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Over the past decade, new models of hybrid electric vehicles have been released worldwide, and the fuel efficiency of said vehicles has increased by more than 5%. To further improve fuel efficiency, vehicle manufacturers have made efforts to design modules (e.g., engines, motors, transmissions, and batteries) with the highest efficiency possible. To do so, the fuel economy test process, which is conducted primarily using a chassis dynamometer, must produce reliable and accurate results. To accurately analyze the fuel efficiency improvement rate of each module, it is necessary to reduce the test deviation. When the test conducted by human drivers, the test deviation is somewhat large. When the test is conducted by a physical robot driver, the test deviation is improved; however, these robots are expensive and time-consuming to install and take up considerable amount of space in the driver’s seat. To compensate for these shortcomings, we propose a simple, structured robot system that manipulates electrical signals without using mechanical link structures. The controller of this robot driver uses the widely used PI controller. Although PI controllers are simple and perform well, since the dynamics of each test vehicle is different (e.g., acceleration response), the PI controller has a disadvantage in that it cannot determine the optimal PI gain value for each vehicles. In this work, the fuzzy control theorem is applied to overcome this disadvantage. By using fuzzy control to deduce the optimal value of the PI gain, we confirmed that our proposed system is available to conduct tests on vehicles with different dynamics.

electronics

Development of a Simple Robotic Driver System (SimRoDS) to Test Fuel Economy of Hybrid Electric and Plug-In Hybrid Electric Vehicles Using Fuzzy-PI Control