scholarly journals Study on Mechanism Analysis of Skidding Prediction for Electric Vehicle Based on Time-Delay Effect of Force Transmission

2021 ◽  
Vol 12 (4) ◽  
pp. 171
Author(s):  
Ying Yang ◽  
Xiaoyu Wang ◽  
Yangchao Zhang
Keyword(s):  
Electric Vehicle ◽  
Friction Force ◽  
Prediction Method ◽  
Slip Rate ◽  
Typical Feature ◽  
Previous Method ◽  
Detection Methods ◽  
Mechanism Analysis ◽  
Force Transmission ◽  
Slip Factor

The electric vehicle anti-skidding control system is used to ensure the stability of the vehicle under any circumstances. There is a typical feature in most anti-skidding detection methods; the skidding occurs first, and then the detection is performed. For methods that rely on slip rate detection, more accurate vehicle speeds are required, which are often difficult to accurately observe. The previous method was detection and could not do prediction. Skidding prediction can improve driver reaction time and increase safety. Therefore, this paper proposes a prediction method that does not depend on the slip rate. The skidding prediction can be performed by relying on the driving torque, as well as the wheel speed. In this paper, the characteristics of the transmission from the driving force to the friction force in the vehicle model are analyzed. As for the distributed electric vehicle, the slip factor was designed with traction torque and friction force for skidding prediction by its sharp increase before the maximum adhesion point. The variation in the slip factor and time period of skidding are revealed. A multi-information merged prediction model is designed to improve reliability. The co-simulation and experimental verification based on the physical skidding simulation platform are carried out.

scholarly journals Trajectory planning method of robot sorting system based on S-shaped acceleration/deceleration algorithm

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881380 ◽  
Author(s):  
Qizhi Chen ◽  
Chengrui Zhang ◽  
Hepeng Ni ◽  
Xue Liang ◽  
Haitao Wang ◽  
...  
Keyword(s):  
Trajectory Planning ◽  
Prediction Method ◽  
Maximum Velocity ◽  
Previous Method ◽  
Planning Method ◽  
Robot Arm ◽  
Dynamic Prediction ◽  
Efficiency And Effectiveness ◽  
Sorting Efficiency ◽  
Sorting System

To improve the sorting accuracy and efficiency of sorting system with large inertia robot, this article proposes a novel trajectory planning method based on S-shaped acceleration/deceleration algorithm. Firstly, a novel displacement segmentation method based on assumed maximum velocity is proposed to reduce the computational load of velocity planning. The sorting area can be divided into four parts by no more than three steps. Secondly, since the positions of workpieces are dynamically changing, a dynamic prediction method of workpiece picking position has been presented to consider all the possible positions of the robot and the workpiece, so as to realize the picking position prediction of the workpiece at any positions. Each situation in this method can constitute an equation with only one solution, and the existence of the solution can be verified by the proposed graphical method. The simulations of the motion time of the sorting process show that the proposed method can significantly shorten the sorting time and improve the sorting efficiency compared with the previous method. Finally, this method was applied to the Selective Compliance Assembly Robot Arm (SCARA) robot for experiments. In the physical picking experiment, the missing-pick rate was less than 1%, which demonstrates the efficiency and effectiveness of this method.

scholarly journals Follow-up: Prospective compound design using the ‘SAR Matrix’ method and matrix-derived conditional probabilities of activity

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 75 ◽  
Author(s):  
Disha Gupta-Ostermann ◽  
Yoichiro Hirose ◽  
Takenao Odagami ◽  
Hiroyuki Kouji ◽  
Jürgen Bajorath
Keyword(s):  
Related Compound ◽  
Chemical Space ◽  
Prediction Method ◽  
Previous Method ◽  
Conditional Probabilities ◽  
Activity Prediction ◽  
Structure Activity ◽  
Related Series ◽  
Compound Series

In a previous Method Article, we have presented the ‘Structure-Activity Relationship (SAR) Matrix’ (SARM) approach. The SARM methodology is designed to systematically extract structurally related compound series from screening or chemical optimization data and organize these series and associated SAR information in matrices reminiscent of R-group tables. SARM calculations also yield many virtual candidate compounds that form a “chemical space envelope” around related series. To further extend the SARM approach, different methods are developed to predict the activity of virtual compounds. In this follow-up contribution, we describe an activity prediction method that derives conditional probabilities of activity from SARMs and report representative results of first prospective applications of this approach.

scholarly journals Predicting antibody affinity changes upon mutations by combining multiple predictors

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yoichi Kurumida ◽  
Yutaka Saito ◽  
Tomoshi Kameda
Keyword(s):  
Machine Learning ◽  
Molecular Mechanics ◽  
Prediction Method ◽  
Previous Method ◽  
Antibody Engineering ◽  
Antibody Affinity ◽  
High Affinity ◽  
Feature Importance ◽  
Importance Analysis ◽  
Improved Accuracy

Abstract Antibodies are proteins working in our immune system with high affinity and specificity for target antigens, making them excellent tools for both biotherapeutic and bioengineering applications. The prediction of antibody affinity changes upon mutations ($${{\Delta \Delta {\mathrm{G}}}}_{\mathrm{binding}}$$ Δ Δ G binding ) is important for antibody engineering. Numerous computational methods have been proposed based on different approaches including molecular mechanics and machine learning. However, the accuracy by each individual predictor is not enough for efficient antibody development. In this study, we develop a new prediction method by combining multiple predictors based on machine learning. Our method was tested on the SiPMAB database, evaluating the Pearson’s correlation coefficient between predicted and experimental $${{\Delta \Delta {\mathrm{G}}}}_{\mathrm{binding}}$$ Δ Δ G binding . Our method achieved higher accuracy (R = 0.69) than previous molecular mechanics or machine-learning based methods (R = 0.59) and the previous method using the average of multiple predictors (R = 0.64). Feature importance analysis indicated that the improved accuracy was obtained by combining predictors with different importance, which have different protocols for calculating energies and for generating mutant and unbound state structures. This study demonstrates that machine learning is a powerful framework for combining different approaches to predict antibody affinity changes.

Road Adhesion Coefficient Estimation

2011 ◽  
Vol 52-54 ◽  
pp. 1503-1508
Author(s):  
Yan An Sun ◽  
Lu Xiong ◽  
Zhuo Ping Yu ◽  
Yuan Feng ◽  
Long Jie Ren
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Slip Rate ◽  
Research Subject ◽  
Adhesion Coefficient ◽  
The Real ◽  
Coefficient Estimation ◽  
Improved Algorithm

Taking four in-wheel-motor electric vehicle as research subject, the paper focuses on the estimation of road adhesion coefficient based on curve. Through adding a forget factor, an improved algorithm on the basis of Forget Kalman Filter is proposed to estimate the slope of the curve at low slip rate. This new algorithm is proved effective through the real car test.

scholarly journals Rotor Temperature Safety Prediction Method of PMSM for Electric Vehicle on Real-Time Energy Equivalence

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Anjian Zhou ◽  
Changhong Du ◽  
Zhiyuan Peng ◽  
Qianlei Peng ◽  
Datong Qin
Keyword(s):  
Permanent Magnet ◽  
Real Time ◽  
Electric Vehicle ◽  
Load Capacity ◽  
Estimation Error ◽  
Permanent Magnet Synchronous Motor ◽  
Prediction Method ◽  
Time Algorithm ◽  
Synchronous Motor ◽  
Thermal Safety

The load capacity of the permanent magnet synchronous motor is limited by the rotor temperature, and the excessive temperature of the rotor will bring potential thermal safety problems of the system. Therefore, the accurate prediction of the rotor temperature of the permanent magnet synchronous motor for the electric vehicle is crucial to improve the motor performance and system operation safety. This paper studied the heating mechanism and the energy flow path of the motor and built the heat energy conversion model of the stator and rotor. The real-time algorithm to predict the rotor temperature was constructed based on the dissipative energy conservation of the stator of the motor rotor temperature. And the prediction method of the initial rotor temperature is fitted using the experimental results when the system is powered on. Finally, the test platform was set up to validate the rotor temperature accuracy. The results show that the motor rotor temperature estimation error under the dynamic operating condition is within ±5. The research provides a solution to improve the performance and thermal safety of the permanent magnet synchronous motor for electric vehicles.

scholarly journals Optimal Slip Ratio Based Fuzzy Control of Acceleration Slip Regulation for Four-Wheel Independent Driving Electric Vehicles

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Guodong Yin ◽  
Shanbao Wang ◽  
Xianjian Jin
Keyword(s):  
Fuzzy Logic ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Fuzzy Logic Control ◽  
Fuzzy Controller ◽  
Angular Acceleration ◽  
Slip Rate ◽  
Wheel Slip ◽  
Slip Ratio ◽  
Logic Control

To improve the driving performance and the stability of the electric vehicle, a novel acceleration slip regulation (ASR) algorithm based on fuzzy logic control strategy is proposed for four-wheel independent driving (4WID) electric vehicles. In the algorithm, angular acceleration and slip rate based fuzzy controller of acceleration slip regulation are designed to maintain the wheel slip within the optimal range by adjusting the motor torque dynamically. In order to evaluate the performance of the algorithm, the models of the main components related to the ASR of the four-wheel independent driving electric vehicle are built in MATLAB/SIMULINK. The simulations show that the driving stability and the safety of the electric vehicle are improved for fuzzy logic control compared with the conventional PID control.

Co-Simulation of Composite ABS Control for In-Wheel Motor Drive Electric Vehicle Based on Threshold Control Algorithm

2013 ◽  
Vol 690-693 ◽  
pp. 3036-3041 ◽  
Author(s):  
Jian Hua Li ◽  
Chuan Xue Song ◽  
Li Qiang Jin
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Electric Motor ◽  
Control Method ◽  
Slip Rate ◽  
Motor Drive ◽  
Control Logic ◽  
Threshold Control ◽  
Road Friction ◽  
Logic Threshold

According to the brake characteristics of in-wheel motor drive electric vehicles, and basing on threshold control method, we describe one kind of composite ABS control theory about electric motor ABS combined with hydraulic friction ABS, and establish a co-simulation vehicle model. The composite ABS control method is a control method that the electric motor ABS control works together with the hydraulic ABS control. Both of the two modes of ABS control logic were using logic threshold control method. The model of the in-wheel motor drive electric vehicle was established with AMESim, and the model of the composite ABS controller was built with Simulink. The control performance of composite ABS in different braking strength and different road friction coefficients is simulated. Co-simulation was carried out. Through analysis, a number of parameters curves were obtained. It proves that the composite ABS control method for in-wheel motor drive electric vehicles can effectively control the slip rate, and ensure braking stability.

scholarly journals Humanrecognition Using ‘Pso-Ofa’ In Low Resolution Videos

2021 ◽  
Vol 12 (11) ◽  
pp. 697-703
Author(s):  
K.Ranga Narayana, Et. al.
Keyword(s):  
Optical Flow ◽  
Moving Objects ◽  
Previous Method ◽  
Video Data ◽  
Detection Methods ◽  
Support Vector ◽  
Low Resolution ◽  
Swarm Optimization ◽  
Detection Approach ◽  
Novel Method

In present scenario, tracking of target in videos with low resolution is most important task.  The problem aroused due to lack of discriminatory data that have low visual visibility of the moving objects. However, earlier detection methods often extract explanations around fascinating points of space or exclude mathematical features in moving regions, resulting in limited capabilities to detect better video functions. To overcome the above problem, in this paper a novel method which recognizes a person from low resolution videos is proposed. A Three step process is implemented in which during the first step, the video data acquired from a low-resolution video i.e. from three different datasets. The acquired video is divided into frames and converted into gray scale from RGB. Secondly, background subtraction is performed using LBP and thereafter Histogram of Optical Flow (HOF) descriptors is extracted from optical flow images for motion estimation. In the third step, the eigen features are extracted and optimized using particle swarm optimization (PSO) model to eliminate redundant information and obtain optimized features from the video which is being processed. Finally to find a person from low resolution videos, the features are classified by Support Vector Machine (SVM) and parameters are evaluated. Experimental results are performed on VIRAT, Soccer and KTH datasets and demonstrated that the proposed detection approach is superior to the previous method

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