Adopt model-based systems engineering in vehicle development
The most successful businesses are adopting model-based systems engineering (MBSE). This helps them remain competitive, responsive, and cost-effective while tackling the challenges of quality, increased regulations, and sustainability. While the MBSE approach significantly improves efficiency, it does have a few challenges. Those are addressed by using the right tools to overcome them.
Download this white paper to discover how to create better designs and improve the performance of electric drive systems with Siemens MBSE solution.
Introduce MBSE concepts in electric drive development
The concept of MBSE is introduced in this white paper by highlighting what takes place in the development of an electric drive (e-drive) system. MBSE can be used to create a model that represents all aspects of the system, from the initial requirement to the final product. The model can represent the system components to include the electric motor, inverter, gearbox and cooling system.
The goal of MBSE is to provide improved collaboration and communication among everyone involved in developing an e-drive system.
Download this white paper to learn how using MBSE to develop an electric drive system can lead to a more streamlined, effective development process.
Use MBSE to streamline the development process to improve vehicle performance
Simcenter Amesim and Simcenter Studio are part of the Siemens Xcelerator portfolio. By combining the power of these simulation platforms, engineers can explore a wide range of e-drive architectures and identify the optimal design for an e-drive system. The result is a streamlined and efficient process for exploring e-drive architecture options, reducing development time and costs while ensuring optimal performance and efficiency.
Discover how to leverage the benefits of MBSE in the vehicle development process
To fully leverage the benefits of MBSE in the vehicle development process, it is imperative to invest time early in a project to take advantage of the advancement in artificial intelligence. This establishes the right architecture to minimize modeling rework in later project phases.