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.
To meet the growing trend of stringent fuel economy regulations, automakers around the world are designing modules such as engines, motors, transmissions and batteries to be as efficient as possible. In order to verify the effect of these designs on the overall fuel efficiency of the vehicle, the vehicle equipped with each module is placed on the chassis dynamometer, driven to follow the target vehicle speed, and actual fuel efficiency is measured. These tests are traditionally performed by human operators, but are now being replaced by robots (physical or software) to ensure the accuracy and reliability of test results. Although the conventionally proposed proportional integral (PI)-based controller has a simple structure and is easy to implement, it requires the process of finding the optimal gain whenever the test conditions such as vehicle or drive cycle change, which is difficult and time consuming. In this study, we propose a proportional integral controller gain adjustment algorithm using deep reinforcement learning. The reinforcement learning agent learns to dynamically modify the PI gain value of the acceleration/deceleration pedal to better follow the target vehicle in a simulation environment. The perturbation is used in each training episode to reduce the difference between the simulation and real testing environment. Upon completion of the training process, the trained agent performs an adjustment process that generates a reference gain table. We then use this reference gain table to perform a real test. The performance of the proposed system was evaluated using Hyundai Tucson HEV (NX4) on an AVL chassis dynamometer. We also compared the performance of our proposed algorithm to traditional fuzzy logic-based PI controllers. The obtained experimental results show that the proposed control system achieved a performance improvement of aounrd 46.8% compared to the conventional PI control system in terms of root mean square error.
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