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Advanced Battery Management Strategies for a Sustainable Future

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (23 December 2021) | Viewed by 3105

Special Issue Editors


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Guest Editor
WMG, The University of Warwick, Coventry CV4 7AL, UK
Interests: battery manufacture and management; transportation-grid interfacing system; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Information Engineering Department, The Chinese University of Hong Kong, Shatian, HKSAR, China
Interests: battery management with the applications to transportation electrifications and maritime grids

Special Issue Information

Dear Colleagues,

Energy storage systems (ESSs) enable the flexibility of electricity usage in industrial applications, for example, acting as the energy/power supply for e-mobilities or smart grids. For the purpose of supplying high-quality power to electric vehicles, hybrid electric vehicles and stationary storages, the ESS requires a suitable battery management system (BMS) that will act as an enabling technology for energy conversion, safety, balancing, charging, and life extension. Without a doubt, the advancement of measurement and control of the batteries would directly influence the performance, efficiency, reliability, financial profits, and lifespan of all-related ESSs, including pure battery systems and the hybrid battery systems that are integrated with devices such as wind turbines, solar panels, super-capacitors, fuel cells, and conventional fossil power generators or engines. In this thread, the hardware design, modelling, and control strategies of the BMS is of great importance to unlocking the potential performance, such as enhanced efficiency and prolonged service life, of ESSs. Thus, more advanced BMSs should be designed to utilize the full potential of high-performance energy storage elements.

In this sense, this Special Issue serves to solicit and foster new research achievements on the recently emerging and cross-disciplinary field of advanced hardware design, modelling and control strategies for BMSs. We are pleased to receive new contributions from researchers, scientists, students from companies, universities, and research institutes all over the world to build an academic network for developing advanced BMSs for a sustainable future. Topics of interest for this Special Issue include, but are not limited to, the following:

  • Advanced BMS for electrical vehicles and smart grids;
  • Battery state estimation, thermal/safety management in BMS;
  • Real-time hardware in the loop (HIL) simulation for BMS;
  • Advanced artificial intelligence and machine learning solutions for BMS;
  • Cyber-physics, big data, and internet of energy data related to BMS;
  • Advanced control and diagnosis of battery in BMS;
  • Smart management strategies for advanced BMS;
  • Battery-based hybrid-energy systems: architecture, sizing, energy management;
  • Smart battery manufacturing and management;
  • Fast charger technique for battery;
  • Battery equalizer and self-heater;
  • Fault prognosis and diagnosis of power electronics in BMS.

Dr. Kailong Liu
Dr. Mattia Ricco
Dr. Jinhao Meng
Dr. Sidun Fang
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. Sustainability 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 2400 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 system
  • Power electronics
  • Transportation electrification
  • Smart grid
  • Artificial intelligence
  • Estimation, prognosis and diagnosis
  • Fast charger
  • Cyber-physics system
  • Control and management strategies.

Published Papers (1 paper)

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Research

16 pages, 2399 KiB  
Article
State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter
by Jie Xing and Peng Wu
Sustainability 2021, 13(9), 5046; https://doi.org/10.3390/su13095046 - 30 Apr 2021
Cited by 26 | Viewed by 2196
Abstract
State of charge (SOC) of the lithium-ion battery is an important parameter of the battery management system (BMS), which plays an important role in the safe operation of electric vehicles. When existing unknown or inaccurate noise statistics of the system, the traditional unscented [...] Read more.
State of charge (SOC) of the lithium-ion battery is an important parameter of the battery management system (BMS), which plays an important role in the safe operation of electric vehicles. When existing unknown or inaccurate noise statistics of the system, the traditional unscented Kalman filter (UKF) may fail to estimate SOC due to the non-positive error covariance of the state vector, and the SOC estimation accuracy is not high. Therefore, an improved adaptive unscented Kalman filter (IAUKF) algorithm is proposed to solve this problem. The IAUKF is composed of the improved unscented Kalman filter (IUKF) that is able to suppress the non-positive definiteness of error covariance and Sage–Husa adaptive filter. The IAUKF can improve the SOC estimation stability and can improve the SOC estimation accuracy by estimating and correcting the system noise statistics adaptively. The IAUKF is verified under the federal urban driving schedule test, and the SOC estimation results are compared with IUKF and UKF. The experimental results show that the IAUKF has higher estimation accuracy and stability, which verifies the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Advanced Battery Management Strategies for a Sustainable Future)
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