How to Optimize Hybrid Vehicle Design using Simulation

As emissions regulations tighten and an electrified future grows more and more imminent, automotive engineers have been tasked with applying decades of traditional vehicle engineering knowledge to increasingly complex xEV architectures. The good news is that automakers have become very proficient at building conventionally powered vehicles – so much so that they still comprise the basic underpinnings for most electric propulsion architectures. But even implementing hybrid-electric components into an existing conventional vehicle architecture introduces a long list of questions, such as:

• Where in my driveline should I integrate my motor?
• What size battery do I need to meet design requirements?
• And how should I optimally distribute power between the engine and (potentially multiple) motors?

Fortunately, GT-SUITE offers a variety of embedded tools that help answer these questions.

This will be the first blog in a two-part blog series highlighting solutions for hybrid-electric component sizing, assisted electric motor design, power split controls optimization, and much more. Proper use of these tools will significantly aid development and increase efficiency throughout various stages of the design process.

Hybrid Component Integration and Optimization

To demonstrate the power of incorporating these tools into your hybrid vehicle design process, we will walk through a simple example of hybrid component integration and optimization. The goal of the exercise will be to appropriately size and optimize control of an electric motor in a standard compact passenger vehicle.

This process can traditionally be burdensome. Assessing multiple motor configurations often requires repeated manual manipulation of models. Defining motor characteristics typically relies on data from expensive testing, and developing effective hybrid controls strategies is often a time-intensive, trial-by-error process. Fortunately, GT-SUITE’s embedded tools address these problems within one easy workflow. The efficiency of these tools also allows extra time to continually iterate and fine-tune your design.

Figure-1.-Hybrid-Design-Tools-Workflow

Requirements:

For this example, we will focus on hybthat are reasonable for a compact hybrid sedan. Selecting a smaller, lower output motor will save costs, so we will seek to minimize motor size while still meeting these requirements.

Quickly generate a hybrid vehicle model

GT-DRIVE+ starts with a model generator that allows for easy generation of vehicle systems and quick evaluation between multiple architectures and components. When creating a hybrid vehicle, the user will be presented with several options for vehicle architectures as well a large library of pre-defined engines, transmissions, electric motors, batteries, and drive types.

The components were selected in the model generator to closely match many of the hybrid vehicle offerings currently on the market:

Now, after just a few simple selections, a full vehicle model is generated – complete with test cases for city/highway driving cycles, and a pre-configured hybrid control strategy. If necessary, the exact same components can also be generated within a P0, P2, or P0/P4 hybrid architecture to compare how motor placement impacts vehicle performance.

Evaluate motor sizing

Next, maximum and minimum motor torque curves can be modulated to determine the minimum acceptable motor output. This can be done by configuring GT’s integrated design optimizer to sweep through one or more parameters to target a specific output. Here, the 8.5 second acceleration performance requirement is targeted. The design optimizer can be setup to modify torque values, run the model, evaluate the outcome, then modify torque values again, rerunning until results converge on the requirement. In this case, 151 possibilities were automatically evaluated in under 10 minutes. Upon completion, the design optimizer outputs several plots that help visualize the evaluation process, along with an updated model containing the final optimized parameter values.

Figure-2.-Example-of-Integrated-Design-Optimizer-Results

After doing so, a motor with the following characteristics was selected to meet the acceleration performance requirement:

Figure 3. Selected Motor Characteristics

Note that while two of the requirements are satisfied, city fuel economy does not meet the 50 mpg requirement. This is not cause for immediate concern. GT-DRIVE+ automatically generates a rule-based hybrid controls strategy that effectively follows standard drive cycles but is not necessarily optimized for city or highway driving. A hybrid control optimization tool should be applied to this model to better understand city/highway fuel consumption. The second blog posting in this series will demonstrate the application of these optimization tools to this example.

Generate motor efficiency maps

GT has partnered with JSOL to bring their motor design tool, JMAG-Express, directly into GT-SUITE. The approximated motor characteristics determined in our initial investigation can be input directly into this integrated tool to calculate a complete motor efficiency map, as well as estimate many other fundamental motor characteristics, in only a few minutes.

Figure 4. Integrated JMAG Express Interface

This efficiency map can be pulled into our initial model to more accurately characterize the efficiency and power demands of a motor this size.

Instantly generating efficiency and loss maps eliminates the need to choose between over-simplifying motor efficiency for convenience, or spending time and money to test motor hardware. Instead, we can quickly calculate efficiency of our 27.5 kW machine, plug it in to our original model, and move on towards applying our hybrid controls optimization tools.

In this blog, we walked through three different GT tools that facilitate the hybrid vehicle design process. These tools assist and accelerate a highly iterative design process where components can be evaluated, optimized, changed, and evaluated again without sacrificing large amounts of time or resources. The next blog entry showcases GT’s hybrid control optimization solutions and how it can be used in the context of the example presented here.

If you would like to learn more about GT’s integration with JMAG-Express, [CLICK HERE] to read an additional blog post on the topic.