Rising Demand in Electric and Autonomous Vehicles
The automotive industry is in a state of flux due to the trend towards “CASE” vehicles – Connected, Autonomous, Shared and Electric vehicles. In this rapidly changing environment, manufacturers need to accelerate vehicle development and bring new products to market faster. Almost 14 million new electric cars were registered globally in 2023. The global electric vehicle (EV) market surged to new heights in 2024, marking a 25% year-over-year growth. Computer-Aided Engineering plays a crucial role in the design and development of electric vehicles (EVs) by facilitating simulations and analysis of various aspects, from initial design to manufacturing planning. It helps optimize designs for efficiency, range, and safety, addressing unique challenges in EV propulsion like battery management and thermal performance. CAE also aids in early identification of potential issues, reducing development costs and time to market. Companies like SMT, Dassault Systèmes and others now offer EV specific software solutions. CAE allows for rapid iteration of designs by simulating their performance under various conditions, enabling engineers to optimize components for weight reduction, strength, and efficiency. CAE tools can model and simulate complex EV systems, such as battery management systems, electric drivetrains, and thermal management systems.
Adoption of Artificial Intelligence in CAE Tools
AI-driven generative design enables the creation of optimized design alternatives by analyzing vast datasets, leading to more efficient and innovative solutions. For example, deep generative models have been effectively utilized in engineering design to create new, optimized structures. AI improves simulation procedures by eliminating repetitive tasks and intelligent design suggestions, thus optimizing complicated workflows and lowering manual effort.Many leading automakers make use of AI in design and simulation.For instance, Ford also applies AI in simulations and testing to improve the accuracy of design outcomes, ensuring that vehicles meet stringent safety and performance standards.
Large Language Models (LLMs) like those which drive ChatGPT are set to revolutionize Computer-Aided Engineering (CAE) significantly with the ability to make sophisticated simulation tools more accessible, user-friendly, and efficient.By enabling natural language interactions, LLMs can simplify user interfaces, automate model creation, assist in tool selection, and potentially drive fully generative design processes. However, the success of LLM integration in CAE is constrained by the scarcity of high-quality, domain-specific training data—much of which resides as untapped intellectual property within organizations..
Rising Demand for Sustainable and Lightweight Designs
With industries working to minimize material consumption, increase energy efficiency, and comply with demanding environmental regulations, CAE tools are critical in designing, simulating, and optimizing sustainable and lightweight components without any sacrifice in performance, safety, and durability. With the use of CAE software, engineers are able to model new lightweight materials' behavior, forecast product performance under actual conditions, and quickly iterate designs before prototyping physically—saving time, money, and resources. This is especially important in industries such as automotive, aerospace, and consumer electronics, where weight reduction has a direct influence on fuel efficiency, range, and sustainability objectives. CAE facilitates multi-physics simulations, topology optimization, and generative design, which are key for developing innovative, light-weight structures optimized for circular economy models. These methods facilitate material distribution optimization, designing possibilities, and compliance to structural performance with least material consumption and environmental footprint. Sustainable design is focuses on minimizing the environmental footprint of products throughout their life cycle, and lightweight design addresses the need of efficiency, particularly in industries such as automotive, aerospace, and renewable energy. CAE software is central to this, as it offers sophisticated simulation and optimization capabilities that enable engineers to test new materials, including composites and alloys, without having to create expensive physical prototypes.
opportunity Impact Forecast
TRENDS
adoption of cloud-based CAE solutions
With cloud usage growing rapidly among both large enterprises and SMEs majority of engineering companies are shifting from using traditional, on-premises CAE software to cloud-based solutions. The shift to cloud-based CAE (Computer-Aided Engineering) solutions is driven by factors like cost-effectiveness, scalability, and collaboration opportunities. Companies of all sizes view cloud technologies as an opportunity to conduct their operations in a more efficient and cost-effective way, which is especially important within today’s design environments where the demand for faster development and better results at lower cost is increasing. Organizations are increasingly adopting cloud platforms to manage simulation workloads, reduce upfront infrastructure costs, and enable remote collaboration across teams. Globally distributed design and engineering teams can easily share and collaborate on their projects in real time. Industry experts further predict that most software services will start moving to being cloud-only and that the use of Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS) will increase exponentially. Cloud-based CAE solutions provide users with the ability to access powerful simulation tools without the need for heavy on-premises infrastructure. Cloud platforms offer faster data processing and increased computational power, leading to quicker simulation results and faster time-to-market.
integration of CAE into the product lifecycle management (PLM) process
Source
With the increasing complexity of modern products, CAE tools are now being embedded deeper into PLM processes to enable real-time simulation and validation of designs. For instance, cloud-based PLM solutions are integrating CAE tools to allow engineering teams to run simulations on-demand and access real-time results. This integration helps bridge the gap between design, testing, and manufacturing, making decision-making faster, enabling iterative design and more realistic predictions of product behaviour in real-world situations. The use of AI and machine learning in these systems is also evolving, enabling more predictive analytics that help optimize design choices early in the development process. Coordination of these different CAE tools in a unified product lifecycle management (PLM) setting will improve communication, speed up the PLM process and boost the influence of simulation on all stages of product development. For example, Siemens Teamcenter offers a comprehensive suite of PLM functionality with strong integration capabilities for CAE tools like Ansys, Hypermesh, and NX Nastran. Many automakers and aerospace companies integrate Lifecycle Management (LM) and Computer-Aided Engineering (CAE) capabilities to streamline product development.
The COVID-19 pandemic has affected the Information Communication Technology (ICT) sector, serving as a driver of accelerated digitalization across sectors. Disruptions in the supply chain resulted in hardware component shortages that impacted the production and supply of technology products. The COVID-19 crisis heavily affected the ICT industry with supply chain disruption, delays in manufacturing hardware, and delays in large infrastructure developments initially as the businesses adapted to the uncertainty.
