## 3 Results  
### Identification of Transient Endpoints  
A program was needed to be developed in order to determine where variations in voltage and current occurred. These variations were used to classify the transient regime of the throttle tests within the program. The first step involved cropping data around each throttle transient window so that the data only contained the transient and the steady-state period before and after the transient points. The data generally appeared, as shown in the example in Figure 7. Each test point had the turbine or load settled at one throttle point and was rapidly changed to another pre-determined point. The abrupt change noted can be seen in Figure 7, where the current is steady until it rapidly increases and settles at a new, higher steady point. Start and endpoints needed to be identified to extract the dynamic section from these data successfully. A running average of the last 20 points (n-20, n) was generated for every point in the data set to identify the starting point. The standard deviation of the previous 20 points was also calculated. An example of these averages can be seen in Figure 8. Once these values were calculated, the program would step through each point, n. The program would check if the next point, n + 1, exceeded \(\pm\)1 standard deviation from the running average. If the n + 1 point exceeded +1 standard deviation, the program would check if the n + 2 point exceeded +2 standard deviations from the original (n) point. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \begin{tabular}{c} **Theoretical** \\ **Specific** \\ **Energy** \\ **[W-hr/kg]** \\ \end{tabular} & \begin{tabular}{c} **Practical Specific** \\ **Energy** \\ **[W-hr/kg]** \\ \end{tabular} & \
\begin{tabular}{c} **Specific Power [W/kg]** \\ **[V]** \\ \end{tabular} &
\begin{tabular}{c} **Cell Voltage** \\ **[V]** \\ \end{tabular} \\ \hline Lead-acid (Pb/) & 170 & 30–50 & 180 & 1.2 \\ Nickel cadmium (NiCd) & 240 & 60 & 150 & 1.2 \\ Nickel metal hydride (NiMH) & 470 & 23–85 & 200–400 & 0.94–1.2 \\ Lithium-ion (Li-Ion) & 700 & 100–135 & 250–340 & 3.6 \\ Lithium polymer (Li-Po) & 735 & 50.7–220 & 200–1900 & 3.7 \\ Lithium sulfur (LiS) & 2550 & 350 & 600–700 & 2.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specific energy of different battery chemistries. Figure 6: Screen capture of the BMS showing cell voltages nearly leveled during charging. Similarly, if the n + 1 point exceeded \(-1\) standard deviation, the program would check if n + 1 exceeded \(-2\) standard deviations. This process was repeated for n + 3 and n + 4 against \(\pm 3\) and \(\pm 4\) standard deviations, respectively. If all four standard deviation comparisons were exceeded, then point n was considered the beginning point of the throttle transient. A similar process was used for the endpoint of the throttle transient. The endpoint program differed in that instead of using the last 20 points for the running average, the following 20 points (n, n + 20) were used. The program also ran from the end of the data set backward. This method was used instead of one which checks for consecutively rising points because there was a possibility that some data sets involved an extremely rapid change within a few points. There was also the possibility of consecutively rising points not part of the transient test, as seen at approximately the 16 s mark in Figure 7. An algorithm based on the absolute magnitude of change at the beginning and end of the throttle transient could not be used because the magnitude of change varied between throttle tests (e.g.,  
Figure 8: A plot showing measured currents, a running average, and positive multiples of the standard deviation. Figure 7: An example of transient throttle data displaying current as a function of time. The endpoints of the transient are marked in red. the 50-100% transient would have a more considerable shift in current than the 50-75%).