MBSE for Automotive
- Efficiency in Design and Development: Feature models streamline the design process by allowing engineers to manage different product configurations within a single model. This reduces the need for redundant development efforts for each variant, saving time and resources.
- Consistency Across Variants: By using feature models, engineers can ensure that all variants of a product line share a consistent architecture, improving overall quality. The use of common modules across different variants ensures that changes to one component (e.g., a software update) are automatically propagated to all relevant configurations.
- Enhanced Traceability: Feature models improve traceability in MBSE by linking system requirements to specific features and variants. For example, safety requirements from the ISO 26262 functional safety standard can be associated with specific ADAS features, ensuring that safety compliance is maintained across all vehicle configurations.
- Scalability: Automotive systems are becoming more complex, with increased customization options. Feature models provide a scalable approach to manage this complexity by offering a structured way to handle variability across different system levels—software, hardware, and system behavior.
- Support for Compliance and Safety Standards: Feature models help ensure compliance with standards such as ISO 26262 (functional safety) and ISO/SAE 21434 (cybersecurity). By modeling features and their dependencies, engineers can trace safety and security requirements to the relevant system components, ensuring that all variants meet regulatory standards.
Tools and Standards Supporting Feature Models in MBSE
- SysML (Systems Modeling Language): SysML is often used to represent feature models in MBSE. SysML’s requirement and block definition diagrams help capture feature hierarchies and dependencies, ensuring that variants are fully modeled and traceable throughout the system architecture.
- Product Line Engineering (PLE) Tools: Tools like pure::variants, BigLever Gears, and PTC’s Windchill provide robust support for managing product lines and variants in MBSE environments. These tools allow engineers to define, manage, and validate product variants based on feature models.
- ISO 26262: The ISO 26262 standard for functional safety is closely aligned with feature models, as it requires detailed traceability between safety goals and system implementations. Feature models allow engineers to model safety-critical features and ensure they are applied consistently across all vehicle variants.
- ISO/SAE 21434: This cybersecurity standard for road vehicles can also be integrated into feature models. By modeling cybersecurity features (e.g., secure communication, encryption) within the system, engineers can ensure that all variants are compliant with cybersecurity requirements.
Conclusion
Feature models and variant management are critical components of MBSE, especially in industries like automotive where product customization and complexity are high. By integrating feature models into the MBSE workflow, engineers can manage product variability efficiently, ensure compliance with safety and cybersecurity standards, and reduce the time and cost associated with developing different vehicle configurations.
As the automotive industry moves towards highly automated and connected vehicles, the use of feature models will continue to grow, providing a structured, scalable, and flexible way to manage the complexity of modern vehicle systems.
References:
- ISO 26262: Road vehicles – Functional safety
- ISO/SAE 21434: Road vehicles – Cybersecurity engineering
- “Feature Models and Product Lines: Variability in Software Systems,” Klaus Pohl, Günter Böckle, Frank J. van der Linden
- SysML: Systems Modeling Language Specification