Optimal Gear Ratio Research Articles

Optimal and prototype dimensioning of 48V P0+P4 hybrid drivetrains

This paper presents a virtual toolchain for the optimal concept and prototype dimensioning of 48 V hybrid drivetrains. First, this toolchain is used to dimension the drivetrain components for a 48 V P0+P4 hybrid which combines an electric machine in the belt drive of the internal combustion engine and a second electric machine at the rear axle. On an optimal concept level, the power and gear ratios of the electric components in the 48 V system are defined for the best fuel consumption and performance. In the second step, the optimal P0+P4 drivetrain is simulated with a prototype model using a realistic rule-based operating strategy to determine realistic behavior in legal cycles and customer operation. The optimal variant shows a fuel consumption reduction in the Worldwide harmonized Light Duty Test Cycle of 13.6 % compared to a conventional vehicle whereas the prototype simulation shows a relatively higher savings potential of 14.8 %. In the prototype simulation for customer operation, the 48 V hybrid drivetrain reduces the fuel consumption by up to 24.6 % in urban areas due to a high amount of launching and braking events. Extra-urban and highway areas show fuel reductions up to 11.6 % and 4.2 %, respectively due to higher vehicle speed and power requirements. The presented virtual toolchain can be used to combine optimal concept dimensioning with close to reality behaviour simulations to maximise realistic statements and minimize time effort.

New Engineering Solutions for the Traction Electric Drive of a Gazelle-Next Medium-Duty Van

A procedure for choosing the optimal gear ratio for a reducing gear train is proposed for the traction electric drive of the workshop electric automobile based on the GAZ A22R22/32 chassis and its modified versions, taking into account the requirements for the top linear speed of the automobile, acceleration, and power plant energy efficiency that will allow achieving a required reserve. The optimization procedure consists of three phases. In phase one, the traction electric drive’s top speed was achieved. Then, the time was minimized for which the electric drive reached the rated speed. In phase two, the gear ratio was adjusted proceeding from the conditions in which the drive was used in the field attenuation zone. Taking into account the system’s operation in the attenuation mode against the loss minimization criteria, the procedure of selecting the gear ratio allowed reaching a cruising range of 200 km and the traction electric drive’s acceleration to the rated speed in 9 s. A charge control system is proposed that allows making the battery work stably in operational and charge modes. It is confirmed by experiment that, at a cell imbalance of no more than 3%, the full balancing takes only up to 4 min.

A Virtual Driveline Concept to Maximize Mobility Performance of Autonomous Electric Vehicles

<div class="section abstract"><div class="htmlview paragraph">In-wheel electric motors open up new prospects to radically enhance the mobility of autonomous electric vehicles with four or more driving wheels. The flexibility and agility of delivering torque individually to each wheel can allow significant mobility improvements, agile maneuvers, maintaining stability, and increased energy efficiency. However, the fact that individual wheels are not connected mechanically by a driveline system does not mean their drives do not impact each other. With individual torques, the wheels will have different longitudinal forces and tire slippages. Thus, the absence of driveline systems physically connecting the wheels requires new approaches to coordinate torque distribution. This paper solves two technical problems. First, a virtual driveline system (VDS) is proposed to emulate a mechanical driveline system virtually connecting the e-motor driveshafts, providing coordinated driving wheel torque management. The VDS simulates power split between driving wheels. Conceptually, VDS is founded on generalized tire and vehicle parameters. Generalized slippages are utilized to determine virtual gear ratios from a virtual transfer case to each wheel. The virtual gear ratios serve as signals to the electric motors. Secondly, a new velocity-based mobility performance index is used as the ratio of the actual velocity of a vehicle, with individual wheel management, to the theoretical velocity of the same vehicle equipped with a mechanical driveline without controllable gear ratios. Using the index as the objective function, a maximization problem of vehicle mobility is formulated and solved. Optimal virtual gear ratios are determined for maximal mobility in a given terrain condition. Simulations of a 4x4 tactical vehicle in stochastic soil conditions demonstrated a 17% increase in mean values of the velocity-based mobility performance index when the vehicle is electrically driven by the optimal virtual gear ratios as compared to the mechanical driveline system with non-controlled differentials.</div></div>

The Influence of Main Design Parameters on the Overall Cost of a Gearbox

This study is aimed at determining optimum partial gear ratios to minimize the cost of a three-stage helical gearbox. In this work, eleven input parameters were investigated to find their influence on the optimum gear ratios of the second and the third stages ( u 2 and u 3 ). To reach the goal, a simulation experiment was designed and implemented by a cost optimization program. The results revealed that in addition to the input parameters, their interactions also have important effects in which the total ratio gearbox ratio ( u t ) and the cost of shaft ( C s ) have the most impact on u 2 and u 3 responses, respectively. Moreover, the proposed models of the two responses are highly consistent to the experimental results. The proposed regression equations can be applied to solve optimization cost problems.

Optimized Design of Multi-Speed Transmissions for Parallel Hybrid Electric Vehicles

In this paper, the optimal design of a multi-speed transmission system in terms of gear ratio, number of gears and gear shifting strategy is investigated for a parallel hybrid electric vehicle. The design procedure starts with the optimization of the transmission configuration to identify the optimal gear ratios for a specified number of gears. In order to avoid solving a complex co-optimization problem that involves numerous control variables for hybrid powertrain energy management (EM), gear ratios and gear shifting, the gear ratio optimization is properly decoupled from the co-optimization problem, while the optimal gear shifting strategy for the optimized gear ratios is determined jointly with the powertrain EM. The separation of the co-optimization makes it possible to solve individual problems by dynamic programming (DP), which guarantees global optimality. To show the impact of optimally designed and controlled transmission on fuel savings, the fuel economy solution of the proposed scheme is compared with the traditional EM and gear shifting optimization method that applies non-optimized gear ratios. Simulation examples verify the effectiveness of the proposed methodology and show the fuel savings incurred by the configuration optimization of the multi-speed transmission system.

Model Predictive Controller Development for controlling Actuators of Automated Manual Transmission

Automotive transmission technology is evolving rapidly to match up with the growing need of increased power transfer efficiency, tractive demands and powertrain electrification. In this, Automated Manual Transmission (AMT) systems have made their way to the global market as popular choice by automotive industry. AMT applications have grown substantially in Hybrid Electric Vehicles, where hybrid controller decides optimum gear ratio and the gearshift needs to be automated. Advanced model-based predictive techniques for AMT supervisory controllers have allowed complex clutch control, gear shift control and optimal gear-ratio selection. However, the AMT’s lower level actuator control is usually done with the help of simple linear controllers like PID. This work aims to evaluate the performance of the AMT system when using a Model Predictive Controller (MPC) for controlling generic AMT actuators. This generic AMT and its control system are modeled and implemented within a HEV vehicle closed loop system model. For gearshift control problem, MPC controller is developed and implemented as actuator controller within the AMT control system framework. A simple PID based actuator controller is also developed to serve as benchmark. The comparative performance of the developed MPC and PID based controllers are evaluated by simulating them during gear-shift operation under a realistic drive cycle. Finally, suitability of MPC for the AMT actuator controller is illustrated through the obtained results.

Determination of optimum gear ratios of a two-stage helical gearbox with second stage double gear sets

This paper introduces a study on the optimum calculation of the gear ratios of a two-stage helical gearbox with second stage double gear sets. In this study, for finding the optimum gear ratios of the gearbox, an optimization problem was conducted. In the optimization problem, the objective function was the acreage of the cross section of the gearbox. Besides, the effect of the input parameters including the total gearbox ratio, the wheel face width coefficient, the allowable contact stress and the output torque were considered. For investigation of the influence of these factors on the optimum gear ratios, an “experiment” was designed and a computer program was built for conducting the “experiment”. Also, models for determination of the optimum gear ratios of the gearbox were proposed. By using these models, the gear ratios can be calculated accurately and simply.

A study on determination of optimum gear ratios of a worm -helical gearbox

This paper introduces a new study on the optimum calculation of the gear ratios of a worm-helical gearbox. For finding the gear ratios, the cross section area of the gearbox was chosen as the objective function of the optimization problem. Moreover, the influence of the input parameters including the total gearbox ratio, the allowable contact stress and the wheel face width coefficient of the helical gear set, the output torque was investigated. For evaluation of the influence of these parameters on the optimum gear ratios, an “experiment” was designed and a computer program was built for performing the “experiment”. In addition, models for determining the optimum gear ratios of the gearbox were proposed. Based on those models, the gear ratios can achieve high accuracy with simplification.

Optimal design of the gear ratio of a power reflux hydraulic transmission system based on data mining

The power reflux hydraulic transmission system (PRHTS) with a capacity adjustment device presents a high transmission efficiency, a wider range of high-efficiency area (η ≥ 75%), low speed, high torque, and adjustable capacity factor to correspond to the various working modes of a wheel loader. Herein, based on the working principle of a capacity adjustment device and cluster analysis of V-pattern working condition data, the shift strategy of a capacity adjustment device was designed. By combining the output characteristics of the PRHTS and V-pattern working condition data analysis, the optimal gear ratio was developed. The simulation results demonstrated that, in a V-pattern working condition, the average transmission efficiency of the PRHTS and traction were improved by 6.94% and 8.74%, respectively, based on the shovel efficiency and fuel consumption which are consistent with the traditional hydraulic transmission system. In traction conditions, the average traction force increased by 20.92%, the dynamic factor increased by 19.75%, and the average constant-speed fuel consumption per 100 km decreased by 11.66%, which considerably improved the power and fuel economy of the wheel loader during traction conditions.

Determination of optimum gear ratios of a three stage bevel helical gearbox

This paper presents a study on the determination of optimum gear ratios of a three stage bevel helical gearbox. In the study, for finding the optimum gear ratios, an optimization problem was conducted. In the optimization problem, the cross sectional area of the gearbox was chosen as the objective function. Also, the influence of input factors including the total gearbox ratio, the coefficient of the face width of the bevel and the helical gear sets, the allowable contact stress and the output torque were considered. evaluating the effect of these parameters on the optimum gear ratios, an “experiment” was designed and a computer program was constructed to perform the “experiment”. From the results of the study, the effect of the input factors on the optimum gear ratios were learned and several models for determination of the optimum gear ratios were proposed.

Calculating optimum gear ratios of a two-stage helical reducer with first stage double gear sets

This paper presents a study on the determination of the optimum gear ratios of a two-stage helical reducer with first stage double gear sets. In the study, an optimization problem was built in order to find the optimum gear ratios of the reducer. In the optimization problem, the objective function was the reducer cross section area. Also, the influence of the input factors including the total reducer ratio, the wheel face width coefficient, the allowable contact stress and the output torque were evaluated. In order to investigate the effects of these factors on the optimum gear ratios, a simulation experiment was designed and conducted. In addition, equations for calculating the optimum gear ratios were introduced. By using these equations, the gear ratios can be determined accurately in a simple way.

A Study on Calculation of Optimum Gear Ratios of a Two-Stage Helical Gearbox with Second Stage Double Gear Sets

A Study on Calculation of Optimum Gear Ratios of a Two-Stage Helical Gearbox with Second Stage Double Gear Sets

Electric Vehicle with Multi-Speed Transmission: A Review on Performances and Complexities

Electric vehicles (EVs) with multi-speed transmission offer improved performances compared to those with single speed transmission system in terms of top speed, fast acceleration, or gradeability along with driving range. In this study, relevant literature is extensively analyzed to explore the performances and associated complexities with multi-speed automatic manual/mechanical transmission (AMT) system in EVs. In EV powertrain, the only torque generator component is electric motor, which is not equally efficient throughout wider speed range. To the other end, vehicles need to run at different speeds in diverse driving conditions. The study shows that multi-speed transmission system enables efficient operation of electric motor by choosing an appropriate gear at different driving torque-speed demands and thus contributes to achieve desired vehicle performances at minimum energy consumption. To demonstrate the differences, both dynamic and economic performances with multi-gear system and single speed system are compared, and the results of various techniques are presented in the form of tables and bar charts. A quantitative analysis is also conducted to show the performance improvement achievable by employing multi-speed transmission concept in EVs. Apart from additional mass, gear ratio selection and torque interruption during gear shifting are major obstacles to improve drivetrain efficiency and riding comfort in EVs with multi-speed transmission system. For optimal gear ratio in transmission system, genetic algorithm (GA) is found to be implemented in most articles. It is also observed that variable shift schedule needs to be considered during the optimization process to get the paramount gear ratios. Tables and bar charts are used to show the results of recent relevant works to these issues. In this article, the readers would gain a comprehensive knowledge on how multi-speed transmission technology can outperform the single speed system within EV platform and what the inherent challenges are.

Optimization of Gear Ratio of In-Wheel Motor Vehicle Considering Probabilistic Driver Model

A reduction gear of an in-wheel motor vehicle is mounted between a traction motor and wheel, to increase the wheel torque and decrease the rotational speed. To improve the energy efficiency of a vehicle, the determination of the optimal gear ratio is an important factor in the design of the reduction gear. This paper presents an optimization procedure to obtain the optimal gear ratio of an in-wheel motor vehicle that minimizes the electric energy consumption. Using a model-based design, a dynamic simulation model of a vehicle was developed for an analysis of the energy efficiency. Owing to a variation in energy efficiency across drivers, a probabilistic driver model that includes driver characteristics is employed. To reduce excessive calculations, a surrogate model was constructed for the optimization. The optimal gear ratio for saving energy was obtained, and the usefulness of the proposed optimization procedure was shown through a comparison of the results of the optimal gear ratio and the energy efficiency achieved between deterministic and probabilistic approaches.

Cooperative look‐ahead control of vehicle platoon travelling on a road with varying slopes

In the current literature, little research has been done in the eco-driving of vehicle platoon. To increase the fuel efficiency of vehicle platoon and ensure vehicle safety, this study proposes a switching control strategy for vehicle platoon travelling on a road with varying slopes, where the platooning and eco-driving technologies are used. To improve the fuel efficiency of vehicle platoon, the cooperative look-ahead controller is proposed by formulating a cooperative look-ahead control problem of vehicle platoon based on distributed model predictive control (CLCbDMPC), which is a constrained nonlinear non-convex multi-objective optimisation problem. To solve the CLCbDMPC problem quickly, after the minimum fuel consumption table and its corresponding optimal reduction gear ratio table are constructed and calculated off-line, the improved particle swarm optimisation algorithm with multiple dynamic populations is presented. Furthermore, the safety controller acting as the emergency brake to guarantee vehicle safety is also designed. The switch between the look-ahead controller and the safety controller forms the switching control strategy of vehicle platoon. Simulation results demonstrate, compared with benchmarks, the proposed strategy can significantly save up to 21.88% of fuel for vehicle platoon, and vehicle safety can also be guaranteed.

Development Trends of Transmissions for Hybrid Electric Vehicles Using an Optimized Energy Management Strategy

Energy conservation and emissions reduction have become increasingly significant for automobiles due to the severity of the current energy situation. Hybrid electric vehicle (HEV) technology is one of the most promising solutions. This study investigated the total efficiency of a HEV powertrain. To improve the total efficiency, the engine should be regulated to work at its highest efficiency and drive the wheels directly as much as possible. To accomplish this, we developed an energy management strategy based on the direct drive area (DDA) of the engine’s efficiency map. Several typical HEV models were built to compare the fuel consumption using DDA and rule-based strategies. Furthermore, the function of the HEV transmission system with DDA was considered. The transmission in a HEV should regulate the engine to work at its highest efficiency as much as possible, which is rather different than the regulation in an internal combustion engine vehicle. The functional change may lead to transmission systems with fewer gears but optimal gear ratios. If this trend is realized, the manufacturing cost of HEVs could be largely reduced.

Vehicle power train optimization using multi-objective bird swarm algorithm

In this paper, a multi-objective bird swarm algorithm (MOBSA) is proposed to cope with multi-objective optimization problems. The algorithm is explored based on BSA which is an evolutionary algorithm suitable for single objective optimization. In this paper, non-dominated sorting approach is used to distinguish optimal solutions and parallel coordinates is applied to evaluate the distribution density of non-dominated solution and further update the external archive when it is full to overflowing, which ensure faster convergence and more widespread of Pareto front. Then, the MOBSA is adopted to optimize benchmark problems. The results demonstrate that MOBSA gets better performance compared with NSGA-II and MOPSO. Since a vehicle power train problem could be treated as a typical multi-objective optimization problem with constraints, with integration of constrained non-dominated solution, MOBSA is adopted to acquire optimal gear ratios and optimize vehicle power train. The results compared with other popular algorithm prove the proposed algorithm is more suitable for constrained multi-objective optimization problem in engineering field.

Component Sizing of Parallel Hybrid Electric Vehicle Using Optimal Search Algorithm

In designing a parallel hybrid electric vehicle, it is essential to select the optimal capacity of power sources and the optimal gear ratio of the torque coupler. The capacity of the power sources and the gear ratio of the torque coupler should be optimized simultaneously. However, since this process is excessively time-consuming, previous studies have selected the gear ratio of the torque coupler and then selected the capacity of power source. However, this approach cannot guarantee global optimization. In this paper, a feasible region is defined to satisfy the required performance of vehicle such as maximum speed, hill-climbing. and feasible points are selected inside the feasible region. In the conventional technique, the global optimal solution is obtained by simulating all feasible points. In the optimization technique, optimal points are simulated within the feasible region using several optimal search algorithms, such as the golden section search algorithm and the hillclimbing search algorithm. And using these algorithms, the number of simulations is reduced and the capacity of the power source and the gear ratio of the torque coupler are optimized simultaneously. Finally, the validity of the component sizing results is verified by comparing the global optimal solution obtained by applying the conventional technique with the solution obtained by applying the proposed optimization technique.

Design, Modelling and Analysis of Tilted Human Powered Vehicle

Human Powered Vehicle are powered only by muscular-strength. HPV’s are moving in road, air, in and under the water. Recumbent bike is one of the type of HPV. Recumbent bike is bicycle with inclined backward seat position and the pedals and bottom bracket are front. In this paper human powered Vehicle (HPV) is designed with a new dimension and analysed. Chassis designed with aerodynamic calculation, its CAD model analysed in Ansys for strength. Fairing is analysed with CFD. So that we can develop new low weight aerodynamic fairing, and four bar tilting mechanism. New design also gives optimum gear ratio with recumbent sitting position. The tricycle has the goals of developing innovative processes and designs for a trike that can be tilted at high speeds for enhanced turning at corners and stable at very low speeds too. The objective is to improve reliability and stability by equipping the vehicle with an aerodynamic fairing such that it can outperform conventional Human Powered Vehicle in all common applications.

Gear Ratio Optimization of a Multi-Speed Transmission for Electric Dump Truck Operating on the Structure Route

Research demonstrated that the application of a multi-speed transmission could improve the dynamic and economic performance of electric vehicles. This paper deals with a novel multi-speed transmission for the electric dump truck (EDT) operating on the structure route (SR), which has a definite starting point and end point without complex traffic conditions. To optimize the gear ratio and shift schedule to reduce energy consumption in such conditions, the mathematical model of the transmission and the dynamic model of the EDT are initially required. Following this, the shift schedule is presented according to the motor efficiency map. After that, the gear ratio optimization is carried out by a particle swarm optimization (PSO) algorithm. Moreover, the proposed EDT is compared with an EDT with a single-speed transmission. The simulation results show that the energy consumption is reduced by 6.1%.