directions_car Solutions for Automotive
The main task in the automotive industry is to organize a nonstop development of safer, better performing and more appealing vehicles within strict timelines and limited budgets.
Key automotive trends today are the electrification driven by growth in emerging markets and emission regulations, autonomous technologies and connectivity.
pSeven solutions enable automotive OEMs and tier suppliers (powertrain, transmission, chassis, electronics, HVAC, batteries and other) to implement new technologies and meet productivity and sustainability goals.
Addressing challenges
in the Automotive industry
Today’s economies are undergoing dramatic changes, driven by advances in emerging markets, the rapid growth of new technologies, sustainability policies and evolving consumer preferences. In the automotive sector, these forces have given rise to technology-driven trends such as electrification, autonomous driving and connectivity. As the industry navigates these trends, it faces multiple challenges, including shortening product development cycles, reducing production costs, complying with safety and emissions regulations, reducing warranty expenses and more.
Software and electrification will remain the two main drivers of the automotive sector’s transformation.
- Pedro Pacheco, VP Analyst at Gartner.
pSeven Desktop and pSeven Enterprise enable OEMs and part manufacturers to adapt the top trends driving the future of the automotive industry and address the associated challenges.
Reduce development time
and production costs
- Automate repetitive engineering processes and simulation tasks.
- Find the best product design parameters.
Improve fuel economyand reduce emissions
- Reduce aerodynamic drag and rolling resistance.
- Reduce vehicle weight.
- Improve combustion efficiency.
Improve collaboration withinand between teams
- Co-author and share simulation worflows server-side.
- Turn workflows into web apps and expand the use of simulation.
Develop electric vehicles
- Increase traction motor efficiency and reliability.
- Reduce battery costs and maximize battery life.
- Minimize usage of rare-earth components.
Implement reliability prediction
- Identify input parameters that affect quality the most.
- Explore the limits of engine operating ranges.
Explore autonomous
driving scenarios
- Fully automate the virtual testing.
- Adaptively explore and set up driving scenarios.
- Reduce cycle for ADAS validation campaigns.
Achieve optimal and sustainable designs faster
with Multidisciplinary Design Optimization
In the automotive industry, accelerated time-to-market is key to boosting competitiveness, profitability, and growth. Reduced development timelines also enable the quick and flexible implementation of new technologies. As fuel costs rise and emissions standards tighten worldwide, the need to optimize vehicle propulsion system performance grows.
Achieving optimal automotive designs faster in terms of cost and emission reduction requires a multifaceted approach. This includes optimizing aerodynamics, reducing vehicle weight, improving engine efficiency, exploring alternative fuels and implementing advanced emission control technologies. Therefore, the automotive design process requires the implementation of automated simulation workflows that consider the multidisciplinary effects and overall behavior of the system, such as aerodynamics, combustion, acoustics, vibration and harshness (NVH), durability and other attributes.
Multidisciplinary design optimization (MDO) and SmartSelection technology, which automatically selects and tunes algorithms, help optimize vehicle design, including the use of lightweight materials to reduce drag and vehicle weight, improved aerodynamics to reduce rolling resistance, and increased powertrain efficiency to enhance fuel efficiency and reduce emissions. These capabilities can help identify the ultimate vehicle body geometry and equipment characteristics, including optimization of:
- External aerodynamics to reduce drag.
- Vehicle body elements and exhaust systems to reduce weight and minimize NVH.
- Radiator and heat exchangers geometry to reduce size and maximize heat transfer.
- Impellers in the turbocharger to maximize efficiency.
- Catalytic converter geometry to reduce emissions.
- Breaks geometry and materials to reduce squeal.
- Electric motor efficiency in different operating conditions.
- HVAC systems to enhance temperature and moisture comfort.
- Cams geometry to enhance shape of lift, velocity, acceleration, jerk curves and dozens of other characteristics in valve train.
- Valve and intake/exhaust channels geometry to enhance in-cylinder flows.
- Piston head geometry to enhance combustion.
- Car seat structures to reduce weight and improve reliability.
Improve collaboration
with process integration, low-code automation and multi-user environment
Effective MDO requires strong collaboration between engineers from different disciplines. pSeven Enterprise offers process integration and collaborative engineering capabilities to capture complex multidisciplinary workflows and improve collaboration between departments and engineers and ultimately lead to development cycle and cost reduction. As a server-side platform, it allows running many resource-consuming MDO studies simultaneously with a built-in resource manager and running workflows offline ("fire-and-forget") without interruption.
Democratize simulation
with workflow-powered web apps
For a technology to have a significant impact, it must be affordable and scalable, ensuring widespread adoption. Achieving environmental targets, increasing productivity and supporting digital transformation in automotive industry requires a huge boost in technology consumption. Simulation methodologies need to be democratized, ensuring they reach the vast engineering population and are produced at the scale required to truly make a difference.
With pSeven Enterprise, Methods & Tools department and advanced users can share their knowledge and simulation methodologies with other users across and outside the organization in a form of workflow-powered web apps that are stored in a centralized location called AppsHub. AppsHub helps to automate routine engineering tasks and democratizes the use of simulation at enterprise scale – all in your browser.
Embrace digital transformation
with accelerated SPDM adoption
Automotive industry is actively embracing digital transformation technologies and strategies to remain competitive and meet the evolving needs of consumers and the market. SPDM systems are one of the drivers of digital transformation in automotive as they centralize and facilitate the management of simulation data, improve collaboration across teams and streamline engineering workflows. Still, according to NAFEMS, the adoption of SPDM systems by simulation engineers is very low, at 1%-2%. One of the reasons is that it is very difficult for an SPDM system to establish a direct connection to the entire engineering software ecosystem that engineers, coming from all disciplines, used throughout the product development cycle.
Leveraging pSeven’s process automation and integration framework accelerates SPDM deployment. It is able to remove the traditional technology lock of these SPDM platforms in terms of interoperability with engineering applications. It encapsulates hundreds of applications and micro-services in standard blocks and becomes a kind of universal connection to these applications for the SPDM platform.
Related publications
NWC2025 - Enhancing engineering efficiency: the synergy of SPDM and PIDO integration
Laurent Chec, General director, pSeven SAS
NAFEMS World Congress 2025
P7UC2023 - The power of combining SPDM with engineering workflow automation
Michael S. Murgai, Executive Board Member, PDTec
pSeven User Conference 2023
P7UC2022 - 20 years of SPDM in production - towards a convergence of SPDM and PIDO
Mark Norris, CEng MIMechE MBA, theSDMconsultancy
pSeven User Conference 2022
Accelerate MBSE initiatives
with "black-box" predictive models
The automotive sector is shifting from a document-based to a model-based approach and is progressively adopting model-based system engineering (MBSE). MBSE as a valuable methodology addressing the complexities inherent to modern vehicle design, particularly with the emergence of electric and autonomous vehicles. Virtual prototyping and simulations enabled by MBSE help identify and address potential risks early in the design process. MBSE requires ensuring the accuracy and consistency of data within the models, as well as ensuring that MBSE tools and models can exchange data seamlessly with other engineering tools and systems while preserving intellectual property rights.
Fast and robust predictive models can answer this need and drastically speed up system simulation while preserving IP rights. Models created in pSeven products from simulation, analytical and/or experimental data can be exported for the use in external software products like Systems Engineering modeling software via C, FMU or Matlab/Octave, or can be sent as an Excel file to contractors for developing auxiliary equipment.
settings Use case
Creating internal combustion predictive model for Mitsubishi Motors
pSeven Desktop was used as a powerful and helpful tool to predict combustion model parameters. The created predictive model is used in 1D engine simulations via export through m-file or FMI. Such predictive models are very fast in terms of computation, so implementing them in the product development process leads to significant time savings.
Develop electric vehicles
with predictive models and MDO
By 2030, Gartner predicts that more than 50% of all vehicle models marketed by automakers will be EVs. However, achieving widespread adoption requires overcoming significant engineering challenges. These challenges span from from improving battery technology to increasing vehicle efficiency and lowering production cost.
Fast and accurate predictive models for system level simulation and Multidisciplinary Design Optimization with pSeven Desktop and pSeven Enterprise empower electric vehicles manufacturers to develop efficient and reliable electric vehicles’ components by meeting key success factors:
- Keeping the energy balance.
- Making the traction motor highly efficient.
- EV batteries: reducing cost and increasing life, improving thermal/cooling efficiency.
settings Use case
Optimization of microstructure properties of Lithium-Ion batteries
To use lithium-ion batteries for EV, their performance and capacity should be increased while saving weight. In this use case, pSeven was applied to optimize batteries microstructure thus improving their overall performance and to build accurate predictive models of battery properties for different applications without heavy simulations.
Automate virtual test bed and explore driving scenarios
with adaptive DOE and Cloud deployment
pSeven products help the developers of advanced driver-assistance systems and automated driving design safe, convenient and efficient systems by the means of predictive models for operating systems and fully automated workflows.
pSeven Desktop enables scalable test beds in the virtual testing environment with a variety of real-world driving scenarios. Fully automated workflows, Adaptive DoE technique and predictive modeling toolkit ensure full automation of the virtual testing, adaptive exploration and flexible setup of driving scenarios, collecting, storing and reusing the data. Reuse of automated workflows reduces the effort during further testing.
pSeven Enterprise, a cloud-native low-code platform, can be deployed on-premises or in the cloud to leverage additional computing power on-demand. Combining power of aDoE which globally reduces number of simulations on one hand, with scalable power of Cloud architecture on the other hand, can lead to drastic cycle reduction for ADAS validation campaigns.
settings Use case
Virtual test bed automation and exploration of driving scenarios
FDTech is developing the simulation-based testing environment of automated driving systems. Virtual test beds imitate a variety of real-world driving scenarios, described by a huge set of parameters. In this study, company applied Adaptive DoE methods in pSeven Desktop to focus on the most important parts of design space, and created predictive models for remaining test space to predict the responses for any driving scenario.
Implement reliability prediction and quality system
with Design Optimization, Sensitivity Analysis and Uncertainty Quantification
Automakers spend billions of dollars annually on warranty costs. Warranty reduction programs are among the highest priorities for financial departments. To reduce warranty expenses, automotive companies implement systems combining design robustness methodology, reliability prediction and a robust quality system.
Sensitivity analysis plays a crucial role in automotive reliability prediction by helping to identify the most influential parameters affecting a system's performance and failure rate. This analysis helps engineers understand which design choices or input variables have the biggest impact on reliability, allowing for targeted design improvements and risk mitigation. By quantifying the impact of uncertainties in these parameters, sensitivity analysis enhances the accuracy and confidence of reliability predictions.
Using Design Optimization, Sensitivity Analysis and Uncertainty Quantification (UQ) functionality of pSeven solutions allows automotive engineers studying design space and estimating failure chances to address them before they become warranty claims.
settings Use case
Design Space Exploration for harness components
LEONI is a cable and harness manufacturer and developer for various automotive industry customers. LEONI uses simulations in the design and validation processes for cable channels, but they are time- and resource-consuming. Learn how company applied pSeven at the validation and quality check step to find better solutions faster.
Explore industrial use cases
Related publications
P7UC2022 - Enabling digital transformation in engineering: Trends and challenges
Donald Tolle, Director, Simulation-Driven Systems Development Practice
pSeven User Conference 2022
P7UC2022 - Automatic generation of stability charts for telehandler vehicles
Joan Mas Colomer, Application engineer, on behalf of Manitou company
pSeven User Conference 2022
P7UC2022 - Optimization study of bumper structure
Güven Nergiz, CFD Engineer, BIAS
pSeven User Conference 2022
What's in the eBook?
done pSeven products description
done Application scenarios
done Selected industrial use cases