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

Abstract

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.

Systematic Literature Review on Smart Mobility: A Framework for Future “Quantitative” Developments

“…Electric vehicles have an important role in the future energy system (Cooper et al 2019; Xiang et al 2019). Their use represents a powerful eco-friendly initiative that, if integrated well with an urban environment, could be a key element of the smart city concept (Aymen and Mahmoudi 2019). However, the increased demand for mobility, and therefore energy, can create constraints on the power network, which can reduce the benefits of electrification as a certain and reliable source.…”

A Novel Ensemble Method for Electric Vehicle Power Consumption Forecasting: Application to the Spanish System

“…From the reported results, the authors claimed that this method can reduce the number of decision variables, and require less computational time and memory. Another interesting energy optimization proposal for EV in smart cities can be found in [28]. The main idea underlying the approach is that EVs and buildings can share some particular information regarding both vehicle and roads status.…”

A novel energy management framework incorporating multi‐carrier energy hub for smart city

“…Therefore, the elements of transportation system, as a promising solution, can productively contribute to the balance between generation and consumption of the whole grid [1,10]. This usually takes place through vehicle-to-anything (V2X) and anything-tovehicle (X2V) technologies [8,11,12], and is mainly due to the emergence of high technology batteries [13]. It is worth nothing that EV-related technologies have some strengths and weaknesses that all described by authors in [14,15].…”

“…To provide better understanding, RBE appears to be the most efficient way of improving the performance of STS as it can be simply obtained via traction motors installed on ETs [1,19]. In fact, this type of motors has the ability to reproduce the energy while ETs are decelerating [13]. RBE can be consumed by EVs in a real-time manner provided that the availability of ETs, that is, arrival and departure times is specified or optimally scheduled [20].…”