Special Issue "Battery Management Systems"

A special issue of Batteries (ISSN 2313-0105).

Deadline for manuscript submissions: closed (31 December 2017).

Special Issue Editor

Prof. Dr. James Marco
Website
Guest Editor
WMG, University of Warwick, Coventry CV4 7AL, UK
Interests: systems engineering; real-time control; systems modelling; design optimization; design of energy management control systems
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

A complete battery system may be comprised of many hundreds of individual cells (connected in series or parallel), cell interconnects, safety devices, control electronics, energy balancing circuits, high voltage and low voltage cabling, and thermal management. Manufacturing tolerances, coupled with demanding duty-cycles and a heterogeneous environment, in particular the presence of thermal differences within the battery system, results in significant variations in the rate of cell degradation and the energy distribution amongst the cells. Within the field of battery system design and integration, a key enabling technology is the design of the Battery Management System (BMS). The term BMS has come to encompass a broad spectrum of control and monitoring functions that collectively: (a) integrate the high voltage battery within the complete control architecture of the larger system (e.g., an electric vehicle), (b) manage the availability of power and energy from the battery, and (c) manage the ancillaries that are integrated within the complete battery system.

The aim of this Special Issue of Batteries is to explore recent advances highlighting new innovations in BMS design, analysis and implementation.

Dr. James Marco
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 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. Batteries is an international peer-reviewed open access quarterly 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 1400 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

  • the design, verification and implementation of enhanced algorithms for battery control and monitoring, including:
    • state of charge (SOC)
    • state of health (SOH)
    • state of power (SOP)
    • state of function (SOF)
  • integration of active charge balancing algorithms within the BMS
  • battery diagnostic and prognostic functions
  • battery system thermal management
  • the use of model-based design and verification methods to improve functionality and the efficiency of the development process.
  • the impact and analysis of different battery pack design options and hardware architectures on the operation of the BMS
  • the use of novel sensing methods to enhance BMS operation
  • functional safety within the context of BMS design and verification
  • the role of the BMS for extending battery pack functionality and service life (e.g., 2nd-life and vehicle-2-grid operation)

Published Papers (4 papers)

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Research

Open AccessArticle
Prognosis and Remaining Useful Life Estimation of Lithium-Ion Battery with Optimal Multi-Level Particle Filter and Genetic Algorithm
Batteries 2018, 4(2), 15; https://doi.org/10.3390/batteries4020015 - 23 Mar 2018
Cited by 4 | Viewed by 3723
Abstract
Prognosis and remaining useful life (RUL) estimation of components and systems (C&S) are vital for intelligent asset-integrity management. The implementation of the traditional multi-level particle filter (TRMPF) has improved prognosis when compared with the one-step traditional particle filter that depended on the first-order [...] Read more.
Prognosis and remaining useful life (RUL) estimation of components and systems (C&S) are vital for intelligent asset-integrity management. The implementation of the traditional multi-level particle filter (TRMPF) has improved prognosis when compared with the one-step traditional particle filter that depended on the first-order state equation. However, despite this improvement, the need to enhance the accuracy of fault prognosis, diagnosis and detection cannot be overemphasized. To this end, an optimal multi-level particle filter (OPMPF) algorithm that combines genetic algorithm (GA) optimization and multi-level particle filter (MPF) techniques is used to predict the RUL of the C&S in order to enhance the accuracy of the estimation at different forms of deterioration in operation. A 9-fold cross-validation ensemble MPF that utilized lithium-ion (Li+) batteries’ charge capacity decay to test the developed OPMPF algorithm showed an improvement of over 200% in the estimated RUL when compared with the TRMPF estimation. Full article
(This article belongs to the Special Issue Battery Management Systems)
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Open AccessArticle
Statistical Characterization of the State-of-Health of Lithium-Ion Batteries with Weibull Distribution Function—A Consideration of Random Effect Model in Charge Capacity Decay Estimation
Batteries 2017, 3(4), 32; https://doi.org/10.3390/batteries3040032 - 16 Oct 2017
Cited by 7 | Viewed by 5134
Abstract
Effective prognosis of lithium-ion batteries involves the inclusion of the influences of uncertainties that can be incorporated through random effect parameters in a nonlinear mixed effect degradation model framework. This study is geared towards the estimation of the reliability of lithium-ion batteries, using [...] Read more.
Effective prognosis of lithium-ion batteries involves the inclusion of the influences of uncertainties that can be incorporated through random effect parameters in a nonlinear mixed effect degradation model framework. This study is geared towards the estimation of the reliability of lithium-ion batteries, using parametric effects determination involving uncertainty, using a multiphase decay patterned sigmoidal model, experimental data and the Weibull distribution function. The random effect model, which uses Maximum Likelihood Estimation (MLE) and Stochastic Approximation Expectation Maximization (SAEM) algorithm to predict the parametric values, was found to estimate the remaining useful life (RUL) to an accuracy of more than 98%. The State-of-Health (SOH) of the batteries was estimated using the Weibull distribution function, which is found to be an appropriate formulation to use. Full article
(This article belongs to the Special Issue Battery Management Systems)
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Open AccessArticle
On-Line Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Optimized Gray Model GM(1,1)
Batteries 2017, 3(3), 21; https://doi.org/10.3390/batteries3030021 - 08 Jul 2017
Cited by 9 | Viewed by 5004
Abstract
Lithium-ion battery on-line remaining useful life (RUL) prediction has become increasingly popular. The capacity and internal resistance are often used as the batteries’ health indicator (HI) for quantifying degradation and predicting the RUL. However, the capacity and internal resistance are too difficult to [...] Read more.
Lithium-ion battery on-line remaining useful life (RUL) prediction has become increasingly popular. The capacity and internal resistance are often used as the batteries’ health indicator (HI) for quantifying degradation and predicting the RUL. However, the capacity and internal resistance are too difficult to measure on-line due to the batteries’ internal state variables being inaccessible to sensors under operational conditions. In addition, measuring these variables requires accurate measurement devices, which can be expensive, and have limited applicability in practice. In this paper, a novel HI is extracted from the operating parameters of lithium-ion batteries for degradation models and RUL prediction. Moreover, the Box–Cox transformation is applied to improve the correlation between the extracted HI and the battery’s real capacity. Then, Pearson and Spearman correlation analyses are utilized to assess the similarity between the real capacity and the estimated capacity derived from the HI. An optimized gray model GM(1,1) is employed to predict the RUL based on the presented HI. The experimental results show that the proposed method is effective and accurate for battery degradation modeling and RUL prediction. Full article
(This article belongs to the Special Issue Battery Management Systems)
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Open AccessArticle
Lithium Ion Cell/Batteries Electromagnetic Field Reduction in Phones for Hearing Aid Compliance
Batteries 2016, 2(2), 19; https://doi.org/10.3390/batteries2020019 - 15 Jun 2016
Cited by 1 | Viewed by 6756
Abstract
“The Hearing Aid Compatibility Act of 1988 (HAC Act) generally requires that the Federal Communications Commission (FCC) ensure that telephones manufactured or imported for use in the United States after August 1989, and all ‘essential’ telephones, are hearing aid-compatible”. The electromagnetic field (EMF) [...] Read more.
“The Hearing Aid Compatibility Act of 1988 (HAC Act) generally requires that the Federal Communications Commission (FCC) ensure that telephones manufactured or imported for use in the United States after August 1989, and all ‘essential’ telephones, are hearing aid-compatible”. The electromagnetic field (EMF) emission is generated by electrical currents in the phones’ circuit boards and components, including the battery. Here, we have investigated design changes to reduce the EMF from Lithium Ion (Li-ion) batteries. Changes mainly include: (1) Li-ion cell internal positive/negative tab location and length on cathode/anode layers; and (2) Li-ion cell external positive/negative connectors spacing. Results show that the cell’s internal tab locations and spacing between the cell’s external connectors play critical roles in reduction of battery EMF emissions. It is important that cells’ design changes are compatible with the manufacturing processes. Full article
(This article belongs to the Special Issue Battery Management Systems)
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