FREE WEBINAR: Electrified Powertrain Motion Control (ePMC) Meets Vehicle Motion Control (VMC)

Posted on 4th September 2026

Date: 17 September 2026 | Time: 1pm – 2pm

Vehicle Motion Control (VMC) is rapidly moving from a chassis specialist topic to a decisive discipline for electrified drivetrains. With software-defined vehicle architectures and centralised computing on the rise, traction, stability and energy recuperation can no longer be optimised in separate silos. Instead, propulsion and braking must be designed as one coordinated envelope—spanning software, electronics, sensors, computing hardware and actuators – with robust interfaces to steering and suspension.

Who should attend

This webinar is designed for professionals working in:

• Vehicle dynamics and chassis systems
• Electrified powertrain development
• Brake systems and brake-by-wire
• E/E architecture and software-defined vehicles
• Controls engineering and systems engineering
• Advanced development at OEMs, Tier 1s and technology suppliers

Learning Points

In this webinar, we unpack why electrification makes interdependence unavoidable: regenerative braking, brake blending and wheel-slip control require precise coordination between the e-machine and friction brakes (especially with brake-by-wire). What used to be “downstream” calibration becomes an early cross-domain design challenge – balancing efficiency, safety and compliance as regulations tighten and new methodologies (including AI-supported virtual development) reshape how complexity is managed.

You’ll also learn how electrified Powertrain Motion Control (ePMC) is emerging as an integral part of VMC, giving teams a shared language for the intersection where propulsion, braking and energy flows become one coherent control task – critical for performance, range and development speed.

Register now for free

How AI Redefines Automotive Engineering Workflows

Posted on 2nd September 2026

Théophile Allard, CTO and co-founder at Neural Concept

The automotive industry is undergoing a structural shift. Development cycles compress from years to months, regulatory requirements multiply and customer expectations rise. The automotive industry’s structural challenges feel more burning than ever: deliver better vehicles, faster, without sacrificing quality or cost.

For decades, the answer was adding more tools and specialists. But engineering organisations are fragmented, iteration loops are long, and system-level insights arrive too late. In perspective, AI looks like the perfect solution that will magically absorb workloads and shrink timelines. In reality, despite heavy investment, most initiatives stall at the pilot stage.

The issue is workflow fit, not AI capability. What is needed is a different engineering playbook, one built around how AI-native development actually works.

The fragmentation problem

Modern vehicle development involves hundreds of specialists across aerodynamics, thermal management, crashworthiness, NVH, and manufacturing, each operating with separate tools and timelines.

This workflow is deeply sequential: designers create geometry, analysts spend weeks on simulations, results return, changes are requested, the loop repeats. By the time conflicts surface, design freedom has evaporated. Today, AI can generate and evaluate thousands of design variants per day across multiple physics domains. The constraint is the old workflow, not ready to leverage these new technology capabilities.

From iteration to exploration

The shift happens when teams move from design iteration to design space exploration. Traditionally, engineers work on one design at a time, modifying CAD, running simulations, and interpreting results.

In AI-native workflows, engineers define a design space with rules, constraints, and performance targets. AI populates that space with thousands of variants. Engineers navigate possibilities, select promising designs, and refine constraints. This fundamentally changes how engineering knowledge is captured, shared and applied.

The intelligence layer

For this to scale, AI must embed in tools that engineers already use, not exist in isolated environments. In practice, AI functions as an intelligence layer connecting to CAD, CAE, and PLM systems through APIs. Engineers work in familiar interfaces but gain AI-powered features: automated geometry generation, real-time performance feedback and cross-discipline constraint propagation.

AI-based surrogate models trained on historical simulation data predict aerodynamic drag, thermal performance, or structural behavior with near-CFD/FEA accuracy at a fraction of computational cost. Engineers see design changes consequences in real time, early exploration happens where iteration is cheap and fast, and promising candidates get validated with high-fidelity tools only when needed.

At scale, this approach collapses development timelines, reduces late-stage redesigns, and shifts critical decisions to phases where design freedom remains high.

The path forward

For organizations ready to make this shift, the first step is identifying workflows where fragmentation causes the most friction and faster exploration would deliver clear value. Aerodynamics and thermal management are common entry points: physics are well understood, simulation data is abundant, and geometry changes have measurable impact.

But success requires more than deploying technology. Engineers need training, infrastructure must support AI workloads at scale, and organizations must accept that this transition will not happen overnight. Those who get it right will make designers more productive, decisions more informed, and better products.

We Need to Talk About the Grounding Ring

Posted on 26th August 2026

Goeff Lewis, Technical Director, Duvelco

OEMs love SiC and GaN [1]; higher switching speeds lead to more efficient motors, which in turn mean greater range or lighter, less expensive batteries for the same BEV range.

Higher switching speeds, dV/dt, can mean increased displacement current coupled onto the motor shaft [2].

For a given common-mode voltage (CMV) amplitude, this causes the floating shaft voltage to reach its limit more quickly. Oil films on the bearing races can break down at around 20 to 50 volts, which in turn can lead to bearing damage through electrical erosion (EDM) and related pitting. Higher-speed, higher-frequency multipole motors mean the number of possible EDM events increases broadly in proportion to the voltage excitation frequency, hastening possible bearing failure.

One widely adopted solution is to fit a shaft grounding ring, which effectively clamps the floating shaft voltage to near zero — typically within a few volts in practice.

Why don’t OEMs love this solution?

• An additional component with associated cost, complexity, and assembly considerations.
• Axial packaging space requirement, which can be challenging in highly integrated e-axle designs.
• Requirement for a dry operating environment (i.e. separation from the lubrication system), which may necessitate additional sealing and space.
• A wear component that may not reliably meet the ~15-year design life expected of BEV drivetrains.
• Potential source of NVH (Noise, Vibration and Harshness), particularly noticeable in inherently quiet electric vehicles.
• Most critically, a hidden degradation mechanism can be difficult to detect and may lead to non-graceful bearing failure, potentially immobilising the vehicle.

Engineers and product managers have therefore sought to design out the risk of EDM entirely – the ideal solution. However, despite the use of advanced filters and common-mode chokes, this has not yet been achieved without compromising the efficiency gains offered by SiC- and GaN-based inverters.

A commonly adopted mitigation strategy is the use of electrically insulated bearings. These may take the form of bearings with ceramic insulating coatings or hybrid bearings with ceramic rolling elements.

Such bearings can be relatively expensive and, to some extent, more fragile. In addition, thin ceramic coatings with a moderate dielectric constant can still support capacitive coupling, allowing displacement currents to flow and reducing the overall impedance at high frequencies.

A dimensionally stable, creep-resistant polymer insulator with a low dielectric constant (e.g. ~2.7 at 1 MHz and 150 °C) could enhance the robustness of such mitigation strategies.

Research from Leibniz Universität Hannover (IMKT) has shown that polyimide coatings can exhibit higher electrical impedance than conventional ceramic coatings, approaching that of hybrid bearings while providing improved robustness [3].

Building on this work, Ducoya — a highly crystalline, PFAS-free PMDA–ODA polyimide from Duvelco — offers excellent flow characteristics, enabling high-precision moulding of thinner-walled sections. Its relatively low dielectric constant also helps reduce parasitic capacitance and associated displacement currents. Together, these attributes enable reduced material usage and improved dimensional control, providing an economically attractive route to enhance the robustness of grounding strategies and mitigate EDM-related bearing damage.

Duvelco Ducoya also exhibits high compressive and tensile strength and toughness at elevated temperatures, which may be advantageous across a wider range of applications.

Duvelco Ducoya is available both as a moulding-grade polymer for use with suitable modern precision processing equipment and as finished components for integration by bearing manufacturers or OEMs.

The preliminary data in the graphs below, demonstrates how Ducoya also exhibits high compressive and tensile strength and toughness at elevated temperatures, which may be advantageous across a wider range of applications.

As electrified powertrains continue to evolve, the focus is increasingly shifting from mitigating EDM risk to addressing it at source. Advancedmaterials such as Ducoya may offer new possibilities for more robust, spaceefficient insulation strategies, helping engineers explore alternatives to traditional approaches. The question is: could a material-led solution play a greater role in future drivetrain design?

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[1] Silicon Carbide and Gallium Nitride.
[2] I=C. dV/dt
[3] Pauchard, P., Berger, C., and Jackowiak, R., “Effective Solutions for bearing Insulation to prevent electrical corrosion in E-Drive Systems” CTI Magazine, December 2025, pp. 25.