Automated Manual Transmission Research Articles

Efficiency Analysis of Electric Vehicles with AMT and Dual-Motor Systems

With the rapid development of automobiles, energy shortages and environmental pollution have become a growing concern. In order to decrease the energy consumption of electric vehicles (EVs), this study aims to improve EV efficiency with AMT and dual-motor systems. Firstly, the paper establishes an Automated Manual Transmission (AMT) model for EVs, which is then simulated using MATLAB R2022a software. In order to eliminate the impact of gear ratio selection, the genetic algorithm is used to optimize the AMT gear ratios. Meanwhile, a dual-motor EV model is constructed, and three different torque distribution schemes are simulated and analyzed. The results indicate that due to the elongation of the energy transmission chain in AMT-equipped EVs, energy losses increase, leading to some improvement in optimized power consumption. However, these EVs remain inferior to those with only a single-stage main reducer. The study also found that the torque distribution based on optimal efficiency further improves results.

Analysis of KIA’s Intelligent Manual Transmission

Abstract: It is difficult to decide that the new Intelligent Manual Transmission in the KIA motor’s vehicle is an automatic or a manual transmission from the point of view of user. In general, iMT is a manual gearbox, but talking about it’s mechanism it has gears with mechanism that looks precisely like any other manual transmission. The iMT transmission does not have clutch, it contains only a break and throttle. To be noticed, it’s not totally automated. iMT is the next generation gearbox technology in Indian automobiles ater the AMT (automated manual transmission) that Maruti Suzuki popularized. It is now available in the KIA Seltos, Carens etc.

Application of particle filter variants to estimate the remaining useful life

Dynamic loadings encountered by clutch motor of an Automated manual transmission (AMT) passenger vehicle require apposite assessment of nonlinear characteristic of the degrading system to accurately predict the remaining useful life (RUL) for implementation of condition based maintenance (CBM) schedule. The present study involves the application of three variants of particle filter (PF) with various resampling techniques to account for heavy tailed observations and non-Gaussian characteristic of noise to improve the accuracy of RUL estimation. Degradation dataset was generated using accelerated life cycle test (ALCT) on hardware-in-loop (HIL) laboratory of a large Indian automotive company. A comparative analysis of the results showed 91.5, 93.2, and 94.9% enhancement in the efficacy of RUL estimation due to canonical, unscented, and improved unscented particle filters (i-UPF) vis-a-vis the ordinarily fitted exponential degradation model.

An Adaptive Shift Schedule Design Method for Multi-Gear AMT Electric Vehicles Based on Dynamic Programming and Fuzzy Logical Control

This paper proposes an adaptive shift schedule design framework based on dynamic programming (DP) algorithm and fuzzy logical control to promote the shift schedule’s adaptability whilst improving the comprehensive performance of the multi-gear automated manual transmission (AMT) electric vehicles in real-time application. First, the DP algorithm is employed to extract an offline optimal gear-shift schedule based on a set of driving conditions, including 11 groups of typical driving cycles. Second, a fuzzy logical controller is formulated considering the variation in the vehicle load and acceleration, where a velocity increment is exported online to adjust the gear-shift velocity of the predesigned DP-based schedule to develop a Fuzzy-DP shift schedule. In addition, multi-objective particle swarm optimization (MOPSO) is utilized to construct a comprehensive shift schedule by simultaneously considering the dynamic and economic performance of the vehicle. Then, the dynamic and economic shift schedules are deployed as the benchmark to examine the performance of the proposed shift schedule. Finally, the effectiveness of the Fuzzy-DP shift schedule is evaluated by comparison with others under various combined driving cycles (including vehicle load and velocity). The comparisons demonstrate the remarkable promotion in the adaptability of the Fuzzy-DP shift schedule in terms of acceleration time, energy-saving potential, and shift frequency. The most significant improvements in the dynamic, economic, and shift frequency can reach 8.86%, 10.12%, and 8.56%, respectively, in contrast to the MOPSO-based shift schedule.

Mathematical modelling of gear upshift and downshift schedule for two-wheeler amt transmission

Gear shifting strategy is the key for the successful working of an automated manual transmission (AMT) as it affects the driving experience, fuel economy and dynamic performance of the vehicle. Depending upon the driver’s requirement, AMT can be designed for maximum acceleration performance and fuel efficiency. This project involves generation of gear shift schedule for both the maximum acceleration and fuel economy performance. To generate the gear shift schedule, a mathematical model is developed in MATLAB environment which takes input parameters as: vehicle speed, acceleration and throttle position. For generation of gear upshift schedule for maximum acceleration and fuel economy equation relating acceleration with tractive forces and vehicle’s fuel consumption equation at different throttle position is used respectively. Fuel consumption equation relates to brake specific fuel consumption (BSFC) along with vehicle structure. In order to obtain BSFC data, a GT-SUITE model of motorcycle is developed which incorporates data like gear ratio, power, torque and motorcycle weight. Finally, a buffer zone is applied to generated upshift schedule for both maximum acceleration and fuel economy case to get the downshift schedule. The generated gear shift schedule can be utilized for the development of the retrofit AMT kit on manual transmission motorcycle.

MPC-Based Coordinated Control of Gear Shifting Process for a Power-split Hybrid Electric Bus with a Clutchless AMT

Two-speed clutchless automated manual transmission (AMT) has been widely implemented in electric vehicles for its simple structure and low cost. In contrast, due to the complex response characteristics of powertrain, utilizing clutchless AMT in a hybrid power system comes with complex coordination control problems. In order to address these issues, a power-split hybrid electric bus with two-speed clutchless AMT is studied in this paper, and a coordinated control method based on model predictive control (MPC) is used in gear shifting control strategy (GSCS) to improve gear shifting quality and reduce system jerk. First, the dynamic model of power sources and other main powertrain components including a single planetary gear set and AMT are established on the basis of data-driven and mechanism modeling methods. Second, the GSCS is put forward using the segmented control idea, and the shifting process is divided into five phases, including (I) unloading of drive motor, (II) shifting to neutral gear, (III) active speed synchronization by drive motor, (IV) engaging to new gear, and (V) resuming the drive motor’s power, among which the phases I and V have evident impact on the system jerk. Then, the MPC-based control method is adopted for these phases, and the fast compensation of driving torque is realized by combining the prediction model and quadratic programming method. The simulation results show that the proposed GSCS can effectively reduce shift jerk and improve driving comfort. This research proposes a coordinated control strategy of two-speed clutchless AMT, which can effectively improve the smoothness of gear shifting and provides a reference for the application of two speed clutchless AMT in power-split hybrid powertrains.

Development of low-cost data logger system for capturing transmission parameters of two-wheeler using Arduino

The data collection system is one of the powerful devices for getting information on the current state of the system. This Project work conceptualizes a low-cost data collection system popularly called a Data Logger using Gear, Throttle, Clutch, Brake position sensor, and wheel speed sensor. The focus is to develop a methodology for generation of gear shifting schedule using experimental data logger system. These sensors are mounted on a vehicle and signals are recorded and displayed with help of Arduino. The Data Logger is designed to acquire data in form of transmission parameters such as vehicle linear speed, Throttle and Gear position, Clutch engagement, and brake depression of Two-Wheeler (L2 Category according to Automotive Industry Standard, AIS-053). This data is used to capture the driver’s intelligence while shifting gears of a two-wheeler under different road conditions. Further, the data is useful for making the actual control strategy of vehicles by a generation of Shift Schedule in the Automated Manual Transmission (AMT) vehicle.

A Lyapunov based robust control of the EV gearshift process

This paper presents a novel Lyapunov based robust control algorithm for gearshift system of automated manual transmission (AMT) in electric vehicle (EV). Gearshift process is analyzed and modeled considering a two-gear powertrain. Trajectory-tracking control is designed regarding the engagement rule of the shifting fork as the desired trajectory. Model uncertainties and external disturbance are lumped into a function with the assumed upper bound. The control algorithm consists of a PD feedback component and a piecewise function component, which is error-based and model-based inheriting from conventional PID control and robust control. Furthermore, this proposed control is based on Lyapunov approach, and is theoretical proved to render uniform boundedness and uniform ultimate boundedness. The proposed control can guarantee the deterministic performance of the gearshift system, and its effectiveness is illustrated by numerical simulations and sample vehicle test.

Multi-Objective Optimization of Gear Ratios of a Seamless Three-Speed Automated Manual Transmission for Electric Vehicles Considering Shift Performance

Multi-speed transmission can greatly improve the power and economic performance of electric vehicles (EVs) compared with single-speed transmission. Gear ratio is the key design parameter of multi-speed transmission. Optimizing gear ratios can further improve vehicle performance. Most of the existing optimization methods of gear ratios take the power and economy of vehicles in gear as the optimization objectives, but rarely consider the shift performance of the transmission, such as shift time, friction, and shift jerk. Considering the shift performance in the process of gear ratio optimization can not only optimize the vehicle performance in gear, but also improve the shift performance of the transmission. Therefore, this paper proposes a multi-objective optimization method of gear ratios considering the shift performance. Firstly, a seamless three-speed automated manual transmission (AMT) of EVs is selected as the research object, the structure and the shift process without power interruption of the three-speed AMT are introduced, and the detailed EV simulation model is established. Then, the multi-objective optimization method of gear ratios considering shifting performance is described. Specifically, the acceleration time, energy consumption, and jerk of the vehicle in gear are taken as the objective functions, and the shift time, clutch friction, and the shift jerk are added to the corresponding objective functions, respectively. Finally, the multi-objective optimization algorithm is used to solve the gear ratio optimization problem. The simulation results show optimization of the gear ratios significantly improves the power, economy, and comfort of the vehicle compared with the original. More importantly, compared with the optimization method without shift performance, gear ratios optimized by the proposed optimization method has better shift performance, and the feasibility of the proposed method is verified by simulations.

Hierarchical mode optimization strategy for gear engagement process of automated manual transmission with electromagnetic actuator

When vehicles equipped with automated manual transmission (AMT) are in the gear engagement process (GEP), the problems of longitudinal shift jerk and synchronizer wear deterioration is prominent, which seriously affects the GEP quality. This paper develops and evaluates a hierarchical mode optimization strategy (HMOS) for GEP. Firstly, the models of gear-shift actuator and three-stage GEP are established, and the correlation between sleeve displacement and cone torque is revealed based on the established models. Next, the HMOS is proposed for cone torque optimization and sleeve trajectory tracking during GEP. At the top layer of the HMOS, a novel sliding-mode predictive controller (SMPC) with constraints is proposed for the cone torque decision during the synchronization stage of the GEP. At the bottom layer of the HMOS, an MPC-based sleeve trajectory tracking controller integrated with a load torque disturbance observer (L-DOB) is presented. Finally, the simulation and hardware-in-the-loop (HIL) test are carried out in a driving condition of power upshift from the first gear to the second gear to verify the performance of the optimization strategy. The verification results show that the proposed optimization strategy achieves satisfactory longitudinal shift jerk, synchronizer sliding work, and thermal stress.

Energy Management Strategy of a Novel Electric Dual-Motor Transmission for Heavy Commercial Vehicles Based on APSO Algorithm

With the development of electric vehicles, dual-motor transmission has become a potential alternative for automated manual transmission (AMT) due to the solution of power interruption and the improvement of energy efficiency. In this paper, a novel electric dual-motor transmission (eDMTP) for heavy commercial vehicles is proposed. Then, a 4-layer energy management strategy is developed to optimize dynamics performance and energy efficiency. Subsequently, a real vehicle operation is performed to validate the control strategy and the performance of eDMTP. The results demonstrate that the operating points of the two motors are both in and around the high-efficiency area under normal mode. This research lays the foundation for the development of a pure electric vehicle transmission system.

A mathematical model for vibration analysis of a parallel hybrid electric bus powertrain

This paper presents a parallel hybrid electric bus equipped with an automated manual transmission (AMT) and a mathematical model of the hybrid powertrain is developed for vibration analysis. The powertrain dynamic model is established by a modular modelling approach. A detailed AMT dynamic model, considering gear time-varying meshing stiffness, shaft elastic deformation, and bearing elastic support, is incorporated into the powertrain dynamic model. The damping and gyroscopic effect of gear-rotor in the AMT are considered as well. The AMT dynamic model is validated by experiment data from the time and frequency domain comparisons. Finally, the parameter analysis of the dual-mass flywheel (DMF) is utilised to illustrate how to use the proposed powertrain dynamic model for vibration reduction. The influence of the DMF parameters on vibration responses of the system with varying engine rotation speed is investigated. This study provides a basis for further vibration control of the hybrid powertrain during the engine driving mode.

Dynamics modeling and shift control of a novel spring-based synchronizer for electric vehicles

This paper investigates the gearshift transient response and the shift control of a new spring-based synchronizer used in the automated manual transmission (AMT) for electric vehicles (EVs). The spring-based synchronizer uses torque spring to provide a torque to synchronize the speed difference between the target gear and the shaft, which reduces the wear caused by friction in traditional synchronizer, as well as decreases the engaging time and the vehicle jerk. The dynamics model of a two-speed AMT in EV equipped with the spring-based synchronizer is built in AMESim software, and the shift control model is established in MATLAB/Simulink environment. The simulation results of the dynamics model demonstrate that the spring-based synchronizer reduces the engaging time and the vehicle jerk compared to the traditional synchronizer. Furthermore, the influences of the motor inertia, speed difference, spring stiffness and shift force on the engaging time and vehicle jerk are quantitatively analyzed. Finally, several control strategies of shift force are proposed, including the single-closed loop PI position control, dual-closed loop PI control, and dual-closed loop fuzzy PI control, which further reduce the vehicle jerk.

Implementation of automatic manual transmission for a two wheeler

The concept of Automatic Manual Transmission (AMT) is picking up speed in India after the launch of the Maruti Suzuki Celerio. But very few of the two-wheeler manufacturers in the world have tried to implement the AMT concept in a motorcycle. There are gearless motorcycles available which use a Continuously Variable Transmission (CVT) and a Centrifugal clutch, but this setup is very inefficient compared to a positive gear drivetrain. Thus these gearless motorcycles get low mileage and are also more expensive to manufacture. Geared motorcycles, on the other hand, have better starting torque, greater mileage and a higher span of gear ratios. They are also cheaper, but the average buyer is discouraged by the complexity of the operation. Even those familiar with riding a motorcycle do not shift optimally to obtain the best mileage/performance. The aim of this paper is to present an innovative AMT system developed and implemented on a manual geared motorcycle having a sequential constant-mesh gearbox with a multi-plate clutch. This was carried out by interfacing the manual gear shifting motorcycle with a microcontroller and using an electronic actuation mechanism to make it an automatic gear shifting motorcycle. The significant steps of this project would involve the sensor readouts of the parameters necessary, the actuation of the controls and, most importantly, the algorithm for the optimal shift points for a given motorcycle. These would be obtained experimentally and using the manufacturer data, if available. This AMT setup has been successfully developed and implemented. This setup can then be applied to any geared motorcycle with minimal structural changes without any modification to its existing gearbox. This converts the manual geared motorcycle into the automatic gear shifting motorcycle having better fuel economy and lower maintenance costs as compared to the conventional CVT motorcycles.

Design and modeling of an in-wheel two-speed AMT for electric vehicles

To improve the performance of the electric vehicles (EVs), significantly reduce unsprung mass, and improve system reliability, in the present paper, the authors propose a novel in-wheel two-speed automatic manual transmission (WH-AMT) structure which weighs only 16.8 kg. The proposed WH-AMT is composed of a high-speed drive motor unit, gear transmission unit, and shift actuator unit. The gear transmission system has no free planet carrier and ring gear, which improves the reliability of the WH-AMT in the harsh working environment within the wheel. Owing to the integrated design in the wheel, the free space in the wheel is increased, and unsprung mass is greatly reduced. In this study, kinematic analysis of the WH-AMT, derivation of its dynamic equations, and development of a simulation model of the shifting process are carried out. The results indicate that there is no power interruption in the WH-AMT during gear shifting, and the shift quality is high, which considerably enhances the power performance of EVs.

Robust augmented H∞ shift control strategy for power-split system with a two-speed AMT gearbox of a heavy commercial vehicle

The coordination control of a transmission system has gradually attracted more attention with the development of hybrid electric vehicles. However, nonlinear coupling of multiple power sources, superposition of different dynamic characteristics in multiple components, and withdrawal and intervention for a power-split powertrain with a two-speed automated manual transmission (AMT) gearbox can cause jerk and vibration of the transmission system during the shift, which has higher requirements and challenges for the overall performance improvement of the system. This paper designs a novel, robust, augmented H∞ shift control strategy for a power-split system with a two-speed AMT gearbox of a heavy commercial vehicle and verifies the strategy’s effectiveness with simulations and experiments. First, the dynamic plant model and kinetic equations are established, and the shift is divided into five stages to clearly reveal the jerk and vibration problem. Based on augmented theory, a robust H∞ shift control strategy is proposed. Shift coordination is transformed into a speed tracking problem, and state variable and disturbance are reconstructed to obtain a new augmented system. Simulation and hardware-in-the-loop test are carried out to verify the effectiveness of the strategy, which mainly includes simulation of pneumatic actuator and H∞ control strategy. Results show that the proposed H∞ control strategy can greatly reduce the jerk of the transmission system. The jerk produced by the proposed strategy is decreased from 20.4 to 4.07 m/s3, leading to a substantial improvement of 80%. Therefore, the proposed strategy may offer a theoretical reference for the actual vehicle controller during the shift.

Engine Speed Regulation During Gear Shift Process of Torque Decoupled HEV Using Triple-Step Nonlinear Method

For the torque decoupled hybrid electric vehicles (HEVs) equipped with automated manual transmission (AMT), the motor torque can be used to compensate for traction loss during gear shift. Thus, engine speed regulation can be used to achieve speed synchronization, and the clutch can be removed, which simplifies the overall structure of AMT. A triple-step nonlinear method is proposed in this paper to improve the engine speed tracking accuracy, which utilizes the steady-state, reference trajectory dynamics, and the tracking error information comprehensively. By the expense of the intake manifold pressure tracking performance, the developed controller can guarantee the asymptotic convergence of the engine speed tracking error. After the triple-step controller is deduced, it is verified through co-simulation between AMT HEV plant model in AMESim and controller model in Simulink. The simulation results demonstrate that the proposed controller can exert the advantages of the two control inputs: throttle opening α and spark advance deviation Δθ, and certificate the overall gear shift quality, including shift time and vehicle jerk.

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.

Corresponding drivability control and energy control strategy in uninterrupted multi-speed mining trucks

An hybrid uninterrupted multi-speed transmission (HUMST), based on the integration of a planetary gear set and a 3-speed automatic manual transmission (3-AMT), is developed to satisfy the specific performance indexes of mining trucks. The power-split device can alleviate and eliminate the inherent torque interruption of the 3-AMT during gear shift by implementing the designed cooperative shift control strategy which is optimized by quadratic performance index. In order to achieve fast torque coordination while guaranteeing the driving comfort performance, the torque profiles of the power split device and the traction motor are optimized by Linear-quadratic regulator (LQR) algorithm. Dynamic programming (DP) is implemented as a benchmark to demonstrate the maximum fuel efficiency of the proposed HUMST. Because of the high computational cost of optimal control strategies such as DP, an improved real-time control strategy (IRTCS) using modified Gaussian distribution function is proposed to significantly reduce the computing load. As efficiency-oriented energy control strategy would result in frequent gear shifts, to achieve a desirable tradeoff between the overall efficiency and the shift stability, multi-objective genetic algorithm (MGA) is integrated to optimize the overall performance. The detail mathematical and dynamic model shows that the proposed shifting strategy with LQR can effectively suppress shift jerk, and the proposed IRTCS with MGA can reduce shift frequency by 70.78% to improve the drivability, only sacrificing 4.86% of overall efficiency compared to that of DP.

Integrated Prognostics Observer for Condition Monitoring of an Automated Manual Transmission Dry Clutch System

The closed loop feedback control system of an Automated Manual Transmission (AMT) electro-pneumatic clutch actuator is used for intelligent real time condition monitoring, enhanced diagnostics and prognostic health management of the dry clutch system, by integrating with the existing gearbox prognostics observer. The real-time sensor data of the clutch actuator piston position is analyzed for monitoring the condition of the clutch system. Original parameters of the new clutch are stored in the Electrically Erasable Programmable Read-only Memory (EEPROM) of the AMT controller and the real-time data is used by the observer for assessing the degradation/wear of the frictional clutch parts. Also, clutch slip during torque transmission is monitored, using the engine speed and the gearbox input shaft speed from Controller Area Network (CAN). Condition monitoring of clutch system provides enhanced prognostic functionality for AMT system which ensures consistent clutch performance, gear shift quality and timely warning for recalibration, repair and/or replacement of the critical wear and tear parts. Also, systematic analysis of the monitored data provides an accurate diagnosis of a developing fault. Thus, with the advanced control systems in place for AMT, a closed loop feedback based condition monitoring system is modelled for improved diagnostics and prognostics of AMT clutch system.