Direct Torque Feedback for Accurate Engine Torque Delivery and Improved Powertrain Performance

2015 ◽  
Author(s):  
Anwar Alkeilani ◽  
Le Yi Wang ◽  
Hao Ying
Keyword(s):  
Feedback Control ◽  
Fuel Economy ◽  
Torque Control ◽  
Estimation Accuracy ◽  
Torque Sensor ◽  
Cruise Control ◽  
Sensor Reading ◽  
Engine Torque ◽  
Torque Estimation ◽  
Gain Margin

At the present time, both control and estimation accuracies of engine torque are causes for under-achieving optimal drivability and performance in today’s production vehicles. The major focus in this area has been to enhance torque estimation and control accuracies using existing open-loop torque control and estimation structures. Such an approach does not guarantee optimum torque tracking accuracy and optimum estimation accuracy due to air flow and efficiencies estimations errors. Furthermore, current approach overlooks the fast torque path tracking which does not have any related feedback. Recently, explicit torque feedback control has been proposed in the literature using either estimated or measured torques as feedback to control the torque using the slow torque path only. We propose the usage of a surface acoustic wave (SAW) torque sensor to measure the engine brake torque and feedback the signal to control the torque using both the fast and slow torque paths utilizing an inner-outer loop control structure. The fast torque path feedback is coordinated with the slow torque path by a novel method using the potential torque that is adapted to the sensor reading. The torque sensor signal enables a fast and explicit torque feedback control that can correct torque estimation errors and improve drivability, emission control, and fuel economy. Control-oriented engine models for the 3.6L engine are developed. Computer simulations are performed to investigate the advantages and limitations of the proposed control strategy, versus the existing strategies. The findings include an improvement of 14% in gain margin and 60% in phase margin when the torque feedback is applied to the cruise control torque request at the simulated operating point. This study demonstrates that the direct torque feedback is a powerful technology with promising results for improved powertrain performance and fuel economy.

Direct Torque Feedback for Accurate Engine Torque Delivery and Improved Powertrain Performance

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Anwar Alkeilani ◽  
Le Yi Wang ◽  
Hao Ying
Keyword(s):  
Feedback Control ◽  
Fuel Economy ◽  
Torque Control ◽  
Torque Sensor ◽  
Cruise Control ◽  
Estimation Errors ◽  
Sensor Reading ◽  
Engine Torque ◽  
Torque Estimation ◽  
Gain Margin

At the present time, both control and estimation accuracies of engine torque are causes for underachieving optimal drivability and performance in today's production vehicles. The major focus in this area has been to enhance torque estimation and control accuracies using existing open loop torque control and estimation structures. Such an approach does not guarantee optimum torque tracking accuracy and optimum estimation accuracy due to air flow and efficiency estimation errors. Furthermore, current approach overlooks the fast torque path tracking which does not have any related feedback. Recently, explicit torque feedback control has been proposed in the literature using either estimated or measured torques as feedback to control the torque using the slow torque path only. We propose the usage of a surface acoustic wave (SAW) torque sensor to measure the engine brake torque and feedback the signal to control the torque using both the fast and slow torque paths utilizing an inner–outer loop control structure. The fast torque path feedback is coordinated with the slow torque path by a novel method using the potential torque that is adapted to the sensor reading. The torque sensor signal enables a fast and explicit torque feedback control that can correct torque estimation errors and improve drivability, emission control, and fuel economy. Control oriented engine models for the 3.6L engine are developed. Computer simulations are performed to investigate the advantages and limitations of the proposed control strategy versus the existing strategies. The findings include an improvement of 14% in gain margin and 60% in phase margin when the torque feedback is applied to the cruise control torque request at the simulated operating point. This study demonstrates that the direct torque feedback is a powerful technology with promising results for improved powertrain performance and fuel economy.

Engine Torque Estimation Based on BP Neural Network

2011 ◽  
Vol 403-408 ◽  
pp. 2848-2851
Author(s):  
Kai Sheng Huang ◽  
Dong Liang Wang ◽  
Zhi Hua Lin ◽  
Xiang Rui Zeng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Estimation Method ◽  
Time Estimation ◽  
Torque Control ◽  
Steady Condition ◽  
Estimation Model ◽  
Engine Torque ◽  
Unsteady Condition ◽  
Torque Estimation

Engine torque estimation function is the base of engine torque control. This paper establishes the model for engine torque estimation respectively under steady condition and unsteady condition based on BP Neural network, and develops a new engine torque real-time estimation method. The experiment results under steady condition and unsteady condition show that the engine torque estimation model can estimate the engine output torque and the precision is remarkable.

A Redundant Manipulator Joint Torque Estimation Method Based on Disturbance Observer

2020 ◽  
Vol 29 (07n08) ◽  
pp. 2040015
Author(s):  
Xun Liu ◽  
Yaqiu Liu ◽  
Hanchen Zhao
Keyword(s):  
Error Compensation ◽  
Disturbance Observer ◽  
Control Method ◽  
Estimation Method ◽  
Industrial Robots ◽  
Torque Control ◽  
Joint Torque ◽  
Torque Sensor ◽  
Torque Estimation ◽  
Joint Torque Sensor

With the continuous development of the robot industry, both industrial robots and collaborative robots are developing towards light type and intelligence. The core issue is that how to improve the dynamic control performance of robots and reduce costs. The accurate torque feedback control can be achieved by introducing a joint torque sensor. The disadvantages brought by it are higher cost and the limited performance of the torque sensor. Therefore, on the basis of the traditional current estimated torque, combined with the accurate joint torque data fed back by the torque sensor, a method to estimate the harmonic transmission torque in the joint based on the disturbance observer is proposed, and a joint torque model is constructed. At the same time, the compensation factor is introduced to improve the accuracy of torque estimation. In the method proposed in this paper, the theoretical position and actual position, speed difference and motor current of the dual encoder on the motor side and the link side are used to estimate the harmonic transmission torque through the disturbance observer, and the corresponding coefficient is identified. By calibrating the transmission error compensation term and friction force with the torque sensor, the joint torque estimation model is obtained, and the sensorless joint torque estimation can be realized. This method does not require additional torque error compensation caused by harmonic drive deformation in the controller. Therefore, the torque control method without torque sensor is adopted in batch, which is not affected by the configuration and dynamic parameters of the manipulator. In the experiment, the output data of the joint torque sensor is used for testing and comparison. Through the single joint and redundant robot manipulator integration testing, the effectiveness of the proposed joint torque estimation method is verified.

On the estimation of optimal state-of-charge trajectory for plug-in hybrid electric buses using trip information

2021 ◽  
pp. 095440702110410
Author(s):  
Naser Sina ◽  
Vahid Esfahanian ◽  
Mohammad Reza Hairi Yazdi
Keyword(s):  
Fuel Economy ◽  
Dynamic Programming Algorithm ◽  
Optimal Solution ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Programming Algorithm ◽  
Factors Affecting ◽  
Optimal State ◽  
Energy Management Systems ◽  
Hybrid Electric

Plug-in hybrid electric buses are a viable solution to increase the fuel economy. In this framework, precise estimation of optimal state-of-charge trajectory along the upcoming driving cycle appears to play a pivotal role in the way to approach the globally optimal fuel economy. This paper aims to conduct a parametric study on the key factors affecting the estimation of optimal state-of-charge trajectory, including trip information availability and trip segment distance, and to provide a guideline for the design and implementation of predictive energy management systems. To accomplish this, the dynamic programming algorithm is employed to obtain the solution of optimal control problem for the sampled driving cycles in a particular bus route. A large database comprising of driving features of the cycles and the optimal solution is developed which then is used to construct a neural network based estimator for obtaining the optimal state-of-charge trajectory. The main results show promising performance of the proposed method with about 76% reduction in the root mean square error of the estimated trajectory comparing to the linear state-of-charge trajectory assumption. Moreover, the robustness of the estimator is verified through simulation and it is observed that appropriate choice of trip segment distance is vital to improve the estimation accuracy, especially in case of uncertain prediction of trip information.

scholarly journals An automated driver based on convergent vector fields

2002 ◽  
Vol 216 (4) ◽  
pp. 329-347 ◽  
Author(s):  
T J Gordon ◽  
M C Best ◽  
P J Dixon
Keyword(s):  
Vector Field ◽  
Feedback Control ◽  
Field Analysis ◽  
Driver Model ◽  
Cruise Control ◽  
Essential Information ◽  
Mass Centre ◽  
Reference Path ◽  
Driving Task ◽  
Specific Implementation

This paper describes a new general framework for the action of an automated driver (or driver model) to provide the control of longitudinal and lateral dynamics of a road vehicle. The context of the problem is assumed to be in high-speed competitive driving, as in motor racing, where the requirement is for maximum possible speed along a track, making use of a reference path (racing line) but with the capacity for obstacle avoidance and recovery from large excursions. While not necessarily representative of a human driver, the analysis provides worthwhile insight into the nature of the driving task and offers a new approach for vehicle lateral and longitudinal control; it also has applications in less demanding applications such as Advanced Cruise Control systems. As is common in the literature, the driving task is broken down into two distinct subtasks: path planning and local feedback control. In the first of these tasks, an essentially geometric approach is taken here, which makes use of a vector field analysis. At each location x the automated driver is to prescribe a vector w for the desired vehicle mass centre velocity; the spatial distribution and global properties of w( x) provide essential information for stability analysis, as well as control reference. The resulting vector field is considered in the context of limited friction and limited mass centre accelerations, leading to constraints on ∇ w. Provided such constraints are satisfied, and using suitable adaptation of w( x) when required, it is shown that feedback control can be applied to guarantee stable asymptotic tracking of a reference path, even under limit handling conditions. A specific implementation of the method is included, using dual non-linear SISO (single-input single-output) controllers.

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