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Batteries
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Recent Advances in Battery Measurement and Management Systems
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Recent Advances in Battery Measurement and Management Systems

A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Modelling, Simulation, Management and Application".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 10906

Special Issue Editors

Prof. Dr. Alessio De Angelis

E-Mail Website
Guest Editor
Department of Engineering, University of Perugia, I-06125 Perugia, Italy
Interests: battery management; instrumentation and measurement; positioning systems (using magnetic field and ultrasound); statistical signal processing; system identification; sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Francesco Santoni

E-Mail Website
Guest Editor
Department of Engineering, University of Perugia, I-06125 Perugia, Italy
Interests: battery management; electronics; positioning systems; OLED; photovoltaic; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rechargeable batteries are now ubiquitous in advanced technological applications, ranging from grid energy storage and electric vehicles, wireless sensor networks, and the Internet of Things to portable electronics, particularly smartphones and laptops.

In this context, internal state measurement and analysis, as well as automated battery management, are critical tools used for the proper operation of various applications. Of particular use is the capability to estimate the state of charge (SOC), state of health (SOH), and remaining useful life (RUL) of batteries in run time. Electrochemical methods are paradigmatically used for the purpose, while a variety of machine learning techniques have also been applied in recent years. Battery modeling using equivalent circuits also provides useful information in simulation and testing, as well as for internal state analysis and fault detection.

These information sources converge in the recently defined concept of battery passports, intended to be a standard and complete digital representation of the battery during its whole lifecycle, in turn providing comprehensive knowledge for effective management and maintenance.

This Special Issue aims to explore the latest advances on all the mentioned topics, and to review the current state-of-art including case studies.

Potential topics include but are not limited to:

  • Electrochemical test methods for batteries, from laboratory to run-time applications;
  • Battery state estimation methods of SOC, SOH, and RUL;
  • Machine learning methods applied to battery characterization and management;
  • Newly conceived battery management systems;
  • Definition and implementation of a battery passport;
  • Current state-of-art and case studies on battery characterization, management, and maintenance.

Prof. Dr. Alessio De Angelis
Dr. Francesco Santoni
Guest Editors

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

  • state of charge
  • state of health
  • remaining useful life
  • electrochemical test methods
  • battery parameters estimation
  • battery passport

Published Papers (6 papers)

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Research

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14 pages, 6593 KiB  
Article
Influence of Pressure, Temperature and Discharge Rate on the Electrical Performances of a Commercial Pouch Li-Ion Battery
by Luigi Aiello, Peter Ruchti, Simon Vitzthum and Federico Coren
Batteries 2024, 10(3), 72; https://doi.org/10.3390/batteries10030072 - 21 Feb 2024
Viewed by 2023
Abstract
In this study, the performances of a pouch Li-ion battery (LIB) with respect to temperature, pressure and discharge-rate variation are measured. A sensitivity study has been conducted with three temperatures (5 °C, 25 °C, 45 °C), four pressures (0.2 MPa, 0.5 MPa, 0.8 [...] Read more.
In this study, the performances of a pouch Li-ion battery (LIB) with respect to temperature, pressure and discharge-rate variation are measured. A sensitivity study has been conducted with three temperatures (5 °C, 25 °C, 45 °C), four pressures (0.2 MPa, 0.5 MPa, 0.8 MPa, 1.2 MPa) and three electrical discharge rates (0.5 C, 1.5 C, 3.0 C). Electrochemical processes and overall efficiency are significantly affected by temperature and pressure, influencing capacity and charge–discharge rates. In previous studies, temperature and pressure were not controlled simultaneously due to technological limitations. A novel test bench was developed to investigate these influences by controlling the surface temperature and mechanical pressure on a pouch LIB during electrical charging and discharging. This test rig permits an accurate assessment of mechanical, thermal and electrical parameters, while decoupling thermal and mechanical influences during electrical operation. The results of the study confirm what has been found in the literature: an increase in pressure leads to a decrease in performance, while an increase in temperature leads to an increase in performance. However, the extent to which the pressure impacts performance is determined by the temperature and the applied electrical discharge rate. At 5 °C and 0.5 C, an increase in pressure from 0.2 MPa to 1.2 MPa results in a 5.84% decrease in discharged capacity. At 45 °C the discharge capacity decreases by 2.17%. Regarding the impact of the temperature, at discharge rate of 0.5 C, with an applied pressure of 0.2 MPa, an increase in temperature from 25 °C to 45 °C results in an increase of 4.27% in discharged capacity. The impact on performance varies significantly at different C-rates. Under the same pressure (0.2 MPa) and temperature variation (from 25 °C to 45 °C), increasing the electrical discharge rate to 1.5 C results in a 43.04% increase in discharged capacity. The interplay between temperature, pressure and C-rate has a significant, non-linear impact on performance. This suggests that the characterisation of an LIB would require the active control of both temperature and pressure during electrical operation. Full article
(This article belongs to the Special Issue Recent Advances in Battery Measurement and Management Systems)
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21 pages, 8884 KiB  
Article
Hybrid Estimation Method for the State of Charge of Lithium Batteries Using a Temporal Convolutional Network and XGBoost
by Jong-Hyun Lee and In-Soo Lee
Batteries 2023, 9(11), 544; https://doi.org/10.3390/batteries9110544 - 05 Nov 2023
Viewed by 1648
Abstract
Lithium batteries have recently attracted significant attention as highly promising energy storage devices within the secondary battery industry. However, it is important to note that they may pose safety risks, including the potential for explosions during use. Therefore, achieving stable and safe utilization [...] Read more.
Lithium batteries have recently attracted significant attention as highly promising energy storage devices within the secondary battery industry. However, it is important to note that they may pose safety risks, including the potential for explosions during use. Therefore, achieving stable and safe utilization of these batteries necessitates accurate state-of-charge (SOC) estimation. In this study, we propose a hybrid model combining temporal convolutional network (TCN) and eXtreme gradient boosting (XGBoost) to investigate the nonlinear and evolving characteristics of batteries. The primary goal is to enhance SOC estimation performance by leveraging TCN’s long-effective memory capabilities and XGBoost’s robust generalization abilities. We conducted experiments using datasets from NASA, Oxford, and a vehicle simulator to validate the model’s performance. Additionally, we compared the performance of our model with that of a multilayer neural network, long short-term memory, gated recurrent unit, XGBoost, and TCN. The experimental results confirm that our proposed TCN–XGBoost hybrid model outperforms the other models in SOC estimation across all datasets. Full article
(This article belongs to the Special Issue Recent Advances in Battery Measurement and Management Systems)
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24 pages, 3173 KiB  
Article
A Lithium-Ion Battery Capacity and RUL Prediction Fusion Method Based on Decomposition Strategy and GRU
by Huihan Liu, Yanmei Li, Laijin Luo and Chaolong Zhang
Batteries 2023, 9(6), 323; https://doi.org/10.3390/batteries9060323 - 12 Jun 2023
Cited by 4 | Viewed by 1961
Abstract
To safeguard the security and dependability of battery management systems (BMS), it is essential to provide reliable forecasts of battery capacity and remaining useful life (RUL). However, most of the current prediction methods use the measurement data directly to carry out prediction work, [...] Read more.
To safeguard the security and dependability of battery management systems (BMS), it is essential to provide reliable forecasts of battery capacity and remaining useful life (RUL). However, most of the current prediction methods use the measurement data directly to carry out prediction work, which ignores the objective measurement noise and capacity increase during the aging process of batteries. In this study, an integrated prediction method is introduced to highlight the prediction of lithium-ion battery capacity and RUL. This approach incorporates several techniques, including variational modal decomposition (VMD) with entropy detection, a double Gaussian model, and a gated recurrent unit neural network (GRU NN). Specifically, the PE−VMD algorithm is first utilized to perform a noise reduction process on the capacity data obtained from the measurements, and this results in a global degradation trend sequence and local fluctuation sequences. Afterward, the global degradation prediction model is established by employing the double Gaussian aging model proposed in this paper, and the local prediction models are built for each local fluctuation sequence by GRU NN. Lastly, the proposed hybrid prediction methodology is validated through battery capacity and RUL prediction studies on experimental data from three sources, and its accuracy is also compared with prediction algorithms from the recent related literature. Experimental results demonstrate that the proposed hybrid prediction method exhibits high precision in the predicting future capacity and RUL of lithium-ion batteries, along with strong robustness and predictive stability. Full article
(This article belongs to the Special Issue Recent Advances in Battery Measurement and Management Systems)
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12 pages, 1408 KiB  
Article
On the Usage of Battery Equivalent Series Resistance for Shuntless Coulomb Counting and SOC Estimation
by Alessio De Angelis, Paolo Carbone, Francesco Santoni, Michele Vitelli and Luca Ruscitti
Batteries 2023, 9(6), 286; https://doi.org/10.3390/batteries9060286 - 23 May 2023
Viewed by 1488
Abstract
In this paper, a feasibility study of a shuntless coulomb counting method for estimating the state of charge (SOC) of a battery is presented. Contrary to conventional coulomb counting, the proposed method does not require an external resistive shunt; it instead only requires [...] Read more.
In this paper, a feasibility study of a shuntless coulomb counting method for estimating the state of charge (SOC) of a battery is presented. Contrary to conventional coulomb counting, the proposed method does not require an external resistive shunt; it instead only requires voltage measurements performed on the battery under test while it is operating. The current is measured indirectly using the battery’s equivalent series resistance (ESR). The method consists of a preliminary calibration phase where the ESR and the open-circuit voltage of the battery are measured for different SOCs and stored in look-up tables (LUTs). Then, in the subsequent operational phase, the method uses these LUTs together with the measured voltage at the battery terminals to estimate the SOC. The performance of the proposed method is evaluated on a sample lithium polymer (LiPo) battery, using a realistic current profile derived from the Worldwide Harmonized Light-Duty Vehicles Test Procedure (WLTP). The results of this experimental evaluation demonstrate a SOC estimation root-mean-square error of 0.82% and a maximum SOC error of 1.45%. These results prove that the proposed method is feasible in a practical scenario. Full article
(This article belongs to the Special Issue Recent Advances in Battery Measurement and Management Systems)
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15 pages, 1439 KiB  
Article
An Adaptive Cutoff Frequency Design for Butterworth Low-Pass Filter Pursuing Robust Parameter Identification for Battery Energy Storage Systems
by Cong-Sheng Huang
Batteries 2023, 9(4), 198; https://doi.org/10.3390/batteries9040198 - 27 Mar 2023
Viewed by 1705
Abstract
Energy storage systems are key to propelling the current renewable energy revolution. Accurate State-of-Charge estimation of the lithium-ion battery energy storage systems is a critical task to ensure their reliable operations. Multiple advanced battery model-based SOC estimation algorithms have been developed to pursue [...] Read more.
Energy storage systems are key to propelling the current renewable energy revolution. Accurate State-of-Charge estimation of the lithium-ion battery energy storage systems is a critical task to ensure their reliable operations. Multiple advanced battery model-based SOC estimation algorithms have been developed to pursue this objective. Nevertheless, these battery model-based algorithms are sensitive to measurement noises since the measurement noises affect the accuracy of battery model identification, thus leading to inaccurate battery SOC estimation consequently due to modeling error. The Butterworth low-pass filter has proven effectiveness in measurement noise filtering for accurate parameter identification, while the cutoff frequency design relies on prior knowledge of lithium-ion batteries, making its capability limited to general cases. To overcome this issue, this paper proposes an adaptive cutoff frequency design algorithm for the Butterworth low-pass filter. Simulation results show that the low-pass filter functions properly in the presence of multiple scales of measurement noises adopting the proposed work. Consequently, the parameters of the battery model and the SOC of the battery are both identified and estimated accurately, respectively. In detail, the parameters: R0, R1, C1, and the time constant τ are all identified accurately with low relative identification errors of 0.028%, 11.12%, 6.21%, and 5.94%, respectively, in an extreme case. Furthermore, the SOC of the battery can thus be estimated accurately, leaving a low of 0.081%, 0.97%, and 0.14% in the mean and maximum absolute SOC estimation error and the standard deviation, respectively. Full article
(This article belongs to the Special Issue Recent Advances in Battery Measurement and Management Systems)
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Review

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16 pages, 1018 KiB  
Review
Artificial Intelligence Opportunities to Diagnose Degradation Modes for Safety Operation in Lithium Batteries
by Edurne Jaime-Barquero, Emilie Bekaert, Javier Olarte, Ekaitz Zulueta and Jose Manuel Lopez-Guede
Batteries 2023, 9(7), 388; https://doi.org/10.3390/batteries9070388 - 21 Jul 2023
Viewed by 1388
Abstract
The degradation and safety study of lithium-ion batteries is becoming increasingly important given that these batteries are widely used not only in electronic devices but also in automotive vehicles. Consequently, the detection of degradation modes that could lead to safety alerts is essential. [...] Read more.
The degradation and safety study of lithium-ion batteries is becoming increasingly important given that these batteries are widely used not only in electronic devices but also in automotive vehicles. Consequently, the detection of degradation modes that could lead to safety alerts is essential. Existing methodologies are diverse, experimental based, model based, and the new trends of artificial intelligence. This review aims to analyze the existing methodologies and compare them, opening the spectrum to those based on artificial intelligence (AI). AI-based studies are increasing in number and have a wide variety of applications, but no classification, in-depth analysis, or comparison with existing methodologies is yet available. Full article
(This article belongs to the Special Issue Recent Advances in Battery Measurement and Management Systems)
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