Learn how modeling and simulation helps evaluate and meet multi-objective requirements.
The correct battery pack design is critical to offer the range, cost, safety, durability, and driving experience customers demand. Integrating this technology into vehicles remains a challenge since the trade-offs between weight, cost and battery performance are so high. Balancing these aspects is key for both new market entrants and existing OEMs. In addition, the lack of physical testing facilities and the length of time testing can take only add to the challenges.
Register to watch this webinar on demand to learn how state-of-the-art modeling and simulation applications enable design and engineering teams to collaborate on the evaluation and improvement of multi-objective battery performance targets.
Learn how multi-disciplinary workflows are used to model and simulate e-Drive systems and performance.
An electric drive is the heart of an electric vehicle. Its performance is measured by many attributes, with key factors being durability, efficiency, harshness, cost and weight.
Achieving the targets for these characteristics requires solving highly non-linear, multi-variable problems involving structural, thermal, electrical, and magnetic physics domains.
Since these design objectives are interdependent, they need to be evaluated on the complete system, including the electric machine and the gearbox.
Watch this on demand webinar to learn how Dassault Systèmes electric drive engineering solution provides multi-disciplinary workflows to design and simulate the complete performance of an e-Drive in a short amount of time.
The quest for designing the optimal electric vehicle that is smart, safe, and connected and delivers a customized user experience is setting new standards in automotive benchmarks. The challenge is that many need to turn to new approaches in the engineering design process to design. validate, and deliver an intelligent vehicle experience. This requires new thinking and processes, along with a convergence of old approaches, and redefining performance and safety measures. Since this is a very new market place there is little historical data or experiences to learn from, those in it are carving out the product innovation advancements in real-time.
The automotive industry is riding this wave as carmakers compete to deliver vehicles that feature their latest innovations to capture consumers’ attention and business. As the Internet of Things continues to grow, companies are now inventing alternative transportation solutions that take advantage of intelligent city services to provide people with the best mobility experiences.
Smart, Safe and Connected solutions based on the 3DEXPERIENCE platform deliver innovative technology that supports end-to-end digital continuity between the multiple disciplines involved in conceptual design and embedded electronics and software development of the intelligent car. Carmakers can address vehicle development using a systems engineering approach to manage the complex task of making cars smarter, safer and more efficient.
The two main areas of design focus that are completely different from combustion vehicles include the electric drive and the battery. A recent blog post on Dassault Systémes blog noted these design challenges:
The electric drive is a complicated system whose parts need to work together effectively and efficiently. These include the gear box and electric machine, which need to be designed carefully to avoid over-designing. Over-designing leads to excess material, weight and cost and system engineering helps to precise and balance targets like maximum torque, power and speed, in the context of the whole vehicle.
The design must fulfill requirements for performance, efficiency, thermal, noise and vibration, etc. It is key to include multi-disciplinary design explorations and optimizations in order to reduce the required time of the development cycle. Once the many components of the electric drive have been implemented, their performance must be verified against the targets defined by Model-Based Systems Engineering, or MBSE.
In addition to the electric drive, the battery is a vital part of an electric car – and it’s not simple to design, either, especially when considering temperature. Batteries are akin to humans in that they perform best within a certain range of temperatures. If a battery’s temperature is too high or too low, it can result in lower vehicle range or shortened battery life.
Batteries involve multiple and connected physics, making them tricky to design, but Dassault Systèmes is working on a solution that connects mechanical and system design, materials and chemistry modeling, and performance assessment of battery cells, modules and packs.
When a battery is evaluated along with the electric drive on a vehicle, engineers and manufacturers can get a real idea of the performance and range of the battery rather than relying on a test at a test cell. The battery performance can be tested with real load on the battery, enabling an accurate picture of how it will perform in the real world.
A recent ebook from Tech-Clarity, “Solving High Tech’s Top Six Critical Business Issues,” reinforces what many in the high-tech product development world know: it’s a tough marketplace out there. New technologies offer exciting opportunities for innovation, but they also create implicit requirements for companies whose success depends on responding to ever-changing consumer desires.
This is a particular challenge given that lack of response to market trends could mean irrelevance and even obsolescence. In the drive to make connected, smart products, the pressure is on to be first or early to the market to capture share, but also to deliver something new and exciting for the market. We are all aware of products we once used that are no longer in existence. I was an early adopter of the PalmPilot (several versions), now they are nowhere. One study projects that 50% of the current S&P 500 will be replaced over the next decade. It happens to the best of them… Polaroid, Prime, DEC, Wang, just to name a few. No one wants that to happen to their company.
What are the critical business issues that organizations need to address to prepare for the future? Tech-Clarity identifies six:
1. Taking cost out
It is noted that 70% of a product’s cost gets locked in during the design cycle. Are you using the right tools to fully optimize your design? Using a platform of simulation tools like 3DEXPERIENCE will help design teams fully assess a given design such as the best material, the ideal weight, the full load anticipated, hot spots on boards. Identifying these characteristics early in the design process will play a significant role in designing right the first time before producing a prototype.
Top Engineering Challenges in High Tech Source: Tech-Clarity
2. Avoiding quality problems
One solution to this is creating a single source of truth for data management. In a separate study, Tech-Clarity found that 20% of time engineers are not working with the right data. This is a big issue for high tech who have mechanical, electrical and systems engineers working on the same project. Using a collaborative PLM platform that manages all product data will enable greater team efficiency and reduce quality issues down the line.
3. Achieving shorter product development schedules
Productivity gains rarely happen by maintaining current processes. When the right process changes get put into place, significant gains can be realized. Creating a collaborative design process ensures that all the teams have access to the right data when it’s needed. In high tech, a combined ECAD-MCAD platform is the key to bringing all the pieces together to shorten the design cycle and mitigate risks by putting the right checkpoints in place. Tech-Clarity identifies that 23% of the time engineers spend their time just “looking” for the right information. This gets even more time consuming when companies have multiple systems to check. A platform approach like 3DEXPERIENCE can remove these roadblocks and increase the throughput of all involved.
4. Facilitating innovation
Being nimble and agile is critical for high tech companies due to the rapidly changing needs of their consumers. Many design teams need to pivot off a current design but improve on it and create more interesting capabilities. Managing across engineering disciplines is critical for facilitating innovation and enables cross-pollination of ideas. Having an ideation repository can help spur new approaches to old designs along with a platform that manages data and other information in one place to support real-time collaboration.
Cost Impact of Design Changes Source: Tech-Clarity
5. Ensuring performance and reliability
Heat and vibration are the two biggest culprits that cause performance and reliability problems. It’s important to work with a team who has deep experience with electromagnetic simulation such as low-frequency applications such as electric motors to high-frequency applications such as sensors and antennas. There are many aspects to setting up multiphysics models correctly, never mind understanding the tools behind them. The good news is when you do work with experienced consultants (like Adaptive), we can help establish a testing and analysis process from concept through to final validation phases. Further, the process will also document and incorporate these tests into your design process so that you know why decisions got made and have access to the supporting data behind it.
6. Compliance with environmental and regulatory requirements
Consumers are hot on environmental-friendly products, and high-tech companies have the opportunity to work with materials that are recyclable and have better sustainability. Beyond pleasing consumers, high tech companies need to make sure they are in compliance with RoHS and other local regulations as it relates to their product life and obsolescence. A strong PLM platform can help ensure that the right steps are documented and signed off to meet compliance regulations.
Overall, the eBook supports an integrated platform for PLM to bring together ECAD and MCAD systems, the mechanical, electrical and system engineering teams and their design processes. A single version of the truth for data and contextual information (2D and 3D drawings) need to be accessible and a fundamental building block for any product lifecycle management (PLM) platform. Implementing tools that will not only enable but also promote and even require collaboration and virtual simulation, among other functionalities, will enable companies to overcome many of the unique challenges faced by the industry.
If you want to learn more about how your organization can address these critical business issues with a powerful PLM platform that addresses many of these challenges, call us at (440) 257-7460 or click below to schedule a demo.
Let’s take a deep dive into dynamic vehicle systems modeling with a step-by-step example – modeling and simulating a series hybrid vehicle in Dymola®.
A series hybrid has an internal combustion engine that is only used to generate electricity. This means there is no direct connection from the engine to the wheels – instead electric motors are used to provide torque to the wheels.
This example not only demonstrates how components from different domains, like internal combustion engines and electric motors, can be combined to build a complete model of your vehicle, it also demonstrates how to model the control systems.
To watch a prerecorded video of this scenario please Click here.
Step 1: Modeling the Engine and Crankshaft
We’ll start by modeling the internal combustion engine model. Models are typically created by dragging and dropping component models into a schematic diagram. Notice that the engine model has two inputs. The first input is used to specify the normalized throttle position for the engine and the other, a Boolean signal, used to specify whether fuel should be injected or not. We’ll revisit the topic of how to control this engine later. For now, we’ll send constant signals into the engine as a starting point and switch to a closed-loop control strategy later. To finish building the engine portion of our model, let’s add a rotational inertia of 0.15 kg m² to represent the crankshaft.
Note that the block diagrams used to model control functions are seamlessly combined with the mechanical components. More importantly, notice the difference in the connectors. The block diagram components have arrows on them, indicating information flows through the system. The mechanical connections on the other hand are directionless, acausal connectors. Acausal connectors allow us to build models that are flexible. In this case, we’ve connected the crankshaft to the engine model but we have the freedom to connect it to any other rotational component. We don’t need to worry about whether that component will be a spring, an engine, or a clutch; whatever is needed, we just instantiate it from the library and connect it up.
Step 2: Modeling the Transmission
With the engine out of the way, let’s start looking at the transmission. Let’s model a simple transmission with a pair of motors from the standard library. One motor is connected to the engine, acting as the generator and the other is connected to the wheels, driving the vehicle. To control the motor, let’s insert a current control block. This component is essentially an actuator, controlled from outside the transmission. The input to this component is the requested motor current. The actual value for the requested current will have to be calculated based on the torque required by the vehicle. For now, let’s simply add a constant input with an initial value zero (motor is not running).
Next, we connect the generator and the motor, and add a ground to the circuit. Our mode is still missing one important thing: batteries to store energy. There are many ways to model batteries. Just to keep things simple, let’s use a large capacitor as the battery and add it in parallel with the motor. This means that electricity generated by the generator can flow either into the battery or into the motor. The motor current actuator determines how much flows one way and how much flows the other. To start the battery out charged, let’s specify the initial voltage of the capacitor as 300V.
Step 3: Modeling the Vehicle
We’ll start with a simple model for the vehicle. The main effects we need to capture are how torque is translated into a force on the vehicle, the drag force present on the vehicle and the overall vehicle inertia. For this model, we are only interested in longitudinal dynamics, that is, we are only interested in modeling the vehicle moving in a straight line. The first step in modeling the vehicle is to add wheels that transform the torque generated from the transmission into forces that move the vehicle forward in a straight line. Note how the wheel model has a rotational connector on one end indicated by a gray circle and a translational connector indicated by green square to the other. Let’s also add the overall vehicle mass and a damper to represent losses that scale up with speed. In reality aerodynamic drag scales differently, this is just an approximation. So far everything looks good!
Step 4: Simulating the Behavior of the Vehicle
Step 5: Decomposing into Subsystems
To get closer to real-world conditions, we need to refactor this model and improve the control systems. We could start by changing and reconnecting components, but there are a couple things to watch out for. First, when reconnecting things, you run the risk of introducing errors. Second, we may want the original open-loop control version for testing. To address these concerns, we should follow standard configuration management guidelines.
Our first step is to organize the components by subsystems. To do this, we select the components that are part of the same subsystem and create a new subsystem model. Let’s create a new subsystem called EngineController out of the engine control components – the throttle indicator and fuel flag, while preserving the original components as a new model called OpenLoopController. We perform the same actions for the engine, transmission, transmission-control and vehicle models. The system is now composed of subsystems. The next step is to standardize the interfaces for each subsystem.
Step 6: Creating Interfaces
Let’s define a standardized interface for the engine-controller. In our model, the engine control decides what the throttle position should be and whether to fuel the engine. Our current engine control model reflects this by including two output signals. One is a Boolean signal for the fueling command and the other is a continuous signal indicating normalized throttle position. The current engine control model defines both the interface, the above two signals that it needs to work with, as well as the implementation, that it uses open-loop commands. We will separate this model into an implementation and an interface.
We’ll also add one additional input signal to the new interface to supply the engine controller with information about the state of the battery voltage.
We now have an interface which defines what is common across all potential engine control models and a specific implementation that just uses open loop commands.
Let’s follow the same procedure for the engine subsystem, splitting it into an interface and a specific implementation. This interface includes inputs from the engine controller and an output shaft for connection to the transmission. The implementation includes the internal combustion engine and the crankshaft. We will follow this procedure for the transmission, the transmission-controller and the vehicle model, adding additional sensors along the way.
Step 7: Creating Vehicle Architecture with Interfaces
To create the vehicle architecture, let’s build a new version of our system model, but this time, using only the interfaces that we have developed. After connecting the interfaces together, we end up with a model that looks very similar to what we had before, except this time we haven’t included any implementation details.
This architecture contains only the interfaces and no implementation has been specified. It captures the structure of our system, regardless of the specific implementations we choose to use. Once the architecture has been created we don’t need to connect subsystem models anymore, all the interfaces have been connected to work across any implementation. Next step is to create a variant of the vehicle and decide which implementations of each subsystem will be included in the variant.
Step 8: Extending the Vehicle Architecture to Implement a Variant
For the base variant, let’s recreate our original model with open-loop control. To do this we need to specify the implementations for each subsystem. At the moment, we only have one implementation for each subsystem. We could directly specify our implementation choices in the architecture model, but a better approach is to leave the architecture model as it is and create a variant from our architecture that captures our specific implementation choices. For this, we simply create a new model that extends from the architecture. When we extend the new model starts from the old model. From there, we can make further changes, like specifying the implementation details for the different subsystems. This allows us to easily create many different variants of the same fundamental architecture. All these models can exist at the same time instead of constantly switching back and forth between different configurations. It is worth pointing out that there is no limit to how many times we can extend from a model. For example, we might extend from our architecture to create a baseline configuration of our vehicle where all the implementations are filled in. From there, the engine designers might extend from the baseline model but insert a more detailed engine model while keeping the transmission and vehicle subsystem models the same. Similarly, the transmission designers might do the same with the transmission while leaving the engine and the vehicle as is. These best practices for configuration management organize the models and support collaborative workflows.
Step 9: Implementing a Closed-loop Transmission Controller
Once we’ve gone through and specified all of our initial implementations, we can again simulate the model. Of course, we’d still have the same uninteresting response because of the open loop elements, but now, we’re in a position to quickly do something about that. For example, let’s create a transmission controller that directs our vehicle to follow a specified speed profile. To do this, we’ll create a new transmission controller implementation by extending from the interface. When we extend, we are not copying the contents on the interface into our implementations. This is important because copying and pasting creates redundancy. By extending, we avoid copying and pasting, making maintaining the models easier.
Once we create our new transmission controller model by extending from the appropriate interface, we just need to fill in the implementation details. Let’s instantiate a PID controller for speed control with a trapezoidal wave pattern for the drive cycle.
To incorporate the new controller, instead of creating a whole new vehicle model, we can extend from the original open loop vehicle model and simply change our selection of the transmission controller. Again, we select the subsystem we are interested in, the transmission controller in this case, and we select from a collection of existing controllers. The relationship between different variants of our model is concisely captured. In this case, our current vehicle model, extends from the open-loop vehicle model, but replaces the transmission controller with a different transmission controller.
Step 10: Implementing a State Machine for the Engine Controller
Let’s repeat the procedure for the engine controller, extending from the interface to create a new model and implement a state machine to turn the engine on and off. Taking in the battery voltage as input, the state machine turns on the engine to charge the battery when the voltage is too low, and turns off the engine to save fuel, when the batter voltage is too high. To select this variant of the engine controller, we select the engine controller subsystem ad switch to this implementation. The variant choices in the architecture for each subsystem is automatically determined based on the interfaces they implement.
Step 11: Simulating Vehicle with the New Controllers
With these two new controller implementations, let’s take the vehicle out for a spin. After we simulate the model, we plot the vehicle speed and compare it to the desired drive cycle profile. Here we see the transmission controller is doing a good job of following the drive cycle speed trace. Now let’s look at what is going on with the engine and the battery. Notice how the engine comes on when the battery voltage gets too low and turns off when the battery voltage gets too high. Another important thing to know about the battery is that it is charging and discharging, even when the engine is off. The discharging comes when the vehicle accelerates because the motor takes energy from the batteries to increase the vehicle speed. But how is the battery recharging when the engine is off? The answer is regenerative braking. When did we implement regenerative braking? We did it in the transmission control. The PID controller in the transmission controller requests positive torque from the motor when the vehicle needs to accelerate, and requests negative torque when the vehicle needs to decelerate. Because we are using acausal models, all of our components include balance equations, for things like mass, momentum, charge and so on. In order for these balance equations to work out, the kinetic energy in the vehicle has to go somewhere. The motor turns it back into electricity when negative torque is requested and resulting current flows into the battery, charging the battery in the process.
The important point here is that we don’t need to implement regenerative braking. We implement a mathematical model of each component and then impose conservation equations across all the connections. As a result, we are always assured of accurate accounting. This is important because if model developers had to implement all the consequences of the different modes for each component it would be very easy to overlook something. With acausal modeling, all of this is taken care of.
Next Step: Leverage the Vehicle Library to Model Your Vehicle
While it’s good to know that all of this is possible with Dymola, it is also important to realize that you don’t need to start from scratch. This architecture based approach is very common and many of the specialized libraries that come with Dymola include not only high quality component and subsystem models but also the interfaces and architectures that give you a head start in building models of your system. You can also import your legacy models into Dymola, either using direct interfaces, or by adopting the standard FMI® interface.
Vehicle Systems Modeling and Analysis (VeSyMA®) is a complete set of libraries for vehicle modeling and simulation. It includes engine, powertrain and suspensions libraries that work in conjunction with the Modelica® Standard Library. In addition, battery with electrified and hybrid powertrain libraries are available as well. Please watch the other videos in this series for more information on Dymola.
It’s no surprise that electric vehicles (EVs) will radically change the automotive industry. The question is if you’re a CIO, do you stay on the sidelines to observe and play conservatively or charge ahead and innovate to grab a share of the market early?
There are suppliers who want to see more certainty and confidence in the market before they dive right in.
But one thing is certain: This shift will drive CIOs to evaluate their current supply chain and rethink their technology. Electrification opens up possibilities that require new software and service platforms for the entire ecosystem. They’ll need to digitally connect with consumers and track things like electricity production, charging stations, and usage. IT can be the driver for mainstream consumer adoption of EVs.
This article gives you a brief summary of a report on electric vehicles by Gartner Analyst Michael Ramsey. You’ll get an overview on the potential sales of EVs, what CIOs in the automotive industry need to know in planning for the future, and what obstacles could slow progress.
Let’s first look at the stats to see the projected growth of EVs.
EV Growth Over the Next Decade
In the last four years, EV sales have tripled, but it’s expected to jump more steadily moved forward. In 2018, production of battery electric and plug-in hybrid vehicles was 1.8 million (1.8% of total vehicles).
According to Gartner, over the next five years automotive companies will spend approximately $260 billion launching more than 200 new EV models.
It is projected to take about 10 years to hit mass EV ownership. By 2030, LMC Automotive forecasts sales of battery electric vehicles to climb to 18.1 million. That means hybrid and EVs could make up 48% or more of all car sales in 2030.
What Changes Will EVs Drive?
With the rise of EVs come big changes—from the auto industry to the electricity system to the consumer level. On the other hand, EVs introduce possibilities of competitive advantages in speed, quality, and lower costs. Gartner explains the adjustments CIOs have to consider and the actions they can take preparing for the long term.
There are several major changes, but here we’ll look at two of them.
2 Changes of the EV Effect:
1. EVs reduce part complexity with fewer moving parts.
Mechanically it’s simpler. Gartner interviewed experts at the engineering firm, Munro & Associates. They explained that EVs don’t need parts like the internal combustion engine, transmission , or exhaust systems making the mechanics simpler.
The complexity shifts to the electrical components for things like the electric drive train, reconfigurable interiors, battery and thermal management systems. Electro-mechanical components will also replace hydraulic parts like oil and water pumps.
Benefits: This affects the entire supply chain and means shorter development and assembly times.
Gartner recommends CIOs evaluate and rethink the following:
Evaluate the current supply chain.
Do your current PLM systems have the capabilities to devleop EVs?
Is manufacturing capable of executing the assembly?
What is needed to revamp the supply chain and does the existing ERP system work well with it?
Compare current software platforms to new third-party software.
CIOs should work with engineers, finance, and manufacturing to evaluate existing software and compare new third-party software designed for EVs. Once they determine the shortest path with the best quality and lowest cost, then they can decide if it’s time to change vendors.
Develop a case to move to the cloud.
IT is responsible for providing smooth communications internally and with customers. now is the time to develop a case to move to cloud-based software that will enable the organization to streamline their operations and information exchange and seamlessly connect to consumers and their EVs.
2. Electrification opens up new opportunities to collaborate.
This is the time to rethink your ERP system and focus on long-term customer experience beyond the sale of the EV. This means collaboration with other companies in the ecosystem and with consumers.
Gartner recommends CIOs use IT to help gather and share information with all parties on critical matters such as charging these vehicles safely, conveniently, and cost effectively. Consumer trust is at stake every step of the way.
Some suggestions include:
Create a new system for charging capabilities.
Collaborate with utility CIOs to provide vehicle location and charging status to help them plan for electricity production and usage.
Work internally and with ERP vendors to create a new system that handles charging issues like battery state of charge (SoC), charging availability, and billing solutions.
Create an ongoing dialogue with consumers through new digital services.
Develop industry standards and universal charging access to network providers.
CIOs can collaborate with and create connections between charging network providers and EV owners through cloud-based identification so they’re not left without resources.
Prepare for blockchain solutions for accountability or innovation.
Understand and prepare for blockchain solutions where the data is decentralized, easily verifiable, continually updated, and securely validated. Blockchain would allow for accountability of energy generation and energy usage credits for charging EVs.
Even though the future shows high projected growth, there are obstacles that could slow the rise and adoption of EVs.
Some of these obstacles are:
Recharging times still too long: Could take 30 minutes to 12 hours at a charging station.
Lack of public charging stations: Limited charging ability in the workplace and along the highways.
Unacceptable driving ranges: Current driving ranges fall short of the average consumer’s expectations of 245-300 miles before battery needs charging.
Lack of universal connector standards: High-voltage fast charging stations require multiple adapters not usable in some EVs.
Lack of universal access to chargers: Chargers are part of either free, subscription-based, or closed-access disparate networks with limited connections between them.
Consumer appeal has dropped: Higher EV pricing, low gas prices, loss of consumer interest in fuel efficiency, and end of tax credits has limited the appeal to switching to EVs.
Billing and accountability challenges: Challenges in billing and accountability for generating energy and tracking production and usage.
Need more education on EVs: Many consumers don’t understand the difference between an EV and a hybrid, the advantages of EVs, and what car manufacturers really offer.
These 8 obstacles are just a few of the challenges today that could impact the growth of EVs. However, they won’t stop the movement to electrification.
Gartner suggest CIOs understand and keep on top of the obstacles, changes, and opportunities. Evaluate their existing supply chain and IT strategy. Then study the recommendations to prepare for this shift.
CIOs must implement changes that make sense for their company to successfully impact the EV market. Automakers have to strike a balance between ROI and developing smarter technologies that delight and build trust with consumers.
There is high projected growth in electric vehicles in the next decade. As with any industry disruption, obstacles will get in the way and could slow the rise and adoption of EVs, but won’t stop it.
Electrification will create disruptive changes with new possibilities for CIOs and their IT systems in the automotive industry.
Two major changes include:
EVs reduce part complexity with fewer moving parts and will disrupt the supply chain.
Electrification opens up new opportunities to collaborate.
IT can be the driver for mainstream consumer adoption of electric vehicles. It’s important for CIOs in this industry to watch the progress, understand the implications, and take advantage of new opportunities with electrified vehicles to succeed.
Nestled on the coast of the Adriatic Sea is the small country of Croatia. It was most famous for summer vacations, but is now becoming known as the destination site for automotive innovation. Mate Rimac, an entrepreneur and Croatian automaker, is changing the game in transportation with his company, Rimac Automobili. Rimac is not only designing and developing new drivetrains, battery systems, and high-performance electric vehicles (EVs), but they’ve also created the hypercar of the 21st Century.
What Gives Them the Edge? Rimac is using state-of-the-art software—an advanced product lifecycle management (PLM) platform with a custom model-based system simulation for global collaboration and better integration.
Rimac was founded five years ago with its mission to take sports cars to the next level and build an electric hypercar. From the start, their development processes were digital and virtual as much as possible. They recognized that the key to building an extremely complex system, such as an entire car, is the ability to model, simulate, rapidly iterate, and repeat, over and over again. In other words, minimize the physical prototypes in favor of digital versions.
Tools for Complex Physical Systems
In the beginning, Rimac successfully used SOLIDWORKS 3D CAD to develop and validate lightweight solutions for battery power in EVs. As their customer base increased, and the electrical system of their new C_Two model became more complex, they migrated to Dassault’s 3DEXPERIENCE platform.
Choosing the right digital software, tools, and processes are key to modern vehicle design and production. Being able to create, simulate, iterate, verify, and test drive an electric vehicle virtually without a physical part saves substantially on development costs that would otherwise be out of reach.
Dassault’s 3DEXPERIENCE Platform enabled Rimac’s development team of 100+ employees to work in CATIA (CAD), ENOVIA (cPDm) and other applications on the digital manufacturing side, such as CAE SIMULIA and DELMIA. They also had access to Dassault’s data-driven database in ENOVIA.
But due to the complexity, Rimac needed even more customization. Fortunately, they were able to partner with Modelon, a Swedish software developer. They specialize in model-based systems engineering (MBSE) and simulation, to create an open-standard, model-based system.
Modelon solutions are based on Modelica (open-standard language) and FMI (open-standard model format). Modelica was created to model complex physical systems containing, for example, mechanical, electrical, electronic, hydraulic, thermal control, electric power, or process-oriented subcomponents—exactly the complexity Rimac needed. Even better, Modelon’s open standard–format means their solutions seamlessly integrate with a wide variety of software platforms, such as 3DEXPERIENCE and other PLM tools, allowing users to share and collaborate consistently from product concept to operation.
Results of Rimac’s Approach
Rimac’s incredible success has proven the value of their approach. With the help of 3DEXPERIENCE and Modelon solutions, they’ve created cutting-edge electric drivetrain technologies, which they supply to several large automotive players, including Aston Martin, Jaguar Land Rover, and Renault. Rimac has also developed two of its own electric hypercars, the second containing an innovative four-engine electric drivetrain in which one engine drives each wheel. Porsche was impressed enough in the company’s technology that they bought a 10% stake in Rimac, forming a development partnership.
To find out more about how Rimac is using 3DEXPERIENCE and Modelon, see engineering.com.
And to find out more about how a comprehensive Digital to Physical PLM platform can help you overcome your challenges in bringing new products to market, contact us.