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Batteries
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Battery Modeling
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Battery Modeling

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

Deadline for manuscript submissions: closed (30 July 2016) | Viewed by 72777

Special Issue Editor

Prof. Dr. Juan Carlos Álvarez Antón

E-Mail Website
Guest Editor
Department of Electrical Engineering, University of Oviedo, 33204 Gijón, Spain
Interests: battery modeling; electronic instrumentation systems; Internet of Things (IoT)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

 

The battery industry is experiencing significant growth driven by an increase in portable battery-powered devices, electric vehicles, and other industrial applications. Several battery chemistries, such as lead-acid, nickel-metal-hydride, lithium-ion, and other technologies, are commonly used in these applications, resulting in a wide range of available performance characteristics. Battery performance is affected by environmental conditions and operational factors. Battery degradation, state of charge, and aging concerns impose boundaries on charge/discharge currents and discharge depths. Despite their relevant importance, many challenges remain unsolved as regards characterizing and managing batteries. In this respect, battery modeling plays an important role in the design and use of batteries. An accurate and realistic battery model is essential to design an efficient battery-powered system.

This special issue invites articles on state-of-the-art developments for battery modeling.

We solicit original, high-quality technical papers, including review articles, which are not under consideration by other publications.

Potential topics include, but are not limited to:

  • Modeling cell or multi-cell batteries for various chemistries
  • State-of-charge estimation
  • State-of-health estimation
  • Remaining useful life estimation
  • Design, control, and optimization of battery management systems (BMS)
  • Cell, module, and system-level performance monitoring
  • Innovative charge/discharge management systems
  • Exploring battery behavior under a range of operational and environmental conditions
  • Experimental characterization techniques for batteries

We sincerely hope that this Special Issue will serve as a reference for initiating and continuing state-of-the-art research in the critical area of battery modeling, with potential applications in portable battery-powered devices and electric vehicles.

Dr. Juan Carlos Álvarez Antón
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 modeling
  • BMS
  • state of charge
  • state of health
  • electric vehicles

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Published Papers (5 papers)

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Research

4634 KiB  
Article
High-Fidelity Battery Model for Model Predictive Control Implemented into a Plug-In Hybrid Electric Vehicle
by Nicolas Sockeel, Masood Shahverdi, Michael Mazzola and William Meadows
Batteries 2017, 3(2), 13; https://doi.org/10.3390/batteries3020013 - 6 Apr 2017
Cited by 16 | Viewed by 10670
Abstract
Power management strategies have impacts on fuel economy, greenhouse gasses (GHG) emission, as well as effects on the durability of power-train components. This is why different off-line and real-time optimal control approaches are being developed. However, real-time control seems to be more attractive [...] Read more.
Power management strategies have impacts on fuel economy, greenhouse gasses (GHG) emission, as well as effects on the durability of power-train components. This is why different off-line and real-time optimal control approaches are being developed. However, real-time control seems to be more attractive than off-line control because it can be directly implemented for managing power and energy flows inside an actual vehicle. One interesting illustration of these power management strategies is the model predictive control (MPC) based algorithm. Inside a MPC, a cost function is optimized while system constraints are validated in real time. The MPC algorithm relies on dynamic models of the vehicle and the battery. The complexity and accuracy of the battery model are usually neglected to benefit the development of new cost functions or better MPC algorithms. The contribution of this manuscript consists of developing and evaluating a high-fidelity battery model of a plug-in hybrid electric vehicle (PHEV) that has been used for MPC. Via empirical work and simulation, the impact of a high-fidelity battery model has been evaluated and compared to a simpler model in the context of MPC. It is proven that the new battery model reduces the absolute voltage, state of charge (SoC), and battery power loss error by a factor of 3.2, 1.9 and 2.1 on average respectively, compared to the simpler battery model. Full article
(This article belongs to the Special Issue Battery Modeling)
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1221 KiB  
Article
Thermal Analysis of a Fast Charging Technique for a High Power Lithium-Ion Cell
by Victor Manuel García Fernández, Cecilio Blanco Viejo, David Anseán González, Manuela González Vega, Yoana Fernández Pulido and Juan Carlos Alvarez Antón
Batteries 2016, 2(4), 32; https://doi.org/10.3390/batteries2040032 - 3 Nov 2016
Cited by 15 | Viewed by 9153
Abstract
The cell case temperature versus time profiles of a multistage fast charging technique (4C-1C-constant voltage (CV))/fast discharge (4C) in a 2.3 Ah cylindrical lithium-ion cell are analyzed using a thermal model. Heat generation is dominated by the irreversible component associated with cell overpotential, [...] Read more.
The cell case temperature versus time profiles of a multistage fast charging technique (4C-1C-constant voltage (CV))/fast discharge (4C) in a 2.3 Ah cylindrical lithium-ion cell are analyzed using a thermal model. Heat generation is dominated by the irreversible component associated with cell overpotential, although evidence of the reversible component is also observed, associated with the heat related to entropy from the electrode reactions. The final charging stages (i.e., 1C-CV) significantly reduce heat generation and cell temperature during charge, resulting in a thermally safe charging protocol. Cell heat capacity was determined from cell-specific heats and the cell materials’ thickness. The model adjustment of the experimental data during the 2 min resting period between discharge and charge allowed us to calculate both the time constant of the relaxation process and the cell thermal resistance. The obtained values of these thermal parameters used in the proposed model are almost equal to those found in the literature for the same cell model, which suggests that the proposed model is suitable for its implementation in thermal management systems. Full article
(This article belongs to the Special Issue Battery Modeling)
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Graphical abstract

3679 KiB  
Article
Characterising Lithium-Ion Battery Degradation through the Identification and Tracking of Electrochemical Battery Model Parameters
by Kotub Uddin, Surak Perera, W. Dhammika Widanage, Limhi Somerville and James Marco
Batteries 2016, 2(2), 13; https://doi.org/10.3390/batteries2020013 - 26 Apr 2016
Cited by 142 | Viewed by 22010
Abstract
Lithium-ion (Li-ion) batteries undergo complex electrochemical and mechanical degradation. This complexity is pronounced in applications such as electric vehicles, where highly demanding cycles of operation and varying environmental conditions lead to non-trivial interactions of ageing stress factors. This work presents the framework for [...] Read more.
Lithium-ion (Li-ion) batteries undergo complex electrochemical and mechanical degradation. This complexity is pronounced in applications such as electric vehicles, where highly demanding cycles of operation and varying environmental conditions lead to non-trivial interactions of ageing stress factors. This work presents the framework for an ageing diagnostic tool based on identifying and then tracking the evolution of model parameters of a fundamental electrochemistry-based battery model from non-invasive voltage/current cycling tests. In addition to understanding the underlying mechanisms for degradation, the optimisation algorithm developed in this work allows for rapid parametrisation of the pseudo-two dimensional (P2D), Doyle-Fuller-Newman, battery model. This is achieved through exploiting the embedded symbolic manipulation capabilities and global optimisation methods within MapleSim. Results are presented that highlight the significant reductions in the computational resources required for solving systems of coupled non-linear partial differential equations. Full article
(This article belongs to the Special Issue Battery Modeling)
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1523 KiB  
Article
Method for Determination of the Internal Short Resistance and Heat Evolution at Different Mechanical Loads of a Lithium Ion Battery Cell Based on Dummy Pouch Cells
by Theo Volck, Wolfgang Sinz, Gregor Gstrein, Christoph Breitfuss, Simon F. Heindl, Hermann Steffan, Stefan Freunberger, Martin Wilkening, Marlena Uitz, Clemens Fink and Alexander Geier
Batteries 2016, 2(2), 8; https://doi.org/10.3390/batteries2020008 - 7 Apr 2016
Cited by 27 | Viewed by 11999
Abstract
Within the scope of developing a multi-physical model describing battery behavior during and after the mechanical load (accelerations, intrusions) of a vehicle’s high voltage battery, an internal short circuit model is of deep interest for a virtual hazard assessment. The internal short resistance [...] Read more.
Within the scope of developing a multi-physical model describing battery behavior during and after the mechanical load (accelerations, intrusions) of a vehicle’s high voltage battery, an internal short circuit model is of deep interest for a virtual hazard assessment. The internal short resistance and the size of the affected area must be known as a minimum for determining the released heat and, in consequence, the temperatures. The internal short resistance of purpose-built dummy pouch cells, filled with electrolyte-like solvent without conductive salt, has thus been measured in a given short area under various compressive loads. The resistances for different short scenarios obtained are analyzed and described in a mathematical form. Short circuit experiments with dummy cells using an external power source have also been carried out. This set-up allows the measurement of the temperature evolution at a known current and a determination of the actual short resistance. The post-mortem analysis of the samples shows a correlation between the maximum temperatures, released short heat and the separator melt diameter. Full article
(This article belongs to the Special Issue Battery Modeling)
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4057 KiB  
Article
Fast Characterization Method for Modeling Battery Relaxation Voltage
by An Li, Serge Pelissier, Pascal Venet and Philippe Gyan
Batteries 2016, 2(2), 7; https://doi.org/10.3390/batteries2020007 - 6 Apr 2016
Cited by 51 | Viewed by 17450
Abstract
After the end of a charging or discharging sequence, the battery voltage keeps evolving towards a finite value, during hours or even days, although no current is exchanged with the battery. This corresponds to the battery relaxation. In the context of electric vehicles [...] Read more.
After the end of a charging or discharging sequence, the battery voltage keeps evolving towards a finite value, during hours or even days, although no current is exchanged with the battery. This corresponds to the battery relaxation. In the context of electric vehicles (EV), a good measurement of the voltage at rest allows an accurate estimation of the battery state of charge (SoC). The characterization of the battery voltage at different levels of SoC after the full relaxation would be very time consuming. In this paper, a fast method to extrapolate long relaxation voltage is proposed. It needs only one complete measurement of relaxation at one given SoC and could give accurate voltage estimation at other states of charge from short and partial measurement. This generic method was validated on three different cells and could be easily extended to any type of battery. Full article
(This article belongs to the Special Issue Battery Modeling)
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