582 research outputs found
Energy management system optimization based on an LSTM deep learning model using vehicle speed prediction
The energy management of a Hybrid Electric Vehicle (HEV) is a global optimization problem, and its optimal solution inevitably entails knowing the entire mission profile. The exploitation of Vehicle-to-Everything (V2X) connectivity can pave the way for reliable short-term vehicle speed predictions. As a result, the capabilities of conventional energy management strategies can be enhanced by integrating the predicted vehicle speed into the powertrain control strategy. Therefore, in this paper, an innovative Adaptation algorithm uses the predicted speed profile for an Equivalent Consumption Minimization Strategy (A-V2X-ECMS). Driving pattern identification is employed to adapt the equivalence factor of the ECMS when a change in the driving patterns occurs, or when the State of Charge (SoC) experiences a high deviation from the target value. A Principal Component Analysis (PCA) was performed on several energetic indices to select the ones that predominate in characterizing the different driving patterns. Long Short-Term Memory (LSTM) deep neural networks were trained to choose the optimal value of the equivalence factor for a specific sequence of data (i.e., speed, acceleration, power, and initial SoC). The potentialities of the innovative A-V2X-ECMS were assessed, through numerical simulation, on a diesel Plug-in Hybrid Electric Vehicle (PHEV) available on the European market. A virtual test rig of the investigated vehicle was built in the GT-SUITE software environment and validated against a wide database of experimental data. The simulations proved that the proposed approach achieves results much closer to optimal than the conventional energy management strategies taken as a reference
Development of a Distributed Model Predictive Controller for Over-Actuated Autonomous Vehicle Path Tracking
Widespread interest in the advancement of autonomous vehicle technology is motivated by multiple outstanding issues associated with vehicular travel despite the decades-long ubiquity of this mode of transportation. It is well known that the leading cause of accidents on the road is human error. Furthermore, vehicle hardware faults and harsh environmental circumstances are also common collision factors due to the challenges that they introduce to the driving task. Autonomous vehicles have the potential to greatly exceed the perception, decision making, and control capabilities of human drivers in some applications, and the large-scale adoption of this technology will thereby mitigate the primary driving-related safety concerns. Numerous additional benefits will be realized as a result; for instance, complex planning algorithms will help to reduce traffic congestion, and transportation- and insurance-related costs will be minimized due to the lower collision rates. Though it may be many years before the technology sees extensive use for passenger transportation applications due to the complexity of standard driving environments, autonomous vehicles will likely find use over the short-term in other specialized domains. For example, these vehicles can be used to transport payloads over short distances in a wide variety of applications, including agriculture, mining, and shipping, where the operating environment is less complex. In these scenarios, autonomous vehicle technology will help to lessen the effects of labour shortages while enabling longer operating hours at a lower cost.
A key component of the autonomous stack is the motion controller, which serves to regulate the longitudinal and lateral motion of the vehicle according to a defined set of objectives by precisely manipulating the available actuators. Model predictive control (MPC) is a powerful control strategy commonly used for this purpose; the algorithm can coordinate a large set of control inputs such that the system meets all defined objectives while satisfying any constraints on the states and inputs. Many prior works investigate the use of MPC, and its variants, for vehicle path tracking and stability control applications. One such variant is distributed MPC; with this approach, the controlled plant is modelled as a set of interacting subsystems, each subsystem using its own MPC controller to select a set of optimal control actions in combination with all others. An extension of distributed MPC, agent-based MPC (AMPC), enhances the control capabilities by allowing the controller to additionally consider both the effect of subsystems that are not controllable by the optimal controller and the effects of hardware faults on the system dynamics. While previous works have investigated the application of AMPC to vehicle stability control tasks, in this thesis, AMPC is utilized to perform path tracking.
The vehicle hardware platform considered in this work, WATonoTruck, is modular and over-actuated in design, making it a suitable test platform for AMPC. Built using the corner module platform, the wheels at each corner can be independently driven and steered. A vehicle dynamics reference model to represent the behaviour of WATonoTruck is constructed; this model utilizes a nonlinear tire force model to accurately characterize the tire-road interaction, and incorporates Ackermann geometry to prevent unecessary wheel slip and reduce the control task complexity that results from the over-actuated nature of the system. This model serves as the prediction model for the designed AMPC controller. The controller also considers numerous constraints on the vehicle states, inputs, and input rates to ensure stability, and can incorporate an external longitudinal controller and account for actuator faults. The controller is validated over several simulated and experimental tests that demonstrate its ability to provide effective path tracking and velocity control performance in a varied set of scenarios, including those where actuator failures occur or the driving environment is harsh
Multi-body dynamics in vehicle engineering
Since Euler's original gyro-dynamic analysis nearly two and a half centuries ago, the use of multi-body dynamics (MBD) has spread widely in application scope from large displacement rigid body dynamics to infinitesimal amplitude elastodynamics. In some cases, MBD has become a multi-physics multi-scale analysis, comprising contact mechanics, tribo-dynamics, terramechanics, thermodynamics, biomechanics, etc. It is an essential part of all analyses in many engineering disciplines, including vehicle engineering. This paper provides an overview of historical developments with emphasis on vehicle development and investigation of observed phenomena, including noise, vibration and harshness. The approach undertaken is comprehensive and provides a uniquely focused perspective, one which has not hitherto been reported in the literature
Influence of the Reward Function on the Selection of Reinforcement Learning Agents for Hybrid Electric Vehicles Real-Time Control
The real-time control optimization of electrified vehicles is one of the most demanding tasks to be faced in the innovation progress of low-emissions mobility. Intelligent energy management systems represent interesting solutions to solve complex control problems, such as the maximization of the fuel economy of hybrid electric vehicles. In the recent years, reinforcement-learning-based controllers have been shown to outperform well-established real-time strategies for specific applications. Nevertheless, the effects produced by variation in the reward function have not been thoroughly analyzed and the potential of the adoption of a given RL agent under different testing conditions is still to be assessed. In the present paper, the performance of different agents, i.e., Q-learning, deep Q-Network and double deep Q-Network, are investigated considering a full hybrid electric vehicle throughout multiple driving missions and introducing two distinct reward functions. The first function aims at guaranteeing a charge-sustaining policy whilst reducing the fuel consumption (FC) as much as possible; the second function in turn aims at minimizing the fuel consumption whilst ensuring an acceptable battery state of charge (SOC) by the end of the mission. The novelty brought by the results of this paper lies in the demonstration of a non-trivial incapability of DQN and DDQN to outperform traditional Q-learning when a SOC-oriented reward is considered. On the contrary, optimal fuel consumption reductions are attained by DQN and DDQN when more complex FC-oriented minimization is deployed. Such an important outcome is particularly evident when the RL agents are trained on regulatory driving cycles and tested on unknown real-world driving missions
A study on gear rattle and whine noise reduction of power transmission system for agricultural tractor
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๋์
์๋ช
๊ณผํ๋ํ ๋ฐ์ด์ค์์คํ
๊ณตํ๊ณผ, 2023. 2. ๋ฐ์์ค.๋์
์ฉ ํธ๋ํฐ๋ ์์ง, ๋ณ์๊ธฐ, ์ ์์์คํ
๋ฑ ๋ค์ํ ์์์์ด ์กด์ฌํ๋ฉฐ, ์ ํต์ ์ผ๋ก ์์ง์ด ๊ฐ์ฅ ํฐ ์์์์ด๋ผ๊ณ ์๋ ค์ ธ ์๋ค. ๊ทธ๋ฌ๋ ์์ง ์ค๊ณ ๊ธฐ์ ์ด ๋ฐ์ ์ ๋ฐ๋ผ ์๋์ ์ผ๋ก ๊ฐ๋ ค์ ธ ์๋ ๋ณ์๊ธฐ ๋๋ ฅ์ ๋ฌ๊ณ์ ์์์ด ์ด์๊ฐ ๋๊ณ ์๋ค. ๋์
์ฉ ํธ๋ํฐ ๋ณ์๊ธฐ์ ๋๋ ฅ์ ๋ฌ๊ณ๋ PTO ์ ๋๋ผ์ธ๊ณผ ๊ตฌ๋๋ฅ์ผ๋ก ๋๋ ฅ์ ์ ๋ฌํ๋ ์ฃผํ ๋ณ์๋ถ๋ก ๊ตฌ๋ถ๋๋ค. ์ต๊ทผ ์ฃผ๋ก ์ฑํ๋๋ ๋
๋ฆฝํ PTO ์์คํ
์ ์์ง๊ณผ ์ง์ ์ฐ๊ฒฐ๋์ด ๋๋ ฅ์ ๋ฌ ํจ์จ์ ์ข์ผ๋ ์ง๋/์์์ ์ทจ์ฝํ ๊ตฌ์กฐ์์๋ ์ด์ ๋ํ ์ฐ๊ตฌ๊ฐ ๋ถ์กฑํ๋ค. ๋ํ ๊ตฌ๋๋ฅ์ผ๋ก ์ ๋ฌ๋๋ ์ฃผํ ๋ณ์๋ถ๋ ๊ธฐ์ด ์ ๋ฌ์ค์ฐจ์ ์ํด ๋ฐ์ํ๋ ๊ธฐ์ด ์นํฉ ์์์ด ์ฃผ์ ์ด์์ด๋ค. ๊ทธ๋ฌ๋ ๊ธฐ์ด ์ ๋ฌ์ค์ฐจ๋ฅผ ๊ณ ๋ คํ ์ ์์ ์ฃผํ ๋ณ์๋ถ ์ค๊ณ๋ฅผ ์ํ ์ฒด๊ณ์ ์ธ ์ฐ๊ตฌ๋ ๋ฏธ๋นํ๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ๋์
์ฉ ํธ๋ํฐ์ ๋๋ ฅ์ ๋ฌ๊ณ ์์ ๊ฐ์ ์ ์ํด PTO ์ ๋๋ผ์ธ์ ๊ธฐ์ด ๋ํ ์์๊ณผ ์ฃผํ ๋ณ์๋ถ์ ๊ธฐ์ด ์นํฉ ์์์ ์ ๊ฐ์ ๋ชฉ์ ์ผ๋ก ์ฐ๊ตฌ๋ฅผ ์ํํ์๋ค. ๊ธฐ์ด ๋ํ ์์๊ณผ ๊ธฐ์ด ์นํฉ ์์์ ๋ฐ์ ์์ธ ๋ฐ ๋ฉ์ปค๋์ฆ์ด ์๋ก ๋ค๋ฅด๋ฏ๋ก ํฌ๊ฒ ๋๊ฐ ํํธ๋ก ๊ตฌ๋ถํ์ฌ ์ฐ๊ตฌ๋ฅผ ์ํํ์๋ค.
์ฒซ๋ฒ์งธ ํํธ์ธ PTO ์ ๋๋ผ์ธ์ ๊ธฐ์ด ๋ํ ์ ๊ฐ์ ์ํด ์ํฅ์ธ์๋ฅผ ๋ถ์ํ์ฌ, ๋ฐฑ๋์๋ฅผ ๊ฐ๋ ์คํ๋ผ์ธ ์ฒด๊ฒฐ๋ถ, ๋นํ๋ฆผ ๋ํผ ๋ฑ๊ณผ ๊ฐ์ ๋น์ ํ ๊ฐ์ฑ ์์๋ ํ์ ๊ณ์ ๋์ ๊ฑฐ๋์ ๊ธ๊ฒฉํ ๋ณํ๋ฅผ ์ผ์ผํฌ ์ ์๋ ์์์์ ํ์ธํ์๋ค. PTO ์ ๋๋ผ์ธ์ ๊ธฐ์ด ๋ํ ์์ ์ ๊ฐ์ ์ํ์ฌ ๋น์ ํ ๊ฑฐ๋์ ์ผ๊ธฐํ๋ ์คํ๋ผ์ธ ๋ฐฑ๋์๊ฐ ์ ์ฒด ๋์ ๊ฑฐ๋์ ๋ฏธ์น๋ ์ํฅ์ ์ํ์ ํตํด ํ์ธํ์๋ค. ์ค๋ด์ํ์ ๊ธฐ์ด ๋ํ ์์์ ์ ์ธํ ๋ค๋ฅธ ์์์ด ์ ๊ฑฐ๋ ํ๊ฒฝ์์ ์ํํ์๊ณ , ์์ง ๋ชจ์ฌ๊ฐ ๊ฐ๋ฅํ ๋ชจํฐ์ ์ด ์์คํ
์ ๊ตฌ์ถํ์๋ค. ๋ณ์๊ธฐ ์
๋ ฅ์ถ๊ณผ ๋ค์์ ์คํ๋ผ์ธ ์ฒด๊ฒฐ๋ถ๋ฅผ ๊ฑฐ์ณ ํ์ ์ง๋์ด ์ ๋ฌ๋๋ PTO ๊ตฌ๋๊ธฐ์ด ํ์ ์๋ ์๋ต์ ๊ณ์ธกํ์ฌ ๋์ ๊ฑฐ๋ ๋ณํ๋ฅผ ํ์ธํ์์ผ๋ฉฐ, ๋์์ ๊ธฐ์ด ๋ํ ์์๋ ๊ณ์ธกํ์๋ค. ์์ง ์์ด๋ค ํ์ ์๋ ๊ตฌ๊ฐ์ธ 700 ~ 1,000 rpm์์ ์ํ๋ ์ค๋ด์ํ์ ํตํด 920 rpm์์ ๊ธ๊ฒฉํ ๋์ ๊ฑฐ๋ ๋ณํ์ธ ์ ํ ํ์์ด ๋ฐ์ํ์์ผ๋ฉฐ, ๋์์ ๊ธฐ์ด ๋ํ ์์๋ 10.9 dBA ๊ฐ์ํ์๋ค. ์ํ์ ํตํด ์์ง ์์ด๋ค ํ์ ์๋ ๊ตฌ๊ฐ์์ PTO ์ ๋๋ผ์ธ์ ๋์ ๊ฑฐ๋ ํน์ฑ์ ํฌ๊ฒ 3๊ฐ ๊ตฌ๊ฐ์ผ๋ก ๊ตฌ๋ถ๋์๋ค. ์ฒซ๋ฒ์งธ ๊ตฌ๊ฐ์ ์
๋ ฅ์ถ ๋๋น PTO ๊ตฌ๋๊ธฐ์ด์ ํ์ ์๋ ๋ณ๋๋์ด ์ฆํญํ๋ ๊ณผ์๋ต ๊ตฌ๊ฐ์ด๋ฉฐ, ๋๋ฒ์งธ ๊ตฌ๊ฐ์ ์ ํ ํ์์ด ๋ฐ์ํ๋ ์๊ฐ์ ๊ณผ๋์๋ต ๊ตฌ๊ฐ์ด์๋ค. ์ธ๋ฒ์งธ ๊ตฌ๊ฐ์ ํ์ ์๋ ๋ณ๋๋์ ์ฆํญ ์์ค์ด ๋ฎ๊ฒ ์ ์ง๋๋ ์ ์๋ต ๊ตฌ๊ฐ์ผ๋ก ๊ธฐ์ด ๋ํ ์์ ๋ํ ๋ฎ์ ์์ค์ ์ ์งํ์๋ค.
์ ํ ํ์์ ์ฃผ์ ์ธ์ ๋ถ์์ ์ํด ์คํ๋ผ์ธ ๋ฐฑ๋์๋ฅผ ๋ฐ์ํ 1D ์๋ฎฌ๋ ์ด์
์ ์ํํ์๋ค. ์๋ฎฌ๋ ์ด์
์ ํตํด ๊ณผ์๋ต ๊ตฌ๊ฐ์์๋ ์คํ๋ผ์ธ ๋ด์ธ์ธก ์น๊ฐ ์๋ฐฉํฅ ์ถฉ๋์ ํ์์ผ๋ฉฐ PTO ๊ตฌ๋๊ธฐ์ด์ ํ์ ์๋ ๋ณ๋๋์ด ์ฆํญ๋์๋ค. ๋ฐ๋ฉด์, ์ ์๋ต ๊ตฌ๊ฐ์์๋ ์คํ๋ผ์ธ ๋ด์ธ์ธก ์น๊ฐ ๋จ๋ฐฉํฅ ์ถฉ๋์ ํ์์ผ๋ฉฐ, ํ์ ์๋ ๋ณ๋๋์ด ์ฆํญ๋๋ ํ์์ ๋ํ๋์ง ์์๋ค. ๋ํ ์ ํ ํ์์ ๋ฐ์ ์์ธ์ธ ์คํ๋ผ์ธ ๋ฐฑ๋์์ ํฌ๊ธฐ ๋ณํ์ ๋ฐ๋ฅธ ์ํฅ์ ์๋ฎฌ๋ ์ด์
์ ํตํด ์ถ๊ฐ์ ์ผ๋ก ๊ฒํ ํ์๋ค. ์๋ฎฌ๋ ์ด์
๋ชจ๋ธ์ ๋ฐ์๋ ์คํ๋ผ์ธ ์ค ๊ฐ์ฅ ํฐ ๋ฐฑ๋์ ํฌ๊ธฐ๋ฅผ ๊ฐ๋ PTO ํด๋ฌ์น์ ํ๋ธ์ถ๊ณผ ๋ง์ฐฐํ ์ฌ์ด์ ์คํ๋ผ์ธ ๋ฐฑ๋์๋ฅผ ๋ณ๊ฒฝํ์์ผ๋ฉฐ, ๊ธฐ๋ณธ ๋ฐฑ๋์ ํฌ๊ธฐ์ธ 0.50 mm๋ฅผ ๊ธฐ์ค์ผ๋ก ์ ์ ๊ฐ๋ฅํ ํฌ๊ธฐ์ธ 0.1 mm์ 0.9 mm์ ๋ํด ๋์ ๊ฑฐ๋์ ํ์ธํ์๋ค. ์คํ๋ผ์ธ ๋ฐฑ๋์๊ฐ 0.10 mm, 0.50 mm์ธ ๊ฒฝ์ฐ์๋ ์๋ฐฉํฅ ์ถฉ๋์ด ๋ฐ์ํ์๊ณ , 0.90 mm์ธ ๊ฒฝ์ฐ๋ ์์ง ๊ฐ์ง์ 2์ฐจ ์ฑ๋ถ์ด ์ฆ๊ฐํ์ง์์์ผ๋ฉฐ, ์ ์๋ต ๊ตฌ๊ฐ์ ์ถฉ๋ ๊ฑฐ๋์ธ ๋จ๋ฐฉํฅ ์ถฉ๋์ด ๋ํ๋ฌ๋ค.
์คํ๋ผ์ธ ๋ฐฑ๋์ ํฌ๊ธฐ์ ๋ฐ๋ฅธ PTO ์ ๋๋ผ์ธ์ ๋์ ๊ฑฐ๋ ๋ณํ๋ฅผ ์ํ์ ํตํด ํ์ธํ์๋ค. ์คํ๋ผ์ธ ๋ฐฑ๋์ ์์ค์ PTO ํด๋ฌ์น์ ๋ง์ฐฐํ๊ณผ ํ๋ธ์ถ ์ฌ์ด์ ์คํ๋ผ์ธ ์ฒด๊ฒฐ๋ถ์ ๋ฐฑ๋์์ ํฌ๊ธฐ๋ฅผ 0.10 mm(Type A), 0.50 mm(Type B), 0.90 mm(Type C), 1.30 mm(Type D) ๋ก ์ ์ํ์ฌ ์ํ์ ์ํํ์๋ค. Type A๋ฅผ ์ ์ฉํ ์ํ์์๋ ์ ํ ํ์์ด ๋ฐ์ํ์ง ์์์ผ๋ฉฐ, ์คํ๋ผ์ธ ๋ฐฑ๋์ ์์ค์ด ์ปค์ง์๋ก ์ ํ ํ์์ด ๋ฐ์ํ๋ ํ์ ์๋๊ฐ ์ ์ ๋ฎ์์ก๋ค. ์คํ๋ผ์ธ ๋ฐฑ๋์ ํฌ๊ธฐ์ ๋ฐ๋ผ ๊ธฐ์ด ๋ํ ์์์ด ๊ธ๊ฒฉํ ๊ฐ์ํ๋ ํ์ ์๋ ๋ํ ๋ฎ์์ก๋ค.
์ฒซ๋ฒ์งธ ํํธ์ ์ฐ๊ตฌ๋ฅผ ํตํด ๋์
์ฉ ํธ๋ํฐ์ ์์ง ์์ด๋ค ํ์ ์๋๋ ์ ์๋ต ๊ตฌ๊ฐ์ ์ค์ ๋์ด์ผ ์ ์์ฑ์ ์ ์งํ ์ ์์์ ์ ์ ์์๋ค. ๋ํ ๊ธฐ์ด ๋ํ ์์์ ๊ฐ์์ํค๊ธฐ ์ํด์๋ PTO ์ ๋๋ผ์ธ์ ์ง๋ ์ ๋ฌ ๊ฒฝ๋ก ์์ ์คํ๋ผ์ธ ๋ฐฑ๋์ ํฌ๊ธฐ๋ฅผ ์ฆ๊ฐ์์ผ, ์ค์ ํ ์ ์๋ ์์ง ์์ด๋ค ํ์ ์๋๋ฅผ ๋ฎ์ถ ์ ์์๋ค.
๋๋ฒ์งธ ํํธ์ธ ์ฃผํ ๋ณ์๋ถ์ ๊ธฐ์ด ์นํฉ ์์ ์ ๊ฐ์ ์ํด์ ์ฃผ์ ์์ธ์ธ ๊ธฐ์ด ์ ๋ฌ์ค์ฐจ๋ฅผ ์ต์ํํ ์ ์๋ ๊ธฐ์ด ์ค๊ณ ๊ธฐ๋ฒ์ ๋ํด ์ฐ๊ตฌํ์๋ค. ๊ธฐ์ด ์ ๋ฌ์ค์ฐจ๋ฅผ ์ ๊ฐํ๊ธฐ ์ํด ๋์
์ฉ ํธ๋ํฐ ๋ณ์๊ธฐ์ ์ธ๊ฐ๋๋ ๋ถํ ํน์ฑ ๋ฐ ์ฐจ๋ ์ ์กฐ ํ๊ฒฝ์ ๊ณ ๋ คํ์ฌ ๊ธฐ์ด ๋งคํฌ๋ก ์ ์ ์ต์ ํ ๊ธฐ๋ฒ์ ์ด์ฉํ์๋ค. ๋ณธ ์ฐ๊ตฌ ๋์ ํธ๋ํฐ์ ์ /ํ์ง๋ถ์ ์ ์ง ๊ธฐ์ด, ์ฃผ๋ณ์๋ถ์ 1๋จ, 2๋จ, 4๋จ ๊ธฐ์ด๋ฅผ ๋์์ผ๋ก ์ต์ ํ๋ฅผ ์ํํ์๋ค. ๊ธฐ์ด ๋งคํฌ๋ก ์ ์ ์ต์ ํ์ ๋ชฉ์ ํจ์๋ ๊ธฐ์ด ์ ๋ฌ์ค์ฐจ์ ๋๋ ฅ์ ๋ฌ ํจ์จ๋ก ์ค์ ํ์๊ณ , ์ ์ ์๊ณ ๋ฆฌ์ฆ(NSGAโ
ข)์ ์ด์ฉํ์๋ค. ์ต์ ํ๋ฅผ ํตํด ๋๋ ฅ์ ๋ฌ ํจ์จ์ ๊ธฐ์กด ์์ค์ผ๋ก ์ ์งํ๋ฉฐ, ๊ธฐ์ด ์ ๋ฌ์ค์ฐจ๋ฅผ ์ต์ํํ ์ ์๋ ๊ธฐ์ด ๋งคํฌ๋ก ์ ์์ ๋์ถํ์๋ค. ๊ธฐ์ด ๋งคํฌ๋ก ์ ์ ์ต์ ํ๋ฅผ ํตํด ๋์ถ๋ ๊ธฐ์ด ์ฌ์์ด ์ค์ ๋ก ์์ ์ ๊ฐ ์ ๋๋ฅผ ํ์ธํ๊ธฐ ์ํด ์ํ ์ฅ๋น๋ฅผ ๊ตฌ์ถํ์๋ค.
์ํ์ 3์ถ ๋ค์ด๋๋ชจ๋ฏธํฐ๋ฅผ ์ด์ฉํ์ฌ ์์ง ๋ฐ ์ ์ ์์ ๋ฑ ๋ค๋ฅธ ์์์์ ํต์ ํ์๊ณ , ์ฌ์ฉ ๋น์จ์ด ๋์ ๋ณ์ ๋จ์์ธ 10๋จ, 11๋จ, 16๋จ์์ ์์ง ์ ๊ฒฉ ํ ํฌ์ 20 %์ 50 %์ ๋ถํ์์ค์ ๊ณ ๋ คํ์ฌ ์ํ์ ์ํํ์๋ค. ์์ ์ธก์ ๊ฒฐ๊ณผ, ์์ง ์ด์ ํ์ ์๋ ๊ตฌ๊ฐ์์ ์์ง ์ ๊ฒฉ ํ ํฌ์ 50%์ ๋ถํ๊ฐ ๋ณ์๊ธฐ๋ก ์
๋ ฅ๋์์ ๋ 10๋จ, 11๋จ, 16๋จ์์ ๋ณ์๊ธฐ ์ ์ฒด ์์์ ๊ฐ๊ฐ 2.99 dBA, 3.90 dBA, 3.31 dBA ๊ฐ์ ๋์๋ค. ๋ณ์๊ธฐ๋ก ์ธ๊ฐ๋๋ ๋ถํ์์ค์ด ์์ง ์ ๊ฒฉ ํ ํฌ์ 20 %์ธ ๊ฒฝ์ฐ, 10๋จ, 11๋จ, 16๋จ์์ ๊ฐ๊ฐ 3.30 dBA, 2.21 dBA, 2.42 dBA ์์์ด ๊ฐ์ ๋์๋ค. ๊ฒฐ๊ณผ์ ์ผ๋ก ๊ธฐ์ด ๋งคํฌ๋ก ์ ์ ์ต์ ํ๋ฅผ ํตํด 2๊ฐ ๋ถํ ์กฐ๊ฑด์ ๋ํด ๊ธฐ์ด ์นํฉ ์์์ด ์ ๊ฐ๋๋ ๊ฒ์ ํ์ธํ์๋ค.
๊ธฐ์ด ์ฑ๋ถ์ ์์ ์์ค์ ๋น๊ตํ๊ธฐ ์ํด ์ฐจ์ ๋ถ์์ ์ํํ์๊ณ , ์์ ์ ๊ฐ ์ ๋๋ฅผ ํ์ธํ์๋ค. ์ ์ง๊ธฐ์ด์ ํ๋ชจ๋ ์ฑ๋ถ ์ค์์ 2์ฐจ ํ๋ชจ๋ ์ฑ๋ถ์ ์์ ์์ค์ด ๊ฐ์ฅ ๋์๊ณ ์ต์ ํ๋ฅผ ํตํด 5.13 dBA ์ ๊ฐ๋์๋ค. ๋ํ ์ฃผ๋ณ์ 1๋จ, 2๋จ, 4๋จ ๊ธฐ์ด์ ๊ธฐ์ด ์นํฉ ์์์ ๊ฐ๊ฐ 8.52 dBA(2์ฐจ ํ๋ชจ๋), 4.52 dBA(1์ฐจ ํ๋ชจ๋), 9.95 dBA(1์ฐจ ํ๋ชจ๋) ๊ฐ์ ๋์๋ค. ๋ํ ๋ณ์๊ธฐ ์์คํ
๊ณต์ง์ ๊ณผ ๊ธฐ์ด ์ฑ๋ถ์ ๊ต์ฐจํ ๋์ ์์์์ค์ ๋น๊ตํ ๊ฒฐ๊ณผ, ์ต์ ํ ๋์๊ธฐ์ด์ ์์์ด ํจ๊ณผ์ ์ผ๋ก ์ ๊ฐ๋๋ ๊ฒ์ ํ์ธํ์๋ค.
๋ฐ๋ผ์ ๊ธฐ์ด ๋งคํฌ๋ก ์ ์ ์ต์ ํ๋ฅผ ํตํด ์ฌ๋ฌ ๊ธฐ์ด ์ฑ๋ถ์ด ๋ณตํฉ์ ์ผ๋ก ๊ธฐ์ฌํ ๋ณ์๊ธฐ ์ ์ฒด์์๊ณผ ๊ธฐ์ด ํ๋ชจ๋ ์ฑ๋ถ์ ์์ ๋ชจ๋ ํจ๊ณผ์ ์ผ๋ก ์ ๊ฐ๋จ์ ํ์ธํ์๋ค. ๋ฐ๋ผ์ ๋ถํ๊ฐ ๋น๋ฒํ๊ฒ ๋ณ๋ํ๋ฉฐ, ์์
์ ๋ฐ๋ผ ๋ค์ํ ๋ถํ ์์ค์ด ์ธ๊ฐ๋๋ ๋์
์ฉ ํธ๋ํฐ ๋ณ์๊ธฐ์ ์์ ์ ๊ฐ์ ์ํด ๊ธฐ์ด ๋งคํฌ๋ก ์ ์ ์ต์ ํ ๊ธฐ๋ฒ์ด ์ ํจํจ์ ๋ณธ ์ฐ๊ตฌ๋ฅผ ํตํด ํ์ธํ์๋ค.Agricultural tractors have various noise sources such as engines, transmissions, and hydraulic systems, and it was traditionally known that the engine was the biggest noise source. However, the transmission noise that had been relatively masked by the development of engine design technology has become an issue. The power transmission system of the agricultural tractor transmission was divided into a PTO driveline and a transmission that transmits power to the driving wheel. The engine-direct PTO system, which is mainly applied recently, has good power transmission efficiency because it is directly connected to the engine, but research on this is insufficient even though it was a structure that was vulnerable to vibration/noise. In addition, gear whine noise generated by transmission errors was a major issue in the transmission that was transmitted to the drive wheels. However, systematic research for the design of low-noise transmission considering gear transmission errors was insufficient. In this study, the purpose of this study was to reduce the gear rattle noise of the PTO driveline and the gear whine noise of the transmission to improve the noise of the transmission of the agricultural tractor. Since gear rattle noise and gear meshing noise have different causes and mechanisms, the study was conducted by dividing them into two parts.
The first part of the study, the factors affecting the gear rattle noise were analyzed to reduce noise of the PTO driveline. Among the various influencing factors, it was confirmed that non-linear stiffness components such as spline coupling with backlash and torsional dampers were components that can cause sudden changes in the dynamic behavior of the rotation system. A test was conducted to determine the effect of non-linear stiffness components on dynamic behavior and gear rattle noise in a PTO driveline composed of several spline couplings. The test was constructed in an environment in which other noises except for the gear rattle noise were removed, and a motor control system capable of imitating an engine was composed. The change in dynamic behavior was confirmed by measuring the rotation speed of the PTO driving gear to which rotational vibration was transmitted through the input shaft and several spline couplings, and at the same time, the gear rattle noise was also measured. Through a lab. test performed at 700 ~ 1,000 rpm, which was the engine idle rotation speed range, jumping phenomenon, which was a sudden change in dynamic behavior at 920 rpm, was occurred, and the gear rattle noise was also reduced by 10.9 dBA. Through the lab. test, the dynamic behavior characteristics of the PTO driveline in the engine idle rotation speed range were largely divided into three range. The first range was an over-response range in which the rotational speed fluctuation of the PTO driving gear relative to the input shaft was amplified, and the second range was a transient-response range at the moment when a jumping phenomenon occurs. The third range was a low-response range in which the amplification level of the rotational speed fluctuation was kept low, and the gear rattle noise was also maintained at a relatively low level.
A 1D simulation reflecting the spline backlash was performed to analyze the cause of the jumping phenomenon. It was confirmed through simulation that the rotation speed fluctuation value of the inner and outer teeth of the spline was amplified by two-way collision in the over-response range. On the other hand, in the low-response range, the inner and outer teeth of the spline had a one-way collision, and the phenomenon of amplification of the rotational speed fluctuation did not appear. In addition, the effect of the change in the level of the spline backlash, which is the cause of the jumping phenomenon, was additionally reviewed through simulation. Among the splines applied in the simulation model, the spline backlash between the hub shaft and the friction plate of the PTO clutch, which has the largest backlash level, was changed, and 0.1 mm and 0.9 mm, which were manufacturable levels, were selected based on the basic backlash level of 0.50 mm. When the spline backlash was 0.10 mm and 0.50 mm, a two-way collision occurred, and when the spline backlash was 0.90 mm, the 2nd component due to engine excitation did not increase, and a one-way collision, which is the collision behavior of the low-response range, appeared.
An additional experimental investigation was performed to experimentally confirm the simulation results in which the dynamic behavior changes according to the level of spline backlash. A lab. test was performed by adjusting the backlash of the spline coupling between the friction plate of the PTO clutch and the hub shaft by 0.10 mm (Type A), 0.50 mm (Type B), 0.90 mm (Type C), and 1.30 mm (Type D). In the Lab. test where Type A was applied, no jumping phenomenon occurred, and as the spline backlash level increased, the rotation speed at which the jumping phenomenon occurred gradually decreased. The rotation speed at which the gear rattle noise was rapidly reduced by the dynamic behavior change due to the spline backlash level was also lowered.
Through the study of the first part, it was found that the engine idle rotational speed should be set in the low-response range where the noise level was kept relatively low in order to reduce the PTO gear rattle noise. In addition, by increasing the level of the spline backlash on the vibration transfer path of the PTO driveline in order to reduce the gear rattle noise, the rotational speed at which the engine idle rotational speed can be set was also lowered.
In the second part on the study, a gear design technique that can minimize the transmission error, which was the main cause, was studied in order to reduce the gear whine noise of the transmission. In order to reduce the gear transmission error, the gear macro-geometry optimization technique was used in consideration of the load characteristics and the manufacturing environment applied to the transmission. The gear macro-geometry optimization was performed on the F gear pair of the forward/reverse shift part of the tractor subject to this study, and the main 1st, 2nd and 4th gear pairs of main shift part. The objective function of gear macro-geometry optimization was set as gear transmission error and efficiency, and a genetic algorithm (NSGAโ
ข) was used. A test equipment was built to confirm the noise reduction level of the gear specifications derived through gear macro-geometry optimization.
In the test, other noise sources such as hydraulic noise were controlled using a 3-axis dynamometer, and the test was performed in consideration of load levels of 20% and 50% of the rated engine torque in the 10th, 11th, and 16th gears, which are the most frequently used gears was performed. As a result of the noise measurement, when a load of 50% of the engine rated torque was input to the transmission, the overall noise level of the transmission was improved by 2.99 dBA, 3.90 dBA, and 3.31 dBA in the 10th, 11th, and 16th gears, respectively. When the load level applied to the transmission was 20% of the engine rated torque, overall noise level was reduced by 3.30 dBA, 2.21 dBA, and 2.42 dBA in the 10th, 11th, and 16th gears, respectively. As a result, it was confirmed that gear whine noise was reduced for the two load conditions through gear macro-geometry optimization.
Order tracking analysis was performed to compare the noise level of gear harmonic components, and the level of noise reduction was confirmed in the engine operating rotation speed range. Among the harmonic components of the forward gear, the noise level of the 2nd harmonic component was the highest and was reduced by 5.13 dBA through optimization. Also, the noise level of the 1st, 2nd, and 4th gear pairs of main shift part was improved by 8.52 dBA (2nd harmonic), 4.52 dBA (1st harmonic), and 9.95 dBA (1st harmonic), respectively. In addition, as a result of comparing the noise level at the intersection of the resonance region of the transmission system and the gear component in the test, it was confirmed that noise was effectively reduced through optimization.
Therefore, it was confirmed that through the gear macro-geometry optimization, not only the overall noise of the transmission, which was contributed by various gear components, but also the noise of the gear harmonic component was effectively reduced. Therefore, it was confirmed through this study that the gear macro-geometry optimization technique was effective to reduce the noise of the agricultural tractor transmission, which has severe load fluctuations and various load levels are applied according to agricultural operation.์ 1 ์ฅ ์ ๋ก 1
1.1. ์ฐ๊ตฌ ๋ฐฐ๊ฒฝ 1
1.2. ์ฐ๊ตฌ ๋ชฉ์ 5
1.3. ๋ฌธํ ์ฐ๊ตฌ 8
1.3.1. ๊ธฐ์ด ๋ํ ์์ 8
1.3.2. ๊ธฐ์ด ์นํฉ ์์ 12
์ 2 ์ฅ ํธ๋ํฐ ์์ ์์ค ๋น๊ต 17
2.1. ์ฐ๊ตฌ ๋์ ํธ๋ํฐ 17
2.2. ๊ธฐ์ด ๋ํ ์์ ์์ค ๋น๊ต 18
2.2.1. ์ํ ๋ฐฉ๋ฒ 18
2.2.2. ๊ณ์ธก ๊ฒฐ๊ณผ ๋ถ์ 20
2.3. ๊ธฐ์ด ์นํฉ ์์ ์์ค ๋น๊ต 22
2.3.1. ์์ ๊ณ์ธก ๋ฐฉ๋ฒ 22
2.3.2. ์์ ๊ณ์ธก ๊ฒฐ๊ณผ ๋ถ์ 24
์ 3 ์ฅ ํธ๋ํฐ PTO ์ ๋๋ผ์ธ์ ๊ธฐ์ด ๋ํ ์์ ์ ๊ฐ 27
3.1. PTO ์ ๋๋ผ์ธ ๊ฐ์ 27
3.1.1. PTO ์ ๋๋ผ์ธ์ ๊ธฐ๋ณธ ๊ตฌ์ฑ 27
3.1.2. ์ฐ๊ตฌ๋์ PTO ์ ๋๋ผ์ธ 29
3.1.3. ๊ธฐ์ด ๋ํ ์์ ์ํฅ ์ธ์ 32
3.2. ์ํ ๊ธฐ๋ฐ ์คํ๋ผ์ธ ๋ฐฑ๋์์ ์ํฅ ํ์ธ 35
3.2.1. ์ํ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์ 35
3.2.2. ์ํ ํ๊ฒฝ ๊ตฌ์ฑ ๋ฐ ๊ณํ 37
3.2.3. ์ํ ๊ฒฐ๊ณผ ๊ฒฐ๊ณผ 39
3.3. ์ ํ ํ์์ ์ฃผ์ ์ธ์ ๋ถ์์ ์ํ ์๋ฎฌ๋ ์ด์
44
3.3.1. ์๋ฎฌ๋ ์ด์
๋ชจ๋ธ ๊ตฌ์ฑ 44
3.3.2. ์๋ฎฌ๋ ์ด์
๊ฒฐ๊ณผ 48
3.3.2.1 ์คํ๋ผ์ธ ๋ฐฑ๋์์ ์ํฅ 48
3.3.2.2 ์คํ๋ผ์ธ ๋ฐฑ๋์์ ํฌ๊ธฐ์ ๋ฐ๋ฅธ PTO ์ ๋๋ผ์ธ์ ๋์ ๊ฑฐ๋ ํด์ 60
3.4. ์ํ ๊ธฐ๋ฐ ์คํ๋ผ์ธ ๋ฐฑ๋์ ํฌ๊ธฐ์ ์ํฅ ๋ถ์ 69
3.4.1. ์ํ ๊ณํ 69
3.4.2. PTO ์ ๋๋ผ์ธ์ ๋์ ๊ฑฐ๋ ๋ถ์ 72
3.4.3. ๊ณ ์ฐฐ ๋ฐ ๋ถ์ 80
3.5. ๊ฒฐ๋ก ๋ฐ ๊ณ ์ฐฐ 86
3.5.1. ๊ณผ์๋ต ๊ตฌ๊ฐ(Phase โ
)์ ์๋ต ๋ถ์ 86
3.5.2. ๊ณผ๋์๋ต ๊ตฌ๊ฐ(Phase โ
ก)์ ์๋ต ๋ถ์ 92
3.5.3. ์ ์๋ต ๊ตฌ๊ฐ(Phase โ
ข)์ ์๋ต ๋ถ์ 99
3.5.4. ์ฐ๊ตฌ ๊ฒฐ๊ณผ ํ์ฉ 103
์ 4 ์ฅ ํธ๋ํฐ ์ฃผํ ๋ณ์๋ถ์ ๊ธฐ์ด ์นํฉ ์์ ์ ๊ฐ 105
4.1. ์ฃผํ ๋ณ์๋ถ์ ๊ธฐ์ด ์นํฉ ์์ 105
4.1.1. ๊ธฐ์ด ์นํฉ ์์ ์์ธ 105
4.1.2. ๊ธฐ์ด ์ ๋ฌ์ค์ฐจ 105
4.1.3. ๊ธฐ์ด ์นํฉ ์์ ์ ๊ฐ ๋ฐฉ์ 108
4.2. ๊ธฐ์ด ๋งคํฌ๋ก ์ ์ ์ค๊ณ ์ต์ ํ 109
4.2.1. ์ฐ๊ตฌ๋์ ํธ๋ํฐ 109
4.2.2. ๊ธฐ์ด ์ ๋ฌ์ค์ฐจ ๊ณ์ฐ 111
4.2.3. ๊ธฐ์ด ํจ์จ ๊ณ์ฐ 116
4.2.4. ๊ธฐ์ด ๋งคํฌ๋ก ์ ์์ ์ต์ ํ 118
4.3. ์ค๋ด์ํ : ๊ธฐ์ด ์นํฉ ์์ ์ ๊ฐ ํ์ธ 127
4.3.1. ์ํ ์ฅ์น ๊ตฌ์ฑ 127
4.3.2. ์ํ ๊ณํ 129
4.4. ๊ธฐ์ด ์นํฉ์์ ๊ณ์ธก ๊ฒฐ๊ณผ 132
4.4.1. ๋ณ์๊ธฐ ์ ์ฒด ์์(Overall noise level) ๋น๊ต 132
4.4.2. ์ฐจ์๋ถ์(Order tracking analysis) 137
4.4.3. ๊ธฐ์ด ์นํฉ ์์ ์์ค ๋น๊ต 147
4.4.3.1 ์ ์ง ๊ธฐ์ด 148
4.4.3.2 ์ฃผ๋ณ์ 1๋จ ๊ธฐ์ด 155
4.4.3.3 ์ฃผ๋ณ์ 2๋จ ๊ธฐ์ด 158
4.4.3.4 ์ฃผ๋ณ์ 4๋จ ๊ธฐ์ด 162
4.5. ๊ฒฐ๋ก ๋ฐ ๊ณ ์ฐฐ 166
์ 5 ์ฅ ๊ฒฐ ๋ก 168
์ฐธ๊ณ ๋ฌธํ 174
๋ถ๋ก A 178
๋ถ๋ก B 184
Abstract 193๋ฐ
Data driven techniques for on-board performance estimation and prediction in vehicular applications.
L'abstract eฬ presente nell'allegato / the abstract is in the attachmen
Component Optimization of a Parallel P4 Hybrid Electric Vehicle Utilizing an Equivalent Consumption Minimization Strategy
Advancements in battery and electric motor technology have driven the development of hybrid electric vehicles to improve fuel economy. Hybrid electric vehicles can utilize an internal combustion engine and an electric motor in many configurations, requiring the development of advanced energy management strategies for a range of component configurations. The Equivalent Consumption Minimization Strategy (ECMS) is an advanced energy management strategy that can be calculated in-vehicle in real-time operation. This energy management strategy uses an equivalence factor to equate electrical to mechanical power when performing the torque split determination between the internal combustion engine and electric motor. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimized fuel economy results, while maintaining a target state of charge of the battery. The goal of this work is to analyze how the algorithm operates with the WVU Chevy Blazer to find an optimal equivalence factor that can maintain a strict charge sustaining window of operation for the high voltage battery, while improving the fuel economy based on dynamic programing results calculated for this vehicle architecture. Different electric motor sizes are then explored by changing the max torque and max power to analyze how the equivalence factor changes to operate the ECMS algorithm. This research mainly focused on utilizing both the UDDS drive cycle and HwFET drive cycle to determine the effectiveness of the ECMS algorithm. The results show that as the max torque and max power of the electric motor increased, the equivalence factor found for the UDDS drive cycle and the HwFET drive cycle converged to similar value. The convergence of the equivalence factor allowed the ECMS algorithm to better maintain the target state of charge of the battery while maintaining the fuel economy and improving the fuel economy for the UDDS drive cycle and HwFET drive cycle, respectively
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