Detailed Validation: SDVs and Simulation
Last updated
Last updated
Simulation has long been a cornerstone of vehicle development, supporting everything from physics simulations for crash testing and aerodynamics to energy management, sensor modeling, and control system validation. These tools are essential for improving efficiency, safety, and performance across all stages of design and testing.
However, traditional vehicle simulation systems are complex, comprehensive, and time-consuming to build. To address this challenge in the SDV era, modularization, architectural layering, and the shift north approach—supported by the Vehicle Hardware Abstraction Layer (VHAL)—are critical. This enables faster, more agile simulation environments that decouple hardware from software development, aligning with modern SDV strategies.
Simulation plays a critical role in modern vehicle development, enabling comprehensive virtual testing across multiple domains to reduce costs and time. Key areas include Vehicle Dynamics and Performance, where handling, braking, and aerodynamics are optimized, and Safety and Crashworthiness, which tests crash scenarios to validate occupant protection systems. Environmental Testing assesses vehicle performance across varying weather, terrain, and altitudes, while Electrification and Energy Management models battery range, charging, and energy use.
In ADAS and Autonomous Driving, simulations validate sensors, decision-making algorithms, and vehicle behavior. E/E Systems and Software benefit from Hardware-in-the-Loop (HIL), Software-in-the-Loop (SIL), and Model-in-the-Loop (MIL) testing to ensure seamless integration. Computational Fluid Dynamics (CFD) enhances Aerodynamics and Thermal Management, while Human Factors and UX simulations focus on ergonomics, HMI interfaces, and cabin NVH performance. Additionally, virtual Regulatory Compliance testing ensures emissions, safety, and homologation standards are met.
Simulations also address Modular and Variant Testing for platform flexibility and configuration validation, as well as Sustainability and Lifecycle Analysis to model environmental impacts. Finally, Manufacturing and Assembly processes are optimized virtually, improving factory workflows and reducing production issues. Together, these simulation efforts create a robust, efficient, and agile development process, supporting a "Shift Left" strategy in vehicle engineering.
Simulation in Software-Defined Vehicles (SDVs) predominantly occurs south of the Vehicle Hardware Abstraction Layer (VHAL), where it focuses on physical systems and safety-critical, ASIL-compliant components. These simulations replicate real-world vehicle behavior for areas like vehicle dynamics, battery management, and environmental conditions, ensuring high-fidelity results closer to physical reality.
In contrast, development north of the VHAL follows a code-first approach. Algorithms here, often classified as QM or low-ASIL, are developed iteratively using agile methodologies, MVPs, and continuous improvement. This separation enables rapid innovation north of the VHAL while maintaining stability and accuracy south of it, showcasing the benefits of modularization and layered development.
This modular approach allows for cross-domain integration, even when different domains south of the VHAL rely on distinct simulation platforms, ensuring cohesive, multi-domain validation while accelerating innovation.
In the next step of the Range Extension use case, the basic mock-up data south of the Vehicle Hardware Abstraction Layer (VHAL) is replaced with a more realistic vehicle simulation. This advancement ensures that vehicle behavior below the VHAL is far more accurate, leading to better testing and validation results for the range extension algorithm north of the VHAL.
Importantly, this change does not impact the algorithm itself, as it interacts with the vehicle API above the VHAL. This demonstrates the benefits of loose coupling—allowing improvements south of VHAL without affecting development north of it.
To enable a deeper understanding of simulation methods in SDV development, we will explore Model-in-the-Loop (MIL), Software-in-the-Loop (SIL), and Hardware-in-the-Loop (HIL) approaches, highlighting their roles, benefits, and importance in achieving efficient and reliable testing across different stages of the development cycle.
The diagram illustrates three key simulation approaches in the context of Software-Defined Vehicles (SDVs): Model-in-the-Loop (MIL), Software-in-the-Loop (SIL), and Hardware-in-the-Loop (HIL).
MIL: Simulation inputs are tested against mathematical or functional models. It allows early testing of algorithms or system behaviors in a virtual environment.
SIL: Validates software components by simulating their performance with inputs and outputs. This ensures the software works as intended before integration.
HIL: Combines real hardware with simulated environments to test physical components under realistic conditions.
These simulation "loops" enable iterative testing, providing fast feedback while reducing risks and costs. They also allow cross-domain integration, ensuring systems interact seamlessly, which supports a shift-left approach to development.
