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.

Summary The communication technologies were evolving rapidly and making everything connected. Peoples, vehicles, and buildings become connected everywhere. Concentrating on the targets that vehicle can be connected with, the present paper exposes a new power management approach applicable for three kinds of connected vehicles. Vehicle to buildings or infrastructure, vehicle to network, and vehicle to vehicle are the three communication protocols between vehicle and external targets. The proposed approach is designed for implementing all of these three communication cases. It is based on the principle of vehicle data sharing. Each vehicle will share a package of data, which contain information related to the vehicle status and the road condition. This information will be collected in a database and used for managing the power inside these cars. This approach is built on two stages; the first one is related to vehicles' classification, and the second one is attached to the recommendation, and this is according to the car position and according to the communication protocol case. The first stage was resolved using the support vector classification method. The second stage was treated using the artificial intelligence principle, and the neural network was employed. So the optimal decision will be outputted. According to the vehicle communication method, the optimal decision will be identified inside the building, infrastructure, vehicle, or the cloud database. This will be related to the vehicle position using the old vehicle's data. So the obtained decission will be transferred to the coming vehicle, for optimizing its energy consumption method in the corresponding area. Different possibilities and situations were discussed in this approach. The power management methodology was verified and confirmed. For showing the efficiency of this approach, a comparison of the obtained results in relation to the battery state of charge and the consumed energy results will be done. Matlab simulink was used for doing the necessary tests.

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<p>As world population continues to grow and the limited amount of fossil fuels begin to diminish, it may not be possible to afford the needed amount of energy demanded by the world by only using fossil fuels. Meanwhile, the abundant nature of renewable energy sources brings new beginning for next generations. Greater penetration of electric vehicles will play an important role in building green and healthy world. The main remaining issue to make the switch from conventional to electric vehicle is performance cost; Efficient EVs that can drive for long distances, on single charge, are still expensive for ordinary consumer. To address this range problem, many attempts have been made during last decade. The goal was to conceive a power efficient electric vehicle, capable of managing its energy and reach longer distances. It depends on the electrical architectures and used algorithms.</p><p>This paper adds new perspective for power Management in EVs; The proposed methodology introduces a new power management architecture based on communication and car learning. The conventional software level in EV has been replaced with self readjustable software. EVs are connected through a database, and can upload or download adjustment parameters while software is running.</p><p>To take advantage of the new architecture, a new learning technique concept is introduced too, based on Cloud experience exchange between Electric Vehicles. This enhancement aims to build a better EV experience in power management through Cloud sharing and definitely cut with conventional architecture that may have reached its boundaries.</p>

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