scholarly journals A Novel Energy Optimization Approach for Electrical Vehicles in a Smart City

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 929 ◽  
Author(s):  
Flah Aymen ◽  
Chokri Mahmoudi
Keyword(s):  
Power Management ◽  
Smart City ◽  
Energy Use ◽  
Smart Cities ◽  
Classification Problem ◽  
Optimal Decision ◽  
Support Vector ◽  
Optimization Approach ◽  
Sustainable Mobility ◽  
The Road

Electric Vehicles (EVs) have emerged rapidly across the globe as a powerful eco-friendly initiative that if integrated well with an urban environment could be iconic for the city’ host’s commitment to sustainable mobility and be a key ingredient of the smart city concept. This paper examines ways that will help us to develop a better understanding of how EVs can achieve energy use optimization and be connected with a smart city. As a whole, the present study is based on an original idea that would be useful in informing policy-makers, automotive manufacturers and transport operators of how to improve and embrace better EV technologies in the context of smart cities. The proposed approach is based on vehicles and buildings communication for sharing some special information related to the vehicle status and to the road condition. EVs can share their own information related to the energy experience on a specific path. This information can be gathered in a gigantic database and used for managing the power inside these vehicles. In this field, this paper exposes a new approach to power management inside an electric vehicle based on bi-communication between vehicles and buildings. The principle of this method is established on two sections; the first one is related to vehicles’ classification and the second one is attached to the buildings’ recommendation, according to the car position. The classification problem is resolved using the support vector classification method. The recommendation phase is resolved using the artificial intelligence principle and the neural network was employed, for giving the best decision. The optimal decision will be calculated inside the building, according to its position and using the old vehicle’s data, and transferred to the coming vehicle, for optimizing its energy consumption method in the corresponding building zone. Different possibilities and situations were discussed in this approach. The proposed power management methodology was tested and validated using Simulink/Matlab tool. Results related to the battery state of charge and to the consumed energy were compared at the end of this work, for showing the efficiency of this approach.

scholarly journals A novel energy optimization approach for electrical vehicles in a smart city

2019 ◽  
Author(s):  
FLAH AYMEN ◽  
chokri mahmoudi ◽  
lassaad sbita
Keyword(s):  
Electric Vehicle ◽  
Power Management ◽  
Smart City ◽  
Smart Cities ◽  
Essential Element ◽  
Control Unit ◽  
Optimization Approach ◽  
Electrical Vehicles ◽  
Smart Technologies ◽  
Monetary Savings

Smart cities and smart technologies have been incorporated into several axes to increase the comfort of life. The connected building's concept was introduced for this reason. However, it was utilized in power management for better organizing, greater buildings management, and monetary savings. Cars technologies and the number of vehicles are also involved; Nowadays, each house has at least one car. Technological evolution helped to make those cars intelligent and connected. In the latest versions, the majority of those cars were equipped with several sensors, several communication protocols and a principal electrical control unit (ECU), especially for the electric vehicle model. This type of architecture was an essential element in a smart city, thus, it helps to manage power and decide when a vehicle needs to be charged. Based on the smart city concept and using possible network communication between buildings and vehicles, EVs can share their own information related to the powerful experience on a specific path. This information can be gathered in a gigantic database and used for managing the power inside these vehicles. In this field, we propose in this paper a new approach for power management inside an electric vehicle based on bi-communication between vehicles and buildings. The proposed approach is founded on two essential parts; the first is related to vehicles’ classification and buildings’ recommendation according to different car positions. Two algorithms, related to the SVC and neural network was employed in this work for implementing the final process. Different possibilities and situations were discussed for this approach. The proposed method was tested and validated using Simulink/Matlab application. The state of charge of the used battery was compared at the end of this work, for two specified cases, for showing the contribution of this approach.

scholarly journals Modeling and Visualizing Smart City Mobility Business Ecosystems: Insights from a Case Study

Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 270 ◽  
Author(s):  
Anne Faber ◽  
Sven-Volker Rehm ◽  
Adrian Hernandez-Mendez ◽  
Florian Matthes
Keyword(s):  
Smart City ◽  
Design Science ◽  
Smart Cities ◽  
Knowledge Flows ◽  
Sustainable Mobility ◽  
Ecosystem Models ◽  
Business Ecosystem ◽  
Proactive Management ◽  
Business Ecosystems

Smart mobility is a central issue in the recent discourse about urban development policy towards smart cities. The design of innovative and sustainable mobility infrastructures as well as public policies require cooperation and innovations between various stakeholders—businesses as well as policy makers—of the business ecosystems that emerge around smart city initiatives. This poses a challenge for deploying instruments and approaches for the proactive management of such business ecosystems. In this article, we report on findings from a smart city initiative we have used as a case study to inform the development, implementation, and prototypical deployment of a visual analytic system (VAS). As results of our design science research we present an agile framework to collaboratively collect, aggregate and map data about the ecosystem. The VAS and the agile framework are intended to inform and stimulate knowledge flows between ecosystem stakeholders in order to reflect on viable business and policy strategies. Agile processes and roles to collaboratively manage and adapt business ecosystem models and visualizations are defined. We further introduce basic categories for identifying, assessing and selecting Internet data sources that provide the data for ecosystem models and we detail the ecosystem data and view models developed in our case study. Our model represents a first explication of categories for visualizing business ecosystem models in a smart city mobility context.

scholarly journals Analysis of Machine Learning Techniques Applied to Sensory Detection of Vehicles in Intelligent Crosswalks

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6019
Author(s):  
José Manuel Lozano Domínguez ◽  
Faroq Al-Tam ◽  
Tomás de J. Mateo Sanguino ◽  
Noélia Correia
Keyword(s):  
Machine Learning ◽  
Smart Cities ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Models ◽  
Fuzzy Classifier ◽  
Logistic Regression Models ◽  
The Road ◽  
Learning Agent ◽  
Machine Learning Models

Improving road safety through artificial intelligence-based systems is now crucial turning smart cities into a reality. Under this highly relevant and extensive heading, an approach is proposed to improve vehicle detection in smart crosswalks using machine learning models. Contrarily to classic fuzzy classifiers, machine learning models do not require the readjustment of labels that depend on the location of the system and the road conditions. Several machine learning models were trained and tested using real traffic data taken from urban scenarios in both Portugal and Spain. These include random forest, time-series forecasting, multi-layer perceptron, support vector machine, and logistic regression models. A deep reinforcement learning agent, based on a state-of-the-art double-deep recurrent Q-network, is also designed and compared with the machine learning models just mentioned. Results show that the machine learning models can efficiently replace the classic fuzzy classifier.

scholarly journals Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

2019 ◽  
Vol 9 (13) ◽  
pp. 2714 ◽  
Author(s):  
Le Thi Le ◽  
Hoang Nguyen ◽  
Jian Zhou ◽  
Jie Dou ◽  
Hossein Moayedi
Keyword(s):  
Smart City ◽  
City Planning ◽  
Smart Cities ◽  
Critical Parameters ◽  
Gradient Boosting ◽  
Percentage Error ◽  
Support Vector ◽  
Extreme Gradient Boosting ◽  
Building Systems ◽  
Heating Load

In this study, a novel technique to support smart city planning in estimating and controlling the heating load (HL) of buildings, was proposed, namely PSO-XGBoost. Accordingly, the extreme gradient boosting machine (XGBoost) was developed to estimate HL first; then, the particle swarm optimization (PSO) algorithm was applied to optimize the performance of the XGBoost model. The classical XGBoost model, support vector machine (SVM), random forest (RF), Gaussian process (GP), and classification and regression trees (CART) models were also investigated and developed to predict the HL of building systems, and compared with the proposed PSO-XGBoost model; 837 investigations of buildings were considered and analyzed with many influential factors, such as glazing area distribution (GAD), glazing area (GA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), and relative compactness (RC). Mean absolute percentage error (MAPE), root-mean-squared error (RMSE), variance account for (VAF), mean absolute error (MAE), and determination coefficient (R2), were used as the statistical criteria for evaluating the performance of the above models. The color intensity, as well as the ranking method, were also used to compare and evaluate the models. The results showed that the proposed PSO-XGBoost model was the most robust technique for estimating the HL of building systems. The remaining models (i.e., XGBoost, SVM, RF, GP, and CART) yielded more mediocre performance through RMSE, MAE, R2, VAF, and MAPE metrics. Another finding of this study also indicated that OH, RA, WA, and SA were the most critical parameters for the accuracy of the proposed PSO-XGBoost model. They should be particularly interested in smart city planning as well as the optimization of smart cities.

scholarly journals An AdaptiveWhale Optimization Algorithm Guided Smart City Big Data Feature Identification for Fair Resource Utilization

2019 ◽  
Vol 8 (12) ◽  
pp. 271-281
Keyword(s):  
Optimization Algorithm ◽  
Smart City ◽  
Smart Cities ◽  
Human Life ◽  
Support Vector ◽  
Feature Identification ◽  
Ideal Element ◽  
Course Of Action ◽  
Whale Optimization ◽  
The Ideal

World improvement is the development of every single province of the world. Smart city implies changed hardware to adjusted individuals. Smart cities have the most indispensable part in altering distinctive regions of human life, touching segments like transportation, wellbeing, vitality, and instruction. Productively to make measurements to improve distinctive smart city benefits huge information frameworks are put away, prepared, and mined in smart cities. For the change and course of action of huge information applications for smart cities, different difficulties are faces. In this paper, we propose a wrapper display based ideal element recognizable proof calculation for ideal use of assets given highlight subset age. Nine component determination techniques used for compelling element extraction. At last, which includes best add to the ideal usage of assets got by means of a novel element recognizable proof calculation made by the application out of a Whale Optimization Algorithm with Adaptive Multi-Population (WOA-AMP) system as inquiry process in a wrapper display driven by the notable relapse demonstrate regression model Random Forest with Support Vector Machine (RF-SVM). Our proposed calculation gives the exact method to choose the most agreeable feature blend, which prompts ideal asset usage.

scholarly journals Feasibility of Innovative Smart Mobility Solutions: A Case Study for Vaasa

2021 ◽  
Vol 12 (4) ◽  
pp. 188
Author(s):  
Tomi Paalosmaa ◽  
Miadreza Shafie-khah
Keyword(s):  
Second Life ◽  
Smart City ◽  
Smart Cities ◽  
City Development ◽  
Sustainable Mobility ◽  
Horizon 2020 ◽  
Vehicle To Grid ◽  
Carbon Neutrality ◽  
Smart Mobility ◽  
The City

The global trend of urbanization and growing environmental awareness have risen concerns and demands to develop cities to become smarter. There is a grave need for ambitious sustainability strategies and projects, which can aid cities intelligently and comprehensively in this task. European Union (EU) launched 2014 the Horizon 2020 program (aka Horizon Europe), aiming to encourage the EU nations and their cities to take action to reach carbon neutrality through projects striving to smart city development. By promoting innovative, efficient, far-reaching, and replicable solutions, from the fields of smart energy production and consumption, traffic and mobility, digitalization and information communication technology, and citizen engagement, the objectives of the smart city strategies can be achieved. Horizon 2020 funded IRIS Smart Cities project was launched in 2017. One of the follower cities in the project has been the City of Vaasa in Finland. Vaasa’s climate objective is to reach carbon neutrality by 2030. In order to achieve this goal, the city has taken several decisive measures to enhance de-carbonization during recent years. One essential target for de-carbonization activities has been traffic and mobility. The primary purpose of the research conducted was to study the smart mobility, vehicle-to-grid (V2G), and second life battery solutions in the IRIS Smart Cities project, demonstrated first by the Lighthouse cities and then to be replicated in the City of Vaasa. The aim was to study which importance and prioritization these particular integrated solutions would receive in the City of Vaasa’s replication plan led by the City of Vaasa’s IRIS project task team of 12 experts, with the contribution of the key partners and stakeholders. Additionally, the aim was to study the potential of the integrated solutions in question to be eventually implemented in the Vaasa environment, and the benefit for the city’s ultimate strategy to reach carbon neutrality by 2030. The secondary object was to study the solutions’ compatibility with the IRIS lighthouse cities’ demonstrations and gathered joined experiences concerning the smart and sustainable mobility and vehicle-to-grid solutions, and utilization of 2nd life batteries. The results of the research indicated, that the innovative smart mobility solutions, including vehicle-to-grid and second life battery schemes, are highly relevant not only to the IRIS Lighthouse cities, but they also present good potential for the City of Vaasa in the long run, being compatible with the city’s climate and de-carbonization goals.

scholarly journals Difficulties and Challenges of Anomaly Detection in Smart Cities: A Laboratory Analysis

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3198 ◽  
Author(s):  
Victor Garcia-Font ◽  
Carles Garrigues ◽  
Helena Rifà-Pous
Keyword(s):  
Machine Learning ◽  
Sensor Networks ◽  
Smart City ◽  
Smart Cities ◽  
Technical Difficulty ◽  
Attack Detection ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Detection Techniques ◽  
Complex Scenario

Smart cities work with large volumes of data from sensor networks and other sources. To prevent data from being compromised by attacks or errors, smart city IT administrators need to apply attack detection techniques to evaluate possible incidents as quickly as possible. Machine learning has proven to be effective in many fields and, in the context of wireless sensor networks (WSNs), it has proven adequate to detect attacks. However, a smart city poses a much more complex scenario than a WSN, and it has to be evaluated whether these techniques are equally valid and effective. In this work, we evaluate two machine learning algorithms (support vector machines (SVM) and isolation forests) to detect anomalies in a laboratory that reproduces a real smart city use case with heterogeneous devices, algorithms, protocols, and network configurations. The experience has allowed us to show that, although these techniques are of great value for smart cities, additional considerations must be taken into account to effectively detect attacks. Thus, through this empiric analysis, we point out broader challenges and difficulties of using machine learning in this context, both for the technical complexity of the systems, and for the technical difficulty of configuring and implementing them in such environments.

The Road to SC Transition for Cities in Developing Nations

2022 ◽  
pp. 214-231
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Developing Nations ◽  
Smart Technology ◽  
The Road ◽  
Complex Operation ◽  
Different Types ◽  
Versus Transformation ◽  
Guiding Principles

Smart city transformation is a complex operation and comes with critical challenges that this chapter addresses in a strategic manner. The chapter clearly distinguishes between different types of cities. An overview of the most significant and crucial four qualities of smart cities is discussed. An essential part of the chapter is the review of the foundations of technology in smart cities with emphasis on indispensable types of technology such as communications, smart technology, and connectivity infrastructure. The second important part of the chapter is the issue of developing guiding principles to smart city transformation. A discussion of strategies of migration versus transformation of smart cities is followed by a review of the phases of smart cities implementation.

scholarly journals Transforming ‘traditional’ cities into ‘smart’ cities

2020 ◽  
Vol 170 ◽  
pp. 06013
Author(s):  
Laxmi Nagaraj
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Current Status ◽  
Base Line ◽  
The Road ◽  
Road Map ◽  
Indian Context ◽  
Project Plan

This paper aims to discuss the challenges of transforming ‘Traditional’ cities to ‘Smart Cities’ and the tools that can be used to transform ‘Traditional’ cities to ‘Smart’ cities in the Indian Context. In this context, this paper discusses the expectations and goals of the Smart City India Mission for the 100 Smart cities, the existing scenario of the ‘Traditional’ cities, the current status of the Smart cities in India and concludes that ‘Traditional’ cities can become ‘Smart’ by developing a base line scenario and developing a ‘Road Map’ to become ‘Smart’. The ‘Road Map’ must consist of the following four stages: Assessment, Vision, Project Plan and Metrics.

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