Summary
The AEV Energy Model Lab consisted of checking that the wheel count sensors were working properly on the team’s respective AEV and calculating the net force for the AEV from the data collected so the team could analyze the performance of the AEV. The team used troubleshooting if the recorded wheel counts were different from the actual wheel counts. Verifying accurate sensor readings are very important for the AEV in order to program the AEV to complete certain tasks at specific and exact locations along the track. The team will later use the results to create a more energy efficient AEV. The ability to design more energy efficient vehicle; hence, Advanced Energy Vehicle, is essential in creating the best product possible, which will save the company operating the AEV money as well as reducing the amount of natural resources used and harmful gases put into the environment. The whole purpose of the AEV is to create a vehicle that can complete various tasks on a track while utilizing the least amount of energy. This lab deepened the team’s knowledge on how to do this and displayed the importance of conserving energy.
Results
The AEV had a total mass 0.256 kilograms as recorded in Table 1. There were 275 marks recorded by the sensors. However, after the distance of the AEV traveled was converted into marks the AEV actually went 282.3 marks as shown in Table 2. The team had a significant marks error of 7 marks, requiring the team to get assistance from an instructor. The team worked to resolve the issue but due to time constraints, no data was able to be collected using the improved sensors. Data from the AEV was then uploaded into MATLAB and various pieces of data were recorded and used to calculate the friction force and the propeller force. The friction force was 3.6 gram-force and the propeller force was 7.8 gram-force, as shown in Table 3. The propeller force displayed how powerful the fans were pushing the air back and the friction force exhibited the friction experienced by the AEV on the rail. The ideal AEV should have the lowest friction force and highest propeller force, resulting in the highest net force. The AEV had a lower propeller force compared to the 11.2 gram-force class average as evident in Table 1. The force of friction on the AEV was also lower than the 4.7 gram-force class average as evident in Table 1. This resulted in a 5.2 gram-force net force for the AEV compared to the 6.5 gram-force class average as evident in Table. A higher net force means better energy efficiency, and the AEV was over a point below the class average. The team discussed ways to make the AEV’s propeller create a larger force, but no ideas were executed in this lab. The team will troubleshoot and resolve these issues at later meetings.
There were many setbacks and errors that occurred during this lab. Initially, the code from Figure 1 would not upload to the arduino. The team realized that Sketchbook was connected to the wrong COM port. This was resolved by choosing the correct COM port for the arduino. Adding the code to the arduino was essential to completing the rest of the lab. This therefore took up much of the time and put the team in a rush to finish the rest of the lab. After the computer was successfully connected to the arduino and the code was added, the AEV did not move on the track. After troubleshooting the problem, the team realized the code was directly copied into the sketchbook program and did not include the reverse function needed to reverse the front motor to pull rather than push. This was necessary because the motors are in opposite directions. One potential source of error could have occurred when the measurements were taken on the track. Due to the length of the track and the quantity of teams testing their vehicles, different team members measured the startpoint and endpoint of the AEV. This division of work could have resulted in variations in where the AEV was measured by the two different people. While this error was minimized when the lab partners communicated which part on the AEV would be measured, the two lab members could have had measured slightly different locations within that part. This error could be eliminated by having one member measure both the startpoint and endpoint taking valuable time needed for other teams to test their AEV’s.
The lab procedure was in excel rather pdf. The format made it harder to follow. While data collection was easier to input, reading off the instructions was difficult. In order to decrease the amount of time teams spent waiting for recommendation would be to set times throughout the lab time for each team to test the AEV. This would make for a more organized and efficient method of testing, and would help avoid confusion between different teams.
This lab allowed the team to observe the calculated and actual data points of the AEV in order to determine and fix issues with the wheel count sensors. It is important to verify accurate sensor readings for the AEV so that the AEV will be able to complete certain tasks at specific and exact locations along the track. Figure 2 shows the measured number of marks by the AEV compared to the actual marks the AEV traveled over each time interval. This graph shows how the data of each one differs which represents the error in the sensors. This lab displayed how the AEV compared to the rest of the class. The team’s AEV had a lower propeller force at 7.8 gram-force compared to the 11.2 gram-force class average. Also, the team had a friction force 3.6 gram-force, which was lower than the 5.2 gram-force class average. The team also had a net force of 5.3 gram-force in comparison to the class average of 6.5 gram-force. Additionally, the team had a marks error of 7 which was higher than the class average and will need to be corrected. A more efficient AEV is created with a higher net force, as demonstrated during the lab. The team will need to troubleshoot and make use of problem solving techniques in order to increase the net force in order to create a more energy efficient vehicle.
| Mass of the AEV | 0.256 kilograms |
| Starting point | 24.00 |
| Ending Point | 160.00 |
| Actual Marks | 282.3 |
Table 1
| Marks recorded by sensors | 275 marks |
| Marks Error | 7 marks |
| Distance traveled by AEV | 3.45 meters |
Table 2
| Time when motors stop | 4 seconds |
| Marks when motors stop | 132 marks |
| Distance when the motors stop | 1.616 meters |
| Time when the AEV stops | 12.18 seconds |
| Marks when the AEV stops | 275 marks |
| Distance when the AEV stops | 3.366 meters |
| Average velocity | 0.404 m/s |
| Max velocity | 0.808 m/s |
| Acceleration constant motor speed | 0.099 m/s2 |
| Acceleration for coasting | 0.202 m/s2 |
| Friction force | 3.6 gram-force |
| Net force | 5.3 gram-force |
| Propeller force | 7.8 gram-force |
Table 3
| motorSpeed(4,30); // Sets all motors to 30% power
goFor(4); // Continues for 4 seconds motorSpeed(4,0); // Sets all motors to 0% power goFor(10); // Continues for 10 seconds |
The figure above is the code that was used.
Team Meeting Notes
Date: 3-1-2017
Time: 4:00-6:30 pm
Members Present: Cameron, Alex, Evan, Ahmed
Topics Discussed: We discussed different ideas for codes for the AEV to complete the course on.
Objective: Gain a better understanding of the code the AEV uses
To do/Action Items: Situation for past week, situation for next week, and weekly goals and schedule.
Decisions: The next meeting date was determined to be on 3/5.
Date: 3-5-2017
Time: 7:00-8:00 pm
Members Present: Alex, Evan,Cameron, Ahmed
Topics Discussed: We discussed more ways to make the AEV more energy efficient and updated website.
Objective: Get website updated and get everyone updated o what needs to be done.
To do/Action Items: Get code and make AEV better.
Decisions: Discuss what went on in lab.
Weekly Schedule
| Task | Teammate(s) | Start Date | Due Date | Time Needed |
| Complete Executive Summary | All Members | 3/1/17 | 3/7/17 | 2 hours |