KLK305: Vehicle Performance Simulation, Phases I-III

Principal Investigator:

Donald Blackketter

Project Objectives:

The objectives of this Phase I are to

• Complete the first version of new SmartSolve parser, and

• Identify transportation applications for solution algorithms.

The objectives of Phase II are to

• Complete a final version of new SmartSolve parser and swapper,

• Implement SVD into a modular form for use in other NIATT projects,

• Develop all remaining tools into a modular form for implementation into other applications,

• Complete the “HEV Modeler” application for HEV energy-use simulation,

• Complete first version of the application builder,

• Write one or two proposals, and

• Develop collaboration with Harry Townes in the area of modeling vehicle dynamics and crash simulation.

The objectives of Phase III are to

• Complete a final version of new SmartSolve parser and swapper Complete SmartHEV, alternative fuel vehicle software design tool,
• Integrate SmartHEV with Clean Vehicle Energy Management (David Reiche’s MS Thesis) for a complete design and simulation software tool,
• Investigate ways to include SmartHEV into other vehicle software simulators.,
• Collect data on the UI FutureTruck 2001 Suburban including local driving cycles made with GPS and elevation data. Validate integrated SmartHEV with FutureTruck test data,
• Develop an energy management control strategy for the FutureTruck Suburban that optimizes for efficiency and emissions, and
• Integrate the energy management control strategy into typical driving cycles for better emissions and efficiency in the FutureTruck Suburban.

Task Descriptions:

Tasks for Phase I:

Task 1: Provide training and time for undergraduate and graduate students to become proficient in programming in Visual Basic. This includes developing knowledge about other software and software tools.

Task 2: Complete Parser refinements.

This task includes the development of algorithms for grouping nonlinear terms to linearly simplify nonlinear sets. This will also involve prompting the user for the most important variables to guide the grouping of terms.

The task will also include the development of parser code that can identify and rewrite equations that have “bad” terms. These bad terms include terms that require unknown variables to be in the denominator. We will also alter the parser such that polynomials can be identified and marked for multiple root possibilities.

We will also identify the degree to which an equation is nonlinear. This will be done by initially using the Book-Rameriz literature. This includes log or trigonometric functions. These terms may be grouped into linear terms or guesses may be restricted.

Task 3: Identify transportation applications that can take advantage of these algorithms.

Tasks for Phase II:

This phase of the project will carry previous work to the point that robust applications can be developed and marketed. We will complete the following five tasks:

• Complete the development of the parsing and swapping algorithms and put these into a form (DLL) for general use in software development
• Implement Single Value Decomposition (SVD) into the applications to enhance simulation and design modeling
• Develop the initial “HEV-SIM” program for simulating and designing electric vehicles, based on vehicle size, shape and weight, as well as on components used in moving the vehicle
• Work with other NIATT projects to develop software applications to assist in completing their projects
• Develop all the algorithms so they can be used in a DLL or modular form

The ultimate goal is the construction of a development program that will allow applications to be developed without the developer having to program advanced knowledge. This will allow for the easy development of a variety of transportation modeling and design applications.

Tasks for Phase III:

• Complete SmartHEV, alternative fuel vehicle design software. Integrate SmartHEV with Clean Vehicle Energy Management simulator
• Identify software applications that are best suited for SmartHEV technology. Determine feasibility of integrating SmartHEV into identified software
• Collect data on FutureTruck
• Validate SmartHEV with FutureTruck data. Integrate GPS and elevation data into SmartHEV
• Optimize energy management control strategy using SmartHEV and test data
• Validate control strategy with FutureTruck
• Design local driving cycles and test FutureTruck
• Optimize FutureTruck for fuel economy and emissions over local driving cycles

Milestones:

• Project start date: October 1, 1998

• Begin work on project: February 1, 1999

• Complete training on software: May 1, 1999

• Complete parsing routine: June 15, 1999

• Complete parser and swapping algorithms: December 15, 1999

• Complete initial version of "HEV Modeler": January 2000

• Write proposals: January - March 2000

• Complete implementation of SVD: March 2000

• Complete final version of "HEV Modeler": May 2000

• Complete SmartHEV: August 2000

• Integrate GPS and inclinometer into FutureTruck: September 2000

• Collect FutureTruck operating data: September 2000

• Complete integration of SmartHEV with CVEM: September 2000

• Validate SmartHEV with FutureTruck: October 2000

• Design and test local driving cycles: October 2000

• Determine FutureTruck component modifications: October 2000

• Integrate SmartHEV into another vehicle simulator: November 2000

• Complete energy management control strategy: March 2001

Budget Information:

UTC funds dedicated to this project are as follows: Phase I: $31,790; Phase II: $36,175; Phase III: $41,588.

Student Involvement:

Relationship to the NIATT Strategic Plan and to Other Research Projects:

Many vehicle problems currently facing the nation involve the integration of systems. Often these systems are large and complex. There is a strong need for simulation and design software that can assist in the design of these systems. The proposed algorithms are ideal for application to this type of problem. For example, we will be investigating the use of these algorithms to assist in the design and optimization of electric vehicles. The algorithms can also be used for structural software such as the composite software program being developed by Dr. Edwin Odom (see KLK320).

Technology Transfer Activities:

Beginning next year, we will identify transportation applications in the area of structural optimization, vehicle design, and/or system optimization in which our software can be used and marketed. A final report will be published in html on the NIATT Website and made available for purchase.

Potential Benefits of the Project:

This is a novel and innovative method for solving difficult transportation problems. The primary benefit of the software is the ability to look “backward” in a design problem. This will allow the user to specify the performance of the product, and have the design software describe how the product should be built. This is opposite to the common practice of setting components and determining the performance. The benefit of such a tool should be of interest to many transportation industries.

Project status:

Complete

Final Report:

N01-10 (pdf)