Special Issue "Condition Monitoring and Diagnostics of Energy Storage Systems Components"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: closed (1 April 2022) | Viewed by 4111

Special Issue Editors

Prof. Dr. Antonio J. Marques Cardoso
E-Mail Website
Guest Editor
Dr. Khaled Laadjal
E-Mail Website
Guest Editor
CISE - Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P - 6201-001 Covilhã, Portugal
Interests: electrolytic capacitors; DC-DC converter; inverter; AC machines

Special Issue Information

Dear Colleagues,

Nowadays, the importance of energy storage has become paramount in different areas, such as the production and distribution of electric energy, portable tools and devices, electric vehicles (EVs), etc. Large scale energy storage also allows today’s electrical systems to run significantly more efficiently, thus meaning lower prices, less emissions, and more reliable power. The principle of energy storage systems is based on the use of alternative resources of energy, where they operate in most applications with electricity stored in batteries, fuel cells (FCs), capacitors, and supercapacitors (SCs). However, energy storage systems (ESSs) are currently facing some challenges because they need to be cost-competitive, compact, efficient, safe and reliable, take up little space, and be long-lasting. Periodic maintenance practices currently assess the future failures of energy storage systems components. Alternatively, in order to eliminate unintended failures, it is important to be able to diagnose the components' underlying degradation and to forecast the extent of unsatisfactory performance through an online real-time monitoring system. Furthermore, the hybridization of ESSs with advanced power electronic technologies has a major effect on the optimum use of power to lead advanced ESS technologies.

The aim of this Special Issue is to provide an opportunity for scientists, researchers, and practicing engineers to share and disseminate their latest discoveries and results in the aforementioned fields, indicating the future trends for condition monitoring and diagnostics of energy storage systems components.

Topics include, but are not limited to, the following research areas:

  • Fault detection and fault tolerance in energy storage systems
  • Battery-management systems (BMS)
  • Life time diagnostics of capacitors and supercapacitors
  • State-of-charge and state-of-health estimation
  • Thermal performance of energy storage systems
  • Emerging battery technologies
  • Power electronics for energy storage systems
  • Life cycle analysis
  • Machine learning for the performance analysis, diagnosis, prognostics, and management of energy storage systems

Prof. Dr. Antonio J. Marques Cardoso
Dr. Khaled Laadjal
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 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

  • Fault detection and fault tolerance in energy storage systems
  • Battery-management systems (BMS)
  • Life time diagnostics of capacitors and supercapacitors
  • State-of-charge and state-of-health estimation
  • Thermal performance of energy storage systems
  • Emerging battery technologies
  • Power electronics for energy storage systems
  • Life cycle analysis
  • Machine learning for the performance analysis, diagnosis, prognostics, and manage-ment of energy storage systems

Published Papers (7 papers)

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Research

Article
Integrated Electro-Thermal Model for Li-Ion Battery Packs
Electronics 2022, 11(10), 1537; https://doi.org/10.3390/electronics11101537 - 11 May 2022
Viewed by 332
Abstract
Lithium-ion battery is considered one of the most attractive energy storage systems for electric vehicles. However, one of its main drawbacks is the sensitivity to temperature. In a battery pack composed of lithium-ion batteries, during the charge/discharge operations, the temperature gradually increases, especially [...] Read more.
Lithium-ion battery is considered one of the most attractive energy storage systems for electric vehicles. However, one of its main drawbacks is the sensitivity to temperature. In a battery pack composed of lithium-ion batteries, during the charge/discharge operations, the temperature gradually increases, especially in the batteries positioned in the central part of the battery pack. This leads the central batteries to age faster and exposes them to the risk of a thermal runaway. In order to mitigate these problems, thermal management systems are needed. However, for the implementation of the control, it is important to know the temperature distribution inside the whole pack. In this paper, an integrated electro-thermal model capable of estimating the thermal behavior of each battery cell, composing the battery pack, only knowing the total current and ambient temperature, is proposed and analyzed. The proposed model was tuned and validated by means of experimental results. The circuital approach used in this model gives good results with a low degree of complexity. Full article
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Article
On-Line Diagnostics of Electrolytic Capacitors in Fault-Tolerant LED Lighting Systems
Electronics 2022, 11(9), 1444; https://doi.org/10.3390/electronics11091444 - 29 Apr 2022
Cited by 1 | Viewed by 361
Abstract
As technology advances, the utilization of lighting systems based on light-emitting diode (LED) technology is becoming increasingly essential, given its benefits in terms of efficiency, reliability, and lifespan. Unfortunately, the power electronic components required to drive LEDs are unable to compete with LED [...] Read more.
As technology advances, the utilization of lighting systems based on light-emitting diode (LED) technology is becoming increasingly essential, given its benefits in terms of efficiency, reliability, and lifespan. Unfortunately, the power electronic components required to drive LEDs are unable to compete with LED devices in terms of lifetime. Aluminum electrolytic capacitor (AEC) failures represent the root cause of power electronic equipment breakdown, mainly through both aging and temperature effects. This highlights the importance of designing robust power converter architectures and control methods that allow the evaluation of the condition of electrolytic capacitors while maintaining the performance of converter controllers, even in the presence of capacitor failure. On this basis, this work proposes a novel condition-monitoring system for the diagnosis of capacitor faults on a fault-tolerant LED driver, which is able to deal with the specific architecture and low ratings of the most recent LED lighting systems. The fault-detection task applies the short time least square Prony’s (STLSP) approach to perform an online estimation of the ESR and C parameters, allowing the continuous evaluation of the electrolytic capacitor’s condition and, as a result, the prevention of total system failure. With regard to capacitor failure, the experimental results suggest that the condition-monitoring task is extremely effective, even when considering a limited number of data samples. Full article
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Article
Capacity State-of-Health Estimation of Electric Vehicle Batteries Using Machine Learning and Impedance Measurements
Electronics 2022, 11(9), 1414; https://doi.org/10.3390/electronics11091414 - 28 Apr 2022
Viewed by 495
Abstract
With the increasing adoption of electric vehicles (EVs) by the general public, a lot of research is being conducted in Li-ion battery-related topics, where state-of-health (SoH) estimation has a prominent role. Accurate knowledge of this parameter is essential for efficient and safe EV [...] Read more.
With the increasing adoption of electric vehicles (EVs) by the general public, a lot of research is being conducted in Li-ion battery-related topics, where state-of-health (SoH) estimation has a prominent role. Accurate knowledge of this parameter is essential for efficient and safe EV operation. In this work, machine-learning techniques are applied to estimate this parameter in EV applications and in diverse scenarios. After thoroughly analysing cell ageing in different storage conditions, a novel approach based on impedance data is developed for SoH estimation. A fully-connected feed-forward neural network (FC-FNN) is employed to estimate the battery’s maximum available capacity from a small set of impedance measurements. The method was tested for estimation in long-term scenarios and for diverse degradation procedures with data from real EV batteries. High accuracy was obtained in all situations, with a mean absolute error as low as 0.9%. Thus, the proposed algorithm constitutes a powerful and viable solution for fast and accurate SoH estimation in real-world battery management systems. Full article
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Article
A Machine Learning-Based Robust State of Health (SOH) Prediction Model for Electric Vehicle Batteries
Electronics 2022, 11(8), 1216; https://doi.org/10.3390/electronics11081216 - 12 Apr 2022
Viewed by 438
Abstract
The car industry is entering a new age due to electric energy as a fuel in the contemporary era. Electric batteries are being more widely used in the automobile sector these days. As a result, the inner workings of these battery systems must [...] Read more.
The car industry is entering a new age due to electric energy as a fuel in the contemporary era. Electric batteries are being more widely used in the automobile sector these days. As a result, the inner workings of these battery systems must be fully comprehended. There is currently no accurate model for predicting an electric car battery’s state of health (SOH). This study aims to use machine learning to develop a reliable SOH prediction model for batteries. A correct optimal method was also constructed to drive the modeling process in the right direction. Extensive simulations were performed to verify the accuracy of the suggested methodology. A state of health method for data processing was developed. The method involves a complex data-driven model combining Big Data, Artificial Intelligence (A.I.), and the Internet of Things (IoT) technologies. To establish the most effective technique for certifying the actual condition of real-life battery health, researchers compared the accuracy and performance of several states of health models. For improved understanding and prediction of the condition of health behavior, data-driven modeling has certain significant advantages over older methodologies. The methods used in this study can be seen as a revolutionary low-cost, high-accuracy, and dependable approach to understanding and analyzing the state of health of batteries. At first, an intelligent model was created using a data-driven modeling strategy. Secondly, the concurrent battery data are qualified using the data-driven model. The machine learning (ML) method creates a very accurate and dependable model for forecasting battery health in real-world scenarios. Third, the previously established ML model was used to develop a knowledge-based online service for battery health. This web service can be used to test battery health, monitor battery behavior, and perform a variety of other tasks. A variety of similar solutions for diverse systems can be derived using the same technique. The default efficiency of the ML algorithmic module, R-Squared (R2), and Mean Square Error (MSE) were also utilized as performance measures. The R2 as a standard is used to examine the effectiveness of a fit. The result is a value between 0 and 1, with 1 indicating a better model fit. MSE stands for mean squared error. A lower MSE number implies superior model performance, since it reflects how close the parameter estimates are to the actual values. The training set of the battery model had a score of 0.9999, whereas the testing set had a score of 0.9995. The R2 score was one, with an M.S.E. of 0.03. As a result of these three indicators, the data-driven ML model used in this study proved to be accurate. Full article
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Article
Modular Battery Emulator for Development and Functional Testing of Battery Management Systems: The Cell Emulator
Electronics 2022, 11(8), 1215; https://doi.org/10.3390/electronics11081215 - 12 Apr 2022
Viewed by 390
Abstract
Battery Management Systems are fundamental components of the present battery generation. The development and characterization phases of a BMS often require an emulator of the battery cells with which the Battery Management System functions can be assessed with no safety risks as it [...] Read more.
Battery Management Systems are fundamental components of the present battery generation. The development and characterization phases of a BMS often require an emulator of the battery cells with which the Battery Management System functions can be assessed with no safety risks as it would instead happen using a real battery. This work describes the design and characterization of a modular cell emulator circuit to be used as platform for the Hardware-in-the-loop test of a Battery Management System. The design constraints and choices are first described. Then, the experimental characterization of the cell emulator is shown and discussed. The proposed circuit shows a voltage resolution of 76 μV, an accuracy of 2.17 mV, and a setting time of 340 μs. Its cost is around 40 USD. The circuit results to be a very good trade-off between performance and cost. The Project is available to the scientific community as open hardware platform freely downloadable. It could be useful to small-size laboratories to self-produce a low-cost battery emulator with good performance for the development and the functional test of custom Battery Management Systems. Full article
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Article
Optimal Battery Dispatch Using Finite-Input Set Non-Linear Model Predictive Control: Algorithm Development and Case Study
Electronics 2022, 11(1), 101; https://doi.org/10.3390/electronics11010101 - 29 Dec 2021
Viewed by 388
Abstract
The advancement in battery manufacturing has played a significant role in the use of batteries as a cost-effective energy storage system. This paper proposes an optimal charging and discharging strategy for the battery energy storage system deployed for economic dispatch and supply/demand balancing [...] Read more.
The advancement in battery manufacturing has played a significant role in the use of batteries as a cost-effective energy storage system. This paper proposes an optimal charging and discharging strategy for the battery energy storage system deployed for economic dispatch and supply/demand balancing services in the presence of intermittent renewables such as solar photovoltaic systems. A decision-making strategy for battery charge/discharge operations in a discrete-time rolling horizon framework is developed as a finite-input set non-linear model predictive control instances and a dynamic programming procedure is proposed for its real-time implementation. The proposed scheme is tested on controllable loads and a photovoltaic generation scenario in the premises of a sports centre, as a part of a pilot demonstration of the inteGRIDy EU-funded project. The test results confirm that the implemented stacking of the battery and optimal decision-making algorithm can enhance net saving in the electricity bill of the sports centre, and lead to corresponding CO2 reductions. Full article
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Article
Modeling of Average Current in Non-Ideal Buck and Synchronous Buck Converters for Low Power Application
Electronics 2021, 10(21), 2672; https://doi.org/10.3390/electronics10212672 - 31 Oct 2021
Cited by 1 | Viewed by 591
Abstract
In this paper, a comparative analysis of the average switch/inductor current between ideal and non-ideal buck and synchronous buck converters is performed and verified against a standard LTspice model. The mathematical modeling of the converters was performed using volt-sec and amp-sec balance equations [...] Read more.
In this paper, a comparative analysis of the average switch/inductor current between ideal and non-ideal buck and synchronous buck converters is performed and verified against a standard LTspice model. The mathematical modeling of the converters was performed using volt-sec and amp-sec balance equations and analyzed using MATLAB/Simulink. The transients in the output voltage and the inductor current were observed. The transfer function of the switch current to the duty cycle (Gid) in open loop configuration for low-power converters operating in continuous conduction mode (CCM) was modeled using thestate space averaging (SSA) technique and analyzed using MATLAB/Simulink. Initially, using the volt-sec and amp-sec, balance equations for the converters were modeled. The switch current to duty ratio (Gid) was derived using the SSA technique and verified using standard average models available in LTspice software. Though the Gid was derived using various methods in earlier works, the analyses of parameters such as low frequency gain, stability, resonant frequency and the location of poles and zeros were not presented. It was observed that the converters were stable, and the non-ideal converter showed smaller resonant frequency than the ideal converter due to the equivalent series resistances (ESR) of the inductor and the capacitor. The non-ideal converters showed higher stability than the ideal converters due to the placement of the poles closer to the s-plane. However, the Gid of the non-ideal converters remained the same in the open loop configuration. Full article
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