As the number of electronic controllers in vehicles has increased, the control-design community has turned to model-based design (MBD) to help accelerate and manage the development process.
MBD uses physics-based models of the systems to be controlled (called “plant models”) to provide a basis for the controller design. This process was soon extended to allow the prototype controllers to be tested with the virtual plant model on real-time simulators before integrating them into the real vehicle systems. This allowed the engineer to validate that the system will work within specification or, if it does not, to adjust the design to bring it into spec.
Therefore, by the time the controller is integrated into the first vehicle prototype, most of the major risks to the success of the project are significantly mitigated, while prototyping time and costs are dramatically reduced.
But there is more to this than simply saving time and money: by developing sufficiently high-fidelity models of their systems, the question is evolving from “Will it work?” to “How can we make it work better?”
By applying rigorous analysis and optimization techniques, engineers can determine design parameters that will improve the system’s performance and efficiency before the first prototype is built.
Moving MBD to the system level
To make this design process a reality, engineers need to a take a further step. Increasingly, they shift their focus from the individual subsystem they are working on and consider all the subsystems in the product as a whole.
It is only at this system level that the engineer gains true insight into how all the subsystems and components interact, and what effects a design change in one subsystem can have on another.
This is particularly true in the development of an electrified vehicle, whether electric vehicle (EV), hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV) or any other configuration.
Electric vehicles are extremely complex systems with a multitude of controllers: battery charge/discharge control, state-of-charge control, speed control, engine control (in the case of HEV), generation, regeneration and braking, steering, stability control, and even passenger environmental control all have an effect on each other to various degrees, often changing significantly with different drive cycles, and sometimes in unexpected ways.
Therefore, it is becoming increasingly important to model and simulate the whole vehicle at a system level very early in the design process to get a clear understanding of how these interactions will affect the product performance, and then to make decisions that will help to achieve product design goals. While this is very attractive in theory, without the right tools it is very difficult to achieve in practice.
Maplesoft's system-level modeling and simulation tool, MapleSim, was built to take advantage of symbolic technology. MapleSim allows complex multidomain systems to be modeled quickly and easily, and then generates the governing dynamic equations for the system.
This unique approach produces highly concise representations of the system model that are fully parameterized for further analysis and optimization, either in the tool’s integrated analysis environment, Maple, or in third-party tools.
In addition, MapleSim automatically generates ANSI-C code from the model that is extremely efficient, allowing levels of detail to be implemented in real-time that are not possible with other tools.
Thermal management of electric vehicles
In a recent project, it was important for the customer to fully understand the thermal loading on the cooling systems within an electric vehicle that it was designing, given various drive-cycles and environmental conditions.
The critical design goal was maintaining the battery temperature within a certain range to maximize its charge/discharge efficiencies, reduce damage to the battery, and—in extreme cases—ensure driver safety (overheated batteries have been known to explode).
However, this also meant understanding where the heat was coming from, and there are many sources: engine, transmission, motors, generators, driver circuits, and the chemical reactions within the battery itself are major sources. But there are other factors that need to be considered, such as tropical or arctic weather, changes in the use of the climate control, and even aggressive driving and vehicle maneuvers.
By using a system-level approach, the customer was able to produce a full-vehicle model that not only helped to design a thermal control system for the whole vehicle, but also improved overall fuel efficiency by reducing the need for the range extender.
The increased certainty offered by this approach even allowed the customer to save costs on cooling system components by reducing their size. In later stages in the project, these models will be used in a real-time simulator to test controllers for the thermal management in a hardware-in-the-loop (HiL) test platform.
Model-based systems engineering
Historically, the simulation and analysis part of the design process has been a separate activity from the core design activities performed with CAD tools, but increasingly these two processes are merging into a more integrated design process.
Sometimes called model-based systems engineering (MBSE), this approach sees the dynamic simulation of the whole system at the very beginning of the design process, allowing the engineering group to assess the viability of the design and preempt and address any design issues before starting on the CAD design.
Taking this a step further, it would be possible to store the functional behavior of subsystems and assemblies as dynamic models in a database with all the other product information, such as physical attributes and operational constraints. This would then allow the initial design steps to be automated: the design team simply enters the product requirements and the system offers candidate configurations that would fulfill, or almost fulfill, those requirements.
MBSE and configuration management (CM) are two areas of research that aim to make the previous approach a reality. From Maplesoft’s perspective, there are several areas of development considered to be critical to the success of this activity.
First to be considered is a rich, consistent, multidomain model description language that allows all dynamic behaviors to be characterized for systems that contain many engineering domains, such as electrical, mechanical, hydraulic, pneumatic, and thermal.
While there have been several attempts at this over the years, one language—Modelica—is emerging as the de facto standard. Developed by a consortium of universities and industrial partners, Modelica has been widely adopted in Europe and is seeing increasing adoption in North America and Asia.
MapleSim is the first commercially available Modelica tool to be developed in North America, and Maplesoft is active in ensuring that Modelica fulfills the requirements of system-level engineering within its customer base.
Second is tight integration between many analysis tools. MapleSim is described as a 1-D (i.e., time-domain) lumped-parameter simulation tool. This allows for rapid model development and simulation execution and provides good insight into a system’s overall functional behavior.
However, it is often necessary to include more complex elements from, say, finite element or computational fluid dynamics tools. To achieve this, there is a growing demand on a standard framework that would allow this level of interaction between tools. The MODELISAR project, initiated by the Modelica Association, has been instrumental in defining such a framework.
Called the functional mock-up interface (FMI), it provides the ability for tools to interact on two levels:
• Allowing models created in one tool to be implemented as code blocks within another. These blocks are called functional mock-up units (FMU) and are convenient and fast—though sometimes limited—means of integrating models from one tool into another.
• Defining a co-simulation platform that allows specialized tools to pass results between one another. This is the ultimate goal of FMI, and with the recent release of FMI version 2.0, Maplesoft is now actively developing the appropriate interface.
A third area to be considered is integration with CAD tools. At some stage in the design process, it is necessary to either extract physical design parameters from a CAD design or submit optimized design parameters into it. Maplesoft has developed interfaces with various CAD tools, and these are being further developed to achieve this goal.
Last, integration with all other operational information needs to be considered. To make CM a reality, it is necessary to include all operational information with the functional behaviors of a component or system model: envelopes of operation, failure modes, environmental factors, even manufacturing processes and costs, all need to be considered.
This is an area of very active research, much of it driven by the demands of military vehicle manufacturers. At the core of this research is the Object Management Group and the International Council on Systems Engineering, out of which extensions to the Modelica language are being developed using SyML.
MBSE is in its infancy but is rapidly turning into a reality, and every manufacturer needs to factor this into their product development and business strategies for the future. Ultimately, MBSE holds the promise of dramatically reducing development effort in multidomain engineering products, reducing risk of design flaws and getting the products to market faster.
As a spokesperson for the U.S. Defense Tactical Technology Office commented at a recent DARPA presentation: “We believe model-based systems engineering will do for the design of complex electro-mechanical systems what EDA did for the electronics industry…[through the use of MBSE] we expect at least a five-fold reduction in the development effort for future space and military vehicles.”
Early system-level modeling is the key
In summary, the overarching key to effective MBD is that complex products, like electric vehicles, need to be considered as whole systems in order to understand how the multitude of subsystems interact with each other over the entire range of duties.
This needs to be done very early in the design stage so that as the design evolves, the functional behavior can be validated throughout the process, thus ensuring it continues to fulfill the design goals of the product.
Finally, manufacturers need to be aware of developments as modeling tools mature to deliver on the promises of MBSE and CM. But this should not be done as passive observers—they should be actively engaged with the tool vendors to provide guidance for future developments that address their own challenges and priorities.
It is for this reason that Maplesoft is a founding sponsor of the recently announced North American Modelica Group. This is a forum for exchanging ideas, addressing modeling issues, and promoting the North American perspective within the Modelica Association, and is an excellent way of learning about, and helping to influence, the future direction of model-based product development.