Analysis: Future Truck Thermoelectric System Customer: PBJ Xaustors

1. Analysis: Future Truck Thermoelectric SystemCustomer: PBJ Xaustors Robert Wiegers Richard StatlerKhurram KemalME430 Fall 2003Professor Michael Anderson

2. Introduction Objective: • Experimentally determine the effect of different loads on Peltier chip bank. • Measure the power output of the thermal electric system constructed by the PBJ Xaustors • Use calibration/regression statistical method to analyze the collected data • From the analysis present opinion on the best load for chip bank Motivation: • To help create a lower emission environment friendly vehicle • Exhaust recover system will recover the waste energy off the exhaust of Idaho Future Truck Background: The system has been created by the PBJ Xaustors which can do the following: • capture the heat on the surface of pipes and convert it into the electricity • Does not put extra load on the engine • Utilizes a system of Peltier chips which produces power when a temperature gradient across the chip is present

3. Experiment Diagram Heat sink placed on top of chips Labview running on computer Field point module controller Chip banks Variable heat source

4. Data Reduction • Initial Calculations: • As the data is being read into the file calculations are completed using labview • The above equation is the equation for power = P using Voltage = V and Load Resistance = R • Using this equation allowed for the graph of temperature difference vs. power to be created • Calibration Regression: • Experimented with more powers for fit but found over quadratic extraneous • Data captured by Field point modules was analyzed by Labview • Labview data exported into Excel and read into MathCAD • Statistical analysis was completed with MathCAD • Statistical Analysis: • The above equation is the quadratic used to model the system with the betas being solved for from the data gathered

5. Experiment Design • Controls: • Using Labview and field point • Exported into excel and then MathCAD • This allows for small error in the measurement and saves time entering data • Accuracy: • The equipment being used, Field point and Labview, has high accuracy • This leads to smaller errors in the values that are collected • Since all the collection and analysis is completed independent of operator the is little human error in the recording and analysis of the data.

6. Measured Data • Raw data taken straight from Labview and read by MathCAD. The time interval for gathering the data was once every 10 seconds. The data was captured until the temperature differential was approximately 85F • A distinct difference can be noticed between the different loads on the chip bank • 5.2 Ohm looks like the best from the graph but the statistical analysis needs to be completed to be confident 5.2 Ohm – Black Line 3.8 Ohm – Red Line 11.8 Ohm – Aqua Line

7. Statistical Analysis • The best fit curves for each of the different resistive loads are shown on the graph as the middle line of the three of same color • The confidence intervals are graphed as well as the outer two lines of the three • Methods including using half as many data points and looking into higher power equations. When these were investigated it was found that a quadratic curve fit was sufficient and a halving of the data points didn’t effect the outcome. 5.2 Ohm – Black Line 3.8 Ohm – Red Line 11.8 Ohm – Aqua Line

8. Conclusion Ideal Resistance Load = 5.2 Ohms

9. Questions?