A Review of Artificial Intelligence-Enabled Electric Vehicles in Traffic Congestion Management

Mahardhika, Sakti; Putriani, Okkie

doi:10.4108/eai.11-10-2022.2326421

Cited by 2 publications

(1 citation statement)

References 16 publications

(16 reference statements)

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“…In this domain of research, several proposals are presented in the literature; however, most of the research works related to EVs mainly tackles topics that are concerned with both wired and wireless charging technologies [21], battery industry [22], safety issues [23], marketing and economics [24], traffic load balancing, and congestion management [25][26][27]. As for routing and navigation models, the work of [28] proposes an EV navigation model that routes the EVs via paths that are claimed to cover the power charging requirements toward their destinations.…”

Section: Related Workmentioning

confidence: 99%

PLUG: A City-Friendly Navigation Model for Electric Vehicles with Power Load Balancing upon the Grid

Quttoum,

Alsarhan,

Aljaidi

et al. 2023

WEVJ

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Worldwide, in many cities, electric vehicles (EVs) have started to spread as a green alternative in transportation. Several well-known automakers have announced their plans to switch to all-electric engines very soon, although for EV drivers, battery range is still a significant concern—especially when driving on long-distance trips and driving EVs with limited battery ranges. Cities have made plans to serve this new form of transportation by providing adequate coverage of EV charging stations in the same way as traditional fuel ones. However, such plans may take a while to be fully deployed and provide the required coverage as appropriate. In addition to the coverage of charging stations, cities need to consider the potential loads over their power grids not only to serve EVs but also to avoid any shortages that may affect existing clients at their various locations. This may take a decade or so. Consequently, in this work, we propose a novel city-friendly navigation model that is oriented to serve EVs in particular. The methodology of this model involves reading real-time power loads at the grid’s transformer nodes and accordingly choosing the routes for EVs to their destinations. Our methodology follows a real-time pricing model to prioritize routes that pass through less-loaded city zones. The model is developed to be self-aware and adaptive to dynamic price changes, and hence, it nominates the shortest least-loaded routes in an automatic and autonomous way. Moreover, the drivers have further routing preferences that are modeled by a preference function with multiple weight variables that vary according to a route’s distance, cost, time, and services. Different from other models in the literature, this is the first work to address the dynamic loads of the electricity grids among various city zones for load-balanced EV routing in an automatic way. This allows for the easy integration of EVs through a city-friendly and anxiety-free navigation model.

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