Análisis de Factores de Desarrollo Urbano en el Sector Sur y Norte del casco central de la ciudad de San José

The goal of this senior project is to develop an easy-to-use sensitivities analysis tool to help the Cal Poly Supermileage team maximize fuel efficiency on their internal combustion and battery electric vehicles. The simulation’s primary purpose is to determine the optimum combination of system level parameters while being able to consider uphill, flat, and downhill profiles. The tool is easy to use for a Cal Poly undergrad with little to no experience in vehicle design or MATLAB and allows the user to quickly familiarize themselves and start producing results. The design, manufacturing, and validation sections have been written out in detail to ensure that with the information provided by this final project report, the program is not only easy to use but also easy to modify, diagnose errors, and interpret results.

The purpose of the design tool is to run a sensitivity analysis on multiple different combinations of parameters at a time. The single run design tool isolates the effects of changing a single parameter at a time and outputs MPG/cycle as the final result after determining an appropriate minimum velocity to maintain a 15-mph average burn and coast cycle. The full run design tool looks at the fuel efficiency resulting from using the cycle found in the single run tool across the distance required by the fuel efficiency competitions by taking the fuel penalty into consideration. The velocity tool exists to validate the results of the design tools and help the user understand why certain trends exist. The three tools should be used in conjunction to perform sanity checks and expose the biases that may be present in the results of any given tool as each tool is unique in its set of advantages and limitations. For most applications, everything can be accessed through the GUI which allows the user to input ranges or nominal values of vehicle parameters and gives them full reign on variables that determine the coarseness of results and simulation time. To run a study using a sloped track profile or to change certain power source performance characteristics, amongst others, the MATLAB function files must be accessed.

The program can accept empirical dyno data for both a combustion engine and an electric motor. The basic principles of mechanical power transmission and the effects of the resistive road loads are appropriately modeled as part of the vehicle performance. The vehicle model developed in Simulink is compartmentalized into multiple subsystems and the MATLAB scripts are well documented such that modifications can be easily made as necessary. In this final stage of development, the program still lacks some features and functionality that can help to improve the simulation tool’s accuracy and modularity. A recommended feature to the ICE model would be the addition of a proper clutch subsystem where the dynamics of a centrifugal clutch is considered. However, this issue can also be completely bypassed using data from a transient pull test with the centrifugal clutch at the input shaft to the dyno. A proper dynamometer test, complete with equivalent rotational inertia within the range of what the engine is expected to see when mounted to the vehicle will significantly improve the functionality of the simulation. As it stands, the lack of empirical data regarding key engine performance characteristics and the prototype vehicle’s road load coefficients contribute to the overall lack of accuracy. Specific tests should be developed and performed under the correct conditions to acquire the desired values. Using validated inputs will allow the simulation to not only reflect general trends, but also report accurate results.

As far as improvements to the program are concerned, the optimization tool using dynamic programming outlined in the design section would be the most ideal as it would employ a smart control method regardless of track profile. However, there are much simpler, incremental approaches to improving fuel efficiency through driving strategy as mentioned in the results verification section. For starters, the addition of a smart control method in the full run design tool that automatically adjusts the final burn and coast cycle to force the vehicle velocity to zero at the finish line would significantly improve the noise present in the full run studies. Some minor improvements could involve parametrizing more variables that could be subject to change in future competitions and making them accessible through the GUI so that it’s easier to adjust for

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the user. Although there are no issues with having multiple function files, it would be easy and preferable to combine the single run and full run design tools into one function file. A more comprehensive vehicle performance model can also be developed with the addition of design parameters involving vehicle geometry such that it considers cornering effects like roll over and further tailor the driving strategy to a specific track.

Based on the initial studies that the SIMs have conducted while developing the design tool, the 3 most important parameters to optimize as a set are the gear ratio, wheel radius, and burn time. As mentioned in the interpretation of results in Section 7, in contrast to the other design parameters that require either the lowest or highest value possible, local maxima do exist for these 3 parameters. One option to mitigate the issue with non-ideal gear ratio and wheel radii combinations would be the use of a cassette sprocket such that the drive wheels can receive greater input torque when required without having to sacrifice the top speed of the vehicle. While trying to optimize power transmission and driving strategy, it’s also very important to stay true to the basics such as decreasing road loads through streamlining the vehicle and improving transmission efficiency through proper bearing fits and chain/wheel alignments.

Changes to the GUI are possible, but not necessary, though there is room for improvement. If the SMV club wants to further develop the GUI, they should employ someone who is familiar with object-based programming. App Designer is a relatively new environment within MATLAB and is frequently being updated, so more capability may be possible in the future. The user interface and functions could be altered to study more vehicle parameters; this would require changing the function files, the code in App Designer, and possibly the Simulink models. In addition, if a GUI is created for an Optimization tool, it could be added to the current app or be built separately. Either option works. The function files and App Designer code have been attached in Appendix K. They are also available to SMV members through the Google Drive, along with other important documentation.

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