Wheel Torque Research Articles

Energy reduction by power loss minimisation through wheel torque allocation in electric vehicles: a simulation-based approach

ABSTRACT As vehicles become increasingly electrified, electrical machines for propulsion can be divided into many sources making the vehicle highly over-actuated. For over-actuated vehicles, the allocation of a propulsive force is an underdetermined process with respect to both the number of wheels and electrical machines. Hence, the allocation can be made to favour particular attributes such as energy consumption. In this study, a vehicle equipped with four identical electric motors with a fixed transmission ratio connected through a half-shaft and a coupling to one wheel respectively is driven a 2-h-long city cycle in the vicinity of Göteborg. Two different control allocation methods are presented to distribute torque momentaneously based on driver request while minimising power losses in electric motor and inverter as well as tyres. One method is a quadratic programming optimisation and the other is an offline exhaustive search method resulting in a look-up table based on requested torque and actual speed. The two methods are compared to other torque distribution strategies based on fixed distribution ratio and equal tyre-to-road friction utilisation. It was found that using the developed optimisation algorithms, a reduction of up to 3.9 in energy consumption can be obtained.

MPC-based path tracking with PID speed control for high-speed autonomous vehicles considering time-optimal travel

In order to track the desired path as fast as possible, a novel autonomous vehicle path tracking based on model predictive control (MPC) and PID speed control was proposed for high-speed automated vehicles considering the constraints of vehicle physical limits, in which a forward-backward integration scheme was introduced to generate a time-optimal speed profile subject to the tire-road friction limit. Moreover, this scheme was further extended along one moving prediction window. In the MPC controller, the prediction model was an 8-degree-of-freedom (DOF) vehicle model, while the plant was a 14-DOF vehicle model. For lateral control, a sequence of optimal wheel steering angles was generated from the MPC controller; for longitudinal control, the total wheel torque was generated from the PID speed controller embedded in the MPC framework. The proposed controller was implemented in MATLAB considering arbitrary curves of continuously varying curvature as the reference trajectory. The simulation test results show that the tracking errors are small for vehicle lateral and longitudinal positions and the tracking performances for trajectory and speed are good using the proposed controller. Additionally, the case of extended implementation in one moving prediction window requires shorter travel time than the case implemented along the entire path.

Drag Torque Prediction of Automotive Wheel Bearing Seals Considering Material and Geometrical Uncertainties Using Monte Carlo Simulation

In this study, the drag torque was predicted considering the uncertainties in geometry and material properties of automotive wheel bearing seals. For this, the deformation of the seals was firstly analyzed using numerical analysis based on the finite element method. Three types of experiments for material properties of rubber were conducted to improve the reliability of deformation analysis for the rubber seals. In addition, hyperelastic analysis was conducted using the experimental results. To consider the geometrical and material uncertainties of the seals, Monte Carlo simulation was performed considering variations in dimension and rubber properties. As Monte Carlo simulation to analyze the deformation of the seals takes a very high computational cost, a response surface was created using design of experiment (DOE) and then Monte Carlo simulation was performed using this response surface. Based on the simulation results, sensitivity analysis was conducted to analyze design variables that affect the drag torque of the rubber seal. Finally, variation in the drag torque of the automotive wheel bearing seals was evaluated using the results of Monte Carlo simulation.

COMPUTER SIMULATION OF ELECTRIC VEHICLE ACCELERATION PROCESSES WITH DIFFERENT POSITIONS OF THE MASS CENTER

Due to the electrification of modern vehicles the role of the electric drive is growing as the main mover. In conditions of increasing requirements for the safety controllability and energy efficiency of a vehicle on electric traction,it is actual to take into account the dynamic properties of a vehicle in various driving modes when developing an automatic control system. In the work it is investigated the influence of the mass center position on the redistribution of forces during acceleration on a straight-line section. Taking into consideration the position of the mass center in the control system allows redistributing the desired moment to the wheels with better adhesion to the surface, which increases the safety and controllability of the vehicle, as well as minimizes energy costs on wheels with the worst adhesion. The aim of the work is to investigate the influence of the mass center position on the dynamics of a vehicle with full, rear and front wheel drive using computer simulation. The mathematical description includes analytical expressions for the redistribution of the support reactions for each of the wheels, which makes it possible, on their basis, to carry out computer simulation of the electric vehicle acceleration on a straight-line section. For the indicated types of vehicle drives, a computer model has been developed that includes, in the automatic control system for torque redistribution, the coordinates of the mass center position, which are converted on the basis of analytical expressions into the physical parameters of the system.Computer simulation of acceleration from zero to one hundred km/h with full pressing of the accelerator pedal for nine different positions of the mass center and three types of drive was carried out. Data were obtained on the change in accelerations, support reactions and torque of wheels during acceleration at various mass center positions. Based on the results obtained, the most preferable coordinates of the mass center for each type of drive from the point of view of the acceleration dynamics on a straight section were determined. The developed computer model can be used to study the dynamics of an electric vehicle when cornering, as well as to study energy indicators in all dynamic driving modes.

Analytical procedures for determining global minimum friction requirement for a six wheeled rover negotiating hard uneven terrain

The problem of determining wheel torques of a rover, to minimize friction requirement, has been addressed by many researchers. We address it by trying to understand the ways in which local optima can occur, and on the basis of that understanding, develop analytical and non-iterative algorithms for determining global optima. The problem is posed in two variations, with normal reaction forces on wheels bounded below by (a) zero, and (b) a positive limit. In both cases, the nature of optima is studied comprehensively, and illustrated by solving examples. Explicit expressions are used to find roots of up to a cubic polynomial. Our algorithms are on the average about an order of magnitude faster than an SQP based general optimization solver, when the latter is started from random guesses. Moreover, our algorithms were able to reach global minima without fail for all examples solved, which number more than a thousand.

On Nonlinear Model Predictive Control for Energy-Efficient Torque-Vectoring

A recently growing literature discusses the topics of direct yaw moment control based on model predictive control (MPC), and energy-efficient torque-vectoring (TV) for electric vehicles with multiple powertrains. To reduce energy consumption, the available TV studies focus on the control allocation layer, which calculates the individual wheel torque levels to generate the total reference longitudinal force and direct yaw moment, specified by higher level algorithms to provide the desired longitudinal and lateral vehicle dynamics. In fact, with a system of redundant actuators, the vehicle-level objectives can be achieved by distributing the individual control actions to minimize an optimality criterion, e.g., based on the reduction of different power loss contributions. However, preliminary simulation and experimental studies – not using MPC – show that further important energy savings are possible through the appropriate design of the reference yaw rate. This paper presents a nonlinear model predictive control (NMPC) implementation for energy-efficient TV, which is based on the concurrent optimization of the reference yaw rate and wheel torque allocation. The NMPC cost function weights are varied through a fuzzy logic algorithm to adaptively prioritize vehicle dynamics or energy efficiency, depending on the driving conditions. The results show that the adaptive NMPC configuration allows stable cornering performance with lower energy consumption than a benchmarking fuzzy logic TV controller using an energy-efficient control allocation layer.

PROPOSE SAFETY ENGINEERING CONCEPT SPEED LIMITER AND FATIGUE CONTROL USING SLIFA FOR TRUCK AND BUS

In 2015, there were 55 deaths from 6,231 accident cases that occurred in Jakarta. A severe problem in Indonesia is the absence of a unique safety device in both commercial transport or personal vehicles and the very high complexity problem of human highways. Consequently, there are many traffic accidents caused by the negligence of the driver, such as driving a vehicle in a drunken, tired, drowsy, or over-limit speed. Therefore, it needs to be innovative using devices to increase speed but able to detect the level of tired or sleepy drivers. This paper tries to propose a concept of improving safety engineering by developing devices that can control the speed and level of safety of trucks and buses, named SLIFA. The proposed device captures the driver's condition by looking at the eyes, size of mouth evaporating, and heart rate conditions. Theses condition will be measured with a particular scale to determine the fatigue level of the driver. Some performance tests have been carried out on truck and bus with 122 Nm and 112 Nm torque wheels and 339 HP and 329 HP power values, respectively, and the minimum speed is 62 km/h. At a top speed of 70 km / h, the torque and power of the truck are 135Nm and 370HP, with average fuel consumption of 3.43 liters/km before SLIFA installation and average fuel consumption of 4.2 liters/km after SLIFA installation. SLIFA can be said to have functional eligibility and can cut fuel consumption by 81 percent.

PocketQube Pico-Satellite Attitude Control: Implementation and Testing

Attitude control for CubeSats and small satellites has been widely researched. Nonetheless, most of the literature is based on simulations, with limited availability of actual implementation results. This article tries to fill this gap by highlighting implementation difficulties and proposing appropriate workable solutions. The difficulty of direct reaction wheel (RW) torque feedback for attitude control of a 1p PocketQube (PQ) pico-satellite is noted and addressed. A simple control strategy implementable on a microcontroller is then proposed, showing how direct RW torque control can be achieved through angular velocity measurement without using derivative feedback. A linear quadratic Gaussian controller with integral action in quaternion formulation is used for attitude control, and a reference input boundary caused by the use of the standard integral action scheme is highlighted. A simple solution is then proposed, showing how using the error quaternion removes this boundary. An extended Kalman filter in quaternion formulation is used for attitude determination by fusing data from three sensors, namely, a magnetometer, solar cells, and a gyroscope. An accelerometer is used in place of the solar cells for simplified laboratory testing. Magnetometer error due to surrounding interference is addressed through calibration. A string-suspended model of the UoMBSat-1 PocketQube pico-satellite is used for attitude control testing. Preliminary single-axis tests are performed using a PID controller to determine the satellite parameters and actuators verification. The proposed linear quadratic Gaussian controller is then tested and validated experimentally in the laboratory setting.

MPC-based path tracking with PID speed control for autonomous vehicles

In this paper, a new coupled lateral and longitudinal controller based on model predictive control (MPC) framework was proposed for an autonomous vehicle to track the desired trajectory and speed. Considering the constraints of control input limit and state output admissible, we used a spatial-based 8 degrees of freedom (DOF) vehicle model as the prediction model and used a high-fidelity model, i.e., a 14-DOF vehicle model as the plant model in the formulation of MPC algorithm. For the lateral control, the MPC controller generates the optimal road-wheel steering angle; for the longitudinal control, the PID controller embedded in the optimization solution generates the total driving or braking wheel torque. All these control inputs were passed to the plant simultaneously. The developed vehicle models were simulated with step steering input and compared with the simulation result of CarSim vehicle model for validation. We implemented the proposed controller for path tracking and speed control with MATLAB considering an 8-shaped curved trajectory as the reference. The simulation results showed that the path tracking and speed tracking performance were good using the combined lateral and longitudinal control strategy.

Active Disturbance Rejection Control of Differential Drive Assist Steering for Electric Vehicles

The differential drive assist steering (DDAS) system makes full use of the advantages of independent control of wheel torque of electric vehicle driven by front in-wheel motors to achieve steering assistance and reduce the steering effort of the driver, as the electric power steering (EPS) system does. However, as an indirect steering assist technology that applies steering system assistance via differential drive, its linear control algorithm, like existing proportion integration differentiation (PID) controllers, cannot take the nonlinear characteristics of the tires’ dynamics into account which results in poor performance in road feeling and tracking accuracy. This paper introduces an active disturbance rejection control (ADRC) method into the control issue of the DDAS. First, the third-order ADRC controller of the DDAS is designed, and the simulated annealing algorithm is used to optimize the parameters of ADRC controller offline considering that the parameters of ADRC controller are too many and the parameter tuning is complex. Finally, the 11-DOF model of the electric vehicle driven by in-wheel motors is built, and the standard working conditions are selected for simulation and experimental verification. The results show that the ADRC controller designed in this paper can not only obviously reduce the steering wheel effort of the driver like PID controller, but also have better nonlinear control performance in tracking accuracy and smooth road feeling of the driver than the traditional PID controller.

Torque Control of Electric Power Steering Systems Based on Improved Active Disturbance Rejection Control

In the electric power steering (EPS) system, low-frequency disturbances such as road resistance, irregular mechanical friction, and changing motor parameters can cause steering wheel torque fluctuation and discontinuity. In order to improve the steering wheel torque smoothness, an improved torque control method of an EPS motor is proposed in the paper. A target torque algorithm is established, which is related to steering process parameters such as steering wheel angle and angular speed. Then, a target torque closed-loop control strategy based on the improved ADRC is designed to estimate and compensate the internal and external disturbance of the system, so as to reduce the impact of the disturbance on the steering torque. The simulation results show that the responsiveness and anti-interference ability of the improved ADRC is better than that of the conventional ADRC and PI. The vehicle experiment shows that the proposed control method has good motor current stability, steering torque smoothness, and flexibility when there is low-frequency disturbance.

Analysis of Tillage Depth and Gear Selection for Mechanical Load and Fuel Efficiency of an Agricultural Tractor Using an Agricultural Field Measuring System

This study was conducted to analyze the effects of tillage depth and gear selection on the mechanical load and fuel efficiency of an agricultural tractor during plow tillage. In order to analyze these effects, we developed an agricultural field measuring system consisting of a load measurement part (wheel torque meter, proximity sensor, and real-time kinematic (RTK) global positioning system (GPS)) and a tillage depth measurement part (linear potentiometer and inclinometer). Field tests were carried out using moldboard plows with a maximum tillage depth of 20 cm and three gear selections (M2H, M3L, and M3H) in a rice stubble paddy field for plow tillage. The average travel speed and slip ratio had the lowest M2H and the highest M3L. M3H had the highest theoretical speed, but the travel speed was 0.13 km/h lower than M3L due to the reduction in the axle rotational speed at deep tillage depth. Regarding engine load, the higher the gear, the greater the torque and the lower the axle rotation speed. The front axle load was not significantly affected by the tillage depth as compared to other mechanical parts, except for the M3H gear. The rear axle load generated about twice the torque of the front wheel and overall, it tended to show a higher average rear axle torque at higher gear selection. The rear axle load and fuel rate were found to be most affected by the combination of the tillage depth and gear selection combination. Overall, field test results show that the M3H had the highest fuel efficiency and a high working speed while overcoming high loads at the same tillage depth. In conclusion, M3H is the most suitable gear stage for plow cultivation, and the higher the gear stage and the deeper the tillage depth during plowing, the higher the fuel efficiency. The results of this study will be useful for analyzing mechanical load and fuel efficiency during farm operations. In a future study, we will conduct load analysis studies in other farming operations that consider various soil mechanics factors as well as tillage depths and gear selections.

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>

Control of the nonlinear dynamics of a truck and trailer combination

The directional instability problem of a truck and trailer combination in a forward motion has been investigated by developing a dynamic model of the planar rigid-body dynamics of the system. Two control strategies have been devised based on the reference model following controller configuration. The main objective of the controllers is to ensure that the nonlinear model follows the desired response generated by a linearized version of the truck and trailer model. The first RFMC uses an integral-plus-state feedback controller for its compensator, which was designed based on the eigenstructure assignment methodology. The second RFMC relies on a sliding mode compensator. The control inputs were applied through the actuation of differential wheel torques. Different driving scenarios and road conditions, such as braking and accelerating maneuver with a flat tire on a dry road, and a double-lane change maneuver on a slippery road, were considered. Overall, the simulation results demonstrated the superiority of the sliding mode controller over the integral-plus-state feedback controller in ensuring better tracking of the truck and trailer combination to a target path under different driving maneuvers.

Assistance Quality Analysis and Robust Control of Electric Vehicle With Differential Drive Assisted Steering System

As a novel power steering technology, Differential Drive Assisted Steering (DDAS) technology for the independent-wheel-drive electric vehicle has gradually appealed to researcher’s attention. However, the previous experimental results show that its assistance quality cannot be fully accepted due to its caused sensitive steering wheel torque fluctuation in actual work environment. According to the working principle of the DDAS system, it is founded that the road roughness, the front wheel alignment parameters and sensor noise are the main factors that influence the quality of assisted steering and driver’s road feel. Hence the three factors are added as interference into the ideal vehicle model. The simulation results and its comparison with the previous real vehicle tests confirm this causality between these factors considered and the steering wheel torque fluctuation of the DDAS system. Then a robust $H_{\infty }$ loop-shaping controller is designed to solve the issue caused by these inner interferences and outer noises. Simulations results validate the proposed controller and show better steering wheel torque performance than the traditional anti-windup PID controller published in the literature earlier. The proposed robust controller is further verified via real vehicle tests and the results are similar to the simulation results which can effectively suppress the steering wheel torque ripple, improve the anti-interference ability of DDAS system and greatly improve the driver’s road feel.

Physiological Measurement and Kansei Evaluation of Steering Feeling Affected by Steering Wheel Torque

Physiological Measurement and Kansei Evaluation of Steering Feeling Affected by Steering Wheel Torque

Development of a Fuzzy Logic Controller for Real-time Energy Optimization of a Hybrid vehicle

The ability to combine positive features of an internal combustion engine with those of an electric motor has been fundamental to the advancement of the high-performance energy-optimized hybrid vehicles. However, due to a lack of reliable and realistic hybrid vehicle models, much of the hybrid vehicle controller research has been limited to computer simulations. To overcome this shortcoming, this paper utilizes a highly reliable vehicle model (Autonomie) for simulation. A state-of-the-art fuzzy logic controller was developed that considers the battery state of charge, wheel torque demand and vehicle speed as the input variables. An ARM Cortex M3 microcontroller-based control hardware prototype was developed and the processor in loop simulation was performed to verify the feasibility of the developed controller in an embedded real-time application. The results of this study indicate that the developed fuzzy logic controller significantly improved the performance (up to 48%) of the hybrid vehicle in a real-time application compared to Autonomie's built-in controller. The processor in loop test results provide evidence of the effectiveness of the developed control algorithm in the embedded real-time form

Longitudinal and lateral control for four wheel steering vehicles

Vehicles evolving in harsh terrains are subject to physical phenomena with a much more important impact than in the case of road vehicle. The main problem we have to face is tire slippery which has to be taken into account when designing the control law to ensure an accurate tracking. In this paper we present a controller for cars equipped with 4 steering wheels. An accurate automatic trajectory tracking via vehicle wheel torque, front and rear steering is developed. This controller takes into account nonlinear tire effects to increase vehicle stability in presence of sliding. Promising results have been obtained with numerical simulations.

Automated Lane Changing With a Controlled Steering-Wheel Feedback Torque for Low Lateral Acceleration Purposes

To bridge the gap between autonomous and normal steering during low lateral acceleration maneuvers, a steering-wheel torque controller is developed. It consists of three controllers in series, a high-gain feedback steering-wheel torque controller, a nonlinear steering-angle controller, and a linear yaw-rate controller. A validated two degrees of freedom steering-system model, in combination with a single-track vehicle model, is used to determine the controller parameters. Driving tests with different drivers have been executed to find a comfortable steering-wheel torque/angle relation. This relation is maintained throughout the lane change by specifying the steering-wheel torque as a function of the steering-wheel angle tracking error. Experiments show promising results regarding the driver/vehicle interaction, but also show that a system to detect the lanes and the vehicle position on the road is required for further improvement.

Yaw Stability Control Based on hMPC for Dual-motor Driven Electric Vehicles

Since dual-motor driven electric vehicles (DDEVs) can achieve independent and accurate control of the wheel torque, this paper proposes a hierarchical yaw stability control strategy based on model predictive control (MPC) for DDEVs to improve the performance of vehicle yaw stability. A two-degree-of-freedom (2DOF) vehicle dynamic model is developed to calculate the desired vehicle states, which are used as the reference signals in the upper layer controller, based on linear MPC. In the lower layer, a hybrid MPC (hMPC) method is carried out for the torque distribution considering the nonlinear characteristics of the tire longitudinal force and the piecewise linearization of the longitudinal force of the tire is performed. Finally, the proposed strategy is evaluated in Matlab and the results indicate that the suggested hierarchical yaw stability control strategy can significantly improve the vehicle yaw stability.