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15th Anniversary of Journal of Low Power Electronics and Applications
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15th Anniversary of Journal of Low Power Electronics and Applications

A special issue of Journal of Low Power Electronics and Applications (ISSN 2079-9268).

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2166

Special Issue Editor

Dr. Davide Bertozzi

E-Mail Website
Guest Editor
Reader in Advanced Processing Technologies, Department of Computer Science, University of Manchester, Manchester M13 9PL, UK
Interests: neuromorphic computing; interconnection networks; fault tolerant computing; emerging technologies; cloud-edge continuum

Special Issue Information

Dear Colleagues,

Since it launched in 2011, Journal of Low Power Electronics and Applications has provided readers with high-quality content edited by active researchers in low power electronics and design through a model of sustainable open access. We would like to sincerely thank our readers, authors, anonymous peer reviewers, editors, any individuals who work for the journal and all those who have contributed time and effort throughout the years for their interest and commitment.

To celebrate the significant milestone of the 15th Anniversary, we are delighted to launch the Special Issue entitled “15th Anniversary of Journal of Low Power Electronics and Applications”. It is our pleasure to invite you to contribute research articles and communications, as well as comprehensive review articles on a current, trending topic from the field of low power devices, design, architecture and process technologies.

Dr. Davide Bertozzi
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 250 words) can be sent to the Editorial Office for assessment.

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. Journal of Low Power Electronics and Applications is an international peer-reviewed open access quarterly 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 1800 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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

17 pages, 2015 KB  
Article
Efficient Battery State of Health Estimation Using Lightweight ML Models Based on Limited Voltage Measurements
by Mohammad Okour, Mohannad Alkhalil, Mutaz Al Fayad, Juhyun Bak, Kevin R. James, Sulaiman Mohaidat, Xiaoqi Liu, Fadi Alsaleem, Michael Hempel, Hamid Sharif-Kashani and Mahmoud Alahmad
J. Low Power Electron. Appl. 2026, 16(2), 16; https://doi.org/10.3390/jlpea16020016 - 21 Apr 2026
Viewed by 486
Abstract
Accurate estimation of lithium-ion battery State of Health (SoH) is critical for emerging applications such as reconfigurable battery systems. Although data-driven machine learning methods are promising, they often rely on costly, time-intensive aging experiments and extensive feature engineering. This work proposes a lightweight [...] Read more.
Accurate estimation of lithium-ion battery State of Health (SoH) is critical for emerging applications such as reconfigurable battery systems. Although data-driven machine learning methods are promising, they often rely on costly, time-intensive aging experiments and extensive feature engineering. This work proposes a lightweight SoH-prediction framework validated on both physics-informed synthetic aging data and the NASA battery aging dataset. We evaluated Random Forest (RF) and Feedforward Neural Network (FNN) models that use only a limited number of samples from an early segment of the raw discharge voltage curve as input. Results show that RF consistently outperforms FNN across input sizes in deterministic or noise-free environments, achieving an RMSE of 0.07% SoH using just 5 voltage samples. In inherently stochastic experimental data, however, FNN can achieve an RMSE 50% lower than RF (1.28 vs. 2.87), but requires 37× more mathematical operations per inference. These findings emphasize the predictive value of the early-discharge-voltage region and demonstrate that compact, low-feature-complexity models can deliver accurate SoH estimates. Overall, the approach supports a goal of combining informed synthetic data with limited real measurements to build robust, scalable SoH predictors, reducing dependence on labor-intensive degradation testing and feature-heavy pipelines. Full article
(This article belongs to the Special Issue 15th Anniversary of Journal of Low Power Electronics and Applications)
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16 pages, 1361 KB  
Article
RF/mm-Wave Frequency Doublers in CMOS Technology
by Manfredi Caruso, Andrea Ballo, Minoo Eghtesadi and Egidio Ragonese
J. Low Power Electron. Appl. 2026, 16(2), 14; https://doi.org/10.3390/jlpea16020014 - 13 Apr 2026
Viewed by 538
Abstract
This paper provides a comprehensive analysis of active frequency doubler architectures adopted for efficient generation of millimeter-wave (mm-wave) signals. The operational principles of each topology are explained to address a thorough comparison based on essential performance metrics such as conversion gain, power efficiency, [...] Read more.
This paper provides a comprehensive analysis of active frequency doubler architectures adopted for efficient generation of millimeter-wave (mm-wave) signals. The operational principles of each topology are explained to address a thorough comparison based on essential performance metrics such as conversion gain, power efficiency, and spectral purity. The review covers several topologies from the standard push–push (PP) doubler to its power-efficient evolution, the complementary push–push (CPP) doubler. Furthermore, this paper focuses on more recent and advanced topologies, including the complementary common gate capacitive cross-coupled (CCGCCC) doubler. Finally, this work proposes and evaluates an improved version of the CCCGCC doubler, offering insights into the state of the art and future directions in mm-wave frequency multiplication. Full article
(This article belongs to the Special Issue 15th Anniversary of Journal of Low Power Electronics and Applications)
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21 pages, 9981 KB  
Article
Forward-Flyback Resonant Topology with Edge AI for MPPT Control in Solar Power Generation
by Juan Cruz-Cozar, Javier Mendez, Miguel Molina, Jorge Perez-Martinez, Alberto Martin-Martin, Noel Rodriguez and Diego P. Morales
J. Low Power Electron. Appl. 2026, 16(2), 13; https://doi.org/10.3390/jlpea16020013 - 12 Apr 2026
Viewed by 729
Abstract
Distributed energy systems open up a vast field of research in power electronics. Local solar power generation requires DC-DC converters that adapt the energy generated by the panels to on-site distribution buses. In addition, the control of the power converter to obtain the [...] Read more.
Distributed energy systems open up a vast field of research in power electronics. Local solar power generation requires DC-DC converters that adapt the energy generated by the panels to on-site distribution buses. In addition, the control of the power converter to obtain the maximum possible energy from the solar source is crucial for the correct deployment of these distributed grids. In this work, system-level solutions are proposed for this application as follows: On the one hand, the use of novel resonant forward-flyback converters allows for a higher energy density than that of a conventional flyback and more relaxed withstand voltages on the switching elements. On the other hand, the implementation of maximum power point tracking algorithms for solar energy using Edge AI enables the deployment of algorithms that maximize the energy obtained locally. These improvements are shown by means of a prototype demonstrator, using cutting-edge microcontrollers and the implementation of a DC-DC power converter based on the proposed topology. Full article
(This article belongs to the Special Issue 15th Anniversary of Journal of Low Power Electronics and Applications)
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