Simulation Drives Virtual Development of Software-Defined Vehicles

January 27, 2025

The automotive industry is undergoing a significant transformation with the advent of software-defined vehicles (SDVs). These vehicles, whose functions and features can be expanded and improved through software updates, represent a new era of innovation and efficiency. The development of SDVs relies heavily on the integration of software, tools, and processes, with simulation models playing a crucial role in this virtual development process.

The Rise of Software-Defined Vehicles

Over-the-Air Updates and New E/E Architecture

Software-defined vehicles are characterized by their ability to receive over-the-air (OTA) updates, allowing manufacturers to continuously improve and expand vehicle functions without the need for physical recalls or manual updates. This distinctive feature necessitates a sophisticated electronic/electrical (E/E) architecture designed to support connected vehicles in the digital age. To facilitate these seamless updates, the development of hardware and software components for SDVs must occur independently. This separation ensures that each component can evolve and improve without being constrained by the development timeline or limitations of the other, fostering innovation and flexibility within the industry.

OTA updates enable car manufacturers to enhance vehicle performance and address safety issues by simply pushing the latest software directly to vehicles, akin to how smartphones and computers receive updates. This approach not only enhances the customer experience, ensuring that their vehicles remain up-to-date with the latest features and security enhancements, but also significantly reduces the costs and logistical challenges associated with traditional recall campaigns. Moreover, the introduction of this advanced E/E architecture is critical in supporting the continuous connection required for these updates, making the integration of virtual models and simulations all the more crucial.

Independent Development of Hardware and Software

In the realm of SDVs, the decoupling of hardware and software development is essential for catering to the diverse requirements and functionalities across multiple vehicle variants. This separation grants manufacturers the freedom to advance software independently, enabling new features and improvements to be efficiently rolled out without necessitating corresponding changes to the vehicle’s hardware. As a result, software can be standardized and reused across different vehicle models, enhancing development efficiency and reducing costs.

By integrating virtual models and simulations into the vehicle development process, engineers can analyze and optimize complex vehicle systems without the need for physical prototypes. This virtual approach not only accelerates development timelines but also allows for the rigorous testing of various scenarios, including those that are too dangerous or impractical to replicate in the real world. By leveraging these simulations, manufacturers can ensure that both hardware and software components meet stringent safety and performance standards before they are physically implemented, ultimately leading to more robust and reliable software-defined vehicles.

Integration of Software, Tools, and Processes

The process of integrating software, tools, and processes into an organization’s workflow is crucial for achieving operational efficiency and effectiveness. Proper integration ensures that various components work seamlessly together, reducing redundancy and improving productivity. It requires careful planning, coordination, and implementation to align with the organization’s goals and to accommodate any existing systems. By doing so, organizations can optimize their resources, enhance communication, and streamline operations, ultimately fostering a more cohesive and responsive work environment.

Continuous Updates and Safety-Relevant Functions

A high level of integration between software, tools, and processes is required to support the continuous updates characteristic of SDVs, particularly for functions that are critical to vehicle safety. Ensuring seamless and secure updates necessitates the use of models and simulations as indispensable tools in the analysis and optimization of vehicle technical systems. These models enable engineers to virtually test and validate updates before they are deployed, ensuring that the changes will perform as expected and meet all safety requirements.

By employing virtual models, engineers can conduct exhaustive testing and validation processes that would be prohibitively time-consuming or costly with physical prototypes. This approach also allows for the examination of extreme or hazardous scenarios that might otherwise be too risky to explore in real-world conditions. For instance, simulation models can test collision-avoidance systems in a variety of controlled and repeatable scenarios, ensuring their reliability in imminent danger situations. Safety standards, such as ISO 26262, underscore the importance of using Model-in-the-Loop (MiL), Software-in-the-Loop (SiL), and Hardware-in-the-Loop (HiL) simulations for the validation of safety-critical functions in self-driving vehicles.

Role of Simulation Models

Simulation models provide a cost-effective and efficient means of analyzing complex vehicle systems without the need for costly and time-consuming physical prototypes. By creating highly accurate virtual representations of vehicle components and systems, engineers can conduct reproducible tests that yield reliable data about how the system will perform in a wide range of scenarios. One of the significant advantages of these simulations is their ability to examine situations that extend beyond the vehicle’s real-world performance limits, such as extreme weather conditions or near-impossible crash scenarios.

Moreover, simulation models facilitate the identification of potential issues early in the development process, allowing for corrective measures to be taken before physical components are produced. This predictive capability significantly reduces development costs and timelines, as fewer physical prototypes are needed, and any design flaws or safety concerns can be addressed proactively. By leveraging simulations, manufacturers can ensure that their vehicles adhere to the highest safety standards and perform optimally under a vast array of conditions, ultimately resulting in safer and more reliable software-defined vehicles.

Model Development in SDVs

Validating Complex Vehicle Functions

To validate the complex functions of modern vehicles, engineers employ models within the broader context of the overall vehicle system. These virtual representations must be integrated within the Software Factory—a collaborative development environment where engineers can design, develop, test, and validate vehicle components and systems. By incorporating these models, engineers can simulate and analyze the behavior of various vehicle functions in a controlled virtual environment, ensuring that they meet design specifications and performance standards before moving on to physical testing.

The use of such models is instrumental in predicting how individual components will interact within the larger vehicle system. This holistic approach enables engineers to identify potential issues that might not be apparent when components are tested in isolation. Moreover, models provide a platform for extensive testing, allowing engineers to perform complex simulations of scenarios that would be impractical or hazardous to replicate in real life. This capability is invaluable in validating the performance and safety of new vehicle functions, ultimately leading to more reliable and secure software-defined vehicles on the market.

Attributes of Effective Models

For simulation models to be effective in the virtual development of SDVs, they must possess certain key attributes—namely suitability, efficiency, and flexibility. Suitability refers to the model’s ability to accurately represent the system it is designed to simulate, ensuring that the results obtained from the simulation are both relevant and reliable. Efficiency is crucial because the computational resources required to run simulations can be significant; therefore, models must be designed to perform these tasks as quickly and resource-effectively as possible. Flexibility is essential to accommodate the wide range of scenarios and variations that might need to be simulated during the development process.

A model that embodies these attributes allows engineers to conduct comprehensive and reliable analyses of vehicle systems in a virtual environment. Suitability ensures that the simulations provide accurate insights into real-world behavior, efficiency allows for the practical execution of complex and large-scale simulations, and flexibility enables the model to adapt to various design changes or new development requirements. Collectively, these attributes make effective models indispensable tools in the virtual development of software-defined vehicles, facilitating innovation while ensuring stringent performance and safety standards are met.

Types of Models

First-Principle Models

First-principle models, rooted in fundamental physical laws, offer a high degree of accuracy and traceability, making them invaluable for understanding and predicting the behavior of complex vehicle systems. These models are based on well-established principles, such as Newtonian mechanics or thermodynamics, and provide precise and reliable predictions of system performance. However, the computational demands of simulating these models can be substantial, often requiring significant processing power and time to analyze intricate systems accurately.

Despite these challenges, the accuracy and traceability of first-principle models make them essential tools in the development of SDVs. Their ability to provide detailed insights into the physical behavior of vehicle systems ensures that engineers can make informed decisions about design and performance adjustments. This level of precision is particularly crucial for safety-critical functions, where even minor inaccuracies in the model could have significant real-world consequences. As a result, first-principle models remain a cornerstone of simulation-based development, providing the foundational accuracy needed to build reliable and effective software-defined vehicles.

Behavioral Models

Behavioral models take a different approach by leveraging data-driven techniques, such as statistical methods or artificial intelligence (AI), to simplify complex systems while maintaining a reasonable level of accuracy. These models often use methods like Reduced Order Models (ROMs) to distill the essential dynamics of a system into a more manageable form, requiring fewer computational resources than first-principle models. By focusing on the observed behavior and response patterns of the system, behavioral models can provide valuable insights with less computational overhead.

The application of statistical models or AI in the creation of behavioral models allows engineers to capture nonlinearities and complex interactions that might be challenging to represent with first-principle methods. These models are particularly useful for tasks that require real-time analysis or for scenarios where quick approximations of system behavior are sufficient. While they may sacrifice some degree of accuracy compared to first-principle models, their efficiency and ease of implementation make behavioral models an effective tool in the iterative development process of software-defined vehicles, offering a balanced trade-off between detail and performance.

Model Integration

Integrating models into a system involves several critical steps, including data preprocessing, model training, model validation, and deployment. Proper data preprocessing ensures that the data fed into the model is clean and suitable for analysis. Model training involves using this preprocessed data to teach the model to recognize patterns and make accurate predictions. Model validation is an essential step to ensure that the model performs well on unseen data, thereby avoiding overfitting. Finally, deployment involves integrating the trained model into a production environment where it can be used to make real-time predictions. This end-to-end process requires careful planning and execution to ensure the model functions correctly and efficiently within the system.

Decoupling Hardware and Software Development

The virtual development process of SDVs facilitates the decoupling of hardware and software development, which is pivotal for achieving flexibility and efficiency across various vehicle variants. When hardware and software development are decoupled, software updates and enhancements can be developed, tested, and deployed independently of hardware changes. This separation enables the reuse of software components across multiple models, reducing the need for redundant development efforts and streamlining the overall process.

Advanced tools and methodologies play a critical role in managing this decoupling, ensuring that virtual models accurately represent both the hardware and software components interfacing with each other. This integration within a unified development environment allows engineers to verify software functionality and performance on virtual hardware representations before any physical components are manufactured, mitigating risks and reducing development costs. The ability to simulate and validate software updates in this manner ensures that the final product meets performance expectations and adheres to safety standards, leveraging the full potential of software-driven innovation in the automotive industry.

Managing Large-Scale Projects

The development of SDVs often involves large-scale projects that require meticulous management of interfaces, files, and version control. Tools such as Simulink with System Composer and MATLAB Projects are indispensable in handling these complexities, providing integrated environments that streamline the design, simulation, and testing processes. These tools support hierarchical model structures and collaborative workflows, enabling teams to work on different aspects of the project simultaneously while maintaining consistency and coherence across the entire system.

Standard formats like Functional Mock-up Interface (FMI) and Functional Mock-up Units (FMUs) are instrumental in facilitating the integration of multiple components from different vendors or development teams. These standards ensure compatibility and interoperability between various simulation tools and models, enhancing the efficiency and reliability of the development process. By managing the large-scale integration of these components effectively, manufacturers can ensure that their software-defined vehicles perform optimally and reliably, even as they incorporate a diverse array of sophisticated features and functionalities.

Credibility and Validation

Ensuring Reliable Simulations

In the world of data science and engineering, the reliability of simulations is paramount. To achieve dependable results, it’s essential to validate the model with real-world data and continuously update the parameters to reflect current conditions accurately. This process involves rigorous testing and revision to ensure that the simulations can be trusted for making critical decisions.

The credibility of simulation models is paramount to the successful development of software-defined vehicles, necessitating rigorous validation and verification processes to ensure the accuracy and reliability of the results. Validation ensures that the models accurately represent the real-world systems they are designed to simulate, while verification confirms that the simulations are implemented correctly and perform as intended. These processes are critical in building confidence among engineers and stakeholders that the simulation outcomes can be trusted to predict actual vehicle behavior.

Reliable simulations are essential not only for performance optimization but also for ensuring compliance with stringent safety standards. For instance, regulatory requirements for automotive safety mandate thorough testing and validation of vehicle systems to guarantee their reliability under various conditions. Through meticulous validation and verification processes, simulation models can be fine-tuned to provide highly accurate and trustworthy predictions, reducing the need for extensive physical prototyping and testing. This approach streamlines the development process, saving both time and resources while ensuring that the final product meets all necessary safety and performance criteria.

Importance of Validation and Verification

Validation and verification are critical processes in ensuring the accuracy and reliability of systems, products, and services. These steps help to identify and address potential flaws or issues, ensuring that the end result meets the required standards and performs as intended.

The importance of validation and verification extends beyond ensuring the accuracy of individual simulation models; it also encompasses the tools and processes used in virtual development. Ensuring that the development tools are functioning correctly and that the simulations are conducted using validated methodologies is crucial for maintaining the integrity of the entire development pipeline. Proper validation and verification build the foundation for credible simulations, which in turn leads to more reliable and safer software-defined vehicles.

In addition to technical validation, maintaining a stringent verification process establishes a standardized protocol for development teams to follow, ensuring consistency and repeatability in simulation outcomes. This structured approach enhances the credibility of the development process and aids in regulatory compliance, facilitating smoother approval and certification processes. By prioritizing validation and verification, manufacturers can mitigate risks, increase development efficiency, and produce more dependable and advanced software-defined vehicles that meet both industry standards and consumer expectations.

Cloud for Scaling

In the ever-evolving landscape of digital businesses, leveraging cloud computing has become essential for scaling operations efficiently and effectively. Cloud services offer scalability, flexibility, and cost-efficiency that traditional on-premises solutions cannot match. By utilizing cloud infrastructure, companies can quickly adjust their resources to meet growing demands, enhance their computing power, and optimize their storage capabilities without the need for significant upfront investments. Furthermore, cloud solutions enable businesses to innovate rapidly, deploy new applications swiftly, and maintain competitive agility in a fast-paced market.

Benefits of Cloud-Based Simulations

Simulation studies stand to benefit significantly from the scalability and computational power offered by cloud technologies. By leveraging the cloud for their simulations, manufacturers can perform large-scale, parallelized computations that would be impractical with traditional on-premises hardware. This capability allows for extensive simulation studies that can run multiple scenarios simultaneously, providing rapid and comprehensive results that expedite the development process. Cloud-based simulations enable automotive companies to maintain flexibility and agility, seamlessly transitioning between desktop and cloud environments in MATLAB to manage and execute large-scale simulations efficiently.

The cloud’s ability to handle vast amounts of data and complex computations is particularly advantageous for the iterative development process of SDVs, where numerous design iterations and extensive testing are required. By utilizing cloud resources, development teams can accelerate their workflow, often achieving in hours or days what might take weeks or months with conventional computing setups. This increased efficiency not only reduces time-to-market for new features and models but also allows for more thorough testing and validation, ensuring higher quality and performance in the final product.

Enhancing Development Efficiency

The automotive industry is experiencing a major shift with the rise of software-defined vehicles (SDVs). These next-generation vehicles are capable of having their functions and features enhanced and expanded through software updates, marking a new era of innovation and operational efficiency in the sector. SDVs signify a profound change as they move away from traditional hardware-dependent models to more flexible, software-centric designs. This transformation is deeply rooted in the integration of advanced software, state-of-the-art tools, and streamlined processes.

A critical component of this development is the use of simulation models, which are indispensable in the virtual development of these advanced vehicles. Simulation models allow for extensive testing and refinement of vehicle functions in a controlled virtual environment, thereby significantly reducing the need for physical prototypes and accelerating the development timeline. This not only cuts costs but also enhances the overall quality and safety of the vehicles before they hit the market.

As the automotive landscape continues to evolve, the reliance on sophisticated software and comprehensive simulation techniques will only grow. The ability to update and improve vehicle performance through over-the-air software updates provides unparalleled convenience and adaptability, ensuring that SDVs remain at the forefront of technological advancements and consumer expectations.

Subscribe to our weekly news digest.

Join now and become a part of our fast-growing community.

Invalid Email Address
Thanks for Subscribing!
We'll be sending you our best soon!
Something went wrong, please try again later