Special Issue "Advances in Li-Ion Battery Management for Electric Vehicles"

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

Deadline for manuscript submissions: closed (30 June 2020).

Special Issue Editors

Prof. Dr. Federico Baronti
Website
Guest Editor
Department of Information Engineering, University of Pisa, V. Caruso 16, 56122 Pisa PI, Italy
Interests: energy storage systems; battery management systems; embedded systems; data acquisition systems
Prof. Dr. Roberto Saletti
Website
Guest Editor
Department of Information Engineering, University of Pisa, V. Caruso 16, 56122 Pisa PI, Italy.
Interests: energy storage; battery managements systems; supercapacitors/Li-based batteries; digital electronics and embedded systems for smart grid
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Special Issue Information

Dear Colleagues,

Li-Ion batteries are undoubtedly the most prominent energy storage technology for electric vehicles and will play a decisive role in achieving a more sustainable and green transport system. In order to take full advantage of their superior characteristics, advanced management is mandatory to ensure safe and reliable operation of the battery and to prolong its lifetime, while reducing costs. The achievement of this goal requires a strong multi-disciplinary approach combining the most recent researches in the fields of battery modeling, control theory, sensor technologies, embedded system design, and thermal management.

The main objective of this Special Issue is to compile recent researches and development efforts contributing to advances in Li-Ion battery management systems (BMSs) for electric vehicles. High-quality papers that explore this area and provide emerging solutions and visions for future research activities are sought.

Prospective authors are invited to submit manuscripts for review for publication in this Special Issue. Original research and practical contributions, as well as state-of-the-art surveys, are welcome.

Prof. Dr. Federico Baronti
Prof. Dr. Roberto Saletti
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 papers will be 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 1800 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

  • Modeling
  • State estimation
  • Diagnosis and prognostics
  • Charge equalization
  • New BMS architecture
  • Safety, fault tolerance, fail-operational
  • Thermal management
  • System integration

Published Papers (2 papers)

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Research

Open AccessArticle
Effects of Overdischarge Rate on Thermal Runaway of NCM811 Li-Ion Batteries
Energies 2020, 13(15), 3885; https://doi.org/10.3390/en13153885 - 30 Jul 2020
Abstract
Overdischarge often occurs during the use of battery packs, owing to cell inconsistency in the pack. In this study, the overdischarge behavior of 2.9 Ah cylindrical NCM811 [Li(Ni0.8Co0.1Mn0.1)O2] batteries in an adiabatic environment was investigated. [...] Read more.
Overdischarge often occurs during the use of battery packs, owing to cell inconsistency in the pack. In this study, the overdischarge behavior of 2.9 Ah cylindrical NCM811 [Li(Ni0.8Co0.1Mn0.1)O2] batteries in an adiabatic environment was investigated. A higher overdischarge rate resulted in a faster temperature increase in the batteries. Moreover, the following temperatures increased: Tu, at which the voltage decreased to 0 V; Ti, at which the current decreased to 0 A; and the maximum temperature during the battery overdischarge (Tm). The following times decreased: tu, when the voltage decreased from 3 to 0 V, and ti, when the current decreased to 0 A. The discharge capacity of the batteries was 3.06–3.14 Ah, and the maximum discharge depth of the batteries was 105.51–108.27%. Additionally, the characteristic overdischarge behavior of the batteries in a high-temperature environment (55 °C) was investigated. At high temperatures, the safety during overdischarging decreased, and the amount of energy released during the overdischarge phase and short-circuiting decreased significantly. Shallow overdischarging did not significantly affect the battery capacity recovery. None of the overdischarging cases caused fires, explosions, or thermal runaway in the batteries. The NCM811 batteries achieved good safety performance under overdischarge conditions: hence, they are valuable references for battery safety research. Full article
(This article belongs to the Special Issue Advances in Li-Ion Battery Management for Electric Vehicles)
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Open AccessArticle
Research on Parameter Self-Learning Unscented Kalman Filtering Algorithm and Its Application in Battery Charge of State Estimation
Energies 2020, 13(7), 1679; https://doi.org/10.3390/en13071679 - 03 Apr 2020
Abstract
A novel state estimation algorithm based on the parameters of a self-learning unscented Kalman filter (UKF) with a model parameter identification method based on a collaborative optimization mechanism is proposed in this paper. This algorithm can realize the dynamic self-learning and self-adjustment of [...] Read more.
A novel state estimation algorithm based on the parameters of a self-learning unscented Kalman filter (UKF) with a model parameter identification method based on a collaborative optimization mechanism is proposed in this paper. This algorithm can realize the dynamic self-learning and self-adjustment of the parameters in the UKF algorithm and the automatic optimization setting Sigma points without human participation. In addition, the multi-algorithm collaborative optimization mechanism unifies a variety of algorithms, so that the identification method has the advantages of member algorithms while avoiding the disadvantages of them. We apply the combination algorithm proposed in this paper for state of charge (SoC) estimation of power batteries and compare it with other model parameter identification algorithms and SoC estimation methods. The results showed that the proposed algorithm outperformed the other model parameter identification algorithms in terms of estimation accuracy and robustness. Full article
(This article belongs to the Special Issue Advances in Li-Ion Battery Management for Electric Vehicles)
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