Improving Smart Charging Prioritization by Predicting Electric Vehicle Departure Time

Frendo, Oliver; Gaertner, Nadine; Stuckenschmidt, Heiner

doi:10.1109/tits.2020.2988648

Cited by 72 publications

(43 citation statements)

References 30 publications

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“…Oliver et al [56] developed regression models to predict the departure time of EVs. The models were trained on historical data containing over 100000 charging sessions spanning over 3 years.…”

Section: Supervised Learning For Analysis and Prediction Of Charmentioning

confidence: 99%

“…Reduced peak load by 27%, and reduced charging cost by 4% when integrated to scheduler. [55] Predict session duration and energy needs for non-residential public charging space, CA, USA Probabilistic GMM SMAPE user duration: 12.25%, SMAPE energy consumption: 12.73% [56] Predict EV charging departure time Regression including XGBoost and LR Best result using XGB: MAE of 82 minutes for departure [57] Predict start time, end time, energy consumption. LR for consumption - [58] Predict arrival and departure time EVs in a university campus (non-residential)…”

Section: Supervised Learning For Analysis and Prediction Of Charmentioning

confidence: 99%

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Machine Learning Approaches for EV Charging Behavior: A Review

et al. 2020

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As the smart city applications are moving from conceptual models to development phase, smart transportation is one of smart cities applications and it is gaining ground nowadays. Electric Vehicles (EVs) are considered one of the major pillars of smart transportation applications. EVs are ever growing in popularity due to their potential contribution in reducing dependency on fossil fuels and greenhouse gas emissions. However, large-scale deployment of EV charging stations poses multiple challenges to the power grid and public infrastructure. To overcome the issue of prolonged charging time, the simple solution of deploying more charging stations to increase charging capacity does not work due to the strain on power grids and physical space limitations. Therefore, researchers have focused on developing smart scheduling algorithms to manage the demand for public charging using modeling and optimization. More recently, there has been a growing interest in data-driven approaches in modeling EV charging. Consequently, researchers are looking to identify consumer charging behavior pattern that can provide insights and predictive analytics capability. The purpose of this paper is to provide a comprehensive review for the use of supervised and unsupervised Machine Learning as well as Deep Neural Networks for charging behavior analysis and prediction. Recommendations and future research directions are also discussed.

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Section: Supervised Learning For Analysis and Prediction Of Charmentioning

confidence: 99%

Section: Supervised Learning For Analysis and Prediction Of Charmentioning

confidence: 99%

Machine Learning Approaches for EV Charging Behavior: A Review

et al. 2020

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“…Recently, several prediction models appeared in the literature [23,37,35,36,10] explaining performance indicators of charging infrastructure also from location features. The candidate set of influence factors that can be described by location features is large.…”

Section: B Our Contributionmentioning

confidence: 99%

“…Applications of data science methods to EV charging already include the whole spectrum of supervised learning methods (e.g. K-nearest neighbour [9], linear regression [10], decision trees and their aggregations [11], support vector regression [12], etc. ), unsupervised learning methods (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Only two location features were used, the number of points of interest and the number of competitive charging stations. Regression models (linear regression, XGBoost, artificial neural networks) were used to predict the departures of vehicles to improve smart charging heuristic [10]. The set of considered features consisted of five categorical and four numerical features, among them three location features (floor of the car park, car park and charge point) were used.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Energy Consumption at Slow Charging Infrastructure for Electric Vehicles

Straka

Carvalho

Poel³

et al. 2021

IEEE Access

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Here, we develop a data-centric approach to analyse which activities, functions, and characteristics of the environment surrounding the slow charging infrastructure impact the distribution of the electricity consumed at slow charging infrastructure. We analysed the probability distribution of energy consumption and its relation to indicators characterising charging events to gain basic insights. The energy consumption can be satisfactorily modelled by a transformed beta distribution and the number of charging transactions is the driving factor among the characteristics constituting the energy consumption. We collected geospatial datasets and prepared a large number of candidate features modelling the spatial context in which the charging infrastructure operates. Using statistical methods, we identified and interpreted a relatively small subset of the most influential features correlated with energy consumption. The majority of these features are related to the economic prosperity of residents. Residents and businesses with high (low) income, situated nearby charging infrastructure, are linked to a positive (negative) impact on energy consumption. Similarly, charging infrastructure located close to expensive newly built housing shows higher energy consumption. The largest adverse impact has the high concentration of residents receiving social assistance. By applying the methodology to a specific charging infrastructure class, e.g. determined by the used rollout strategy, we differentiated the selected features. Business types, working sector of residents and public venues in the proximity are linked to higher consumption of energy at charging infrastructure deployed strategically. Characteristics linked with the age structure of the population are linked to the energy consumption at charging infrastructure placed based on the demand. Data collection and data processing are among the most time-consuming activities. The paper provides valuable insights into which data to collect and use as features when developing prediction models to inform charging infrastructure deployment and planning of power grids. INDEX TERMS electric vehicles; charging infrastructure; energy consumption; variable selection

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Predictive machine learning in optimizing the performance of electric vehicle batteries: Techniques, challenges, and solutions

Naresh,

Ratnakara Rao,

Prabhakar

2024

WIREs Data Min & Knowl

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This research paper explores the importance of optimizing the performance of electric vehicle (EV) batteries to align with the rapid growth in EV usage. It uses predictive machine learning (ML) techniques to achieve this optimization. The paper covers various ML methods like supervised, unsupervised, and deep learning (DL) and ways to measure their effectiveness. Significant battery performance factors, such as state of charge (SoC), state of health (SoH), state of function (SoF), and remaining useful life (RUL), are discussed, along with methods to collect and prepare data for accurate predictions. The paper introduces an operation research model for optimizing the performance of EV Batteries. It also looks at challenges unique to battery systems and ways to overcome them. The study showcases ML models' ability to predict battery behavior for real‐time monitoring, efficient energy use, and proactive maintenance. The paper categorizes different applications and case studies, providing valuable insights and forward‐looking perspectives for researchers, practitioners, and policymakers involved in improving EV battery performance through predictive ML.This article is categorized under: Technologies > Classification Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning

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Improving Smart Charging Prioritization by Predicting Electric Vehicle Departure Time

Cited by 72 publications

References 30 publications

Machine Learning Approaches for EV Charging Behavior: A Review

Machine Learning Approaches for EV Charging Behavior: A Review

Analysis of Energy Consumption at Slow Charging Infrastructure for Electric Vehicles

Predictive machine learning in optimizing the performance of electric vehicle batteries: Techniques, challenges, and solutions

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