Automotive engineering software

Vehicle performance relates to the overall capabilities, efficiency and functionality of a vehicle, encompassing aspects such as acceleration, battery range, safety, aerodynamics and water management, advanced driver-assistance systems (ADAS), energy and thermal management, noise, vibration and harshness (NVH) and acoustics, strength and durability, vehicle controls and vehicle handling.

Automotive performance engineering is driven by business requirements and delivers end-to-end system optimization through a continuous virtual testing and monitoring loop. Using a shift-left strategy, car makers and suppliers aim to provide better business value by discovering potential issues early in the development cycle. By using simulation early in the design process and virtually testing if products meet the requirements, they can detect problems early and rectify them in a comprehensible digital twin. Additionally, they can explore design alternatives earlier in the design phase when changing designs is easier and less disruptive.

A multiphysics simulation refers to the analysis of multiple, simultaneous physical phenomena of a system or systems and the interactions among them. Driven by consumer demands and business requirements, multiphysics automotive engineering software aims to design safe, high-performing products faster. It enables one to model the complexity, explore the possibilities of products operating under real-world conditions and discover potential issues early in the development cycle.

Take for example vehicle battery development, design and engineering teams need to integrate electrochemical, electrical, thermal and structural engineering. Running isolated campaigns targeted at these separate attributes will generate redundant and sometimes conflicting models and data sets. Instead, using an all-in-one multiphysics approach allows you to understand exactly how components will perform when integrated with others and ensures that even the first prototype works as planned.

The time that automotive (system) testing was performed on prototypes is long gone. Today, the development doesn’t stop at the end of the V-cycle. Instead, it continues in an infinity loop by using historical data, test data and data from vehicles in use to create test-based models that define the relationship between vehicle characteristics and its performance.  

A good example is model-based system testing (MBST). MBST is an engineering framework employing three key solutions: virtual models, virtual-physical systems and physical prototypes. It utilizes test data to build, validate and improve simulation models, identifies interactions through XiL in various scenarios and enhances test data with simulation models for physical prototypes. Manufacturers using MBST are more efficient, faster and avoid errors in data transfer or post-processing. 

Another outstanding example is virtual prototyping. Virtual prototyping is an efficient testing method that allows the exploration of vehicle assembly alternatives before physical prototypes are built. It specifically addresses NVH performance and uses component-based transfer path analysis (TPA) to swiftly evaluate various designs, saving costs by detecting and controlling issues early.

Traditional design and engineering methods struggle with today's complex workflows. Leveraging artificial intelligence (AI) and machine learning (ML) accelerates decision-making, enhancing engineering teams' productivity and developing innovative products in shorter time frames. Fitting any stage in the development cycle, Simcenter offers a framework for applying artificial intelligence to leverage historical data, avoid mistakes, speed up the analysis and optimize designs with confidence.

For instance, in autonomous vehicle systems, AI and ML are used to embed human behavior into machine perception. While current autonomous vehicle perception systems nearly match human sensory abilities, especially in standard conditions, they fall short in extreme weather and complex scenarios. Simcenter's scenario-creation process helps generate critical scenarios for OEMs and AV suppliers, ensuring compliance with safety standards like the safety of the intended functionality (SOTIF).

The only sustainable way to integrate vehicle systems is to approach the design and engineering as a complete ecosystem. This enables manufacturers to rapidly redesign and re-use the workflow as requirements change and trade off attributes between multiple domains. On a process level, it is key to meet requirements while staying connected with the product lifecycle management (PLM) system to ensure that changes made in one domain are reflected across the entire system. A model-based systems engineering (MBSE) approach in vehicle development connects systems engineering with the rest of the development organization, improving collaboration and decision-making.

Take the example of electric drive engineering. An e-drive consists of an electric motor, a gearbox and power electronics. Multiple experts from different domains need to work together to achieve an efficient, integrated electric drive. It is hence essential to leave the traditional development silos in the past and allow cross-functional and cross-domain systems engineering. Only a connected engineering approach, with integrated, reusable workflows through the application of MBSE and a digital twin, provides manufacturers with the automotive engineering software toolchain to manage the spiraling complexity of electric vehicle engineering – not only of the product but also of the development process.