Automatic Transmission Vehicle Research Articles

Intelligent gear decision method for vehicle automatic transmission system based on data mining

The gear decision logic of automatic transmission directly affects the vehicle's dynamic, fuel economic, and comfort performance. This study employs data mining techniques to address the issues of low adaptability and low recognition rate in the intelligent gear decision of vehicle automatic transmission systems. The research further proposes the utilization of Kalman filter, Hidden Markov Models, and Long Short-Term Memory networks for condition feature recognition and time series classification. Subsequently, dynamic programming algorithms are employed to optimize intelligent gear decisions. Combining driver intent and driving environment, an intelligent gear decision method is formulated. The results indicate that, during a 430 s driving segment, the intelligent gear decision method consumes only 464 mL of fuel, closely resembling the economic strategy's 457 mL, with a gear shift frequency of 53, significantly better than the 79 shifts in the economic strategy. Moreover, the error rate for slope condition recognition is only 0.062 %. In a 200 s coupled condition, the intelligent gear decision results in fuel consumption of 207 mL, approximating the actual vehicle's 219 mL, while power-shifting consumes 316 mL, and economic shifting only 202mL. This study not only improves the accuracy of gear decisions but also effectively enhances vehicle operational efficiency, providing valuable insights for future automatic transmission systems with significant practical value.

Investigation on steady state performance of hydrodynamic automatic transmission vehicle

The aim of this research is to glance the feasible scientific theory and performance investigations of steady-state hydraulic automatic transmission vehicle. Numerous studies that inform about new powertrain designs in steady-state conditions look at how well manual transmission vehicles operate. However, one of the most cutting-edge of conventional automotive vehicle power transmission for modern automobile designs is hydraulic automatic transmission. Consequently, in this research the realistic methodical scheme is achieved using classical optimization techniques, utilizing spatial case functions that signify engine and torque convertor performance curve in automatic transmission vehicle. The advantage is to test the vehicle performance that meets the experimental or numerical design enactment considering the exceptional correlation coefficient obtained at global extreme. Analysis comprises, engine's joint work with hydraulic torque converter examined in relation to vehicle speediness, observing the matching between engine and convertor based on the fundamental operational performances resembling, full use of engine power, economic fuel consumption, and the requirements of vehicle operation. Finally the vehicle powertrain feasibility is confirmed by, the maximum speed of the vehicle and fuel consumption in extra urban driving 274.5 km/hr and 5.5 L/100 km respectively. This shows, there is 4 % and 7 % deviation from vehicle evidence speed and fuel consumption respectively.

Dynamics modeling and analysis of the multistage planetary gear set-bearing-rotor-clutch coupling system considering the tooth impacts of clutches

Previous studies about the dynamics of the planetary gear system are mainly focused on transmission systems with a fixed transmission ratio, whereas the dynamic studies of vehicle automatic transmission with multiple transmission ratios achieved by engaging different clutches were seldom investigated. This is largely due to the lack of modeling strategies for the stochastic tooth impacts of detached clutches on the dynamics of transmission systems. Ignoring the stochastic tooth impacts on the system’s performance will result in inaccurate analysis that may overestimate the machine’s service performance. In this paper, an original dynamic model of the multistage planetary gear set-bearing-rotor-clutch coupling system with multi-shift and multi-speed transmission is established considering the tooth impacts of detached clutches. An embedded measurement method is developed to measure the in-situ torsional vibration responses of rotating parts under various shift and speed conditions, which are directly compared to simulated responses of the proposed model for validation purposes. Finally, the effects of the rotating speed and clutch’s tooth backlash on the random tooth impact characteristics of clutches are revealed. The modeling strategy of this work provides a more accurate and realistic simulation of automatic transmission’s dynamic performance and supplies theoretical guidance for the strength evaluation and early fatigue failure analysis between mating teeth of clutches.

Data Mining Approach for the Identification of the Intelligent Gear Decision Strategy for the Stepped Automatic Transmission

The primary goal of automatic transmission vehicle design and optimization is to ensure that drivers can operate automatic transmission vehicles with the same driving experience as skilled drivers driving manual transmission vehicles. However, the existing gear decision strategies of stepped automatic transmission vehicles have poor adaptability to different driving intentions and complex driving environments. When operating manual transmission vehicles, skilled drivers can make intelligent gear decision to satisfy their driving intentions and adapt to different driving environments. Therefore, this study's objective is to establish the design and formulation process for the identification of the intelligent gear decision strategy for the stepped automatic transmission that adapts to different driving intentions and complex driving environments. This is achieved using the data mining method to identify skilled drivers' gear decision strategy from their driving data. To this end, the driving data of skilled drivers operating manual transmission vehicles are collected. The shift boundary points of each gear under the target driving condition are obtained through data preprocessing, data cleaning, outlier processing, and boundary points extraction. Thereafter, the gear decision strategy of skilled drivers is established by fitting the shift boundary points. Taking the flat-straight road condition as an example, the gear decision strategy is established and experimental comparisons are made to verify the effectiveness of proposed gear decision design method.

Design of automotive mechanical automatic transmission system based on torsional vibration reduction

Automatic transmission system is the core part of vehicle transmission processing, which can improve driving safety. In order to improve the shift effect of automotive automatic mechanical transmission and narrow the gap between the vehicle speed and the expected speed, an automotive automatic mechanical transmission system based on torsional damping was designed in the experiment. On the basis of hardware composed of different modules and fuzzy control algorithm, the system realizes the software design of vehicle automatic mechanical transmission system. The experimental results show that when the system is applied in practice, the gear selection time of the vehicle is between 0.2 s-0.3 s, and the gear shift time is between 0.3 s-0.4 s. The gap between the vehicle speed and the expected speed, and between the vehicle speed and the expected speed is small. The practical application effect is good.

Intelligent Gear Decision Method for Automatic Vehicles Based on Data Mining Under Uphill Conditions

Existing gear decision methods are affected by the limiting factors of deviation of numerical models as well as the subjectivity of the designer, which results in poor adaptability to the driving intentions and driving environments. Excellent drivers are able to choose the appropriate gear to satisfy their driving intentions and respond to changes in driving environments when driving manual transmission vehicles. Therefore, an intelligent gear decision method and design methodology of vehicle automatic transmission system that adapts to different driving intentions and complex driving environments are constructed from the massive driving data of excellent drivers by using data mining method. Excellent drivers are employed to drive vehicles with manual transmission to obtain a large amount of driving data. Subsequently, data preprocessing, data cleaning, and outlier removal are performed on the collected driving data to extract the shift boundary points of each gear aiming at constructing the shift rule surface of each gear. Using the uphill condition as an example, a data-mining-based shift control strategy is established, and the comparison results verify that the proposed gear decision design method can mine the shift strategy of excellent drivers from massive driving data, and the constructed strategy can better adapt to the driver’s intention while obtaining better fuel economy and avoiding unreasonable gearshifts compared with the automatic transmission’s default strategy.

Stability analysis and development of gear shift patterns for an eight speed automatic transmission vehicle

The increasing stringency of exhaust emission regulations, as well as the rising index of carbon dioxide emissions from the road transport, has driven auto manufacturers and designers to develop solutions for even more efficient vehicles, with well-designed vehicle transmission and gear shift patterns among the alternative approaches used. The current article proposes a design method for developing an appropriate gear ratio set and gear shift patterns for an eight-speed automatic transmission. The BMW M4 Eight speed transmission model was developed using the MATLAB/Simulink platform, and the results were validated using experiment data. The effectiveness of the previously chosen gear ratio set and gear shift patterns was then assessed using a eight-speed transmission model based on the validated six speed transmission model. Based on a mathematical simulation approach, this article presents a novel microscopic model to illustrate the traffic phenomenon. The stability of the proposed model is investigated using the perturbation technique in order to analyze it and determine its stability conditions. The mathematical experimental tests were carried out, and the results reveal that the proposed model is valid. Patterns for sport mode, eco mode, and an accomplished by two modes were analyzed and the results.

Automatic Clutch Engagement Control for Parallel Hybrid Electric Vehicle

Automatic clutch engagement control is essential for all kinds of vehicle power transmissions. The controllers for vehicle power transmissions may include model-based or model-free approaches and must provide high transmission efficiency, fast engagement and low jerk. Most vehicle automatic transmissions are using torque converters with transmission efficiencies up to 96%. This paper presents the use of fuzzy logic control for a dry clutch in parallel hybrid electric vehicles. This controller can minimize the loss of power transmission since it can offer a higher transmission efficiency, up to 99%, with faster engagement, lower jerk and, thus, higher driving comfortability with lower cost. Fuzzy logic control is one of the model-free schemes. It can be combined with AI algorithms, neuro networks and virtual reality technologies in future development. Fuzzy logic control can avoid the complex modelling while maintaining the system’s high stability amid uncertainties and imprecise information. Experiments show that fuzzy logic can reduce the clutch slip and vibration. The new system provides 2% faster engagement speed than the torque converter and eliminates 70% of noise and vibration less than the manual transmission clutch.

Ensuring the required law implementation accuracy of the transmission clutches control by regulating the working fluid volume in the hydraulic cylinder compensation chamber

Introduction (problem statement and relevance). Testing the vehicle automatic transmission and shifting gears without interrupting the power flow undesirable dynamic phenomena were revealed. The occurrence of “parasitic” centrifugal pressure in the clutch boosters was observed which resulted in self-activation of the control element, clamping the discs friction linings, which made the hydraulic cylinder emptying and piston removing from the package disks complicated. As a consequence of it there occurred a comfort decrease in the vehicle, and in some cases, the destruction of friction clutches. It was found that the reason for this occurrence was the filling degree instability of the compensation and piston chambers of the hydraulic cylinder clutch, which was not taken into account by the existing calculation and design methods under various initial conditions.The purpose of the study was to improve the implementation accuracy of the required control law of the transmission clutches by purposeful regulating the working fluid volume in the compensation chamber of the hydraulic cylinder clutch.Methodology and research methods. A road test technique was proposed for identifying and reproducing the conditions of the dynamic phenomenon manifestation. The developed mathematical model of the piston stroke made it possible to assess the dependence of the implementation quality of the required clutch control law when individual gears were engaged on the following parameters: the filling degree of the compensation chamber; features of solenoid valves operation; the stiffness of the return spring; the number of clutch friction pairs.Scientific novelty and results. On the experimental research basis of the worked out design and the use of scientifically grounded technical solutions the dynamic effect manifestation of the unbalanced “parasitic” centrifugal pressure rise was excluded.Practical significance. The developed and implemented technical solutions for stabilizing the pressure in the compensation chamber made it possible to ensure the required quality of gear shifting when limiting the dynamic and thermal loading of the friction discs, which made it possible to ensure the required level of dynamic characteristics and comfort ability of the product when shifting gears.

Tool Evaluation & Design Analysis for Parking Pawl Mechanism

Abstract: Automatic Transmissions in vehicles are becoming the norm today and with the need to reduce fuel consumption, electric vehicles are increasing in number too. As the vehicles get smarter, transmissions have also felt the need to adapt so as to cope with the demands of the 21st century. Apart from providing the user with different driving modes, vehicles with an Automatic Transmission and electric vehicles have a device called a Park Lock. Park Lock Mechanisms are devices that are fitted to vehicles with an automatic transmission or electric vehicles which can secure the vehicle mechanically in addition to the parking brakes to prevent an unintended movement of the vehicle when the vehicle is brought to a stop. This system can face various kinds of loads coming from the transmission or from the wheel side. So, it is necessary to design a system which can withstand against it. Today, when a park lock mechanism is designed, it is designed in a way that it not only fits one vehicle variant but many variants (these variants can include either front wheel drive, rear wheel drive, vehicles with varying final gear to park lock gear ratios etc). Therefore, one Park Lock Mechanism needs to satisfy various conditions and requirements. Carrying out calculations with different notations for each variant becomes a cumbersome procedure, therefore it is prudent to have one common platform which can do the calculation in the early phases of design. In this paper, a closed form calculationsbased Excel VBA tool can able to estimate the loads coming on to the Park lock mechanism by doing some background calculation is presented followed by the simulation performed using Multi Body Dynamics software (‘ADAMS’). Results from MBD tool and a 1D tool are corelated to the test data to gain some confidence on the tool which created in Excel VBA. Keywords: Park Lock Mechanism, ADAMS, 1D Tool, Excel VBA, Drop in Speed, Torque Build up.

Identification of drivers’ driving habits and shift schedule correction for vehicles with automatic transmission

Resource conservation has become a hot topic, and driving habits also have a significant impact on a car’s fuel consumption. In view of the different needs of drivers with different driving habits for automatic transmission and shift characteristics, a vehicle automatic transmission, and shift correction control strategy based on driving habit recognition is proposed. Based on the analysis of drivers’ driving behavior, the phase space reconstruction method is first used to reconstruct the time series of driving control signals, and the driving habit identification and gear shift correction control are conducted based on the correlation dimension and Kolmogorov entropy evaluation index. Simulation and real car test show that the identification method based on phase space reconstruction method and driving habits evaluation index can accurately identify drivers’ driving habits. The gear shift correction control strategy based on driving habit recognition fully meets the different requirements of different drivers for the gearshift performance of vehicles and improves the intelligence degree of automatic transmission of vehicles.

Evolution of gear shift patterns for six speed automatic transmission vehicle

The increasing level of stringent exhaust emission regulations and the rising index of carbon dioxide emissions from the road transport sector have pushed vehicle manufacturers and designers to develop solutions for more efficient vehicles, with well designed vehicle transmission and gear shift patterns among the alternative approaches employed. For a six speed automatic transmission, the current article proposes a design method for developing a suitable gear ratio set and gear shift patterns. The MATLAB/Simulink platform was used to create the Maruti Suzuki 2020 four speed transmission model and the results were verified using experiment data. The effectiveness of the new selected gear ratio set and gear shift patterns was then evaluated using a six speed transmission model based on the validated four speed transmission model. To investigate fuel consumption and acceleration performance, all models were simulated based on motor vehicle emissions groups. Sport mode, eco mode, and combined both mode patterns were developed and analyzed. Based on the eco mode pattern, the six speed transmission model has a 2.74% lower fuel consumption rate than the four speed transmission model, indicating that the proposed design method is feasible and effective.

Integrating Gear Shifting Preference into Personalized Shift-Scheduling Calibration

Shift-scheduling calibration of automatic transmission (AT) vehicles is vital for both driving experience and automobile industry. Shifting schedules are usually calibrated with the consideration of fuel economy and drivability while neglecting the individual driving preference. In this work, we propose to exemplify the individual shifting preference by integrating the manual transmission (MT) shifting habit into the AT shift-scheduling calibration, where the habit is reflected as the shifting points and is available in most automated manual transmission (AMT) vehicles. The automated calibration of AT shifting schedules is directed by using the particle swarm optimization (PSO), during the virtual automobile cycle test, i.e., FTP-72. Candidate shifting-schedules are generated in the overlapped zone of MT shifting points and the space around the base map, and are evaluated on both shifting quality and fuel economy. Through iterations, the generated candidate shift schedules are tested and assessed until the overall performance reach the optimum. Experimental results are presented to show the effectiveness of the proposed method, which retains the shifting preferences as well as enhances the performance index by about 5%, 4%, and 2% for the drivers with aggressive, moderate, and mild styles, respectively.

Semi-Autonomous Parking system for Automatic Transmission vehicles

This paper proposes an automated perpendicular parking strategy for a car which is to be designed. The design considers general cases of perpendicular parking for a rectangular body within a rectangular space. The system works in phases named: The Scanning phase, the parking environment is detected by ultrasonic sensors mounted on the extreme ends of body and a parking position and maneuvering path is produced if the space is enough. Then in the positioning phase, the model positions itself and get aligned with the parking space avoiding potential collisions. Finally, in maneuvering phase, the model moves to the parking position in the parking space in a specified path, which requires clutch, brake control, backward and forward maneuvers depending on the dimensions of the parking space. Based on the characteristics, a collision-free path is planned about the surroundings. The strategy is to be integrated into an automated parking system, and implemented in the car, showing capable of safe parallel parking in tight situations and as automated parking device to help vehicle drivers.

The static structural analysis of torque converter material for better performance by changing the stator angle

Torque converter is a commonly used machine device in automobiles to transmit the rotating power from a source like I.C engine, to a rotating driven load. It is widely used in automatic transmission vehicles to change the speed from the engine to the transmission and also multiply the torque. Effective modeling of torque converters leads to increase the performance and efficiency. The inlet and outlet blade angles take part in a significant role in determining the performance and efficiency of a torque converter. In this paper the performance of torque converter has been evaluated by changing the angle of stator blades by using ANSYS software. Four torque converter with different stator blade angle (15°, 25°, 35°, 40°) are designed and simulated to investigate the effects of blade angles on their overall performance and efficiency.

Study on the Transmission Characteristics of the Multi-gear Multi-degree-of-freedom Hybrid Planetary Gear Automatic Transmission Based on the Line Method

Planetary gear transmission is widely used in vehicle automatic transmission because of its compact structure and good working conditions of gears and bearings. The transmission characteristic analysis of planetary gear automatic transmission has become one of the most important contents of automatic transmission design and maintenance. This paper takes the typical multi-gear multi-degree-of-freedom hybrid planetary gear automatic transmission Ford 10R80 as an example. Firstly, its structure is analysed, and its degree of freedom is calculated to be 5. According to this, Further analysis is made on the shift logic of the sequential up and down gears, skipping-level shifting gears and the multi-level down gears such as from© to© orfrom © to©. Then, the overall transmission nomogram is established based on the improved line method, and the power transmission characteristics of typical gears are analyzed. Finally, the transmission ratios of all gears are calculated according to the nomogram, and the calculated results are completely consistent with the data provided in the patent documents. The results show that the nomogram can not only clearly describe the speed of all the basic components in each gear but also calculate the transmission ratio of each gear quickly.

Shift control of vehicle automatic transmission based on traffic congestion identification

This work builds a T-S fuzzy neural network that identifies traffic congestion conditions by using average vehicle speed, average throttle opening and frequency of brake pedal actuation as evaluation factors. A strategy that controls the shift of vehicle automatic transmission based on the identified congestion conditions is also devised. This strategy divides the vehicle automatic transmission system into the upper identification and decision-making layer and the lower shift execution layer. Simulation and real vehicle tests are performed to verify the effectiveness of the proposed strategy. The results show that congestion conditions can be accurately identified by using the T-S fuzzy neural network and that the proposed layered correction shift control strategy can prevent the frequent changing of gears under congestion conditions, thereby reducing the wear of the shift execution parts and the braking system.

Shift control of vehicle automatic transmission based on traffic congestion identification

This work builds a T-S fuzzy neural network that identifies traffic congestion conditions by using average vehicle speed, average throttle opening and frequency of brake pedal actuation as evaluation factors. A strategy that controls the shift of vehicle automatic transmission based on the identified congestion conditions is also devised. This strategy divides the vehicle automatic transmission system into the upper identification and decision-making layer and the lower shift execution layer. Simulation and real vehicle tests are performed to verify the effectiveness of the proposed strategy. The results show that congestion conditions can be accurately identified by using the T-S fuzzy neural network and that the proposed layered correction shift control strategy can prevent the frequent changing of gears under congestion conditions, thereby reducing the wear of the shift execution parts and the braking system.

Driving Habit Recognition and Shifting Correction Control of Vehicle Automatic Transmission

Driving Habit Recognition and Shifting Correction Control of Vehicle Automatic Transmission

Fault Identification of Vehicle Automatic Transmission based on Sparse Autoencoder and Support Vector Machine

Support vector machine(SVM) got a good classification ability, but the recognition accuracy was easily affected by the value of the kernel parameters. Aiming at this problem, sparse autoencoder(SAE) has its unique advantages in dealing with complex structured data, so the combination of sparse autoencoder and support vector machine(SAE+SVM) was proposed on the fault identification of vehical automatic transmission. Firstly, eight indicators such as engine speed, throttle opening, water temperature and so on are collected from acquisition automobile automatic transmission under 3 running conditions. The data was used as input dataset of the sparse autoencoding model to extract the features. Then the features was used for the fault classification and identification based on support vector machine. Compared with using support vector machine only, the experiment results showed that the recognition accuracy based on the combination of sparse autoencoder and support vector machine(SAE+SVM) was less affected by the value of the kernel parameters and got better recognition accuracy. So the combination of sparse autoencoder and support vector machine can be better used in the real-time fault identification and diagnosis of automatic transmission.