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Modeling Electric Vehicle Charging
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Modeling Electric Vehicle Charging

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (21 September 2020) | Viewed by 20763

Special Issue Editor

Prof. Dr. Joakim Munkhammar

E-Mail Website
Guest Editor
Department of Engineering Sciences, Uppsala University, SE-751 21 Uppsala, Sweden
Interests: electric vehicle charging; photovoltaic power generation and electricity use in the built environment; statistical modeling; machine learning

Special Issue Information

Dear Colleagues,

Electrification of transport is rapidly expanding globally, with large amounts of electric and hybrid vehicles introduced and sold in recent years. Where and how to charge this growing global fleet of electric vehicles is an open problem, and it is also a pertinent question as large amounts of high-power electric vehicle charging could strain the local electricity grid. Thus, there is a need to quantify the impact of electric vehicle charging, in rural and city locations, in space and time. There are many electric vehicle charging models in the literature, which prompts a need for standardization of electric vehicle charging models, and there is also a need for charging models to be rigorously based on charging or mobility data.

To address this problem, this Special Issue, entitled “Modeling Electric Vehicle Charging”, was proposed to the international journal Energies, which is an SSCI and SCIE journal (2018 IF = 2.707). In an attempt to gather state-of-the-art research on electric vehicle charging modeling, this issue mainly covers modeling electric vehicle charging, based on statistics or machine learning, validated by charging or mobility data. In particular, models that are also based on charging or mobility data are encouraged. These models should also aspire to be general so that they are applicable for many locations and could qualify in a standardization process of electric vehicle charging models. Papers selected for this Special Issue are subject to a rigorous peer review procedure with the aim of rapid and wide dissemination of research results, developments, and applications.

I am writing to invite you to submit your original work to this Special Issue. I look forward to receiving your outstanding research.

Assoc. Prof. Dr. Joakim Munkhammar
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Electric vehicle charging
  • Statistical modeling
  • Machine learning
  • Electric vehicle charging data

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Published Papers (4 papers)

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Research

18 pages, 7158 KiB  
Article
Electric Vehicle Charging and Discharging Algorithm Based on Reinforcement Learning with Data-Driven Approach in Dynamic Pricing Scheme
by Jaehyun Lee, Eunjung Lee and Jinho Kim
Energies 2020, 13(8), 1950; https://doi.org/10.3390/en13081950 - 15 Apr 2020
Cited by 44 | Viewed by 5833
Abstract
In the smart grid environment, the penetration of electric vehicle (EV) is increasing, and dynamic pricing and vehicle-to-grid technologies are being introduced. Consequently, automatic charging and discharging scheduling responding to electricity prices that change over time is required to reduce the charging cost [...] Read more.
In the smart grid environment, the penetration of electric vehicle (EV) is increasing, and dynamic pricing and vehicle-to-grid technologies are being introduced. Consequently, automatic charging and discharging scheduling responding to electricity prices that change over time is required to reduce the charging cost of EVs, while increasing the grid reliability by moving charging loads from on-peak to off-peak periods. Hence, this study proposes a deep reinforcement learning-based, real-time EV charging and discharging algorithm. The proposed method utilizes kernel density estimation, particularly the nonparametric density function estimation method, to model the usage pattern of a specific charger at a specific location. Subsequently, the estimated density function is used to sample variables related to charger usage pattern so that the variables can be cast in the training process of a reinforcement learning agent. This ensures that the agent optimally learns the characteristics of the target charger. We analyzed the effectiveness of the proposed algorithm from two perspectives, i.e., charging cost and load shifting effect. Simulation results show that the proposed method outperforms the benchmarks that simply model usage pattern through general assumptions in terms of charging cost and load shifting effect. This means that when a reinforcement learning-based charging/discharging algorithm is deployed in a specific location, it is better to use data-driven approach to reflect the characteristics of the location, so that the charging cost reduction and the effect of load flattening are obtained. Full article
(This article belongs to the Special Issue Modeling Electric Vehicle Charging)
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12 pages, 1083 KiB  
Article
Goal Programming Application for Contract Pricing of Electric Vehicle Aggregator in Join Day-Ahead Market
by Parinaz Aliasghari, Behnam Mohammadi-Ivatloo, Mehdi Abapour, Ali Ahmadian and Ali Elkamel
Energies 2020, 13(7), 1771; https://doi.org/10.3390/en13071771 - 7 Apr 2020
Cited by 14 | Viewed by 2426
Abstract
Selecting an appropriate contract price between electric vehicle aggregators and electric vehicle owners is an uncertain, multi-criteria decision-making issue. In addition, the results can cause strong conflict due to different aims: the optimal value for increasing electric vehicle aggregator (EVA) profit negatively affects [...] Read more.
Selecting an appropriate contract price between electric vehicle aggregators and electric vehicle owners is an uncertain, multi-criteria decision-making issue. In addition, the results can cause strong conflict due to different aims: the optimal value for increasing electric vehicle aggregator (EVA) profit negatively affects the cost for owners. The value of the contract price can change the optimal scheduling of EVAs in the day-ahead market. Taking into consideration this context, the current paper proposes to solve the multi-objective scheduling problem of an aggregator with a goal programming approach. The presented approach sets a satisfaction level for each goal according to decision-makers’ preference. Numerical results illustrate the validity of this approach to balance different performance measures. Furthermore, optimal scheduling of electric vehicle aggregators in the day-ahead market is created. Full article
(This article belongs to the Special Issue Modeling Electric Vehicle Charging)
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Graphical abstract

16 pages, 3260 KiB  
Article
Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features
by Sahar Khaleghi, Yousef Firouz, Maitane Berecibar, Joeri Van Mierlo and Peter Van Den Bossche
Energies 2020, 13(5), 1262; https://doi.org/10.3390/en13051262 - 9 Mar 2020
Cited by 27 | Viewed by 3878
Abstract
The success of electric vehicles (EVs) depends principally on their energy storage system. Lithium-ion batteries currently feature the ideal properties to fulfil the wide range of prerequisites specific to electric vehicles. Meanwhile, the precise estimation of batteries’ state of health (SoH) should be [...] Read more.
The success of electric vehicles (EVs) depends principally on their energy storage system. Lithium-ion batteries currently feature the ideal properties to fulfil the wide range of prerequisites specific to electric vehicles. Meanwhile, the precise estimation of batteries’ state of health (SoH) should be available to provide the optimal performance of EVs. This study attempts to propose a precise, real-time method to estimate lithium-ion state of health when it operates in a realistic driving condition in the presence of dynamic stress factors. To this end, a real-life driving profile was simulated based on highly dynamic worldwide harmonized light vehicle test cycle load profiles. Afterward, various features will be extracted from voltage data and they will be scored based on prognostic metrics to select diagnostic features which can conveniently identify battery degradation. Lastly, an ensemble learning model was developed to capture the correlation of diagnostic features and battery’s state of health (SoH). The result illustrates that the proposed method has the potential to estimate the SoH of battery cells aged under a distinct depth of discharge and current profile with a maximum error of 1%. This confirms the robustness of the developed approach. The proposed method has the capability of implementing in battery management systems due to many reasons; firstly, it is tested and validated based on the data which are equal to the real-life driving operation of an electric vehicle. Secondly, it has high accuracy and precision, and a low computational cost. Finally, it can estimate the SoH of battery cells with different aging patterns. Full article
(This article belongs to the Special Issue Modeling Electric Vehicle Charging)
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19 pages, 565 KiB  
Article
Improved Photovoltaic Self-Consumption in Residential Buildings with Distributed and Centralized Smart Charging of Electric Vehicles
by Reza Fachrizal and Joakim Munkhammar
Energies 2020, 13(5), 1153; https://doi.org/10.3390/en13051153 - 4 Mar 2020
Cited by 99 | Viewed by 7877
Abstract
The integration of photovoltaic (PV) and electric vehicle (EV) charging in residential buildings has increased in recent years. At high latitudes, both pose new challenges to the residential power systems due to the negative correlation between household load and PV power production and [...] Read more.
The integration of photovoltaic (PV) and electric vehicle (EV) charging in residential buildings has increased in recent years. At high latitudes, both pose new challenges to the residential power systems due to the negative correlation between household load and PV power production and the increase in household peak load by EV charging. EV smart charging schemes can be an option to overcome these challenges. This paper presents a distributed and a centralized EV smart charging scheme for residential buildings based on installed photovoltaic (PV) power output and household electricity consumption. The proposed smart charging schemes are designed to determine the optimal EV charging schedules with the objective to minimize the net load variability or to flatten the net load profile. Minimizing the net load variability implies both increasing the PV self-consumption and reducing the peak loads. The charging scheduling problems are formulated and solved with quadratic programming approaches. The departure and arrival time and the distance covered by vehicles in each trip are specifically modeled based on available statistical data from the Swedish travel survey. The schemes are applied on simulated typical Swedish detached houses without electric heating. Results show that both improved PV self-consumption and peak load reduction are achieved. The aggregation of distributed smart charging in multiple households is conducted, and the results are compared to the smart charging for a single household. On the community level, both results from distributed and centralized charging approaches are compared. Full article
(This article belongs to the Special Issue Modeling Electric Vehicle Charging)
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