Machine Learning for Solving Charging Infrastructure Planning Problems: A Comprehensive Review

Deb, Sanchari

doi:10.3390/en14237833

Cited by 10 publications

(4 citation statements)

References 105 publications

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“…Additionally, unsupervised techniques such as k-means clustering, Gaussian mixture model, and kernel density estimator have been employed for this purpose across various research studies. As an example, Random Forest [29] and SVM [30] were employed for predicting the charging capacity of a charging infrastructure. The former supports the adoption of Random Forest, asserting that the SVM is inadequate due to its lack of universality and limitations in handling data.…”

Section: Load Forecastingmentioning

confidence: 99%

Optimal placement of electric vehicle charging infrastructures utilizing deep learning

Alansari,

Al‐Sumaiti,

Abughali

2024

IET Intelligent Trans Sys

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The increasing level of air pollution caused by the transport sector necessitates countries to adopt Electric Vehicles (EVs). To espouse EVs, the charging infrastructures' location should be optimal to fulfill the mass‐market consumer needs and reduce the governmental expenses. In this work, the placement of two categories of charging infrastructures, specifically Charging Station (CS) and Dynamic Wireless Charging (DWC) infrastructure is planned in Dubai, United Arab Emirates (UAE) as a case study. For this study, Dubai is divided into 14 districts as per its new addressing system, and the allocation of the two types of charging infrastructures is based on the projection of population growth, EVs adoption forecasting, and other factors with the objective of meeting the consumers' needs and minimizing the government's expenditure. The proposal introduces a novel hybrid model for forecasting, integrating the strengths of the Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX) model for capturing time‐series statistical characteristics, and the deep learning Attention‐based Convolutional Neural Network (ACNN) for modeling nonlinear relationships in time‐series data. The model's effectiveness was validated through comparative analyses against state‐of‐the‐art (SOTA) models on standard benchmarks, showing significant improvements: 29.70% reduction in Mean Absolute Error (MAE), and 19.15% reduction in Root Mean Square Error (RMSE).

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Section: Load Forecastingmentioning

confidence: 99%

Optimal placement of electric vehicle charging infrastructures utilizing deep learning

Alansari,

Al‐Sumaiti,

Abughali

2024

IET Intelligent Trans Sys

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“…A review of the critical impacts of grid-tied EVs is presented in [21], where the authors focused on the interaction of EVs in a smart grid environment. In [22], the issues of charging infrastructure are also presented and the authors presented a critical review on applications of machine learning for solving the issues around infrastructure planning.…”

Section: Introductionmentioning

confidence: 99%

“…However, the bulk of this systematic review done in [26] also focused on the various technological challenges associated with integrating EVs into smart grids and smart cities, particularly the problems of charging infrastructure. As seen in [13][14][15][16][17][18][19][20][21][22][23][24], most reviews and research papers concerning the risks and challenges of integrating electric vehicles into smart cities are focused on technical challenges. However, there are other associated risks and challenges associated with EV integration into smart cities, such as the security risks of electric vehicle infrastructure.…”

Section: Introductionmentioning

confidence: 99%

The Risks and Challenges of Electric Vehicle Integration into Smart Cities

Apata

Bokoro

2023

Energies

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The integration of electric vehicles (EVs) into smart cities presents a promising opportunity for reducing greenhouse gas emissions and enhancing urban sustainability. However, there are significant risks and challenges associated with the integration of EVs into smart cities, which must be carefully considered. Though there are various reviews available on the challenges of integrating EVs into smart cities, the majority of these are focused on technical challenges, thereby ignoring other important challenges that may arise from such integration. This paper therefore provides a comprehensive overview of the risks and challenges associated with the integration of EVs into smart cities in one research paper. The different challenges associated with the integration of EVs into smart cities have been identified and categorized into four groups, namely: technical, economic, social, and environmental, while also discussing the associated risks of EV integration into smart cities. The paper concludes by highlighting the need for a holistic approach to EV integration into smart cities that considers these challenges and risks. It also identifies possible future trends and outlooks to address these challenges and promote the successful integration of EVs into smart cities. Overall, this paper provides valuable insights for policymakers, city planners, and researchers working towards sustainable urban transportation systems.

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“…Battery SoC and time series load curve are used in [7], [8] not only to route the EVs towards appropriate charging stations but to suggest the type of charging also. The charging station environment discussed in [9] uses pre-defined timings with variable pricing for the charging of EVs without considering the practicality in detail. Charging demand prediction, site selection, utilization of the charger, pricing and scheduling are the key points involved in the infrastructure planning of EVCS reported in [10].…”

Section: Introductionmentioning

confidence: 99%

Communication frame work in an electric vehicle charging station supporting solar energy management

George

Dixit

Dawnee

et al. 2022

IJEECS

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Exploiting Renewable energy to the maximum extent possible in an electric vehicle charging station (EVCS) is the key in supporting the anticipated carbon reduction from the electric vehicles (EVs). Knowing the expected load and the solar energy in advance at the EVCS can be crucial in framing a proper energy management strategy. Selection of suitable parameters associated with the participating EVs and EVCS are vital in utilizing them for predicting the probable EV load and expected solar energy for a given period under consideration. A prototype EVCS with smart communication infrastructure is developed considering solar pv as the energy source. Real time communication of the parameters between multiple agents has been established effectively using an interactive website, cloud server and an short message service (SMS) application programming interface (API). The data generated from the prototype models have been utilized in a random forest regression (RFR) classifier model in order to predict the probable solar energy and the expected EV load for every minute duration. The integrated communication frame work is found to be less complex to implement for an autonomous direct current (DC EVCS). The details provided at the graphical user interface (GUI) designed at the EVCS can be instrumental in developing a proper energy management strategy.

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Machine Learning for Solving Charging Infrastructure Planning Problems: A Comprehensive Review

Cited by 10 publications

References 105 publications

Optimal placement of electric vehicle charging infrastructures utilizing deep learning

Optimal placement of electric vehicle charging infrastructures utilizing deep learning

The Risks and Challenges of Electric Vehicle Integration into Smart Cities

Communication frame work in an electric vehicle charging station supporting solar energy management

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