scholarly journals Algorithm of Energy Efficiency Improvement for Intelligent Devices in Railway Transport

2016 ◽  
Vol 10 (1) ◽  
pp. 29-34
Author(s):  
Anna Beinaroviča ◽  
Mihails Gorobecs ◽  
Anatolijs Ļevčenkovs
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Data Storage ◽  
Motor Vehicle ◽  
Minimum Energy ◽  
Target Function ◽  
Main Task ◽  
Energy Efficiency Improvement ◽  
Minimum Energy Consumption ◽  
Intelligent Devices

Abstract The present paper deals with the use of systems and devices with artificial intelligence in the motor vehicle driving. The main objective of transport operations is a transportation planning with minimum energy consumption. There are various methods for energy saving, and the paper discusses one of them – proper planning of transport operations. To gain proper planning it is necessary to involve the system and devices with artificial intelligence. They will display possible developments in the choice of one or another transport plan. Consequently, it can be supposed how much the plan is effective against the spent energy. The intelligent device considered in this paper consists of an algorithm, a database, and the internet for the connection to other intelligent devices. The main task of the target function is to minimize the total downtime at intermediate stations. A specific unique PHP-based computer model was created. It uses the MySQL database for simulation data storage and processing. Conclusions based on the experiments were made. The experiments showed that after optimization, a train can pass intermediate stations without making multiple stops breaking and accelerating, which leads to decreased energy consumption.

scholarly journals A Q-Learning Rescheduling Approach to the Flexible Job Shop Problem Combining Energy and Productivity Objectives

2021 ◽  
Vol 13 (23) ◽  
pp. 13016
Author(s):  
Rami Naimi ◽  
Maroua Nouiri ◽  
Olivier Cardin
Keyword(s):  
Artificial Intelligence ◽  
Energy Consumption ◽  
Job Shop ◽  
Learning Algorithm ◽  
Minimum Energy ◽  
Flexible Job Shop ◽  
Q Learning ◽  
Minimum Energy Consumption ◽  
Machine Failure ◽  
Flexible Job Shop Problem

The flexible job shop problem (FJSP) has been studied in recent decades due to its dynamic and uncertain nature. Responding to a system’s perturbation in an intelligent way and with minimum energy consumption variation is an important matter. Fortunately, thanks to the development of artificial intelligence and machine learning, a lot of researchers are using these new techniques to solve the rescheduling problem in a flexible job shop. Reinforcement learning, which is a popular approach in artificial intelligence, is often used in rescheduling. This article presents a Q-learning rescheduling approach to the flexible job shop problem combining energy and productivity objectives in a context of machine failure. First, a genetic algorithm was adopted to generate the initial predictive schedule, and then rescheduling strategies were developed to handle machine failures. As the system should be capable of reacting quickly to unexpected events, a multi-objective Q-learning algorithm is proposed and trained to select the optimal rescheduling methods that minimize the makespan and the energy consumption variation at the same time. This approach was conducted on benchmark instances to evaluate its performance.

scholarly journals Optimization of Energy-Efficient Speed Profile for Electrified Vehicles

2018 ◽  
Author(s):  
Hadi Abbas ◽  
Youngki Kim ◽  
Jason B. Siegel ◽  
Denise M. Rizzo
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Minimum Energy ◽  
Efficient Operation ◽  
Operating Modes ◽  
Minimum Energy Consumption ◽  
System A ◽  
Trip Time ◽  
Route Information

This paper presents a study of energy-efficient operation of vehicles with electrified powertrains leveraging route information, such as road grades, to adjust the speed trajectory. First, Pontryagin’s Maximum Principle (PMP) is applied to derive necessary conditions and to determine the possible operating modes. The analysis shows that only 5 modes are required to achieve minimum energy consumption; full propulsion, cruising, coasting, full regeneration, and full regeneration with conventional braking. The minimum energy consumption problem is reformulated and solved in the distance domain using Dynamic Programming to optimize speed profiles. A case study is shown for a light weight military robot including road grades. For this system, a tradeoff between energy consumption and trip time was found. The optimal cycle uses 20% less energy for the same trip duration, or could reduce the travel time by 14% with the same energy consumption compared to the baseline operation.

From Construction to Operation: Achieving Indoor Thermal Comfort via Altering External Walls Specifications in Egypt

2013 ◽  
Vol 689 ◽  
pp. 250-253 ◽  
Author(s):  
Mohamed M. Mahdy ◽  
Marialena Nikolopoulou
Keyword(s):  
Energy Consumption ◽  
Thermal Comfort ◽  
Natural Ventilation ◽  
Minimum Energy ◽  
Residential Energy ◽  
Indoor Thermal Comfort ◽  
Minimum Energy Consumption ◽  
Different Types ◽  
Running Cost ◽  
The Cost

The objective of this research is to study the effect of using different material specifications for the external walls on the cost of the energy consumption for achieving internal thermal comfort. We refer to this as operation running cost, which in turn is compared to initial construction cost for each type of the used external walls. In order to achieve this objective, dynamic thermal simulation were carried out for four different types of external walls – commonly used in Egypt – in two different sets of cooling: natural ventilation and mechanical means. Experiments recommend that using the Egyptian Residential Energy Code (EREC) to achieve inner thermal comfort with the minimum energy consumption (consequently the minimum CO2 emissions) and the minimum running cost as well.

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