auto industry with cae value


We work side-by-side with our clients to shorten development time using the following deliverables:

  • High-fidelity modeling of chassis and powertrain dynamics
  • Multi-objective Design Optimization (MDO) - using Design Exploration to generate balanced solutions meeting desired performance for attributes such as noise, vibration, harshness (NVH), handling, ride, driveability, durability, and more.  
  • Automation of simulation processes to increase efficiency
  • Tire measurement for Durability, Handling and Ride applications
  • Driving Simulator hardware, software and services
  • Mechatronics simulation with connected mechanical and ADAS models
  • Process assessments to identify bottlenecks, define roadmaps, and maximize return on Computer Aided Engineering activities.


6 Use Cases for CAE Simulation in
Electric Vehicle Drivetrains

As electric vehicles take over a larger share of the automotive market, OEMs and suppliers alike need to make use of every tool at their disposal to improve the performance of their vehicles. At present, no tool is more essential than CAE simulation when it comes to helping manufacturers test the design of EM components and how they contribute to the structure, efficiency, and performance of a vehicle.

While a lot of attention is often focused on battery performance in EVs, the electric drivetrain also presents completely new engineering challenges that require extensive simulation in order to understand how interconnecting systems affect each other. From our experience working in the automotive industry, we have identified six key areas where simulation helps EV engineers improve drivetrain performance. Let’s take a closer look.


1. NVH

In automotive engineering, reducing NVH (noise, vibration, and harshness) is essential to providing a comfortable passenger experience. Combustion engines are significantly more noisy than electric motors, which might lead some to believe that NVH reduction doesn’t require the same level of care and attention. However, the noise of the combustion engine masks a lot of NVH issues that would otherwise be problematic. With the engine noise gone, passengers are more likely to notice outside noises, as well as sounds from the gearbox or coolant systems.The issues are similar for vibration. A vehicle that moves and shakes is disquieting to passengers, and gives an impression of low quality. While passengers expect vibration from a combustion engine, electric vehicles will still experience vibration conditions when they are on the road. These can be more noticeable to passengers, leading them to feel less satisfied with the vehicle.

These factors are what make NVH simulation in electric vehicles so important. CAE simulations can show how disparate parts work together to contribute to passenger experiences of NVH. These can then be remedied in the design.

2. Electric Motor Performance

Electric vehicles have typically lagged behind those with internal combustion engines (ICEs) in terms of power and performance. This has been a barrier to wider adoption, as improvements in motor and transmission performance have come at a tradeoff to battery longevity. However, newer EV technologies have increased the efficiency of electric powertrains, allowing them to compete with and even outperform their ICE counterparts.

Multiphysics simulations have been integral to these improvements, and can help automotive manufacturers develop even better designs. This can be particularly important in optimizing motor efficiencies with gear ratios, to improve performance on highways or during uphill driving.

3. Thermal Management

Proper thermal management keeps components operating at optimal efficiency, which results in less drain on the battery and greater range for the vehicle. Thermal management systems in electric vehicles are more complex than their ICE counterparts, as batteries must be either cooled or heated depending on operating conditions, and because EMs don’t generate any waste heat that might be used to warm the cabin.Analyzing the way motors, inverters, generators, and batteries work together to consume energy is an important part of thermal management. Simulations can be used to model the entire vehicle to better integrate these systems for more efficient use.

4. Lubrication

Electric vehicles require different lubricants than ICEs. Without fossil fuels and with fewer moving parts, these systems run cleaner, meaning the engine oils will collect fewer contaminants over time from burning fuel. However, EV motors operate at very high speeds, and still require both oil and coolant. And, while EV transmissions are different from ICEs, they do still require lubrication for the gear reducer.

Thus far, there is no one solution to which lubricants should be used for different EV transmissions. While some manufacturers use traditional transmission fluid, others are relying on grease bearings with oil lubricants. CFD simulation can help engineers understand which lubricants work best based on their design choices.

5. New Materials

A key strategy for improving fuel efficiency in electric vehicles is to reduce the weight of the vehicle itself. Increased strength and stiffness can also improve the responsiveness and speed of electric motors, leading to better operating conditions. However, for a manufacturer to move from traditional metals to new, lightweight alloys, they must be able to ensure that these new materials can withstand the strains of use within an electric vehicle.CAE simulation can show how these new materials behave when used in EV driveline components, from the motor to the gearbox to the structural housing, so that manufacturers can continue to find new and more effective materials for their vehicle designs.

6. Durability

The reputations of automotive manufacturers are made or broken on the reliability and longevity of their products. Premature failure of components leads to lost trust among consumers, as well as extra expenses for manufacturers in case of a large recall.

Using CAE simulation, engineers can create models to run fatigue tests and predict failure conditions for every component in the EV driveline. These simulations can help manufacturers determine repair schedules and write better warrantees for their vehicles.

CAE VAlue can support your simulation needs for electric vehicle drivetrains.
CAE VAlue offers CAE simulation support for OEMs and suppliers in two key ways. First, we are value-added resellers (VARs) of VI-grade simulation apps and driving simulators, Siemens Simcenter HEEDS and Amesim, SES-Agile Tire, and our own ParaMount bushing simulation tool. For manufacturers who hope to expand their CAE departments we offer support for using this software to set up custom workflows.

As CAE consultants, we bring our multidisciplinary expertise to the table, helping our clients find solutions to complex problems. We can perform CAE simulations for our clients, increasing the capabilities for those who do not have CAE engineers on staff, and expanding the capacity for those who need extra assistance during peak production times.

If you are looking for a CAE partner to help test designs for EV components, contact us

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If your company plans to be one of tomorrow's leading OEM or vehicle producers, you need to be fast, agile, reliable, financially sustainable, and able to meet pressing global emission reductions in order to future-proof your business.

Simulation-driven product development is no longer a strategy option and is business critical for your next generation methodology. Your competitors may already be there.

CAE Value knows how to get you into pole position. We've road-tested and identified the industry's best simulation software and driving simulator solutions. We take the guesswork out of the 'Which solution works best for us?' process so you can maintain focus on designing next generation products demanded by the next generation.




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specialized Engineering

Our CAE Value Design and Engineering team has extensive experience with multiple OEM platforms
and top-hat development programs including Volvo Cars, BMW, Siemens Energy, ABB, NEVS, & Saab 

Vehicle dynamics
Suspension optimization
Driveline dynamics & integration
Work package  |  Time- and materials-based setup
Support cover concepts through development loops to industrialization

process automation

Due to the unwavering demand for the transition to digital business models, hyperautomation has quickly gone from being optional to essential. To achieve agility and resiliency, business leaders must leverage hyperautomation as a constant catalyst to optimize task-level digitization through process-level enablement. With hyperautomation, your projects are given a new boost of energy by this supercharged automation plan, which also provides your enterprises with more advanced tools and technology like:

Platforms with No/Low-Code
Organizational Process Management (BPM) Workflow Automation Platforms
Instruments for Process Mapping Integration Platforms (iPaaS)
Automating Process Robotically (RPA)
Processing Documents Intelligently (IDP)
Applied Machine Learning and Artificial Intelligence

This is just the tip of the iceberg. Hyperautomation compells organizations to examine their procedures even more closely and explore everywhere for innovative automation opportunities.

Process modeling
Programming - Python, C, + more
Scripting - Matlab, ADAMS, Apex, Amesim, + more 

Read Gartner's Top Trends in Hyperautomation



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applied optimization

Although simulation and optimization address different issues, we offer the expertise and experience of how they combine to effectively advance your company objectives. Get a sense of the factors that have the biggest impact on outputs by applying simulation approaches to your system on the basis of observed factors that affect the effectiveness, throughput, or other metrics. Develop better assumptions about the mathematical relationships between the parameters using these insights and data simulation methods, leading to better optimization - faster choices about what to modify and by how much. 

Establish multi-objective design optimization methods/workflow, integrate into daily product development activities
Process modelling
Programming - Python, C, + more
Scripting - Matlab, ADAMS, Apex, Amesim, + more


In order to realize a systems engineering approach for new vehicle innovations required to meet the Paris Agreement goal of limiting global emissions to 1.5% above pre-industrial levels, a model-based systems engineering (MBSE) method is required. Through model-based simulation and validation via the integation of 1D and 3D simulation, CAE Value propels manufacturers to meet their emission reduction and sustainability objectives.

Advanced modeling of 1D/3D systems including electric driveline, active chassis, components, etc.
Advanced pre/post processing automation for specific applications

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sectors in focus


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