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Enhancement of Battery Lifespan, Safety, and Performance through Estimation and...
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Enhancement of Battery Lifespan, Safety, and Performance through Estimation and Control Theory and Algorithms

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

Deadline for manuscript submissions: closed (20 January 2022) | Viewed by 16962

Special Issue Editors

Dr. Balakumar Balasingam

E-Mail Website
Guest Editor
Department of Electrical & Computer Engineering, University of Windsor, 401 Sunset Avenue, Windsor, ON N9B 3P4, Canada
Interests: battery management systems; human–machine systems; signal processing; machine learning; information fusion
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Krishna Pattipati

E-Mail Website
Guest Editor
Department of Electrical & Computer Engineering, University of Connecticut, Storrs, CT 06269, USA
Interests: automated testing; model-based and data-driven diagnostics and prognostics; multi-target tracking; multi-user interference; computer system performance optimization; scheduling of manufacturing systems

Special Issue Information

Dear Colleagues,

We would like to invite you to submit original works for a Special Issue of Energies in the area of “Enhancement of Battery Lifespan, Safety, and Performance Through Estimation and Control Theory and Algorithms”. Rechargeable batteries have become ubiquitous in wide-ranging applications, such as electric vehicles, consumer electronics, power equipment, household appliances, and aerospace equipment. Batteries promise a way to greener transportation; however, cost and limited battery lifespan impede progress in the electrification of transportation. An increasing number of battery fire incidents indicate the inadequacy of state-of-the-art battery management systems. Consequently, it is vital to develop an in-depth understanding on the issues affecting the battery lifespan, safety, and performance so that appropriate control algorithms can be developed to improve them.

This Special Issue aims to publish recent works that are focused on improving battery lifespan, safety, and performance. The topics of interest to this Special Issue include but are not limited to:

  • State-of-health modeling of rechargeable batteries;
  • Thermal modeling and management strategies of battery packs;
  • Strategies for optimized battery charging under different constraints;
  • Battery-pack cell balancing strategies;
  • Experimental studies on battery life, safety, and performance.

Prof. Dr. Balakumar Balasingam
Prof. Dr. Krishna Pattipati
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

  • Rechargeable batteries
  • Nonlinear estimation, filtering, and control
  • Optimization
  • Fault detection and isolation
  • Diagnostics and prognostics and battery health management

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Published Papers (3 papers)

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Research

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33 pages, 1028 KiB  
Article
A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries
by Kiarash Movassagh, Arif Raihan, Balakumar Balasingam and Krishna Pattipati
Energies 2021, 14(14), 4074; https://doi.org/10.3390/en14144074 - 6 Jul 2021
Cited by 126 | Viewed by 10651
Abstract
In this paper, we consider the problem of state-of-charge estimation for rechargeable batteries. Coulomb counting is a well-known method for estimating the state of charge, and it is regarded as accurate as long as the battery capacity and the beginning state of charge [...] Read more.
In this paper, we consider the problem of state-of-charge estimation for rechargeable batteries. Coulomb counting is a well-known method for estimating the state of charge, and it is regarded as accurate as long as the battery capacity and the beginning state of charge are known. The Coulomb counting approach, on the other hand, is prone to inaccuracies from a variety of sources, and the magnitude of these errors has not been explored in the literature. We formally construct and quantify the state-of-charge estimate error during Coulomb counting due to four types of error sources: (1) current measurement error; (2) current integration approximation error; (3) battery capacity uncertainty; and (4) timing oscillator error/drift. It is demonstrated that the state-of-charge error produced can be either time-cumulative or state-of-charge-proportional. Time-cumulative errors accumulate over time and have the potential to render the state-of-charge estimation utterly invalid in the long term.The proportional errors of the state of charge rise with the accumulated state of charge and reach their worst value within one charge/discharge cycle. The study presents methods for reducing time-cumulative and state-of-charge-proportional mistakes through simulation analysis. Full article
(This article belongs to the Special Issue Enhancement of Battery Lifespan, Safety, and Performance through Estimation and Control Theory and Algorithms)
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19 pages, 3765 KiB  
Article
Online State-of-Charge Estimation Based on the Gas–Liquid Dynamics Model for Li(NiMnCo)O2 Battery
by Haobin Jiang, Xijia Chen, Yifu Liu, Qian Zhao, Huanhuan Li and Biao Chen
Energies 2021, 14(2), 324; https://doi.org/10.3390/en14020324 - 8 Jan 2021
Cited by 8 | Viewed by 1750
Abstract
Accurately estimating the online state-of-charge (SOC) of the battery is one of the crucial issues of the battery management system. In this paper, the gas–liquid dynamics (GLD) battery model with direct temperature input is selected to model Li(NiMnCo)O2 battery. The extended Kalman [...] Read more.
Accurately estimating the online state-of-charge (SOC) of the battery is one of the crucial issues of the battery management system. In this paper, the gas–liquid dynamics (GLD) battery model with direct temperature input is selected to model Li(NiMnCo)O2 battery. The extended Kalman Filter (EKF) algorithm is elaborated to couple the offline model and online model to achieve the goal of quickly eliminating initial errors in the online SOC estimation. An implementation of the hybrid pulse power characterization test is performed to identify the offline parameters and determine the open-circuit voltage vs. SOC curve. Apart from the standard cycles including Constant Current cycle, Federal Urban Driving Schedule cycle, Urban Dynamometer Driving Schedule cycle and Dynamic Stress Test cycle, a combined cycle is constructed for experimental validation. Furthermore, the study of the effect of sampling time on estimation accuracy and the robustness analysis of the initial value are carried out. The results demonstrate that the proposed method realizes the accurate estimation of SOC with a maximum mean absolute error at 0.50% in five working conditions and shows strong robustness against the sparse sampling and input error. Full article
(This article belongs to the Special Issue Enhancement of Battery Lifespan, Safety, and Performance through Estimation and Control Theory and Algorithms)
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Review

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22 pages, 3015 KiB  
Review
Comprehensive Review on Smart Techniques for Estimation of State of Health for Battery Management System Application
by Sumukh Surya, Vidya Rao and Sheldon S. Williamson
Energies 2021, 14(15), 4617; https://doi.org/10.3390/en14154617 - 30 Jul 2021
Cited by 23 | Viewed by 3885
Abstract
Electric Vehicles (EV) and Hybrid EV (HEV) use Lithium (Li) ion battery packs to drive them. These battery packs possess high specific density and low discharge rates. However, some of the limitations of such Li ion batteries are sensitivity to high temperature and [...] Read more.
Electric Vehicles (EV) and Hybrid EV (HEV) use Lithium (Li) ion battery packs to drive them. These battery packs possess high specific density and low discharge rates. However, some of the limitations of such Li ion batteries are sensitivity to high temperature and health degradation over long usage. The Battery Management System (BMS) protects the battery against overvoltage, overcurrent etc., and monitors the State of Charge (SOC) and the State of Health (SOH). SOH is a complex phenomenon dealing with the effects related to aging of the battery such as the increase in the internal resistance and decrease in the capacity due to unwanted side reactions. The battery life can be extended by estimating the SOH accurately. In this paper, an extensive review on the effects of aging of the battery on the electrodes, effects of Solid Electrolyte Interface (SEI) deposition layer on the battery and the various techniques used for estimation of SOH are presented. This would enable prospective researchers to address the estimation of SOH with greater accuracy and reliability. Full article
(This article belongs to the Special Issue Enhancement of Battery Lifespan, Safety, and Performance through Estimation and Control Theory and Algorithms)
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