It would not have been practical to run tests with actual vehicles, as we would have had to build a new operating system in a vehicle. Therefore, outside of research and data collected from online, all of our data will be collected from running our Simulink model. The data that we collect will serve to improve the components of the model. The model ultimately has two objectives: achieving the user specified objective and ensuring a smooth driving ride. Making tweaks to our Simulink model to improve the perceived acceleration or fuel efficiency and making comparisons will comprise most of the data we collect. Likewise, checking for frivolous actions and mitigating those edge cases will ensure that our system could be integrated into a vehicle and not squander the user experience.
First, to ensure a smooth driving ride, we ran hundreds of tests of our model using different vehicles and different road conditions. We continually tweaked our cruise control system design to ensure there was never too much overshoot, a reasonable response time, and minimal steady-state error. The results of these final tests can be seen in our Appendix D. As you can see, the output of velocity and of acceleration differ depending on the car model and the road grade. We took many calculations of these values and were able to settle on certain values in our controller to ensure a smooth ride regardless of vehicle and road conditions.
With regards to achieving the user-specified objective, as we have discussed, we were unable to run the continual optimization in Simulink. So, we decided to do a manual optimization as best we could. When trying to optimize acceleration, we ran our Simulink model numerous times while experimenting with various gasoline and electric engine utilizations. We held the model of the car and road grade constant to ensure we were only comparing between engines. We experimented with changing each engine’s usage from 0% to 100%. We then recorded the outputs of many data points: if it reaches the desired velocity, the peak time, the max acceleration, the max velocity, the overshoot, and the joules used (both from electric and gasoline). A the brief example of this data can be seen in the table below.
Then, we were able to analyze the complete dataset and find many observations of the usage of these two engines. We were finally able to realize certain usages of each engine that would optimize the user’s specified goal of either maximizing acceleration or maximizing fuel efficiency.