Accelerate Vehicle Feature Deployment and Prepare for Future Needs
In this webinar, we're focusing on software-defined vehicles to enhance adaptability, enabling rapid feature updates and robust validation through simulations and digital twins.
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In this webinar, we're focusing on software-defined vehicles to enhance adaptability, enabling rapid feature updates and robust validation through simulations and digital twins.
Software-defined vehicles (SDVs) capitalize on the adaptability of software functionalities throughout the vehicle's lifecycle. Achieving this flexibility requires an apparent decoupling of software from the underlying hardware, enabling independent development, deployment, and evolution of vehicle features. By separating software from hardware dependencies, automakers gain the freedom to implement new capabilities without being constrained by hardware upgrades.
However, achieving this flexibility requires rigorous validation of software functionalities, given their dependencies on electronic hardware and their direct impact on controlling the physical asset. These validations must be integrated throughout the entire product development lifecycle to account for ongoing software and hardware evolution. The validation framework must support incremental software updates delivered via over-the-air (OTA) mechanisms, ensuring that vehicles in the field remain reliable and free from defects introduced by evolving algorithms.
A virtual replica of the physical system (sensors & actuators) and electronic hardware integrated with software components such as application software, middleware, and base software, enables comprehensive validation of system behavior. Ansys physics-based simulations can help you develop highly accurate virtual models of complex physical assets. These models can be integrated with various software components to analyze how they respond to control functions.
By abstracting the representation of middleware and base software alongside application software, developers can achieve a realistic representation of the ECU within a virtual environment. This virtual vehicle model, created through this methodology, can be embedded into an Industrial Internet of Things (IIoT) framework as a digital twin, supporting validation during operational stages.