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Energies
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State of Charge Estimation for Battery Systems
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State of Charge Estimation for Battery Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D1: Advanced Energy Materials".

Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 22811

Special Issue Editor

Prof. Dr. Andreas Jossen

E-Mail Website
Guest Editor
Institute for Electrical Energy Storage Technology (EES), Technical University München (TUM), Arcisstrasse 21, 80333 Munich, Germany
Interests: battery models; battery system technology; multicellular battery systems; storage for renewable energy; storage for electric mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The state of charge (SOC) of a battery is the most important, indirectly measurable state of a battery. It is used to control the battery system, as in many applications the SOC must be held within defined levels or give away important information, as it is strongly linked with the runtime of a system or the possible range of an electric vehicle. Many approaches based on a large variety of technologies have been published in recent decades. However, in practical applications simple algorithms are still used today. This is due to the sensitivity of many methods to the temperature, the aging of the battery, and the individual cell characteristics within a large battery system. Therefore, I want to invite all researchers in the field of battery state determination to participate in this Special Issue with your original research article or your review paper. Articles related to measurement methods (sensor- or model-based), validation methods, and experience in field applications are welcome. Work related to the reliability, stability, and drift of SOC determination methods are of interest. There are no limitations on the battery technology or the battery size.

Prof. Dr. Andreas Jossen
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. 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

  • Sensor based methods
  • Model based methods
  • Empirical methods
  • Validation of SOC
  • Stability
  • Dynamic
  • Drift
  • Definitions

Published Papers (2 papers)

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Research

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16 pages, 2277 KiB  
Article
An Optimized Impedance Model for the Estimation of the State-of-Charge of a Li-Ion Cell: The Case of a LiFePO4 (ANR26650)
by Victor Pizarro-Carmona, Marcelo Cortés-Carmona, Rodrigo Palma-Behnke, Williams Calderón-Muñoz, Marcos E. Orchard and Pablo A. Estévez
Energies 2019, 12(4), 681; https://doi.org/10.3390/en12040681 - 20 Feb 2019
Cited by 8 | Viewed by 3056
Abstract
This article focused on the estimation of the state of charge (SoC) of a Li-con Cell by carrying out a series of experimental tests at various operating temperatures and SoC. The cell was characterized by electrochemical impedance spectroscopy (EIS) tests, from which the [...] Read more.
This article focused on the estimation of the state of charge (SoC) of a Li-con Cell by carrying out a series of experimental tests at various operating temperatures and SoC. The cell was characterized by electrochemical impedance spectroscopy (EIS) tests, from which the impedance frequency spectrum for different SoC and temperatures was obtained. Indeed, the cell model consisted of a modified Randles circuit type that included a constant phase element so-called Warburg impedance. Each circuit parameter was obtained from the EIS tests. The obtained were been used to develop two numerical models for each parameter, i.e., one based on numerical correlations and the other based on the artificial neural network (ANN) method. A genetic algorithm was used to solve and optimize the numerical models. The accuracy of the models was examined and the results showed that the ANN-based model was more accurate than the correlations-based model. The root mean square relative error (RMSRE) of the parameters Rs, R1, C1 and W for the ANN-based model were: 4.63%, 13.65%, 10.96% and 4.4%, respectively, compared to 7.09%, 27.45%, 34.36% and 7.07% for the correlations-based model, respectively. The SoC was estimated using the extended Kalman filter based on a Randles model, with an estimation RMSRE of about 1.19%. Full article
(This article belongs to the Special Issue State of Charge Estimation for Battery Systems)
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Review

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33 pages, 3388 KiB  
Review
Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation
by Muhammad Umair Ali, Amad Zafar, Sarvar Hussain Nengroo, Sadam Hussain, Muhammad Junaid Alvi and Hee-Je Kim
Energies 2019, 12(3), 446; https://doi.org/10.3390/en12030446 - 30 Jan 2019
Cited by 260 | Viewed by 19338
Abstract
Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart [...] Read more.
Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation. Full article
(This article belongs to the Special Issue State of Charge Estimation for Battery Systems)
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