Executive Summary
The AEV project has led teamwork-oriented groups to use several methods of research and testing to develop improving results to each group’s design. The group demonstrated skills in documentation, cooperation, decision-making, and the overall design process. Over the course of the semester, an AEV has been created in order to achieve improvement in such skills. An AEV, or Advance Energy Vehicle, is a small, autonomous, electric motor-driven, monorail-guided vehicle that groups have free reign over what modifications are to be made. This may include: deciding on a frame and hanger to build upon; what orientation the motors will be in; or whatever adjustments the code may need. The end goal is to produce an AEV that is able to travel on the rail system, able to come to a complete stop at a marked gate and wait until allowed passage (7 seconds), dock and haul a payload with an R2 unit as passenger, and return back to start after another gate passage- all while keeping energy consumption to a minimum.
The following review shows group H’s progress, findings, and courses of action taken throughout the past few weeks of testing and development. First, the group became familiar with the arduino coding and the system itself. The light sensors used to measure the distance traveled had to be tested to ensure helpful data collection in future development. Next, a wind tunnel was used along with telemetry equipment to determine whether a pusher or puller and which diameter propeller would more efficiently propel the AEV. It was decided that a 2510 pusher propeller should be used based on the results. Each group member then devised a unique design for the AEV to be implemented over a reference AEV provided to the class to achieve more desirable qualities, such as: low weight; adjustable center of mass; and durability. One of the suggested designs was then agreed upon and rebuilt accordingly (the group’s AEV went through an additional redesign later to further simplify and reduce the weight of the craft.) Two more contending designs were later ran with an identical code to compare the two as accurately as possible. Design 2 performed significantly better, using 35 fewer Joules than Design 1. For this reason, the group adopted this new AEV for future testing. Development of the code began using the new “T-frame” design and numerous trial and error runs were performed in order to dial in optimal settings of power, at what times, for how long, etc.. A reverse-thrust “brake” function was utilized to bring the AEV to complete stops throughout the mission and was also calibrated. Successful runs of the mission have been made, yet there is still room for improvement.
Considering the group’s current progress, further development on the code will be required in order to ensure a more consistent program. It has also been an aspect of the project that has only just began to be developed, so its need for development is expected. Future redesigns of the AEV’s construction are not out of reason, as there are currently issues with docking the AEV to the payload. Investing more time and thought into these issues will aid in a better performing AEV and perhaps further improve efficiencies.
Results and Analysis
Figure 1 below shows the results from testing the 2510 pusher propeller in the wind tunnel analysis lab. In Figure 1, the propulsion efficiency is plotted vs. the advance ratio which is unitless and is a function of velocity, diameter, and the RPM values of the propeller.
Figure 1: Propulsion Efficiency for the 2510 Pusher Propeller vs. Advance Ratio
The results from the wind tunnel analysis lab showed that the 2510 pusher propeller has the potential to reach 60% efficiency while the 3030 pusher, 2510 puller, and the 3030 puller propellers never reached above 35%, 7.5%, and 14.5% efficiency respectively.
The knowledge gained from the System Analysis Tests 1-2 enhance our understanding in the AEV’s performance by showing the range that the efficiency can vary depending on the type of propeller used. Moving forward from the System Analysis Tests 1-2 the group has prioritized having a 2510 pusher propeller in the AEV design. System Analysis Test 2 helped the group convert EEPROM data that is collected on the Arduino during a run to meaningful data. The group was not able to test a self written code in System Analysis Test 2 due to time constraints, but the group used sample data to get used to the MATLAB software that will aid in evaluating energy consumption data for the AEV.
The group made orthographic drawings of 3 AEV designs in Lab04. Concept screening and concept scoring matrices were made to compare the reference AEV, the 3 designs the the group individually generated in Lab04, and the 2 designs tested in performance test 1 as well as the reference AEV. Table 1 shown on the next page shows the concept screening matrix that compares these designs. Table 2 shows the concept scoring matrix of those same designs against specific success criteria that the group chose to determine which AEV should be selected.
Table 1: Concept Screening for 6 AEV Designs
The concept scoring matrix above shows that Design 2 is the best design compared to the three designs that the group came up with in Lab04, Design 1 from the first performance test, and the reference AEV. The concept scoring matrix shown on the next page provides more clarity when deciding which design is best because it allows the group to apply a weight to each of the success criteria. Looking at Table 2 on the next page, it is clear that Design 2 used in performance test 1 is the best AEV design when considering the success criteria shown in Table 1 and 2 because it has the largest total weighted score of 4.5.
Table 2: Concept Scoring Matrix for 6 AEV Designs
The three designs the team came up in Lab 4 evolved to the two prototype concepts used in Performance Test 1. It is clear from the matrices that the P1 design that Sean came up with is the best design from Lab04. This design was selected moving forward to use in Labs 5-7. Then in performance test 1, or Lab 8, Sean’s design was slightly altered into Design 1 and Design 2 that were scored in Tables 1 and 2.
The two concepts that the group tested in Performance Test 1 are shown in Figure 1 below. The second design that the group will be testing differs from design 1 because it has a T base rather than the cross base and can be seen in Figure 2 below. Both designs use the same hangar and propeller type. The Arduino and battery placement are different for the 2 designs. As shown in Figure 2, the battery is in the center of the back of the AEV as opposed to being in the middle on one side of Design 1. However, it should be noted that both designs are balanced evenly from left to right when hanging. The significant qualitative observations that were made during the run are that both Designs stopped at the correct location near the first gate and Design 2 was equally balanced on the front and back wheel while Design 1 was balanced from left to right well but not front and back.
Figure 2: AEV with Cross Base
Figure 3: AEV with T Base
Figure 4: Power (W) vs Time (sec) for 2 AEV designs from PT1
The results from testing these two designs in performance test 1 can be shown below in Figure 4 above and Tables 3 and 4 on the next page. Figure 4 shows the overlay between the power used by Design 1 and 2 as a function of time. Tables 3 and 4 show the energy supplied to the AEV from the battery for each line of code for Designs 1 and 2.
The two designs behaved differently with the same control program. The group was expecting Design 2 to use less energy because of the weight decrease but was not expecting such a large margin. If you compare the total time after the last command is executed shown in Tables 3 and 4 on the next page, it is clear that Design 2 took about 60% of the time to complete the task as Design 1. However, both designs stayed balanced on the track. It should be noted that the time it takes to complete a task isn’t as relevant to the MCR as energy usage per unit mass.
Table 3: Supplied Energy for each line of code for Design 1
| Time (seconds) | Arduino command | Supplied Energy (J) |
| 0-.360 | reverse (4); | 3.56 |
| .36-1.98 | motorSpeed(4,35);
goFor(2); |
16.01 |
| 1.98-10.8 | motorSpeed(4,25);
goToAbsolutePosition(-300); |
55.86 |
| 10.8-11.1 | reverse(4); | 2.29 |
| 11.1-12.3 | motorSpeed(4,30);
goFor(1.5); |
8.52 |
| 12.3-15.3 | brake(4); | .4373 |
Table 4: Supplied Energy for each line of code for Design 2
| Time (seconds) | Arduino Commands | Supplied energy (J) |
| 0-.18 | reverse(4); | 1.73 |
| .18-1.98 | motorSpeed(4,35);
goFor(2); |
17.75 |
| 1.98-5.16 | motorSpeed(4,25);
goToAbsolutePosition(-300); |
20.29 |
| 5.16-5.46 | reverse(4); | 2.50 |
| 5.46-6.72 | motorSpeed(4,30);
goFor(1.5); |
9.02 |
| 6.7-9.72 | brake(4); | .38 |
There was a significant difference in the total amount of supplied energy required by the 2 designs. The total amount of supplied energy of Design 2 is about 52 J as opposed to Design 1 requiring about 87 J.
Conclusion and Recommendations
The goal of performance test one was to determine the final design of the AEV. The team developed two designs to compare and a standard code. The team ran each AEV and collected the EEPROM data for each run. The team used the data and the design review tools learned in previous labs to determine which design minimized energy usage per unit mass.
As stated in the MCR, the main objective of this project is to develop an AEV that minimize the energy consumed per unit mass. Therefore, the most important factors in determining which AEV to continue with is weight and energy consumption. As shown in Tables 3 and 4, Design 2 required 60% of the amount of energy Design 2 required (52 total J compared to 87 total J). Also, the mass of Design 1 is less than the mass of Design 2. Therefore, based on performance test 1, Design 2 to be selected for the final AEV design.
The group completed the performance test and decided which design to use in future labs. The only error that occurred during this lab was inconsistency with the code. In the coming lab the group’s task is to finalize the code so this error will be resolved in the coming weeks. The group recommends that a minimum of three trials for Designs 1 and 2 should be completed if Performance Test 1 could be repeated. Therefore, planning ahead for performance test 2 and 3, the group will try to allot for multiple trials so that the energy consumption data using the MATLAB analysis is more reliable.
Appendix
Arduino Code
//reverse all motors
reverse(4);
//set all motors to run at 35% power for 2 second
motorSpeed(4,35);
goFor(2);
//set all motors to run at 35% power until the position -300 marks is reached
motorSpeed(4,25);
goToAbsolutePosition(-300);
//reverse all motors
reverse(4);
//set all motors to run at 30% power for 1.5 second
motorSpeed(4,30);
goFor(1.5);
//cuts power to motors
brake(4);
Task Schedule:
| Task | Start | Finish | Due Date | David | Riley | Sean | % Complete |
| AEV 1 Construction | 10/25 | 10/25 | 10/25 | x | 100 | ||
| AEV 1 Testing | 10/25 | 10/28 | 10/31 | x | x | x | 100 |
| AEV 2 Construction | 10/28 | 10/28 | 10/28 | x | x | x | 100 |
| AEV 2 Testing | 10/28 | 10/31 | 10/31 | x | x | x | 100 |
| PDR Executive Summary | 10/28 | 11/3 | 11/4 | x | 100 | ||
| PDR Results and Analysis | 10/28 | 11/3 | 11/4 | x | 100 | ||
| PDR Conclusion and Appendix | 10/28 | 11/3 | 11/4 | x | 100 | ||
| SolidWorks Model | 11/1 | 11/3 | 11/4 | x | 100 |
AEV Design 1
AEV Design 2
