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Sensors for Battery Management
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Sensors for Battery Management

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Electronic Sensors".

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 7093

Special Issue Editor

Prof. Dr. Emilio García

E-Mail Website
Guest Editor
Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain
Interests: process control; factory automation; supervisory control; predictive fault diagnosis; renewable energy

Special Issue Information

Dear Colleagues,

Batteries are promising energy storage devices for portable electronics, electric vehicles, isolated photovoltaic installations and other renewable energy storage applications. At the same time, battery management plays a vital role in providing functions, such as battery performance management, state estimation, thermal management and fault detection. These key functionalities critically rely on the accurate sensing of certain parameters, such as voltage, current and temperature. Then, by estimation algorithms, battery management systems can compute the estimated capacity, internal cell resistance and state of charge (SOC), which contribute to the further estimation of the remaining energy, power, state of health (SOH) and state of life (SOL) of the batteries.

In this sense and with greater specificity, the functionality of the absorption and float-charge voltages of the batteries are adjusted according to their internal temperature. At times, an internal temperature sensor available in the charge regulator is used to detect if the charger becomes overheated, but ideally a battery voltage and temperature sensor should be considered in conjunction with the battery charger regulator, with the aim of compensating and improving the charging efficiency and consequently prolonging the useful battery’s life.

This Special Issue aims to synthesize the state of the art in sensors and sensing technologies for battery management.

The topics of interest include, but are not limited to, the following research areas:

  • The use of novel sensors and sensing methods in battery management.
  • Battery diagnosis and prognostic methods.
  • The design, verification and application of advanced algorithms for battery management, including state of charge (SOC), state of health (SOH) and state of life (SOL).
  • Battery thermal management.

Prof. Dr. Emilio García
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. Sensors 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

  • battery management
  • SOC
  • SOH
  • SOL
  • thermal management
  • electric vehicles
  • fault diagnosis
  • predictive fault diagnosis
  • energy storage management
  • safe operating area

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Published Papers (2 papers)

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Research

38 pages, 9389 KiB  
Article
Distributed Intelligent Battery Management System Using a Real-World Cloud Computing System
by Emilio García, Eduardo Quiles and Antonio Correcher
Sensors 2023, 23(7), 3417; https://doi.org/10.3390/s23073417 - 24 Mar 2023
Cited by 6 | Viewed by 3665
Abstract
In this work, a decentralized but synchronized real-world system for smart battery management was designed by using a general controller with cloud computing capability, four charge regulators, and a set of sensorized battery monitors with networking and Bluetooth capabilities. Currently, for real-world applications, [...] Read more.
In this work, a decentralized but synchronized real-world system for smart battery management was designed by using a general controller with cloud computing capability, four charge regulators, and a set of sensorized battery monitors with networking and Bluetooth capabilities. Currently, for real-world applications, battery management systems (BMSs) can be used in the form of distributed control systems where general controllers, charge regulators, and smart monitors and sensors are integrated, such as those proposed in this work, which allow more precise estimations of a large set of important parameters, such as the state of charge (SOC), state of health (SOH), current, voltage, and temperature, seeking the safety and the extension of the useful life of energy storage systems based on battery banks. The system used is a paradigmatic real-world example of the so-called intelligent battery management systems. One of the contributions made in this work is the realization of a distributed design of a BMS, which adds the benefit of increased system security compared to a fully centralized BMS structure. Another research contribution made in this work is the development of a methodical modeling procedure based on Petri Nets, which establishes, in a visible, organized, and precise way, the set of conditions that will determine the operation of the BMS. If this modeling is not carried out, the threshold values and their conditions remain scattered, not very transparent, and difficult to deal with in an aggregate way. Full article
(This article belongs to the Special Issue Sensors for Battery Management)
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18 pages, 642 KiB  
Article
XGBoost-Based Remaining Useful Life Estimation Model with Extended Kalman Particle Filter for Lithium-Ion Batteries
by Sadiqa Jafari and Yung-Cheol Byun
Sensors 2022, 22(23), 9522; https://doi.org/10.3390/s22239522 - 6 Dec 2022
Cited by 18 | Viewed by 2331
Abstract
The instability and variable lifetime are the benefits of high efficiency and low-cost issues in lithium-ion batteries.An accurate equipment’s remaining useful life prediction is essential for successful requirement-based maintenance to improve dependability and lower total maintenance costs. However, it is challenging to assess [...] Read more.
The instability and variable lifetime are the benefits of high efficiency and low-cost issues in lithium-ion batteries.An accurate equipment’s remaining useful life prediction is essential for successful requirement-based maintenance to improve dependability and lower total maintenance costs. However, it is challenging to assess a battery’s working capacity, and specific prediction methods are unable to represent the uncertainty. A scientific evaluation and prediction of a lithium-ion battery’s state of health (SOH), mainly its remaining useful life (RUL), is crucial to ensuring the battery’s safety and dependability over its entire life cycle and preventing as many catastrophic accidents as feasible. Many strategies have been developed to determine the prediction of the RUL and SOH of lithium-ion batteries, including particle filters (PFs). This paper develops a novel PF-based technique for lithium-ion battery RUL estimation, combining a Kalman filter (KF) with a PF to analyze battery operating data. The PF method is used as the core, and extreme gradient boosting (XGBoost) is used as the observation RUL battery prediction. Due to the powerful nonlinear fitting capabilities, XGBoost is used to map the connection between the retrieved features and the RUL. The life cycle testing aims to gather precise and trustworthy data for RUL prediction. RUL prediction results demonstrate the improved accuracy of our suggested strategy compared to that of other methods. The experiment findings show that the suggested technique can increase the accuracy of RUL prediction when applied to a lithium-ion battery’s cycle life data set. The results demonstrate the benefit of the presented method in achieving a more accurate remaining useful life prediction. Full article
(This article belongs to the Special Issue Sensors for Battery Management)
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