Data-driven smart charging for heterogeneous electric vehicle fleets

Frendo, Oliver; Graf, Jérôme; Gaertner, Nadine; Stuckenschmidt, Heiner

doi:10.1016/j.egyai.2020.100007

Cited by 115 publications

(76 citation statements)

References 38 publications

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“…Using residential charging data, the best performing model in this work achieved MAE of 0.124 minutes and RMSE of 0.158 minutes. Oliver et al [67] used a dataset consisting of charging processes, i.e. timeseries data of charging power, from a workplace to predict charge profiles.…”

Section: Supervised Learning For Analysis and Prediction Of Charmentioning

confidence: 99%

“…Best result using PSF model with average SMAPE: 14.06% [64] Predict energy consumption of a session PSF-based method using kNN SMAPE: 7.85% [65] Classify whether the driver will use fast charging Binary log. regression Accuracy: 0.894 [66] Predict the time to next plug for residential charging SVR with radial basis MAE: 0.124 minutes, RMSE: 0.158 minutes [67] Predict charge profiles in workplace XGBoost, LR and ANN Best result using XGBoost MAE: 126 W. Integration to scheduling lead to in up to 21% increase in charge. [68] Develop model to predict charging speed using temperature, connection time, SOC LR - [69] Predict charging capacity and daily charging times.…”

Section: Various ML Including Psf Svr Rfmentioning

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: Various ML Including Psf Svr Rfmentioning

confidence: 99%

Machine Learning Approaches for EV Charging Behavior: A Review

et al. 2020

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“…While many tractable models for battery charging behavior exist, these models require information about the specific battery pack and the initial state of charge of the vehicle [30], [31]. Other models rely on machine learning to learn the relationship between state of charge and current draw [32]. However, these machine learning models still require access to the state of charge of the vehicle.…”

Section: B Battery Management System Behaviormentioning

confidence: 99%

Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging

Lee

Lee²,

Lee³

et al. 2021

IEEE Trans. Smart Grid

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We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States. The architecture enables real-time monitoring and control and supports electric vehicle (EV) charging at scale. The ACN adopts a flexible Adaptive Scheduling Algorithm based on convex optimization and model predictive control and allows for significant over-subscription of electrical infrastructure. We describe some of the practical challenges in real-world charging systems, including unbalanced three-phase infrastructure, non-ideal battery charging behavior, and quantized control signals. We demonstrate how the Adaptive Scheduling Algorithm handles these challenges, and compare its performance against baseline algorithms from the deadline scheduling literature using real workloads recorded from the Caltech ACN and accurate system models. We find that in these realistic settings, our scheduling algorithm can improve operator profit by 3.4 times over uncontrolled charging and consistently outperforms baseline algorithms when delivering energy in highly congested systems.

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“…With their ability to give precise, local overviews of the flexibility capacity available, AI-based programs could support SOs' manual flexibility management (Esmat et al, 2018;Radecke et al, 2019). Alternatively, flexibility management could be automated: AI could be applied to balance the electricity net autonomously without human involvement (Shen et al, 2018;Frendo et al, 2020). Small-scale experiments in which AI-based programs are taking over the tasks of an SO within a micro-grid are already taking place (Reijnders et al, 2020).…”

Section: Opportunities Of Ai For System Operatorsmentioning

confidence: 99%

Governing AI in Electricity Systems: Reflections on the EU Artificial Intelligence Bill

Niet

Est

Veraart

2021

Front. Artif. Intell.

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The Proposal for an Artificial Intelligence Act, published by the European Commission in April 2021, marks a major step in the governance of artificial intelligence (AI). This paper examines the significance of this Act for the electricity sector, specifically investigating to what extent the current European Union Bill addresses the societal and governance challenges posed by the use of AI that affects the tasks of system operators. For this we identify various options for the use of AI by system operators, as well as associated risks. AI has the potential to facilitate grid management, flexibility asset management and electricity market activities. Associated risks include lack of transparency, decline of human autonomy, cybersecurity, market dominance, and price manipulation on the electricity market. We determine to what extent the current bill pays attention to these identified risks and how the European Union intends to govern these risks. The proposed AI Act addresses well the issue of transparency and clarifying responsibilities, but pays too little attention to risks related to human autonomy, cybersecurity, market dominance and price manipulation. We make some governance suggestions to address those gaps.

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Data-driven smart charging for heterogeneous electric vehicle fleets

Cited by 115 publications

References 38 publications

Machine Learning Approaches for EV Charging Behavior: A Review

Machine Learning Approaches for EV Charging Behavior: A Review

Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging

Governing AI in Electricity Systems: Reflections on the EU Artificial Intelligence Bill

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