AI-Powered Engineering Software for Electric Drives: OPED

Boosting powertrain development with agility, fast time-to-market and optimal product-market fit

Dr. Martin Hofstetter, Head of E-Mobility and Alternative Drivetrains Research Group, Graz University of Technology
Dr. Dominik Lechleitner, Senior Researcher, Graz University of Technology

Designing electric powertrains is challenging: engineers must quickly find competitive designs and optimize the system for multiple key performance indicators (KPIs) at once, e.g., efficiency, cost, and package. The industry-approved engineering software OPED (Optimization of Electric Drives) can do this automatically by combining parametric system models with an AI-based optimization algorithm and exploring hundreds of thousands of design variants within 24 hours.

Development of Electric Drives

The development of electric drives (e-drives) is a highly complex and interdisciplinary process. Engineers must simultaneously design numerous electrical and mechanical subsystems (see Figure 1) that must optimally work together while meeting ambitious system targets for performance, efficiency, cost, and packaging. These objectives are often conflicting – improving one typically worsens another. Moreover, this highly challenging task must be solved under strong time pressure as it is critical for ambitious time-to-market goals. Therefore, engineering of electric drives demands digital tools capable of handling multi-criteria optimization and cross-domain interactions in an integrated way to quickly provide solid answers to complex questions.

Revolutionizing the Development Process with OPED

The software OPED fundamentally changes how e-drives are developed. Instead of relying on sequential design steps and manual iterations, OPED uses parametric system models combined with an AI-powered evolutionary optimization algorithm to explore the full design space automatically. The outline of OPED is shown in Figure 2: Based on specified e-drive system requirements, the design problem is encoded as a multi-objective optimization problem. The intelligent design algorithms then generate different e-drive designs, which are evaluated by system analysis models. Based on the calculated design properties, the optimization algorithm rates the generated designs and aims at improving them based on the best found designs so far. This closed loop of design analysis and synthesis continues until no more improvements are observable and converging behavior is present. Furthermore, self-learning artificial neural networks boost the optimization performance by guiding the optimization algorithm and directing its search towards promising design regions. Within 24 hours of computation time, around 50 design parameters are varied, hundreds of thousands of possible e-drive designs are evaluated, and the most promising ones are identified based on multiple concurrent objectives such as

• performance,
• efficiency,
• cost,
• package integration,
• carbon footprint,

and any other design objective – everything that can be calculated, can be optimized. The result is a Pareto front of optimal solutions, providing engineers and decision makers with a clear overview of achievable trade-offs and design potentials. By merging simulation, optimization, and system understanding in one integrated workflow, OPED enables rapid development cycles. Agility is key: every request for quote (RFQ) or sudden project change request (PCR) is replied with fast and solid answer, tailor-fit to the specific requirements. Taking informed decisions early ultimately leads to an optimal product-market fit for the e-drive system and a fast time-to-market.

Figure 2: From e-drive system requirements to optimal e-drive solutions with OPED

Master Challenging Targets of Package, Efficiency and Cost within 24 Hours

OPED’s strength lies in its versatility. The software can be applied to a wide range of design questions – from component sizing and material selection to system-level trade-offs and product family development. Moreover, as system requirements are often vague and uncertain in the early development stages, OPED can be utilized for requirements engineering. Another powerful capability lies in the full 3D package investigation: OPED not only finds designs that comply with a given 3D target installation space, it also provides possible packaging options within the available space. An example is shown in Figure 3, which depicts two possible design solutions for an e-drive: One with the smallest possible length and one with the smallest possible height (e.g., providing additional trunk space for applications at the rear vehicle axle).

Besides package feasibility, both energy efficiency and cost are always critical and conflicting KPIs. To select the most suitable solution, OPED provides a Pareto front of e-drive designs (Figure 4), where each point represents one optimal design solution. Accordingly, engineers and decision makers are provided with a solid foundation for selecting the most suitable system solution respecting the specific goals of each vehicle application. As each design solution from OPED contains detailed technical information – including a 3D CAD model – a seamless and smooth transition from OPED results to the A-sample development is ensured. This makes OPED a powerful enabler for fast and digital electric powertrain development.

Figure 3: Example of e-drive packaging options; available installation space is shown in blue

Concluding, OPED enables engineers and decision makers to

• respond quickly to requests for quote (RFQs) and project change requests (PCRs),
• solve conflicting KPIs & do requirements engineering,
• develop competitive solutions with product-market fit,
• design optimal product families, utilizing commonality and carry-over-parts.

OPED is established in practical use at a leading global automotive tier 1 supplier – with high potential forscaling across other suppliers and OEMs.

Figure 4: Select the sweet spot of energy efficiency vs. production cost (vehicle model from [1])

Sources:
[1] Holiday, D. (2021). Jaguar I-pace Concept. https://sketchfab.com/3d-models/jaguar-i-pace-concept-3ea106994ec9442eb4b72906026fa215. CC Attribution: https://creative-
commons.org/licenses/by/4.0/, modified. [Online; accessed 13 February 2024

From Lab to Exhaust: A Startup’s Breakthrough in CO2 Transformation

Alicja Stankiewicz, CTO, Coat-It
Marek Turkiewicz, CEO, Coat-It

Pollution kills more people globally each year than war, hunger, or disease. And at the heart of this crisis is carbon dioxide (CO2) – the primary greenhouse gas driving climate change.But what if CO2 could be split and transformed before it ever leaves a tailpipe?

That’s the vision behind RainIons, a U.S.-based startup that has developed a revolutionary powder capable of reducing greenhouse gas emissions – including CO₂, NOx, and hydrocarbons – by transforming them into safe, stable molecules. The application of technology is currently being developed further by COAT-IT, a Polish startup specializing in the engineering of high-temperature-resistant coatings tailored for practical automotive applications.

The Science Behind the Solution

The innovation combines pyroelectric and piezoelectric minerals with naturally occurring radioactive materials (NORM) in a conductive matrix. This unique blend emits alpha particles, negative ions, and electrons – without external power. These particles ionize pollutants and split strong molecular bonds, including those in CO₂.

Independent studies and internal evaluations suggest the solution can significantly reduce CO₂ emissions across a variety of conditions. At elevated temperatures (around 500 °C), reductions have been observed above of 50 %. Even in lower temperature environments (70 – 200 °C), meaningful decreases in CO₂ – typically within a 25 – 30 % range – have been noted, with no harmful byproducts identified. In moisture-rich settings, reductions of up to 50 % indicate that water may play a catalytic role in the
transformation process.

From Tourmaline to Transformation

Inspired by earlier research on tourmaline-infused asphalt, the tourmaline-based “negative ion powder” was infused into a conductive coating and applied it to a diesel muffler. FTIR spectroscopy revealed that CO₂ was being split into graphite and oxygen, with water concentration directly influencing reaction efficiency.

What’s Next?

COAT-IT is now developing durable, high-temperature coatings for commercial deployment. The next phase includes real-world trials using diesel engines and custom exhaust systems. The goal: to quantify transformation products and optimize substrate design for maximum pollution reduction.

If successful, this technology could redefine emissions control – turning exhaust systems into active climate solutions.

Data Driven Cost Measure Generation for Automotive Systems Using Teardown Intelligence and AI

Ivan Jovanovic, Team Leader Incubation InnoHub

Cost optimization in modern drivetrain systems requires more than benchmarking—it demands structured, data driven ideation. This article presents an AI supported methodology that leverages largescale teardown intelligence to systematically generate and assess cost measures at component and system level. The approach bridges the gap between engineering detail and cost strategy decision-making.

Introduction

Automotive drivetrain systems are undergoing rapid transformation driven by electrification, increasing system complexity, and mounting cost pressure. Engineering teams are expected to identify cost reduction opportunities earlier and with greater confidence, while preserving performance, quality, and compliance. Traditional cost

engineering approaches—often based on expert workshops, static benchmarks, or manual analysis—struggle to scale with the growing volume and complexity of available technical data.

This article presents a structured, data driven approach for generating cost measures for systems using largescale teardown intelligence combined with AI supported analysis. The methodology is designed to support engineers and cost analysts in identifying, structuring, and prioritizing cost measures based on factual technical evidence rather than assumptions or isolated examples.

Technical Challenge

Teardown databases today contain thousands of components, materials, manufacturing processes, and architectural design choices across vehicle generations and OEMs. While this data provides a rich foundation for cost insights, extracting actionable cost measures remains challenging due to:

• High data volume and heterogeneity
• Limited traceability between design choices and cost impact
• Dependence on individual expertise to formulate improvement ideas
• Difficulty comparing alternative technical solutions at scale

As drivetrain architectures evolve—especially in electrified powertrains—there is a growing need for systematic methods that transform teardown data into concrete, reusable cost measures.

Methodology

The presented approach is based on three core technical pillars:

1. Structured Teardown Intelligence Componentlevel teardown data is normalized across materials, geometries, manufacturing processes, and system architectures. This enables cross vehicle and cross-generation comparisons within subsystems.
2. Cost Measure Pattern Identification Historical teardown data is analyzed to identify recurring cost relevant design patterns, such as material substitutions, part integration, geometry changes, or process shifts. These patterns are abstracted into reusable cost measure templates that are independent of a single vehicle or OEM.
3. AI Supported Ideation and Filtering An AI based engine combines teardown evidence, cost drivers, and engineering constraints to generate potential cost measures. Measures are contextualized by vehicle segment, drivetrain type, and system boundaries, allowing relevance filtering and prioritization based on feasibility and expected impact.

Concrete Example: Evidence Based Part Elimination

Teardown analysis reveals that the removal of the external brand logo on the hood—first observed on the Tesla Cybertruck and later adopted on recent Model Y versions—constitutes a validated example of part elimination for cost optimization. This decision removes a complete component and associated assembly steps, improving manufacturing efficiency and reducing part count. Such ground truth examples enable data driven challenges to traditional design choices, while highlighting that radical cost measures often require longer internal alignment despite their technical simplicity.

Achieved Innovation

The key innovation lies in shifting cost measure generation from an experience driven activity to a repeatable, data supported process. Instead of manually deriving ideas from individual benchmarks, engineers can explore a structured set of cost measures grounded in real, observed design solutions.

For drivetrain systems, this enables:

• Faster identification of technically realistic cost measures
• Improved transparency between design decisions and cost impact
• Better reuse of historical teardown learnings across projects
• Support for early phase concept and target setting activities

The approach does not replace engineering judgment; rather, it augments it by providing a technically substantiated starting point for cost discussions and design trade-offs.

Conclusion

As drivetrain technologies continue to evolve, cost engineering must evolve in parallel. Leveraging teardown intelligence through structured data models and AI supported ideation offers a scalable way to generate high-quality cost measures rooted in real engineering solutions. This methodology supports more informed, fact-based decisionmaking and helps bridge the gap between technical design and cost strategy in modern automotive development.