Integrated Route Planning Algorithm Based on Spot Price and Classified Travel Objectives for EV Users

Gu, Jiu; Xie, Da; Li, Yongpan

doi:10.1109/access.2019.2937910

Cited by 22 publications

(4 citation statements)

References 14 publications

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“…Despite the proposed model, it does not distinguish different vehicle velocities nor experiment with real-world driving scenarios. To provide the optimal route, some research [57] has combined route planning algorithms to satisfy traffic and charging costs based on various trip objectives via an application where user input has been entered. To Reduce the total tours based on the allocation of the GRU neural network is used to set the time window for the price of electricity, and to predict the optimal path depending on the requirements.…”

Section: ) Hybrid Approaches For Ugvmentioning

confidence: 99%

Routing Approaches used for Electrical Vehicles Navigation: A Survey

Hejres,

Mahjoub,

Hewahi

2024

IJCDS

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The growing demand for Electric Vehicles (EVs) depends on the high integration of this technology in many areas. Therefore, an important area of research raises interest in finding the optimal path-planning solution for electric vehicles. This paper discusses several reviews and analyzes some of the constraints of the techniques used to improve these systems. The paper discusses common models used in Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGV). This paper investigates the planning approaches that lead to finding the optimal route for the tour from the source to the destination. The review outlines the different models and systems of Unmanned Ariel Vehicles (UAV) and Unmanned Ground Vehicles (UGV). This paper can be considered as comprehensive survey research for EV routing techniques to assist researchers choose the appropriate approach for developing a system based on optimization techniques, machine learning, or Hybrid Approaches (HAs) techniques. Optimization techniques are mostly used to find the optimal path and achieve multi-objective goals. Some findings were approved as the best models inspired by natural biological as genetic algorithms, Particle Swarm Optimization, and Ant Colony Optimization. In addition to machine learning techniques as Reinforcement Learning. The hybrid approach techniques that combine optimization and machine learning techniques can increase robustness in solving routing problems.

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Section: ) Hybrid Approaches For Ugvmentioning

confidence: 99%

Routing Approaches used for Electrical Vehicles Navigation: A Survey

Hejres,

Mahjoub,

Hewahi

2024

IJCDS

View full text Add to dashboard Cite

show abstract

“…Equation (13) indicates that the UAV should satisfy the pitch angle constraint. Equation (14) indicates that the UAV should satisfy the actual time constraint throughout the delivery time. Equation (15) indicates that the flight speed of the UAV at each stage should meet its performance speed constraint.…”

Section: Objective Functionmentioning

confidence: 99%

“…They solved the soft time window vehicle path problem with simultaneous set dispensing by combining the domain search strategy with the particle swarm algorithm, which improved the ability to explore the solution space and the algorithm's global search capacity. In addition, Lu et al (14) combined the simulated annealing algorithm with the A* (A-star) algorithm to solve the path planning logistics scheduling problem for different purposes. Tiwari et al (15) proposed a study of UAV trajectory planning in dynamic environments using multiverse algorithms.…”

Section: Introductionmentioning

confidence: 99%

“First Kilometer” Scheduling Task of Multiple Unmanned Aerial Vehicles Based on Multisource Heterogeneous Sensors

Yang¹,

Zhang²,

Pan³

et al. 2022

Sensors and Materials

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To solve the problem of "first-kilometer" distribution difficulties in rural areas, we propose a transportation method using unmanned aerial vehicles (UAVs) for delivery. The mountainous environment of Fengshan County in Guizhou is first simulated as the UAV delivery environment. A differential evolution strategy based on the improved whale optimization algorithm (DEIWOA) combined with multisource heterogeneous sensors is then proposed to solve the UAV obstacle avoidance path. After the UAV's delivery path is planned using the DEIWOA algorithm, the multisource heterogeneous sensor is used to perform obstacle avoidance among multiple UAVs and path correction of UAVs in actual situations. Afterwards, to minimize the delivery cost of UAVs, a multi-UAV cargo delivery model is built with the optimization goal of minimizing the transportation cost and time window violation cost. This UAV scheduling model is solved using the proposed DEIWOA algorithm. Finally, simulations are performed to compare the proposed method with the cutting-edge algorithms. The obtained results show that the proposed DEIWOA algorithm can provide a better plan of the UAV path and reduce the cost of logistics scheduling. It can also provide support for UAV logistics and distribution in mountainous areas in actual situations.

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“…Tese studies lay little emphasis on the coupling characteristics of internal and external factors. When analyzing the factors that infuence users' charging behavior, only the relationship between factors and user behavior was considered [24], and the relationship between factors and interfactors was not analyzed. Te dominant factor among many factors was not identifed.…”

Section: Introductionmentioning

confidence: 99%

Priority Analysis of Influence Factors for Electric Vehicle Demand Response Strategies

Yao

Zhu

et al. 2023

International Transactions on Electrical Energy Systems

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As the penetration of renewable energy continues to increase, the demand-side resources in the grid will become more and more important. Electric vehicles (EVs) account for a relatively large proportion of demand-side resources, but individual and social factors have been less considered in multifactorial studies affecting EV participation in demand response (DR), and the multiscenario DR process has not been adequately studied. Therefore, an EV demand response strategy considering the influence of multiple factors is proposed in this paper. Firstly, a multisource charging load characteristic model is constructed by analyzing the characteristics of EV charging behavior under multiple scenarios. Secondly, the DEMATEL-AISM method is used to analyze the degree of influence of personal and social factors on users’ charging behavior under complex social environments, and the dominant factors in each scenario are identified. Finally, based on the analysis of the dominant factors in multiple scenarios, an EV regulation strategy under the influence of multiple factors is developed to achieve peak shaving. The feasibility of the proposed method is verified through simulation cases. The simulation results reveal that the revenues of aggregators and users are improved, and the stability of the power system is enhanced.

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Integrated Route Planning Algorithm Based on Spot Price and Classified Travel Objectives for EV Users

Cited by 22 publications

References 14 publications

Routing Approaches used for Electrical Vehicles Navigation: A Survey

Routing Approaches used for Electrical Vehicles Navigation: A Survey

“First Kilometer” Scheduling Task of Multiple Unmanned Aerial Vehicles Based on Multisource Heterogeneous Sensors

Priority Analysis of Influence Factors for Electric Vehicle Demand Response Strategies

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