Abstract
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.
Highlights
Regulations regarding fuel efficiency and emissions in the automotive industry have become stricter since 1992, and developing eco-friendly vehicles has become an inevitable challenge for automakers
There are two types of powertrain systems for (P)hybrid electric vehicles (HEVs): (i) add-on type, where electric motors are added to the existing internal combustion engine transmission system and (ii) dedicated hybrid transmissions (DHTs), which are especially designed for use in (P)HEVs [1]
The application of these systems have contributed to the commercialization of (P)HEVs by achieving a 50% reduction in fuel efficiency compared to the conventional ICE vehicles [2]
Summary
Regulations regarding fuel efficiency and emissions in the automotive industry have become stricter since 1992, and developing eco-friendly vehicles has become an inevitable challenge for automakers. A fuzzy-logic-based dynamic speed control method was applied to a robot driver [13]. A dynamic fuzzy PID control-based strategy for vehicle yaw stability was developed; the control strategy they proposed resulted in significant improvements in the safety and stability of the vehicle under different road conditions compared with the conventional control policy [21]. We apply an dynamic fuzzy PID controller to the proposed robot driver system for the purpose of speed tracking. Powered by a simple dynamic PI gain autotuning algorithm based on fuzzy logic, the proposed robot driver system demonstrates much improved target velocity following performance compared to fixed PI gain approach, enabling accurate fuel efficiency testing including industry standards such as the FTP cycle
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