Application of dynamic band brake model for enhanced drivetrain simulation

2002 ◽  
Vol 216 (11) ◽  
pp. 873-881 ◽  
Author(s):  
Y Fujii ◽  
W E Tobler ◽  
E M Clausing ◽  
T W Megli ◽  
M Haghgooie
Keyword(s):  
Automatic Transmission ◽  
Planetary Gear ◽  
Operating Conditions ◽  
Computationally Efficient ◽  
Vehicle Performance ◽  
Shift Quality ◽  
Friction Component ◽  
Experimental Approaches ◽  
And Control ◽  
Analytical Tools

In a modern vehicle design process, analytical tools are widely employed to complement experimental approaches for design evaluation. When effectively utilized, they lead to a reduced development time with improved vehicle performance. The development process of an automatic transmission (AT) system can benefit from an analytical representation which accurately captures AT shifting behaviours. In a typical AT system, friction components such as wet clutches and band brakes are utilized to alter planetary gear configurations for automatic shifting. Thus, an accurate representation of friction component dynamics is critical in predicting AT shifting behaviour. Engagement characteristics of friction components vary widely under different operating conditions. Although the basic engagement physics was identified in the 1970s, it is relatively recently that a predictive, yet computationally efficient model became available. This paper describes the first attempt to utilize a dynamic friction component model in drivetrain simulations. Specifically, a dynamic band brake model is implemented to predict the up-shift behaviour of a four-speed AT system under various operating conditions. Simulation results are qualitatively validated with experimental data obtained from a dynamometer test stand. The dynamic band brake model enhances the shift predictability of a drivetrain model and potentially allows analytical evaluation of shift quality and control strategy.

Automatic Transmission Shift Calibration Based on Parallel-Modulated Neural Network (PMNN) Friction Component Model

2004 ◽  
Author(s):  
M. Cao ◽  
K. W. Wang ◽  
Y. Fujii ◽  
W. E. Tobler
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Automatic Transmission ◽  
Applied Pressure ◽  
Operating Conditions ◽  
Component Model ◽  
Powertrain System ◽  
Friction Component ◽  
Wide Range ◽  
Shift Dynamics

The parallel-modulated-neural-network (PMNN) -based friction component model [19] provides a simple pressure-torque formula, which possesses much improved scalability with respect to the applied pressure. In this paper, the PMNN friction component model is implemented within a comprehensive powertrain model, to simulate the shifting process of an automatic transmission (AT) system under various operating conditions. Simulation results demonstrate that the PMNN model can be effectively applied as a part of powertrain system model to accurately predict transmission shift dynamics. A pressure-profiling scheme through a quadratic polynomial pressure-torque relationship from the PMNN model is developed for the transmission shifting optimization. This scheme is implemented to improve the transmission shifting quality under certain operating conditions. The pressure profiling results illustrate that the proposed pressure profiling technique can be potentially applied to a wide range of operating conditions. This study demonstrates that the PMNN architecture not only outperforms the conventional network modeling techniques in accuracy and numerical efficiency, but is also a new tool for AT controller design.

A heuristic synthesis of multistage planetary gearbox layout for automotive transmission

2018 ◽  
Vol 233 (2) ◽  
pp. 336-347 ◽  
Author(s):  
Debraj Bhattacharjee ◽  
Prabha Bhola ◽  
Pranab K Dan
Keyword(s):  
Power Efficiency ◽  
High Speed ◽  
Power Flow ◽  
Degrees Of Freedom ◽  
Random Search ◽  
Automatic Transmission ◽  
Heuristic Method ◽  
Planetary Gear ◽  
Planetary Gearbox ◽  
Shift Quality

The automatic transmission system is very crucial for the high-speed vehicles, where the planetary or epicyclic gearbox is a standard feature. With the increase in design intricacies of planetary gearbox, mathematical modelling has become complex in nature and therefore there is a need for modelling of multistage planetary gearbox including the shifting scheme. A random search-based synthesis of three degrees of freedom (DOF) high-speed planetary gearbox has been presented in this paper, which derives an efficient gear shifting mechanism through designing the transmission schematic of eight speed gearboxes compounded with four planetary gear sets. Furthermore, with the help of lever analogy, the transmission power flow and relative power efficiency have been determined to analyse the gearbox design. A simulation-based testing and validation have been performed which show the proposed model is efficient and produces satisfactory shift quality through better torque characteristics while shifting the gears. A new heuristic method to determine suitable compounding arrangement, based on mechanism enumeration, for designing a gearbox layout is proposed here. An important finding on automotive gear shift quality due to closer gear ratio is also reported in this work.

Development of a Friction Component Model for Automotive Powertrain System Analysis and Shift Controller Design based on Parallel-Modulated Neural Networks

2004 ◽  
Vol 127 (3) ◽  
pp. 382-405 ◽  
Author(s):  
M. Cao ◽  
K. W. Wang ◽  
Y. Fujii ◽  
W. E. Tobler
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Automatic Transmission ◽  
Analytical Data ◽  
Operating Conditions ◽  
Component Model ◽  
System Model ◽  
Powertrain System ◽  
Friction Component ◽  
Wide Range

In this study, a new hybrid-neural-network-based friction component model is developed for powertrain (PT) dynamic analysis and controller design. This new model, with significantly improved input-output scalability over conventional neural network configuration, has the capability to serve as a forward as well as an inverse system model. The structural information of the available physical and empirical correlations is utilized to construct a parallel-modulated neural network (PMNN) architecture consisting of small parallel sub-networks reflecting specific mechanisms of the friction component engagement process. The PMNN friction component model isolates the contribution of engagement pressure on engagement torque while identifying the nonlinear characteristics of the pressure-torque correlation. Furthermore, it provides a simple torque formula that is scalable with respect to engagement pressure. The network is successfully trained, tested and analyzed, first using analytical data at the component level and then using experimental data measured in a transmission system. The PMNN friction component model, together with a comprehensive powertrain model, is implemented to simulate the shifting process of an automatic transmission (AT) system under various operating conditions. Simulation results demonstrate that the PMNN model can be effectively applied as a part of powertrain system model to accurately predict transmission shift dynamics. A pressure-profiling scheme using a quadratic polynomial pressure-torque relationship of the PMNN model is developed for transmission shift controller design. The results illustrate that the proposed pressure profiling technique can be applied to a wide range of operating conditions. This study demonstrates the potential of the PMNN architecture as a new dynamic system-modeling concept: It not only outperforms the conventional network modeling techniques in accuracy and numerical efficiency, but also provides a new tool for transmission controller design to improve shift quality.

Efficient Exhaustive Search of Power-Split Hybrid Powertrains With Multiple Planetary Gears and Clutches

2015 ◽  
Vol 137 (12) ◽  
Author(s):  
Xiaowu Zhang ◽  
Shengbo Eben Li ◽  
Huei Peng ◽  
Jing Sun
Keyword(s):  
Planetary Gear ◽  
Optimal Designs ◽  
Computationally Efficient ◽  
Energy Management Strategy ◽  
Screening Process ◽  
Automated Modeling ◽  
Operating Modes ◽  
Hybrid Powertrains ◽  
Power Split ◽  
And Control

Planetary gear (PG) power-split hybrid powertrains have been used in producing hybrid and plug-in hybrid vehicles from the Toyota, General Motor, and Ford for years. Some of the most recent designs use clutches to enable multiple operating modes to improve launching performance and/or fuel economy. Adding clutches and multiple operating modes, however, also increases production cost and design complexity. To enable an exhaustive but fast search for optimal designs among a large number of hardware configurations, clutch locations, and mode selections, an automated modeling and screening process is developed in this paper. Combining this process with the power-weighted efficiency analysis for rapid sizing method (PEARS), an optimal and computationally efficient energy management strategy, the extremely large design space of configuration, component sizing, and control becomes feasible to search through. This methodology to identify optimal designs has yet to be reported in the literature. A case study to evaluate the proposed methodology uses the configuration adopted in the Toyota Hybrid Synergy (THS-II) system used in the Prius model year 2010 and the Hybrid Camry. Two designs are investigated to compare with the simulated Prius design: one uses all possible operating modes; and the other uses a suboptimal design that limits the number of clutches to three.

scholarly journals Gold–Carbon Nanocomposites for Environmental Contaminant Sensing

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 719
Author(s):  
Shahrooz Rahmati ◽  
William Doherty ◽  
Arman Amani Babadi ◽  
Muhamad Syamim Akmal Che Mansor ◽  
Nurhidayatullaili Muhd Julkapli ◽  
...  
Keyword(s):  
Environmental Pollutants ◽  
High Sensitivity ◽  
Environmental Crisis ◽  
World Population ◽  
Chemical Contaminants ◽  
Carbon Nanocomposites ◽  
Minimal Sample ◽  
Wide Range ◽  
And Control ◽  
Analytical Tools

The environmental crisis, due to the rapid growth of the world population and globalisation, is a serious concern of this century. Nanoscience and nanotechnology play an important role in addressing a wide range of environmental issues with innovative and successful solutions. Identification and control of emerging chemical contaminants have received substantial interest in recent years. As a result, there is a need for reliable and rapid analytical tools capable of performing sample analysis with high sensitivity, broad selectivity, desired stability, and minimal sample handling for the detection, degradation, and removal of hazardous contaminants. In this review, various gold–carbon nanocomposites-based sensors/biosensors that have been developed thus far are explored. The electrochemical platforms, synthesis, diverse applications, and effective monitoring of environmental pollutants are investigated comparatively.

Unified modelling and control for the power-on gear shift of a planetary transmission

2017 ◽  
Vol 232 (7) ◽  
pp. 958-972
Author(s):  
Erlie Wang ◽  
Huiyan Chen ◽  
Gang Tao ◽  
Xianhui Wang ◽  
Hongliang Wang
Keyword(s):  
Full Range ◽  
Automatic Transmission ◽  
Time Estimation ◽  
Computational Method ◽  
Dissipated Energy ◽  
Hydraulic Pressure ◽  
Characteristic Analysis ◽  
Loop Control ◽  
Gear Shift ◽  
Shift Quality

Estimation of the oil hydraulic pressure for the gear-shift elements can be useful for the development of closed-loop control of the automatic transmission fitted to a heavy off-highway vehicle for a good gear-shift quality, to reduce the dissipated energy and the vehicular shift jerk in complex working conditions. The unified dynamic model for a three-degree-of-freedom planetary automatic transmission is presented, and the power-on upshift from first gear to second gear is considered as an example. The unified model is more efficient than the conventional model for the dynamic analysis; furthermore, it provides a computational method for the inertia of the transmission when in gear. From a phased characteristic analysis, real-time estimation of the oil pressure for the gear-shift elements in the sliding process, i.e. the torque phase and the inertia phase, is addressed; then the improved control scheme for the power-on upshift from first gear to second gear is developed and validated using a heavy off-highway vehicle equipped with a high-power full-range speed-regulating diesel engine. The experimental results show that the model-based oil pressure estimation is able to reflect the dynamic characteristics of the system in changing conditions, and the corresponding control strategy can improve the gear-shift quality and the vehicular performance effectively.

Overview of Path-Planning and Obstacle Avoidance Algorithms for UAVs: A Comparative Study

2018 ◽  
Vol 06 (02) ◽  
pp. 95-118 ◽  
Author(s):  
Mohammadreza Radmanesh ◽  
Manish Kumar ◽  
Paul H. Guentert ◽  
Mohammad Sarim
Keyword(s):  
Path Planning ◽  
Comparative Study ◽  
Operating Conditions ◽  
Computational Time ◽  
Safe Operation ◽  
Sense And Avoid ◽  
Planning Algorithms ◽  
And Control ◽  
Global And Local ◽  
Further Development

Unmanned aerial vehicles (UAVs) have recently attracted the attention of researchers due to their numerous potential civilian applications. However, current robot navigation technologies need further development for efficient application to various scenarios. One key issue is the “Sense and Avoid” capability, currently of immense interest to researchers. Such a capability is required for safe operation of UAVs in civilian domain. For autonomous decision making and control of UAVs, several path-planning and navigation algorithms have been proposed. This is a challenging task to be carried out in a 3D environment, especially while accounting for sensor noise, uncertainties in operating conditions, and real-time applicability. Heuristic and non-heuristic or exact techniques are the two solution methodologies that categorize path-planning algorithms. The aim of this paper is to carry out a comprehensive and comparative study of existing UAV path-planning algorithms for both methods. Three different obstacle scenarios test the performance of each algorithm. We have compared the computational time and solution optimality, and tested each algorithm with variations in the availability of global and local obstacle information.

Development of 2 Stage CVT Shift Control Algorithm to Reduce Torque Variation During 1-2 Upshift of Planetary Gear

2012 ◽  
Author(s):  
Jeongman Park ◽  
Sunghyun Ahn ◽  
Oheun Kwon ◽  
Youngho Jun ◽  
Minhyo Kim ◽  
...  
Keyword(s):  
Control Algorithm ◽  
Planetary Gear ◽  
Gear Ratio ◽  
Shift Quality ◽  
Response Characteristics ◽  
Shift Control ◽  
Variable Transmission ◽  
Vehicle Simulator ◽  
Simulation Results ◽  
Fuzzy Control Algorithm

In this paper, a 2 stage continuously variable transmission (CVT) shift control algorithm is proposed for the 1–2 upshift of the planetary gear to achieve the shift quality. A fuzzy control algorithm is designed considering the relatively slower response characteristics of CVT. In order to evaluate the performance of the control algorithm, a 2 stage CVT vehicle simulator is developed including a dynamic model of the CVT powertrain. From the simulation results, it is found that CVT gear ratio changes faster in the inertia phase and remains constant after the inertia phase of the planetary gear shift, which provides the reduced torque variation by the proposed control algorithm.

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