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Control and Management of Electric Power System in Vehicles

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

Deadline for manuscript submissions: closed (10 May 2022) | Viewed by 7544

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Special Issue Editors

Dr. Anna Pinnarelli

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Department of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria, 87036 Rende, Italy
Interests: FACTS technology; harmonic analysis; electrical system automation and decentralized control; electrical power systems control and management with particular attention on the consequence of market scenario; smart grid; microgrid; nanogrid technologies and demand response modelling and analysis; market model and aggregator framework for energy district and energy communities
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Prof. Dr. Daniele Menniti

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Department of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria, 87036 Rende, Italy
Interests: power generation, operation, stability and control; power electronics; FACTS technology; renewable energies; distributed generation; smart, microgrid and nanogrid technologies; demand response modelling and analysis; energy markets; market models and aggregator framework for energy district and renewable energy communities
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Dr. Giuseppe Barone

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Department of Mechanical, Univ. Calabria, DIMEG, I-87036 Arcavacata Di Rende, Italy
Interests: electrical & electronics engineering; MATLAB simulation; power electronics; electrical power engineering

Dr. Pasquale Vizza

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Department of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria, 87036 Rende, Italy
Interests: power converters; power cloud; energy community; renewable energies; distributed generation; power forecasting; storage systems
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Special Issue Information

Dear Colleagues,

As is well known, key technologies in EVs include motor drive technology, power electronic technology, micro-electronic and control technology, automotive technology, material technology, and energy storage technology. Integration of all these techniques is the key to success in EVs. The most important issues in EV design are System Integration (SI) and Optimization. Moreover, high-performing electric machines are coupled with a high-performing control algorithm to deliver maximum system efficiency and performance.
The objective of this Special Issue is to bring together state-of-the-art research contributions that explore the technology solutions, models, control and management methods, approaches, and technological innovations to obtain more efficient performance and comfort with safety and reliable operations at a cheaper price. Special attention to the most recent experimental applications, in particular, highly efficient solutions over a wide range of driving conditions, considering V2G, V1G and V2H operating conditions, are greatly desirable.

We hope you can join us in this Special Issue by contributing original research papers and unpublished work not currently under review by any other journal/magazine/conference.

Topics of interest for publication include, but are not limited to:

- Modeling simulation and control of EV components;
- Storage system types for electric vehicles;
- Power Electronic Systems—Converters and emerging technologies;
- Hybrid energy storage system for electric vehicles;
- Smart energy management system for EV component;
- Optimization of EV components’ parameters;
- Optimal charging and discharging control strategies of energy storage in an EV;
- Battery thermal management in electric vehicles

Prof. Dr. Anna Pinnarelli
Prof. Dr. Daniele Menniti
Guest Editors

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
Energy storage systems
Power electronics
Smart energy management
DC–DC converter
Energy efficiency
Power losses
Grid-integrated vehicles
Vehicle to grid (V2G)
Vehicle to home (V2H)

Published Papers (3 papers)

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Research

34 pages, 1496 KiB

Open AccessArticle

Techno Economic Analysis of Electric Vehicle Grid Integration Aimed to Provide Network Flexibility Services in Italian Regulatory Framework

by Daniele Menniti, Anna Pinnarelli, Nicola Sorrentino, Pasquale Vizza, Giovanni Brusco, Giuseppe Barone and Gianluca Marano

Energies 2022, 15(7), 2355; https://doi.org/10.3390/en15072355 - 23 Mar 2022

Cited by 7 | Viewed by 1932

Abstract

The recent forecasts regarding the penetration of electric vehicles (EVs) in the transport market and their impact on national electricity distribution grids has presented new challenges in the fields of both of application and research. In this context, vehicle-to-grid (V2G) technology presents itself as an extremely valid solution in terms of application of the “demand side flexibility” paradigm. In this context, the aim of the paper is to analyze from a technical and economical point of view the use of EVs as new flexibility resources to provide network flexibility services in an Italian framework. Within this scope, a methodology for evaluating the flexibility service that a single EV or an EV fleet can offer, and therefore for estimating the EV storage system charge and discharge profile and determining its economic benefit, is proposed. Some numerical results and observations are reported to highlight possible incentive mechanisms for motivating EV end-users to offer flexibility services. Full article

(This article belongs to the Special Issue Control and Management of Electric Power System in Vehicles)

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16 pages, 5963 KiB

Open AccessArticle

Online Prediction of Remaining Useful Life for Li-Ion Batteries Based on Discharge Voltage Data

by Lin Zou, Baoyi Wen, Yiying Wei, Yong Zhang, Jie Yang and Hui Zhang

Energies 2022, 15(6), 2237; https://doi.org/10.3390/en15062237 - 18 Mar 2022

Cited by 7 | Viewed by 1830

Abstract

The state of health and remaining useful life of lithium-ion batteries are key indicators for the normal operation of electrical devices. To address the problem of the capacity of lithium-ion batteries being difficult to measure online, in this paper, we propose an online method based on particle swarm optimization and support vector regression to estimation the state of health and remaining useful life. First, a novel health indicator is extracted from the discharge voltage to characterize the capacity of lithium-ion batteries. Then, based on the capacity degradation characteristics, support vector regression is used to predict the remaining useful life of these batteries, and particle swarm optimization is selected to optimize the parameters of the support vector regression, which effectively enhances the predictive performance of the model. Validated for the NASA battery aging dataset, when training with the first 40% of the dataset, the maximum error of the predicted remaining useful life was four cycles, and when training with the first 50% of the dataset, the maximum error of the predicted remaining useful life was only one cycle. When comparing to a deep neural network, support vector regression, long short-term memory algorithms and existing similar methods in the literature, the particle swarm optimization and support vector regression method can obtain more accurate prediction results. Full article

(This article belongs to the Special Issue Control and Management of Electric Power System in Vehicles)

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22 pages, 4899 KiB

Open AccessArticle

Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries

by Shaheer Ansari, Afida Ayob, Molla Shahadat Hossain Lipu, Aini Hussain and Mohamad Hanif Md Saad

Energies 2021, 14(22), 7521; https://doi.org/10.3390/en14227521 - 11 Nov 2021

Cited by 38 | Viewed by 2856

Abstract

Remaining useful life (RUL) is a crucial assessment indicator to evaluate battery efficiency, robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) applications. RUL prediction is necessary for timely maintenance and replacement of the battery in EVs. This paper proposes an artificial neural network (ANN) technique to predict the RUL of lithium-ion batteries under various training datasets. A multi-channel input (MCI) profile is implemented and compared with single-channel input (SCI) or single input (SI) with diverse datasets. A NASA battery dataset is utilized and systematic sampling is implemented to extract 10 sample values of voltage, current, and temperature at equal intervals from each charging cycle to reconstitute the input training profile. The experimental results demonstrate that MCI profile-based RUL prediction is highly accurate compared to SCI profile under diverse datasets. It is reported that RMSE for the proposed MCI profile-based ANN technique is 0.0819 compared to 0.5130 with SCI profile for the B0005 battery dataset. Moreover, RMSE is higher when the proposed model is trained with two datasets and one dataset, respectively. Additionally, the importance of capacity regeneration phenomena in batteries B0006 and B0018 to predict battery RUL is investigated. The results demonstrate that RMSE for the testing battery dataset B0005 is 3.7092, 3.9373 when trained with B0006, B0018, respectively, while it is 3.3678 when trained with B0007 due to the effect of capacity regeneration in B0006 and B0018 battery datasets. Full article

(This article belongs to the Special Issue Control and Management of Electric Power System in Vehicles)

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