Top 10 Gamma Technologies Blogs of 2023!

From calculating EV range to heat pump design, there is a blog for every simulation! 

As we kick off 2024, let’s look back at the best blogs of 2023! Since the inception of Gamma Technologies, GT-SUITE has optimized system simulation solutions for manufacturers! In no order, these are the top 10 blogs written in 2023 that highlight the vast application use cases and technical capabilities GT-SUITE can deliver!   

1. Decreasing Battery System Simulation Runtime using Distributed Computing
2. Calculating Electric Vehicle Range with Simulation
3. Engine Manufacturers Leverage Simulation to Engineer Ahead of Increasing Regulations
4. Enhancing Model Accuracy by Replacing Lookup Maps with Machine Learning Models (Machine Learning Blog Part 1)
5. Optimizing Neural Networks for Modeling and Simulation (Machine Learning Blog Part 2)
6. Mitigating the Domino Effect of Battery Thermal Runaway with Simulation
7. Designing Thermally Secured Electric Motors with Simulation
8. Understanding Fuel Cell Systems Simulation for Vehicle Integration
9. Addressing Heat Pump Challenges, from Home to Industry with Simulation
10. Simulating Predictive Cruise Control for a Heavy-Duty Truck: Quickly and Easily

Shout-outs to our colleagues for their contributions! 

Learn more about our simulation solutions!  

If you’d like to learn more about how Gamma Technologies can be used to solve your engineering challenges, contact us here! 

Wishing you a healthy & prosperous 2024!   

Top 10 Gamma Technologies Blogs of 2022!

From battery thermal runaway to fleet route optimization, there is a blog for every simulation! 

Since the inception of GT-SUITE, Gamma Technologies has offered state-of-the-art simulation solutions for manufacturers. Our simulation solutions help guide customers and partners toward highly optimized products.  

In no order, these are the top 10 blogs of 2022!  

1. Simulating Your Way to HVACR Innovation 
2. How a Catastrophic Ship Fire Reminded us Why Battery Thermal Runaway Simulation is Important 
3. Reducing Costs & Increasing Efficiency in Power Converter Design  
4. Using Simulation to Model Closed-Cycle Argon Hydrogen Engines 
5. Sensitivity Analysis: How to Rank the Importance of Battery Model Parameters Using Simulation 
6. Accelerate Electric Aircraft Design Certification with Systems Simulation 
7. Vehicle Modeling and Simulation: ICEV & BEV Correlation Procedure 
8. How to Automate Real World Vehicle Route Generation Using Simulation 
9. A Look Inside Large-Scale Electrochemical Storage Systems Simulation 
10. Simulating a NASA Hydrogen Powered Rocket 

Other Gamma Technologies Blogs to check out in 2022! 

1. Using Simulation for Battery Engineering: 12 Technical Blogs to Enjoy 
2. Machine Learning Simulation: HVACR Industry 
3. Fast, Accurate Full Vehicle Thermal Management Simulation with GT-SUITE and TAITherm 
4. Using Simulation To Predict Battery Aging for Real World Applications 
5. How Simulation Can Increase Productivity in Electric Vehicle Thermal Management Design 
6. Using Simulation to Optimize Driving Routes and Vehicle Emissions 
7. How Simulation Is Used To Design ICE vs. Battery Electric Vehicle Thermal Management Systems  
8. Are Your Vehicle Passengers Comfortable? How to Validate An Accurate, Thermal Cabin Management Simulation Solution  

Shout-outs to our colleagues for their contributions! 

Learn more about our simulation solutions!  

If you’d like to learn more about how Gamma Technologies can be used to solve your engineering challenges, contact us here! 

Have a great holiday season and wishing you a healthy & prosperous 2023!   

How to Optimize Electric Vehicle (EV) Drivetrains in Less Than 1 Day Using Simulation

Improving Hybrid Electric Vehicle Controls:

The recent proliferation of hybrid electric vehicles has greatly complicated the world of vehicle controls engineers. Multiple energy sources and propulsion systems applied to sophisticated hybrid drivetrains necessitate a much more intricate controls strategy than conventionally powered vehicles.

Determining when to distribute power to the engine, motor(s), or both is no simple task, and the time typically taken to develop these controls strategies reflects that. Even developing controls for simple hybrid vehicle models can take precious time away from the rest of the design process, and cutting corners can lead to sub-optimal fuel economy and vehicle performance results during simulation. Fortunately, GT-SUITE’s embedded tools include two different methods to automatically generate optimized, charge-sustaining hybrid controls strategies on a per drive cycle basis:

• Equivalent Consumption Minimization Strategy (ECMS)
• Dynamic Programming

Using these tools allows for quick evaluation of a hybrid system’s peak capabilities without the hassle of developing and testing multiple controls options.

Model Generation & Evaluation In Minutes Vs. Days:

In Part 1 of this blog series, we employed GT-DRIVE+, Integrated Design Optimizer, and JMAG-Express to properly size and characterize an electric motor for a P4 hybrid system in a compact passenger car. These tools streamlined a traditionally time-consuming design process, with model generation and evaluation taking minutes rather than days. The goal was to select a motor for a P4 hybrid to meet the following requirements:

Metric	Requirement
Acceleration (0-60 mph)	8.5 seconds
Fuel Economy (City/Highway)	50/52 mpg

 

This blog will build upon our previous work, applying two of GT-SUITE’s hybrid controls optimization solutions to evaluate the previously selected motor’s impact on drive cycle fuel economy. Applying these tools within our workflow allows us to evaluate estimated fuel economy under optimized control without spending time developing complex hybrid controls.

Previous evaluation of our example model revealed that our 27.5 kW motor selection met the acceleration and highway fuel economy requirements but could not meet the city fuel economy demand. These tests, however, were performed using a rule-based control strategy that was not necessarily optimized for city or highway driving. Applying ECMS and Dynamic Programming to the city drive cycle should provide a better idea of this configuration’s fuel economy capabilities.

Equivalent Consumption Minimization Strategy (ECMS)

ECMS in GT-SUITE assigns a “fuel consumption” rate to energy pulled from the vehicle’s battery. Calculation of this energy-equivalent rate is influenced by several user-defined parameters including:

• Equivalence Factor – this represents the relationship between battery energy and fuel energy
• Target State of Charge – this sets a target SOC to develop a charge-sustaining strategy
• Penalty Function Exponent – this influences a penalty function that increasingly penalizes battery energy consumption as the battery deviates farther from the target state of charge

For an ECMS run, the user specifies a variety of independent control variables that are altered at every timestep with the goal of minimizing combined ‘fuel’ consumption from both the engine and the battery. For our example, the following variables were selected:

Variable	Values
P4 Motor Torque (27.5 kW motor)	-105 Nm to 105 Nm
Transmission Gear Number	1st to 6th Gear
Vehicle Mode	Hybrid, Electric, or Conventional

 

At every timestep, all combinations of the independent control variable values are considered. Any combinations that can meet the drive cycle power demand while obeying the defined constraints are evaluated to determine total fuel consumption. This calculation is heavily influenced by the battery energy-equivalent rate parameters. For example, if the SOC deviates too far from its target, then a larger penalty will be levied on battery consumption to incentivize a charge-sustaining strategy – this means scenarios where more engine power and less motor power is used may be deemed more favorable at that timestep. The variable combination that locally optimizes fuel consumption is then selected, and the process repeats for the remaining timesteps. The process at each timestep is summarized below:

Applying an ECMS control strategy to our city driving cycle, we will see a significant improvement in fuel economy that meets our initial requirements:

 

	FTP-75 (City) Minimum Fuel Economy Requirement	Reported FTP-75 (City) Fuel Economy
Heuristic Control	50 mpg	42.93 mpg
ECMS Local Optimization	50 mpg	58.30 mpg

 

 

Despite evaluating 612 different control scenarios at every timestep, this ECMS run completed in less than 3 minutes. After completion, we can see that our motor selection will be sufficient to meet the initial fuel economy requirements – all it needed was a better control strategy. However, optimizing locally at each timestep will likely result in slightly sub-optimal performance over the entire drive cycle.

In other words: This is good, but we can do even better.

Dynamic Programming (Global Optimization)

Dynamic Programming will provide an even clearer picture of our example vehicle’s fuel economy capabilities under optimal control. Dynamic Programming uses similar strategies to minimize fuel consumption but seeks to do so in the context of an entire drive cycle. A global cost function is created and minimized using similar parameters to those defined for ECMS. The run begins at the end of the drive cycle and marches backwards in time to the initial state, where the fuel costs for all possible states and controls are calculated and saved. By referencing these saved values, a controls solution is determined by computing the ‘optimal cost-to-go’. This may not necessarily minimize fuel consumption at every timestep but will produce a solution that cumulatively has the lowest fuel consumption from start to finish.

Applying dynamic programming to our city driving cycle, we will see fuel economy further improve to 62.3 mpg:

 

This blog series has demonstrated 5 different GT-SUITE tools that will significantly streamline your design process. In our motor sizing example, this increased efficiency was apparent:

• GT-DRIVE+ instantly generated a P4 HEV vehicle model to use for evaluation – 5 minutes
• Integrated Design Optimizer automatically selected the correct motor size to meet our acceleration requirements – 20 minutes
• JMAG-Express instantly created an efficiency map from our selected motor characteristics – 10 minutes
• Optimization Tools generated controls for our drive cycles to understand motor/vehicle performance under optimal control – 2 hours

One iteration of this design process could conceivably take less than one day. If we are unhappy with the results after evaluating this final design, we can easily iterate through again – tweaking our initial model and motor characteristics and applying all the tools again with relatively little time lost. If you are interested in learning more about any of these tools, feel free to contact us for additional information!

Accurate, Concept Level Electric Motor Design Using Simulation (Part I)

How to evaluate electric vehicle performance and behavior using simulation

A typical task of a vehicle simulation engineer is to evaluate the effect of different technologies or component selections on overall vehicle performance and behavior. One of the main challenges of this task is the lack of accurate data available for components, especially for engines, batteries, and electric motors. This lack of data availability can lead to false assumptions or extrapolations which may lead to inaccurate results. In this first blog of a two-part series, we will introduce a new integration between GT-SUITE and JMAG-Express Online that provides a method for accurate concept-level electric motor design. In the context of vehicle electrification, motors are a key powertrain component. What is required for a motor is not only high performance as a component but also high consistency with the system. This includes, for instance, matching the motor and battery sizes, but also cooling system size and performance as well. Figure 1 below shows an example of how different losses, and therefore cooling requirements, vary throughout the motor operating range. 

High-fidelity efficiency map-based modeling 

Vehicle engineers use either a map-based approach measured by the prototype or lower fidelity motor model-based approach at the system design phase. When using a map-based approach, the engineer commonly needs to wait for the prototype to be ready, or relies on other empirical approaches. Alternatively, using a lower fidelity motor model-based approach causes a lot of rework as the design matures. To eliminate these errors and inefficiencies, GT and JSOL have partnered together and are excited to release new software functionality. With GT-SUITE v2020, GT users can now create a high-fidelity motor model by using the embedded JMAG-Express Online interface. JMAG is a comprehensive software suite for electromechanically design and development. It enables users to make a high-fidelity efficiency map model with less than 1% error compared to measurement. It allows the user to see various kinds of motor characteristics within 1 second by changing motor types, slot combinations, dimensions and other machine parameters.  

Figure 2 shows a high-level overview of the workflow.

The above workflow is accomplished through an integrated interface, shown in Figure 3.

To the GT user, the experience of concept-level motor design is intuitive and seamless, as well as fast. In this embedded interface, the user has flexibility to change machine types, geometry, as well as requirements for torque, power, and maximum speed. It is also possible to add additional constraints on the system, such as voltage and current limitations, as well as geometry constraints such as maximum motor diameter or stack height, air gap, etc. Based upon “rules of thumb” and common motor design principles, JMAG-Express Online will create a motor which meets the requirements, subject to the constraints. The user can refine the design or proceed with the configured motor design. Because of JMAG’s history in the area of motor design, the end user does not need to be an expert in motor design to be effective in exploring different design possibilities.  

Through this embedded workflow, users quickly and efficiently analyze different motor types and create maps for each, such as in Figure 4. 

Because the JMAG-Express Online interface is natively integrated with GT-SUITE, users not only analyze the motor behavior in a standalone environment but integrate the JMAG motor models directly in a complete system-level model, as shown in Figure 5. 

Such a model can be exercised through standard drive cycles, and by reviewing the residency of plots for motor operating points by going through vehicle simulation, it enables users to reflect immediately on the motor specification and run the next simulations, shown in Figure 6. 

By connecting vehicle simulation engineers with parametric and template-driven motor design solutions with JMAG-Express Online, it is now possible to make earlier, and more confident design decisions, or motor selections. The push-button integration allows for design-space studies which more quickly explore all possibilities for the most effective motor solution at the vehicle level. Check out Part 2 of the blog series, where we discuss further integration possibilities between GT and JMAG, which move beyond map-based models into more predictive capabilities. 

Written By: Jon Zeman and Yusaku Suzuki