Automotive and electric mobility development involves complex design decisions across aerodynamics, thermal management, battery systems, powertrains, structural durability, NVH, cooling circuits, and component performance. Traditional CAE workflows can be powerful but time-intensive when teams need to explore many design options, operating conditions, and performance trade-offs.
ML-Augmented CAE & Digital Twins help engineering teams accelerate development by combining simulation data, machine learning, reduced-order models, and physics-informed modelling. These models make it easier to explore design spaces, predict performance, compare alternatives, monitor systems, and support faster calibration decisions.
Experiqs provides ML-Augmented CAE & Digital Twin services for automotive OEMs, EV manufacturers, mobility startups, component suppliers, and engineering teams. We use surrogate modelling, reduced-order modelling, physics-informed machine learning, simulation data analytics, automotive digital twins, predictive monitoring, and optimization workflows to improve decision-making and reduce development time.
Vehicle development requires evaluating multiple variables such as geometry, material selection, cooling layout, operating loads, thermal limits, aerodynamic performance, vibration response, and control behaviour. Running full CAE simulations for every design condition can be slow, expensive, and difficult to scale.
ML-augmented CAE helps convert detailed simulation results into fast predictive models. These models can estimate performance across many design combinations, identify important parameters, and support rapid optimization without running a full simulation every time.
Digital twins extend this capability by representing real vehicle systems such as battery packs, cooling circuits, powertrains, motors, inverters, HVAC systems, and complete vehicle subsystems. When connected with test or operating data, digital twins can support performance tracking, predictive maintenance, calibration, and system-level decision-making.
For EVs, this is especially valuable because battery thermal behaviour, power electronics cooling, range prediction, charging performance, and system integration depend on multiple interacting variables. Experiqs helps automotive teams use ML and digital twins to reduce uncertainty, shorten development cycles, and improve engineering confidence.
We create fast CAE-based models for rapid design space exploration, sensitivity studies, and optimization.
Our surrogate models help assess:
This helps teams explore more design options in less time.
We build reduced-order and physics-informed models for battery packs, cooling circuits, powertrains, and vehicle systems.
Experiqs supports digital twins for:
This helps create virtual engineering models that support monitoring, prediction, and optimization.
We support real-time performance tracking, predictive maintenance, and faster calibration workflows using physics-based and data-driven models.
We help evaluate:
This helps convert operating data into practical engineering insights.
Physics-informed models combine engineering principles with data-driven methods to improve prediction reliability.
We help develop models for:
This helps improve model accuracy while keeping predictions connected to real engineering behaviour.
Reduced-order models allow complex simulations to be simplified into faster models for system-level evaluation and control support.
We support reduced-order models for:
This helps teams use CAE insights in faster development and control workflows
ML-augmented models can accelerate optimization by quickly testing design and operating changes.
We help support:
This helps engineering teams make faster and more confident decisions.
Experiqs helps automotive OEMs, EV manufacturers, mobility startups, and component suppliers address ML, CAE, and digital twin challenges, including:
Use surrogate models to compare many design options and operating conditions without running full simulations every time.
Convert detailed simulation outputs into faster predictive models for repeated engineering decisions.
Identify key design parameters, sensitivity trends, and trade-offs across performance, safety, efficiency, and cost.
Create virtual models for battery packs, cooling circuits, powertrains, and vehicle systems for monitoring and prediction.
Detect performance drift, thermal issues, degradation trends, and abnormal behaviour earlier.
Use data-driven and physics-informed models to connect CAE results with test data and real operating behaviour.
Experiqs combines CAE simulation, CFD, FEA, thermal engineering, system modelling, machine learning, and digital twin development to help automotive teams accelerate engineering decisions.
Our strength lies in connecting physics-based simulation with data-driven intelligence. We help clients turn large CAE datasets into fast predictive models, build digital twins for complex systems, and create workflows that support optimization, monitoring, and calibration.
By using ML-augmented CAE and digital twins, Experiqs helps automotive and EV teams reduce development time, improve design confidence, optimize performance, and make better decisions before physical testing.
Use surrogate modelling, physics-informed digital twins, reduced-order models, and predictive monitoring to improve automotive and EV development speed, accuracy, and decision-making.
Talk to our experts to evaluate your CAE workflow or vehicle system and identify practical opportunities for faster simulation, smarter optimization, and better predictive engineering.
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Whether you’re exploring a new R&D initiative, seeking advanced simulations, planning experimental validation, or evaluating product feasibility—our experts are ready to assist you.
