A fast-charging navigation strategy for electric vehicles considering user time utility differences

Yang, Ni; Zhang, Xiaohui; Liu, Jing

doi:10.1016/j.segan.2022.100646

Cited by 12 publications

(5 citation statements)

References 8 publications

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“…To alleviate the pressure on charging load, many regions have started constructing new charging stations. However, existing charging strategies [30][31][32] have not addressed their scalability. Adding a new charging area and starting the training of charging recommendation strategies from scratch undoubtedly incurs additional costs.…”

Section: An Algorithmic Framework For Recommending Regional Charging ...mentioning

confidence: 99%

Scalable Accelerated Intelligent Charging Strategy Recommendation for Electric Vehicles Based on Deep Q-Networks

Shen,

Wu,

Zhang

et al. 2024

IJACSA

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With the rapid development of electric vehicles, their charging strategies significantly impact the overall power grid. Solving the spatiotemporal scheduling problem of vehicle charging has become a hot research topic. This paper focuses on recommending suitable charging stations for electric vehicles and proposes a scalable accelerated intelligent charging strategy recommendation algorithm based on Deep Q-Networks (DQN).The strategy recommendation problem is formulated as a Markov decision process, where the continuous sequence of regional charging requests within a time slice is fed into the DQN network as the input state, enabling optimal charging strategy recommendations for each electric vehicle. The algorithm aims to maintain regional load balance while minimizing user waiting time. To enhance the algorithm's applicability, a scalable, accelerated charging strategy framework is further proposed, which incorporates information filtering and shared experience pool mechanisms to adapt to different expansion scenarios and expedite strategy iterations in new scenarios. Simulation results demonstrate that the proposed DQN-based strategy recommendation algorithm outperforms the shortest path-first strategy, and the scalable, accelerated charging strategy framework achieves a 64.3% improvement in iteration speed in new scenarios, which helps to reduce the cloud server load and saves overheads.

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Section: An Algorithmic Framework For Recommending Regional Charging ...mentioning

confidence: 99%

Scalable Accelerated Intelligent Charging Strategy Recommendation for Electric Vehicles Based on Deep Q-Networks

Shen,

Wu,

Zhang

et al. 2024

IJACSA

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“…For this reason, the treatment of this paper is as follows. Firstly, multi-ple x i can be generated by ( 6), as a certain x i is used as a unit mileage, and then the maximum number of unit mileage, n that can be met by the endurance mileage is calculated according to (8). Since the service capacity of a shared vehicle is about 10 traditional private vehicles, keep n no greater than 10.…”

Section: Charging Characteristicsmentioning

confidence: 99%

“…As a means of transportation, the most fundamental function of EV is to fulfill people's traveling demand. Therefore, in terms of research on charging characteristics of EV, it is necessary to consider its traffic characteristics [8]. References [9,10] use Markov model to simulate the driving mode of EV and analyzes the influence of its charging load on the power system.…”

Section: Introductionmentioning

confidence: 99%

Modeling and simulation of charging characteristics of electric vehicle group under the mode of autonomous driving‐shared travel

Zhu,

Wu,

Chen

et al. 2023

IET Intelligent Trans Sys

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Compared with the traditional travel mode, the increasingly mature autonomous driving and shared travel technologies can lead to a higher driving utilization rate and lower car parc; however, at the same time, they will bring unknown impacts to the trans‐energy system. As a new generation of vehicles, the behavior of electric vehicles will also be affected. This paper describes and models the behavior of electric vehicle group in the autonomous driving‐shared travel mode in detail, and uses the multi‐agent technology to establish a large‐scale electric vehicle group simulation model. This model fully considers the constraints of traffic network and charging station and the influence of fuel vehicles, which can simulate the actual scene well and be used to study the charging behavior of electric vehicle group. Finally, the simulation model is used to obtain the charging load curve of the electric vehicle group under the new travel mode and analyze the influence of travel upgrade and network topology on charging load.

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“…Te emergence of electric vehicles (EVs) has opened up an interesting new research area in load and energy storages (ESs). However, the high presence of EVs and their potential for future cooperation with other grid resources present challenges for grid developers [1,2]. In this regard, managing EVs with vehicle-to-grid (V2G) capability is a signifcant operational issue in distribution networks.…”

Section: Introductionmentioning

confidence: 99%

Resilient Operation of Electric Vehicles considering Grid Resiliency and Uncertainties

Armaghan,

Asgharzadeh,

Yousefi-Khanghah

et al. 2023

International Transactions on Electrical Energy Systems

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This paper proposes a novel short-time optimization method based on a resiliency enhancement strategy for a smart distribution network during adverse weather conditions. The key idea is to integrate various electric vehicles (EVs) with different behaviour patterns and other distributed energy resources into emergency tools of distribution network operation such as topology reconfiguration and grid-supported services. In this regard, possible management programs for three types of EVs (with different levels of control potentials), energy resources, reconfiguration, and demand response programs impacts on boosting resiliency are investigated. The framework is organized on coordination and benefit sharing among distribution system operator (DSO), private sector, and EV owners to reduce possible side effects of failures that could be incepted by extreme weather conditions. A Monte Carlo-based stochastic simulation for modelling uncertainties such as EVs movement, state of change, arrival/departure time to charge stations, and weather-based failures is devised. In order to evaluate resiliency, metrics based on a multiphase method are analysed. The performance of the proposed method on extra benefit obtaining for DSO and private sector and also resiliency enhancement in disturbance progress, postdisturbance degraded, and restorative state are carried out on IEEE-33 bus test system, and obtained results are analysed in detail.

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A fast-charging navigation strategy for electric vehicles considering user time utility differences

Cited by 12 publications

References 8 publications

Scalable Accelerated Intelligent Charging Strategy Recommendation for Electric Vehicles Based on Deep Q-Networks

Scalable Accelerated Intelligent Charging Strategy Recommendation for Electric Vehicles Based on Deep Q-Networks

Modeling and simulation of charging characteristics of electric vehicle group under the mode of autonomous driving‐shared travel

Resilient Operation of Electric Vehicles considering Grid Resiliency and Uncertainties

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