Compensating Delays and Noises in Motion Control of Autonomous Electric Vehicles by Using Deep Learning and Unscented Kalman Predictor

2020 ◽  
Vol 50 (11) ◽  
pp. 4326-4338 ◽  
Author(s):  
Yanjun Li ◽  
Guodong Yin ◽  
Weichao Zhuang ◽  
Ning Zhang ◽  
Jinxiang Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Motion Control ◽  
Electric Vehicles ◽  
Kalman Predictor ◽  
Autonomous Electric Vehicles

A Control Algorithm for Autonomous Electric Vehicles by Fuzzy Logic

2018 ◽  
Vol 27 ◽  
pp. 103-110
Author(s):  
Viorel Stoian
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Control ◽  
Motion Control ◽  
Electric Vehicles ◽  
Autonomous Vehicles ◽  
Control Algorithm ◽  
Program Code ◽  
Neighbourhood Level ◽  
Fuzzy Control Algorithm ◽  
Autonomous Electric Vehicles

The domain of autonomous vehicles is of great interest to researchers and engineers and much has been performed in this field. The paper proposes a fuzzy control algorithm for autonomous electric vehicles which are moving next to the obstacle (object) boundaries, avoiding the collisions with them (a “guard motion”). Four motion cycles (programs) which depend of the proximity levels and which are used by the vehicle on its trajectory are described. The directions of the movements corresponding to every cycle and for every reached neighbourhood level are indicated. The sequence of the programs and the conditions of their alternation are shown. The motion control algorithm describes the sequence of the functional cycles by a schernatic program code. The fuzzy rules for evolution (transition) of the cycles and for the motion on x-axis and y-axis respectively are expounded. Finally, some simulations are represented.

State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach

2021 ◽  
Vol 291 ◽  
pp. 116812
Author(s):  
Jinpeng Tian ◽  
Rui Xiong ◽  
Weixiang Shen ◽  
Jiahuan Lu
Keyword(s):  
Deep Learning ◽  
Electric Vehicles ◽  
State Of Charge ◽  
State Of Charge Estimation

scholarly journals A Comprehensive Emissions Model Combining Autonomous Vehicles with Park and Ride and Electric Vehicle Transportation Policies

2021 ◽  
Vol 13 (9) ◽  
pp. 4653
Author(s):  
Mohammed Obaid ◽  
Arpad Torok ◽  
Jairo Ortega
Keyword(s):  
Electric Vehicles ◽  
Autonomous Vehicles ◽  
Emission Reduction ◽  
Transport Sector ◽  
Sustainable Transport ◽  
Mode Shift ◽  
Pollution Levels ◽  
Park And Ride ◽  
Model Combining ◽  
Autonomous Electric Vehicles

Several transport policies reduce pollution levels caused by private vehicles by introducing autonomous or electric vehicles and encouraging mode shift from private to public transport through park and ride (P&R) facilities. However, combining the policies of introducing autonomous vehicles with the implementation of electric vehicles and using the P&R system could amplify the decrease of transport sector emissions. The COPERT software has been used to calculate the emissions. This article aims to study these policies and determine which combinations can better reduce pollution. The result shows that each combination of autonomous vehicles reduces pollution to different degrees. In conclusion, the shift to more sustainable transport modes through autonomous electric vehicles and P&R systems reduces pollution in the urban environment to a higher percentage. In contrast, the combination of autonomous vehicles has lower emission reduction but is easier to implement with the currently available infrastructure.

IoT Based Communication System for Autonomous Electric Vehicles

2021 ◽  
Author(s):  
Vishwajit Rahatal ◽  
Pratik More ◽  
Minesh Salunke ◽  
Sahil Makeshwar ◽  
Radhika D. Joshi
Keyword(s):  
Electric Vehicles ◽  
Communication System ◽  
Autonomous Electric Vehicles

scholarly journals Control and Reconguration of Train of Autonomous Electric Vehicles

2012 ◽  
Vol 10 (1) ◽  
pp. 1543-1551
Author(s):  
Kamel Bouibed ◽  
Abdel Aitouche ◽  
Mireille Bayart
Keyword(s):  
Electric Vehicles ◽  
Autonomous Electric Vehicles

scholarly journals A Novel Deep Learning-Based State-of-Charge Estimation for Renewable Energy Management System in Hybrid Electric Vehicles

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 260
Author(s):  
Mahendiran T. Vellingiri ◽  
Ibrahim M. Mehedi ◽  
Thangam Palaniswamy
Keyword(s):  
Renewable Energy ◽  
Deep Learning ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management System ◽  
Hybrid Electric Vehicles ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Soc Estimation ◽  
Hybrid Electric

In recent years, alternative engine technologies are necessary to resolve the problems related to conventional vehicles. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are effective solutions to decarbonize the transportation sector. It also becomes important to shift from traditional houses to smart houses and from classical vehicles to EVs or HEVs. It is needed to combine renewable energy sources (RESs) such as solar photovoltaics, wind energy systems, and various forms of bio-energies. Among various HEV technologies, an effective battery management system (BMS) still remains a crucial issue that is majorly used for indicating the battery state of charge (SOC). Since over-charging and over-discharging result in inevitable impairment to the batteries, accurate SOC estimation desires to be presented by the BMS. Although several SOC estimation techniques exist to regulate the SOC of the battery cell, it is needed to improvise the SOC estimation performance on HEVs. In this view, this paper focuses on the design of a novel deep learning (DL) with SOC estimation model for secure renewable energy management (DLSOC-REM) technique for HEVs. The presented model employs a hybrid convolution neural network and long short-term memory (HCNN-LSTM) model for the accurate estimation of SOC. In order to improve the SOC estimation outcomes of the HCNN-LSTM model, the barnacles mating optimizer (BMO) is applied for the hyperpower tuning process. The utilization of the HCNN-LSTM model makes the modeling process easier and offers a precise depiction of the input–output relationship of the battery model. The design of BMO based HCNN-LSTM model for SOC estimation shows the novelty of the work. An extensive experimental analysis highlighted the supremacy of the proposed model over other existing methods in terms of different aspects.

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