scholarly journals A Control Algorithm for the Novel Regenerative–Mechanical Coupled Brake System with by-Wire Based on Multidisciplinary Design Optimization for an Electric Vehicle

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2322 ◽  
Author(s):  
Changran He ◽  
Guoye Wang ◽  
Zhangpeng Gong ◽  
Zhichao Xing ◽  
Dongxin Xu
Keyword(s):  
Multidisciplinary Design Optimization ◽  
Control Algorithm ◽  
Multidisciplinary Design ◽  
Regenerative Braking ◽  
The Novel ◽  
Control Parameters ◽  
Torque Distribution ◽  
Distribution Strategy ◽  
Braking Torque ◽  
Sample Points

Current regenerative braking systems in electric vehicles have several problems, such as complex structures, too many control parameters, and inconsistent braking responses. To solve these problems, a control algorithm with multidisciplinary design optimization (MDO) is proposed based on the novel regenerative–mechanical coupled brake-by-wire system. A dynamic model of the novel regenerative braking system was established to analyze the mechanism of coupled braking and propose a braking torque distribution strategy. To realize a better balance between the optimum braking stability and the maximum regenerative energy recovery based on the braking torque distribution strategy and sample points, the MDO mathematical model was developed to optimize the control parameters with the collaborative optimization algorithm. The finite sample points comprising the vehicle speed, battery state-of-charge, and braking severity were obtained through an optimal Latin hypercube design and represent the overall design space. A network was established based on the sample points and the optimization results. Using this network, the in-depth characteristics of the sample points and the optimization results were obtained through supervised learning to develop the control algorithm for vehicle braking. A simulation was performed using the normal braking condition, and the simulation results demonstrated that the control algorithm has higher control precision than conventional methods and better real-time performance than online optimization.

Intelligent optimization of EV comfort based on a cooperative braking system

2021 ◽  
pp. 095440702110044
Author(s):  
Lingying Zhao ◽  
Min Ye ◽  
Xinxin Xu
Keyword(s):  
System Model ◽  
Regenerative Braking ◽  
Coupling Mechanism ◽  
Intelligent Algorithm ◽  
Distribution Strategy ◽  
Braking System ◽  
Logic Diagram ◽  
Braking Torque ◽  
Braking Force ◽  
The Time Domain

To address the comfort of an electric vehicle, a coupling mechanism between mechanical friction braking and electric regenerative braking was studied. A cooperative braking system model was established, and comprehensive simulations and system optimizations were carried out. The performance of the cooperative braking system was analyzed. The distribution of the braking force was optimized by an intelligent method, and the distribution of a braking force logic diagram based on comfort was proposed. Using an intelligent algorithm, the braking force was distributed between the two braking systems and between the driving and driven axles. The experiment based on comfort was carried out. The results show that comfort after optimization is improved by 76.29% compared with that before optimization by comparing RMS value in the time domain. The reason is that the braking force distribution strategy based on the optimization takes into account the driver’s braking demand, the maximum braking torque of the motor, and the requirements of vehicle comfort, and makes full use of the braking torque of the motor. The error between simulation results and experimental results is 5.13%, which indicates that the braking force’s distribution strategy is feasible.

scholarly journals Active torque distribution strategy for electric vehicle independent axle drive

2019 ◽  
Vol 100 ◽  
pp. 00037
Author(s):  
Artur Kopczyński
Keyword(s):  
Electric Vehicle ◽  
Electric Machines ◽  
Torque Distribution ◽  
Distribution Strategy ◽  
Braking Torque

Vehicles equipped with an electric independent axle drive have different properties compared to conventional vehicles. Distribution of driving or braking torque can be achieved by the proper control of the operation of electric machines without applying additional mechanisms. In this paper a method of active torque distribution between front and rear axles is presented. The method allows to use the maximum tyres adhesion and minimize slips. The results of simulation tests are presented, in which the active torque distribution with evenly torque distribution were compared.

scholarly journals Energy Recovery Strategy Numerical Simulation for Dual Axle Drive Pure Electric Vehicle Based on Motor Loss Model and Big Data Calculation

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Huiyuan Xiong ◽  
Xionglai Zhu ◽  
Ronghui Zhang
Keyword(s):  
Numerical Simulation ◽  
Big Data ◽  
Feedback Control ◽  
Energy Recovery ◽  
Recovery Efficiency ◽  
Feedback Control System ◽  
Torque Distribution ◽  
Distribution Strategy ◽  
Braking Torque ◽  
Braking Force

Aiming at the braking energy feedback control in the optimal energy recovery of the two-motor dual-axis drive electric vehicle (EV), the efficiency numerical simulation model based on the permanent magnet synchronous motor loss was established. At the same time, under different speed and braking conditions, based on maximum recovery efficiency and data calculation of motor system, the optimization motor braking torque distribution model was established. Thus, the distribution rule of the power optimization for the front and rear electric mechanism was obtained. This paper takes the Economic Commission of Europe (ECE) braking safety regulation as the constraint condition, and finally, a new regenerative braking torque distribution strategy numerical simulation was developed. The simulation model of Simulink and CarSim was established based on the simulation object. The numerical simulation results show that under the proposed strategy, the average utilization efficiency of the motor system is increased by 3.24% compared with the I based braking force distribution strategy. Moreover, it is 9.95% higher than the maximum braking energy recovery strategy of the front axle. Finally, through the driving behavior of the driver obtained from the big data platform, we analyze how the automobile braking force matches with the driver’s driving behavior. It also analyzes how the automobile braking force matches the energy recovery efficiency. The research results in this paper provide a reference for the future calculation of braking force feedback control system based on big data of new energy vehicles. It also provides a reference for the modeling of brake feedback control system.

Braking torque distribution for hybrid electric vehicles based on nonlinear disturbance observer

2019 ◽  
Vol 233 (13) ◽  
pp. 3327-3341
Author(s):  
Xiuchun Zhao ◽  
Ge Guo
Keyword(s):  
Electric Vehicles ◽  
Disturbance Observer ◽  
Hybrid Electric Vehicles ◽  
State Of Charge ◽  
Regenerative Braking ◽  
Nonlinear Disturbance Observer ◽  
Torque Distribution ◽  
Battery State Of Charge ◽  
Braking Torque ◽  
Nonlinear Disturbance

This article develops a two-layer brake control framework for hybrid electric vehicles equipped with both hydraulic and regenerative braking systems. In order to obtain better braking performance and higher regenerative braking efficiency, a cooperative braking control strategy is presented. In the first layer, a simple but robust brake controller is proposed to overcome the uncertainties of road condition and load variation by introducing a nonlinear disturbance observer. The convergence and stability are proved through the Lyapunov theory. In the second layer, a novel braking torque distribution strategy is proposed based on battery state of charge, which can recover more braking energy and improve the health of the battery. By simulation, the braking strategy is proved to be effective under various conditions and it shows a good compromise between the battery state of charge health and the regenerated energy recovery.

scholarly journals A multidisciplinary coupling relationship coordination algorithm using the hierarchical control methods of complex systems and its application in multidisciplinary design optimization

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668522 ◽  
Author(s):  
Rong Yuan ◽  
Haiqing Li
Keyword(s):  
Complex Systems ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Control Method ◽  
Hierarchical Control ◽  
System Level ◽  
Multidisciplinary Design ◽  
Control Parameters ◽  
Coupling Relationship ◽  
Design Variables

Because of the increasing complexity in engineering systems, multidisciplinary design optimization has attracted increasing attention. High computational expense and organizational complexity are two main challenges of multidisciplinary design optimization. To address these challenges, the hierarchical control method of complex systems is developed in this study. Hierarchical control method is a powerful way which has been utilized widely in the control and coordination of large-scale complex systems. Here, a hierarchical control method–based coupling relationship coordination algorithm is proposed to solve multidisciplinary design optimization problems. Coupling relationship coordination algorithm decouples the involved disciplines of a complex system and then optimizes each discipline objective at sub-system level. Coupling relationship coordination algorithm can maintain the consistency of interaction information (or in other words, sharing design variables and coupling design variables) in different disciplines by introducing control parameters. The control parameters are assigned by the coordinator at system level. A mechanical structure multidisciplinary design optimization problem is solved to illustrate the details of the proposed approach.

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