A data driven typology of electric vehicle user types and charging sessions

Helmus, Jurjen; Lees, Michael; Hoed, Robert van den

doi:10.1016/j.trc.2020.102637

Cited by 97 publications

(43 citation statements)

References 48 publications

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“…Other works have utilized unsupervised learning, mainly clustering techniques to identify EV charging behavior. GMM was used in [70] to find 13 distinct clusters of charging behavior for non-residential charging. Charging sessions containing information about start time, connection duration, distance between two sessions and hours between sessions were considered as features.…”

Section: Unsupervised and Statistical Learning For Analysis And Pmentioning

confidence: 99%

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: Unsupervised and Statistical Learning For Analysis And Pmentioning

confidence: 99%

Machine Learning Approaches for EV Charging Behavior: A Review

et al. 2020

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“…From the observations that weekly charging sessions present two peaks (namely a morning and a late afternoon peak) it was reasonable to consider a mixture of distributions to account for the different modes. In [133], 13 different charging session profiles were identified using Gaussian mixture clustering based on data provided by the G4 cities of the Netherlands. Other recent studies complement this work by using Gaussian mixtures to model the triplet (arrival time, charging duration, energy consumed) in order to characterize EV load.…”

Section: Statistical Characterizationmentioning

confidence: 99%

“…Instead, statistical characterization techniques with unimodal distributions could yield a sufficient approximation of the phenomenon as proposed in [13,14] along with LM [148][149][150]. The remaining statistical characterization models (mixtures [13,24,123,133,134] and KDEs [135][136][137][138][139]) can capture diverse patterns and thus could be applied to medium-sized datasets. The Paris dataset could also be used to ver-ify the consistency of simple queuing models as they usually struggle to find concrete applications [29,142].…”

Section: Suggested Matching Of Ev Load Models With the Datasets Consideredmentioning

confidence: 99%

A Review of Electric Vehicle Load Open Data and Models

et al. 2021

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The field of electric vehicle charging load modelling has been growing rapidly in the last decade. In light of the Paris Agreement, it is crucial to keep encouraging better modelling techniques for successful electric vehicle adoption. Additionally, numerous papers highlight the lack of charging station data available in order to build models that are consistent with reality. In this context, the purpose of this article is threefold. First, to provide the reader with an overview of the open datasets available and ready to be used in order to foster reproducible research in the field. Second, to review electric vehicle charging load models with their strengths and weaknesses. Third, to provide suggestions on matching the models reviewed to six datasets found in this research that have not previously been explored in the literature. The open data search covered more than 860 repositories and yielded around 60 datasets that are relevant for modelling electric vehicle charging load. These datasets include information on charging point locations, historical and real-time charging sessions, traffic counts, travel surveys and registered vehicles. The models reviewed range from statistical characterization to stochastic processes and machine learning and the context of their application is assessed.

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“…), unsupervised learning methods (e.g. clustering [13], Gaussian mixture models [14], and kernel density estimator [15]), and deep learning [16]. Deterministic models, providing scalar predictions, dominate, while the probabilistic models have not received the same attention [17].…”

Section: Introductionmentioning

confidence: 99%

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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A data driven typology of electric vehicle user types and charging sessions

Cited by 97 publications

References 48 publications

Machine Learning Approaches for EV Charging Behavior: A Review

Machine Learning Approaches for EV Charging Behavior: A Review

A Review of Electric Vehicle Load Open Data and Models

Analysis of Energy Consumption at Slow Charging Infrastructure for Electric Vehicles

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