A Time and Energy Efficient Routing Algorithm for Electric Vehicles Based on Historical Driving Data

Bozorgi, Amir Masoud; Farasat, Mehdi; Mahmoud, Anas

doi:10.1109/tiv.2017.2771233

Cited by 65 publications

(31 citation statements)

References 37 publications

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“…The definition of edges' weights was adapted from (Bozorgi et al, 2017). Therefore, the energy cost between two nodes v i and v j can be determined as follows:…”

Section: Environmentmentioning

confidence: 99%

“…Despite being fast, the solution is tedious to formulate and requires adaptation to different EV usage cases. An interesting approach based on learning from historical driving data is demonstrated in ( Bozorgi et al, 2017 ). The proposed solution aims at minimizing both energy consumption and travel time while accommodating particular driving habits.…”

Section: Introductionmentioning

confidence: 99%

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Routing of Electric Vehicles With Intermediary Charging Stations: A Reinforcement Learning Approach

2021

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In the past few years, the importance of electric mobility has increased in response to growing concerns about climate change. However, limited cruising range and sparse charging infrastructure could restrain a massive deployment of electric vehicles (EVs). To mitigate the problem, the need for optimal route planning algorithms emerged. In this paper, we propose a mathematical formulation of the EV-specific routing problem in a graph-theoretical context, which incorporates the ability of EVs to recuperate energy. Furthermore, we consider a possibility to recharge on the way using intermediary charging stations. As a possible solution method, we present an off-policy model-free reinforcement learning approach that aims to generate energy feasible paths for EV from source to target. The algorithm was implemented and tested on a case study of a road network in Switzerland. The training procedure requires low computing and memory demands and is suitable for online applications. The results achieved demonstrate the algorithm’s capability to take recharging decisions and produce desired energy feasible paths.

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“…The definition of edges' weights was adapted from (Bozorgi et al, 2017). Therefore, the energy cost between two nodes v i and v j can be determined as follows:…”

Section: Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Routing of Electric Vehicles With Intermediary Charging Stations: A Reinforcement Learning Approach

2021

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show abstract

“…In optimization studies, all factors affecting energy consumption should be considered to model EVs correctly. In the modeling of EV used in this study, inertial force, gravitational force, rolling resistance force of wheels and aerodynamic force are considered [1], [4] as shown in Fig. 2.…”

Section: Establishing the Ev Modelmentioning

confidence: 99%

Energy Efficient Driving Optimization of Electrical Vehicles Considering the Road Characteristics

Eroğlu¹,

Oğuz²

2021

Balkan Journal of Electrical and Computer Engineering

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Electric vehicles, which are an important part of sustainable energy technologies, occupy an important place in our daily life. More efficient use of electric vehicles will ensure more efficient use of sustainable energy sources. It is not possible for the human brain to determine the most efficient driving characteristics. In this study, energy efficient driving optimization of electric vehicles was realized. Along the route, optimum speeds were determined in terms of energy, by using the road and engine characteristics. Geographical information systems and genetic algorithm have been used effectively in the solution of the problem. The effectiveness of the proposed algorithm was revealed with many test studies. With this study, an algorithm that provides an energy-efficient driving for electrical vehicles was developed. The results will contribute to the development of electric vehicle technologies.

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“…In this manner, data transformation goals must be expected to meet the needs to adjust and standardize node correspondence to accomplish an ideal data transfer. Kacem et al (2018), Liu et al (2019), and Khudayer et al (2020) discussed [8] [13] [14] the node failure. In their perspective, adaptation to internal failure separation of a mobile node causes reduction of the lifetime of nodes, which corresponds to node failure.…”

Section: Introduction or Backgroundmentioning

confidence: 99%

Intelligent route discovery towards rushing attacks in ad hoc wireless networks

Allimuthu

Mahalakshmi

2021

J Ambient Intell Human Comput

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MANET (Mobile Ad-hoc Networks) are distributed or delegated away from a central server, authoritative location of wireless networks that communicate without pre-existing structure. Ad-hoc networks are compromising the many types of attacks and routing. In MANET, the routing plays a vital role in terms of packets interaction and data transmission. Due to decentralized control, the MANET data transmission becomes insecure because of dispersed routing on the mobile ad-hoc nodes. Since the efficient route on MANET only controls the packets and does not simplify the route between the source to the destination, the maintenance of route interaction becomes a crucial process. Maintain effective data transactions over the MANET network, and it is essential to improve the route and locate the attacker. Nevertheless, MANET allows for route interaction against security threads. In this research article, four processing schemes are suggested to preserve the security measures against routing protocols. Especially in node communication, the rushing attacker has a significant impact on packet-based data transmission in MANET. Also, for this research, an Attacker detection automation of the Bees Colony Optimization (ADABCP) method is used, as a result of which the desired result is brought about in the effective attacker detection on the routing process. Moreover, the proposed Hybrid Random Late Detection (HRLD) routing protocol manages the MANET routing and overcomes the MANET congestion communication. The Swift Implicit Response Round Trip Time (SIRT) mechanism is generated by the Route Finding Manipulation (RFM) to enhance the performance. This RFM scheme helps to find the optimal routing in a secured manner. The proposed (SIRT-ADABCP-HRLD) approach was compared to the existing ESCT, ZRDM-LFPM, and ENM-LAC approaches, found to have improved by routing and data transmission. Compared to the conventional method, the method mentioned above achieves a better ratio for the end-to-end delay, communication overhead, packet delivery ratio, network lifetime, and energy consumption.

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A Time and Energy Efficient Routing Algorithm for Electric Vehicles Based on Historical Driving Data

Cited by 65 publications

References 37 publications

Routing of Electric Vehicles With Intermediary Charging Stations: A Reinforcement Learning Approach

Routing of Electric Vehicles With Intermediary Charging Stations: A Reinforcement Learning Approach

Energy Efficient Driving Optimization of Electrical Vehicles Considering the Road Characteristics

Intelligent route discovery towards rushing attacks in ad hoc wireless networks

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