scholarly journals Investigation of the Advanced Rider-Assistance System of a Personal Electric Vehicle Using Personal Space

2022 ◽  
pp. 92-98
Author(s):  
Pham Quoc Thai ◽  
Chihiro Nakagawa ◽  
Atsuhiko Shintani ◽  
Tomohiro Ito
Keyword(s):  
Electric Vehicle ◽  
Personal Space ◽  
Assistance System

scholarly journals Exterior Car Parking Assistance Algorithm Based on Reconfigurable System for Future Industry

2020 ◽  
Author(s):  
Sethakarn Prongnuch ◽  
Suchada Sitjongsataporn
Keyword(s):  
Embedded System ◽  
System Design ◽  
Electric Vehicle ◽  
Software Design ◽  
Design Tool ◽  
Assistance System ◽  
Hardware Accelerator ◽  
Multicore Processor ◽  
Reconfigurable System ◽  
Car Accident

A car accident while parking the car is caused by the car driver, who is invisible around the car. However, there are no solutions for parking assistance when the driver is outside the car. The objective of this paper is to propose a reconfigurable embedded system design by voice controlled parking assistance system for a prototype electric vehicle connected to a smartphone via Bluetooth. Hardware and software co-design using the Xilinx VIVADO as a software design tool is introduced. We design the hardware and software on an ARM multicore processor and the reconfigurable system board model ZYBO: XC7Z010 by considering it as hardware accelerator. The hardware of the proposed voice controlled exterior car parking assistance system is installed on the miniature electric vehicle. The experiments are tested successfully at the parking area for both reverse parking and reverse parallel parking. This proposed system is better suited for users so that they can control their car comfortably while parking safely.

scholarly journals Advanced Driving Assistance System for an Electric Vehicle Based on Deep Learning

2021 ◽  
Author(s):  
Abdelaziz Sahbani ◽  
Hela Mahersia
Keyword(s):  
Electric Vehicle ◽  
Control Method ◽  
Assistance System ◽  
Speed Limit ◽  
Processing Unit ◽  
Vehicle Speed ◽  
Proposed Model ◽  
Driving Assistance ◽  
Driving Assistance System ◽  
System Controller

This chapter deals with a design of a new speed control method using artificial intelligence techniques applied to an autonomous electric vehicle. In this research, we develop an Advanced Driver Assistance System (ADAS) which aims to enhance the driving manner and the safety, especially when traveling too fast. The proposed model is a complete end-to-end vehicle speed system controller that proceeds from a detected speed limit sign to the regulation of the motor’s speed. It recognizes the speed limit signs before extracting from them, a speed information that will be sent, as reference, to a NARMA-L2 based controller. The study is developped specially for electric vehicle using Brushless Direct Current (BLDC) motor. The simulation results, implemented using Matlab-Simulink, show that the speed of the electric vehicle is controlled successfully with different speed references coming from the image processing unit.

Adaptive identification methods of nonlinear random electrical signal

2020 ◽  
Vol 64 (1-4) ◽  
pp. 431-438
Author(s):  
Jian Liu ◽  
Lihui Wang ◽  
Zhengqi Tian
Keyword(s):  
Wavelet Transform ◽  
Electric Vehicle ◽  
Signal To Noise Ratio ◽  
Sudden Change ◽  
Electrical Signal ◽  
Signal Recognition ◽  
State Variable ◽  
Signal Characteristics ◽  
The Matrix ◽  
Kalman Filter Algorithm

The nonlinearity of the electric vehicle DC charging equipment and the complexity of the charging environment lead to the complex and changeable DC charging signal of the electric vehicle. It is urgent to study the distortion signal recognition method suitable for the electric vehicle DC charging. Focusing on the characteristics of fundamental and ripple in DC charging signal, the Kalman filter algorithm is used to establish the matrix model, and the state variable method is introduced into the filter algorithm to track the parameter state, and the amplitude and phase of the fundamental waves and each secondary ripple are identified; In view of the time-varying characteristics of the unsteady and abrupt signal in the DC charging signal, the stratification and threshold parameters of the wavelet transform are corrected, and a multi-resolution method is established to identify and separate the unsteady and abrupt signals. Identification method of DC charging distortion signal of electric vehicle based on Kalman/modified wavelet transform is used to decompose and identify the signal characteristics of the whole charging process. Experiment results demonstrate that the algorithm can accurately identify ripple, sudden change and unsteady wave during charging. It has higher signal to noise ratio and lower mean root mean square error.

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