Micro energy harvesting for IoT platform: Review analysis toward future research opportunities.

Micro-energy harvesting (MEH) is a technology of renewable power generation which is a key technology for hosting the future low-powered electronic devices for wireless sensor networks (WSNs) and, the Internet of Things (IoT). Recent technological advancements have given rise to several resources and technologies that are boosting particular facets of society. Many researchers are now interested in studying MEH systems for ultra-low power IoT sensors and WSNs. A comprehensive study of IoT will help to manage a single MEH as a power source for multiple WSNs. The popular database from Scopus was used in this study to perform a review analysis of the MEH system for ultra-low power IoT sensors. All relevant and important literature studies published in this field were statistically analysed using a review analysis method by VOSviewer software, and research gaps, challenges and recommendations of this field were investigated. The findings of the study indicate that there has been an increasing number of literature studies published on the subject of MEH systems for IoT platforms throughout time, particularly from 2013 to 2023. The results demonstrate that 67% of manuscripts highlight problem-solving, modelling and technical overview, simulation, experimental setup and prototype. In observation, 27% of papers are based on bibliometric analysis, systematic review, survey, review and based on case study, and 2% of conference manuscripts are based on modelling, simulation, and review analysis. The top-cited articles are published in 5 different countries and 9 publishers including IEEE 51%, Elsevier 16%, MDPI 10% and others. In addition, several MEH system-related problems and challenges are noted to identify current limitations and research gaps, including technical, modelling, economic, power quality, and environmental concerns. Also, the study offers guidelines and recommendations for the improvement of future MEH technology to increase its energy efficiency, topologies, design, operational performance, and capabilities. This study's detailed information, perceptive analysis, and critical argument are expected to improve MEH research's viable future.

A Bibliometric Analysis of Low-Cost Piezoelectric Micro-Energy Harvesting Systems from Ambient Energy Sources: Current Trends, Issues and Suggestions.

The scientific interest in piezoelectric micro-energy harvesting (PMEH) has been fast-growing, demonstrating that the field has made a major improvement in the long-term evolution of alternative energy sources. Although various research works have been performed and published over the years, only a few attempts have been made to examine the research's influence in this field. Therefore, this paper presents a bibliometric study into low-cost PMEH from ambient energy sources within the years 2010-2021, outlining current research trends, analytical assessment, novel insights, impacts, challenges and recommendations. The major goal of this paper is to provide a bibliometric evaluation that is based on the top-cited 100 articles employing the Scopus databases, information and refined keyword searches. This study analyses various key aspects, including PMEH emerging applications, authors' contributions, collaboration, research classification, keywords analysis, country's networks and state-of-the-art research areas. Moreover, several issues and concerns regarding PMEH are identified to determine the existing constraints and research gaps, such as technical, modeling, economics, power quality and environment. The paper also provides guidelines and suggestions for the development and enhancement of future PMEH towards improving energy efficiency, topologies, design, operational performance and capabilities. The in-depth information, critical discussion and analysis of this bibliometric study are expected to contribute to the advancement of the sustainable pathway for PMEH research.

Micro Energy Storage Systems in Energy Harvesting Applications: Analytical Evaluation towards Future Research Improvement.

During the last decade, countless advancements have been made in the field of micro-energy storage systems (MESS) and ambient energy harvesting (EH) shows great potential for research and future improvement. A detailed historical overview with analysis, in the research area of MESS as a form of ambient EH, is presented in this study. The top-cited articles in the field of MESS ambient EH were selected from the Scopus database, and based on articles published from 2010 to 2021, and the number of citations. The search for these top-cited articles was conducted in the third week of December 2021. Mostly the manuscripts were technical and contained an experimental setup with algorithm development (65%), whereas 27.23% of the articles were survey-based. One important observation was that the top 20 selected articles, which are the most-cited articles in the different journals, come from numerous countries of origin. This study revealed that the MESS integrated renewable energy sources (RESs) are an enhancement field of research for EH applications. On the basis of this survey, we hope to identify and solve research problems in the field of MESS and RESs integration, and provide suggestions for future developments for EH applications.

Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model.

Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.

Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques.

State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions.