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Batteries
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Battery Management System for Electric Vehicles
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Battery Management System for Electric Vehicles

A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Modelling, Simulation, Management and Application".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 9735

Special Issue Editor

Prof. Dr. Woongchul Choi

E-Mail Website
Guest Editor
xEV System Laboratory, Department of Automotive Engineering, Kookmin University, Seoul 02707, Republic of Korea
Interests: electric vehicle; battery SOC; SOH estimation

Special Issue Information

Dear Colleagues,

Battery management systems (BMSs) are a key element for safe operation of battery packs installed in electric vehicles. Critical missions of the BMS, including estimation of current battery states, protection from abuse and failure cases, and early diagnostics of excessively aging cells, are clearly defined and seem to be straightforward. However, when it comes to establishing stable and reliable theories and implementation of BMSs, they are still quite far from a satisfactory level both developers and consumers can agree to. On top of that, the BMSs should be designed and developed to be practically implementable for the use of electric vehicles; therefore, many limitations are imposed from the perspective of mass production of the electric vehicle.

In this Special Issue, I look forward to collecting various techniques affordably implementable for practical use of BMSs for electric vehicles. Furthermore, I would like to extend the scope to include algorithms that could reliably estimate the state of health of the battery which can possibly be used to evaluate batteries for reuse/recycling when electric vehicles are turned in.

Prof. Dr. Woongchul Choi
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. Batteries is an international peer-reviewed open access monthly 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 2700 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

  • battery management systems
  • battery pack
  • charging (batteries)
  • electric vehicles

Published Papers (2 papers)

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Research

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19 pages, 5646 KiB  
Article
State of Charge Estimation for Lithium-Ion Battery Based on Unscented Kalman Filter and Long Short-Term Memory Neural Network
by Yi Zeng, Yan Li and Tong Yang
Batteries 2023, 9(7), 358; https://doi.org/10.3390/batteries9070358 - 4 Jul 2023
Cited by 11 | Viewed by 3619
Abstract
State of charge (SOC) estimation is the core algorithm of the battery management system. However, the commonly used model-based, data-driven, or experiment-based methods struggle to independently achieve accurate SOC estimation under different working conditions and temperatures, which affects battery performance and safety. To [...] Read more.
State of charge (SOC) estimation is the core algorithm of the battery management system. However, the commonly used model-based, data-driven, or experiment-based methods struggle to independently achieve accurate SOC estimation under different working conditions and temperatures, which affects battery performance and safety. To this end, this paper proposes an online SOC estimation method that combines the model-driven and double-data-driven approaches. The unscented Kalman filter (UKF) based on the first-order RC model is used to achieve robust SOC estimation, while the data-driven long short-term memory (LSTM) neural network is used to achieve fast SOC estimation. The former model has an excellent dynamic performance and the latter has high steady-state accuracy. The SOC estimation results are input into the SOC estimation model of series LSTM so that the stable but inaccurate SOC values estimated by UKF in the first part and the accurate but fluctuating SOC values estimated by LSTM can be correlated and corrected, achieving a fast and accurate SOC estimation under various working conditions. The estimation results show that the above method has strong robustness and high accuracy, and effectively reduces model complexity and data redundancy. In addition, the root mean square error of SOC estimation under different working conditions is controlled within 1–2.3% at 0 °C, 25 °C, and 45 °C, which is better than the traditional single-SOC estimation method. Full article
(This article belongs to the Special Issue Battery Management System for Electric Vehicles)
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Review

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23 pages, 2522 KiB  
Review
Sustainable Development Goals and End-of-Life Electric Vehicle Battery: Literature Review
by Muhammad Nadeem Akram and Walid Abdul-Kader
Batteries 2023, 9(7), 353; https://doi.org/10.3390/batteries9070353 - 2 Jul 2023
Cited by 5 | Viewed by 5469
Abstract
With a global urgency to decrease greenhouse gas emissions, there has been an increasing demand for electric vehicles on the roads to replace vehicles that use internal combustion. Subsequently, the demand and consumption of raw materials have increased, and thus, there has been [...] Read more.
With a global urgency to decrease greenhouse gas emissions, there has been an increasing demand for electric vehicles on the roads to replace vehicles that use internal combustion. Subsequently, the demand and consumption of raw materials have increased, and thus, there has been an increasing number of retired lithium-ion batteries (LIBs) that contain valuable elements. This literature review paper looks at the following: lifecycle assessments (LCA) of EV batteries, the recycling of LIBs while analyzing what studies have been conducted to improve recycling processes, what recycling facilities have been established or are being planned, studies on the circular economy, the environmental benefits of recycling end-of-life (EOL) batteries, and how LIB recycling is aligned with the Sustainable Devel opment Goals, focusing in particular on Goal 13: Climate Action. Full article
(This article belongs to the Special Issue Battery Management System for Electric Vehicles)
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