Blockchain Papers

Jasem M. Alostad, R. Gopi, A. Ananthi, B. Sathiya

No abstract is available for this record.

Ching-Chuan Luo, Cheng-En You, Ming‐Feng Yeh

Sustainable EV charging infrastructure is fragmented by proprietary applications, vendor lock-in, and weakly time-differentiated pricing, blunting its contribution to urban-mobility decarbonisation. This paper asks whether an open-protocol, super-app-mediated photovoltaic–storage charging architecture can jointly resolve these three fragmentations under deployed field conditions and what its sustainability profile then looks like. We report a campus photovoltaic–storage microgrid integrating heterogeneous EV chargers under an open, vendor-neutral charging-control protocol with super-app authentication and payment replacing dedicated charging applications and a time-differentiated tariff aligned at the meter-interval level with the underlying utility wholesale rate; the deployment is exercised through a researcher-scheduled commissioning campaign of 13 sessions designed to establish functional correctness across the operating envelope rather than to measure user behaviour. Three results emerge across cross-vendor compatibility, onboarding friction, and grid alignment. First, basic message-level OCPP compatibility is sustained across two charger vendors under a single cloud management system—in sequential single-vendor sessions—including the full charging profile up to near-rated DC peak power. Second, the super-app-mediated workflow, which requires no charging-specific application installation and no new charger-operator account, structurally eliminates the dedicated application installation and the email/SMS/credit-card verification round-trips of conventional onboarding, compressing measured first-use end-to-end interaction to 31 s; relative to reconstructed commercial-operator baselines, this is, to the best of the authors’ knowledge, an order-of-magnitude reduction rather than a controlled benchmark. Third, mid-day energy delivery aligns incidentally with the utility off-peak window, not user-driven demand shifting, while PV-displacement and BESS-discharge contributions to charging are bracketed by scenario rather than being separately metered. The paper’s contribution is therefore a replicable, policy-embedded sustainable charging architecture validated at field scale within the New Taipei Net-Zero Carbon Demonstration Site Programme, with no claim of global novelty; the same architecture is structurally positioned to convert the observed incidental grid-friendliness into a deliberate, user-facing benefit via a hardware-free mid-day-discount redesign.

Ngoc-Tien Tran

The accelerating adoption of Electric Vehicles (EVs) has intensified the need for efficient and sustainable life-cycle management of Lithium-Ion Batteries (LIBs). However, the complexity of battery supply chains, data fragmentation, and varying technological characteristics among distributed ledger platforms create significant uncertainty in selecting an appropriate infrastructure for implementing Digital Battery Passports (DBP). This study proposes a structured decision-support model to evaluate and differentiate among Distributed Ledger Technologies (DLT) under an uncertain decision environment. The proposed framework integrates the plithogenic set theory to capture expert uncertainty and inconsistency, the Best–Worst Method (BWM) to determine the relative importance of evaluation criteria, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank alternative platforms. The model is applied to assess eight leading DLT platforms for DBP implementation in the automotive context. The results indicate that Hedera is the most suitable platform, achieving the highest TOPSIS closeness coefficient (0.8223), followed by IOTA and EOS. The findings confirm that incorporating contradiction-aware uncertainty modeling into a hybrid MCDM framework enhances the robustness and transparency of DLT platform selection for DBP-oriented applications.

M. Lavanya, V Thiruppathy Kesavan, G. Sathya, R. Gopi

Electric Vehicles (EVs) that use Internet of Things (IoT) networks often involve the exchange of sensitive data between vehicles, charging stations, and other infrastructure, making data security and user privacy critical concerns. Existing methods for securing data in EV IoT networks rely on centralized systems, which create a single point of failure and are vulnerable to cyberattacks, data breaches, and unauthorized access. Furthermore, these systems struggle to address privacy concerns effectively, especially regarding user location and personal information. The proposed solution introduces a Blockchain Technology-based privacy preservation framework for EV networks (BCT-PP-EV). This framework leverages blockchain's decentralized nature to provide secure, transparent, and tamper-proof data exchanges. It ensures user privacy using cryptographic techniques such as zero-knowledge proofs (ZKP) and data anonymization, allowing privacy-preserving transactions without compromising data accuracy. Blockchain's immutability guarantees the integrity of the shared data, while smart contracts automate secure and efficient interactions within the network. The proposed method enhances secure data sharing while preserving privacy across EV IoT networks. By decentralizing data storage and enabling transparent auditing, BCT-PP-EV fosters trust among stakeholders and reduces the risks of unauthorized access or data manipulation. Preliminary findings suggest that implementing BCT-PP-EV significantly improves the security and privacy of data exchanges in EV networks, providing a scalable and resilient solution for the evolving smart transportation ecosystem. Experimental results demonstrate that BCT-PP-EV achieves 94.91% secure data sharing efficiency, reduces data breaches by 92.84%, and ensures data accuracy of 91.44%. Additionally, the framework exhibits high scalability of 96.57% with increasing network nodes, while maintaining controlled latency and throughput. Although unauthorized access resistance is measured at 24.71%, indicating scope for further improvement, the overall results confirm that BCT-PP-EV provides a robust, scalable, and privacy-preserving solution for next-generation smart transportation systems.

Alaa Alqaryuti, Haya Aljaghoub, Khaled Salah, Ahmad Mayyas

The growing adoption of Proton Exchange Membrane (PEM) fuel cell electric vehicles (FCEVS) has increased the need for secure, transparent, and verifiable certification and lifecycle tracking of hydrogen-related components. Current practices rely on fragmented documentation and centralized record-keeping, which creates risks of data manipulation, incomplete maintenance histories, and limited visibility for regulators and service providers. This paper introduces a blockchain-based framework that integrates decentralized storage, oracle-driven automation, and three interoperable smart contracts to manage stakeholder registration, component certification, vehicle assembly validation, and maintenance tracking. Implemented and evaluated in an EVM-compatible environment, the system enforces strict role-based access control, generates immutable audit trails, and automates both failure-based and mileage-based maintenance triggers using real-time inputs. A gas-cost analysis demonstrates that all contract functions operate at minimal cost under current Ethereum conditions, supporting the feasibility of real-world deployment. Overall, the proposed framework improves traceability, regulatory compliance, and operational accountability by enabling near real-time verification of certification records and reducing manual audit processing steps compared to traditional document-based certification workflows. • Blockchain ensures secure, tamper-proof FCEV component traceability. • Smart contracts automate certification, assembly, and maintenance. • Oracle triggers enable real-time, failure-, and scheduled service. • Framework improves compliance, transparency, and lifecycle oversight.

N. V. Ravindhar, A. Manju, S. Murugesan, T. K. S. Rathish Babu

Electric Vehicle-to-Grid (V2G) arrangements stand at the center of bidirectional energy exchange in modern smart grids and are, however, challenged by real-time decision-making, load balancing, and the security of transaction validation. This paper has proposed an energy-efficient optimization framework based on a Bio-Inspired Deep Learning Controller using a Monarch Butterfly Optimization (MBO) algorithm with Gated Recurrent Unit (GRU) network for optimizing charging and discharging schedules across EV fleets. GRU networks forecast short-term grid demand and EV battery availability while MBO tunes the controller weights dynamically to adapt to scheduling under varying conditions. Furthermore, in order to maintain the trust over the transaction in a tamper-resistant fashion, a blockchain layer is embedded with the use of smart contracts to keep a track of authentication, pricing, and energy transfer log records for V2G. The proposed system shows charging cost reduction of 19.6%, peak load shaving efficiency of 23.2%, and forecast accuracy of 96.4%, in all mobility scenarios evaluated. The architecture also contributes to improving grid regulation response time by 28% and reducing EV queuing delay by 31%. Simulated by using MATLAB/Simulink, TensorFlow, and Ethereum-based blockchain, the architecture renders a scalable and secure framework for V2G coordination. It is noted that the findings are based on simulation, and co-simulation experiments, and the actual conditions of deployment like latency in communications, non-idealities of the hardware and regulatory factors are not factored into the analysis. Furthermore, the model facilitates real-time adaptation, strengthens grid resilience, and guides EV operation according to concurrent market conditions for energy.

Jagdish Yadav, Asha Durafe, Perla Anitha, Ch.Phani Kumar · 6 authors

This paper introduces an energy-efficient, Blockchain-Based, Dual-Agent, Reinforcement Learning (BDARL) model of resilient EV-grid integrative effect. All EV energy transactions and agent decisions are validated by the blockchain layer that is integrated through a lightweight proof-of-stake consensus mechanism, and this ensures the tamper-proof functioning and decentralized trust. The proposed framework is applying to a MATLAB/Simulink-Python TensorFlow-Hyperledger Fabric co-simulation environment where the performance analysis shows a 98.8 per cent Resilience Coordination Index (RCI), a 36.7 per cent Energy Efficiency Gain (EEG), a 31.5 per cent Load Stabilization Score (LSS), and a transaction latency of 25 ms. Compared to baseline DRL and non-blockchain schedulers, BDARL offers 7.9% improvement in terms of resilience, 8.4% in terms of energy efficiency, and 14 ms better convergence, so it provides a safe, sustainable, and smart paradigm of managing next-generation EV-grid synergy.

Rong Zhao, Jiaxiang Sun, Haoran Yin, Lehao Lin · 6 authors

The quest for carbon neutrality in the 21st century has led to the rise of decentralized low-carbon energy systems as a promising solution. Blockchain technology has played a pivotal role in catalyzing this transition, with various Web3 projects exploring decentralized operational models and carbon credit markets. However, there is a notable gap in harnessing blockchain’s potential to integrate electric vehicles (EVs) into low-carbon energy systems effectively. This article addresses this gap by proposing a decentralized low-carbon EV charging system that enables transactions between individual low-carbon energy producers and EV owners. Leveraging blockchain and smart contracts, the proposed system issues low-carbon tokens to certify and incentivize environmentally conscious charging behaviors, while enabling token circulation to further promote low-carbon participation. A blockchain-based double auction mechanism is designed to ensure fair and efficient energy allocation, achieving individual rationality, incentive compatibility, and social welfare maximization. By incentivizing user engagement and ensuring fair transactions, this model paves the way for sustainable EV integration within low-carbon energy systems.

Al Mothana Al Shareef, Serap Ulusam Seçkiner

The accelerating adoption of electric vehicles (EVs) is intensifying pressure on urban power grids, particularly during evening peak hours. Existing smart-charging frameworks remain constrained by centralized control, static pricing, and limited integration of predictive intelligence. This study presents SMARGE, a hybrid AI–Blockchain smart charging platform that combines load forecasting, dynamic pricing, and cryptocurrency-based incentives to enhance decentralized EV energy management in Gaziantep Province. An ensemble of forecasting models (SARIMA, LightGBM, N-BEATS, and TFT) predicts 2026 hourly electricity demand, while an adaptive inverse-sigmoid pricing mechanism generates real-time incentives and disincentives for EV charging behavior. A fuzzy logic-based behavioral model simulates both unmanaged and managed charging across three scenarios. Results show that managed charging reduces peak load by 22.43%, shifts 67.45% of energy demand to off-peak periods, and achieves 94.86% charging fulfillment under constrained grid conditions. The blockchain layer—implemented through a custom ERC-20 token (SMARGE) on the Ethereum Sepolia testnet—enables secure, transparent, and low-cost microtransactions with an average confirmation time of 0.63 s. These findings demonstrate that tightly coupling AI forecasting with tokenized blockchain incentives can improve grid stability, lower operational costs, and enhance user autonomy in a scalable and decentralized manner. While promising, the study is limited by assumptions of synthetic user behavior and ideal communication conditions; future work will validate the platform in real-world pilot deployments and across different urban regions.

Idowu Adetona Ayoade, Omowunmi Mary Longe

Electromobility requires transactive coordination that respects distribution-network limits while preserving auditability and privacy. This study presents a reproducible peer-to-peer energy trading system that integrates a network-constrained market with permissioned blockchain settlement. The market solves a convex welfare program with linearized power-flow limits and recovers nodal prices from dual variables to match bids and offers and determine clear quantities. Settlement uses Hyperledger Fabric via the Gateway API, including proposal endorsement, ordering, validation, and commit notifications. Meter evidence is hashed and, when necessary, stored with private data collections. A co-simulation harness links MATLAB/Simulink and MATPOWER for feeder dynamics and price formation with chaincode and client logic for settlement. Three case studies are evaluated: an urban microgrid, a suburban microgrid, and a mobile electric-vehicle swarm. An Ethereum testnet serves as a public-chain baseline. In the testbed, a tuned Fabric configuration sustained approximately 1.6 to 1.7 thousand transactions per second with 99th-percentile submit-to-commit latency near one second and full deadline compliance at a one-second clearing cadence. Energy delivery accuracy remained tight, Multi-Version Concurrency Control conflicts were low, and dynamic nodal prices reduced EV charging cost relative to a flat tariff while signaling congestion through predictable rent patterns. The contribution is a deployable blueprint that connects network economics to verifiable settlement, with an open repository, benchmarking artefacts, and practical targets for endorsement width, block size, and timeouts, and clear pathways to field trials, stochastic and robust clearing, zero-knowledge meter proofs, and city-scale deployment.

Caleb Price, Albert Schmidt

The adoption of emerging transport technologies-such as autonomous vehicles, electric charging infrastructure, and hyperloop systems-increasingly depends not only on regulatory approvals and corporate investment but also on the collective sense-making and knowledge validation that occurs within informal digital spaces. Online communities, including forums, social media groups, and specialized platforms, have become influential arenas where early adopters, enthusiasts, developers, and policymakers co-construct technical knowledge, debate safety standards, and shape public perceptions. However, the governance of these virtual spaces remains critically under-examined. While organizations traditionally rely on formal, top-down mechanisms for technology dissemination and risk management, online communities operate through decentralized, peer-driven dynamics that can accelerate or hinder adoption trajectories. This research investigates the governance structures-both emergent and designed-that enable or constrain knowledge exploitation within transport-focused online communities. Specifically, it examines how community mediators, platform design features, and participant norms influence the credibility, accessibility, and translation of technical knowledge into actionable insights for adoption decisions. Employing a qualitative case study approach, this study analyzes two contrasting transport technology communities: an enthusiast-driven forum for electric vehicle charging standards and a professionally oriented group discussing autonomous freight logistics. Findings are expected to contribute a governance framework that transportation organizations can leverage to engage constructively with online communities, transforming them from peripheral chatter into strategic assets for technology adoption. The research further offers practical recommendations for community managers and transport policymakers on fostering productive knowledge ecosystems that balance openness with accountability.

Seyit Cem Yılmaz, İrfan Kösesoy

The transition to electric vehicles (EVs) plays a critical role in reducing global carbon emissions. However, the end-of-life management of electric vehicle batteries (EVBs) presents significant sustainability and operational challenges. This study proposes a blockchain-based framework that enables full lifecycle tracking of EVBs, from production to disposal or reuse, while addressing issues of transparency, efficiency, and regulatory compliance. The framework incorporates a multi-criteria decision model to guide data-driven end-of-life routing—whether for second-life reuse or direct recycling—based on technical, environmental, and economic indicators. By integrating smart contracts with a hybrid web/mobile platform, the system ensures tamper-proof documentation, stakeholder accountability, and compliance with the EU battery passport regulation. A detailed cost analysis of deploying the framework on Ethereum is also presented. The proposed solution aims to enhance the sustainability of EVB management, reduce environmental impact, and promote circular economy practices within the EV industry.

Quang Huy Duong, Carlos F.A. Arranz, Mao Xu, Li Zhou · 5 authors

The rapid transition to electric vehicles has intensified challenges in electric vehicle battery (EVB) closed-loop supply chains (CLSC), particularly regarding material traceability, supply chain transparency, and recycling efficiency. While decentralised technologies, particularly Web3 and Metaverse, offer promising solutions, their integration into EVB CLSC remains fragmented and insufficiently examined. We introduce an Operational Decentralisation Framework enabling a systematic analysis of centralised operations and a critical evaluation of decentralised alternatives as transformational forces. By adopting a holistic perspective, the framework equips firms with strategic guidance for transitioning from centralised structures to decentralised ecosystems. We analyse 588 academic articles and 1,168 industry documents through two advanced text mining techniques – Dynamic Latent Dirichlet Allocation and Burst Detection. Web3 and metaverse can potentially reconfigure the design, manufacturing, end-of-life diagnostics, procurement, waste management, load balancing, capacity planning, inventory management and service operations of two key areas: (1) EVB CLSC operations and (2) EVB circular energy/grid operations. We also found that while blockchain and digital twins show established applications, Web3 and Metaverse applications face significant barriers, including scalability, technology complexity, and expertise gaps, despite their great potentials. Therefore, we propose four visionary models integrating Web3, Metaverse, and AI technologies that have the potential to overcome existing barriers and enable transformative decentralisation. Extending the TOE framework, the study contributes to the theory by developing an integrated framework for evaluating decentralised technology adoption in EVB CLSCs. For practitioners, we provide actionable insights and pathways for technology implementation across different CLSC stages and guidance for addressing key adoption barriers.

Sarim Zia, Saleha Qureshi, Muhammad Zulfiqar, Arfa Ijaz

The paper discusses the economic and infrastructural challenges preventing the adoption of Electric Vehicles (EVs) in Pakistan.It focuses on key factors such as affordability, consumer preferences, and the overall readiness of the market.Based on a segment-wise comparison, the analysis reveals that four-wheeler EVs carry an initial price premium of 20 to 64 percent over internal combustion engine (ICE) vehicles, with payback periods ranging from 11 to 25 years, placing them out of reach for most middle-income consumers.In contrast, electric two-and three-wheelers-comprising more than 90 percent of registered vehicles-offer a significantly more practical and affordable pathway for mass adoption.These vehicles exhibit minimal upfront cost differences, annual operational savings exceeding PKR 62,000, and short payback periods of just 4 to 6 months, making them highly feasible in the local context.The study adopts a mixed-methods approach using national price data, vehicle registration records, and international case studies from India, Kenya, and Norway.It evaluates financing innovations such as battery leasing, concessional green loans, and carbon-credit-linked microfinance, and outlines a consumer-focused policy framework that emphasizes financial inclusion, decentralized infrastructure development, and phased implementation strategies.By aligning global lessons with Pakistan's socioeconomic and infrastructural realities, the paper offers a scalable and inclusive roadmap for accelerating EV adoption through targeted, consumer-driven solutions.

Mohammad Nasrinasrabadi, Maryam A. Hejazi, Arefeh Jaberi, Hamed Hashemi‐Dezaki · 5 authors

Cryptocurrencies utilize blockchain technology to ensure transparency, decentralization, and immutability in financial transactions. It is expected that blockchain applications will significantly impact renewable energy markets. However, there is a lack of studies addressing the energy requirements of digital currencies. This research proposes optimizing a hybrid energy system consisting of distributed renewable and non-renewable energy sources, focusing on cryptocurrency mining. Although previous studies have not yet addressed energy system optimization considering cryptocurrency mining farms, the increasing prominence of such farms highlights the growing need for research in this area. The primary renewable sources in the proposed hybrid system include photovoltaic (PV) panels and wind turbines. We employ diesel generators as backup systems to compensate for the intermittent nature of solar and wind energy production. Besides meeting the demands of urban loads, cryptocurrency mining devices will be considered a major energy consumer. In this article, the optimal configuration of the energy system will be determined based on technical and economic indicators. Additionally, economic evaluations will be conducted to assess the income generated from cryptocurrency mining farms, and appropriate approaches will be identified from both technical and financial perspectives, focusing on return on investment (ROI).

Joy Nneamaka, Emmanuel Ojo, Chika Oliver Ujah

This critical review examines decentralised renewable energy (DRE) systems as game changers for sustainable energy access in Sub-Saharan Africa (SSA). Although rich in renewable resources, over 570 million people in rural communities lack electricity. Traditional energy models, shaped by colonial histories and marked by inefficiencies, have failed to meet the continent's diverse energy needs. DRE systems provide flexible, community-focused solutions that promote energy equity, foster economic growth, and enhance climate resilience. Using Critical Juncture Theory and the Rational Choice Model, this study examines factors influencing DRE adoption. Analyses show how DRE encourages energy democracy, local ownership, and aligns with Sustainable Development Goals, including SDG 7 (Clean Energy) and SDG 13 (Climate Action). However, these systems face obstacles like fragmented policies, insufficient funding, technical gaps, and governance issues. Case studies from Kenya, Nigeria, South Africa, and Ethiopia demonstrate implementation strategies, revealing supportive environments and challenges. This review synthesises policy discussions, highlights innovations like pay-as-you-go financing and digitalisation and outlines an integrated energy planning roadmap. Recommendations include regulatory reforms, blended financing models, capacity-building initiatives, and regional cooperation. This paper argues that decentralisation should be viewed not as a temporary measure but as a foundation for energy strategies. With visionary leadership, collaborative governance, and targeted investments, decentralised systems can transform Sub-Saharan Africa's energy future, prioritising equity, resilience, and sustainability. • Decentralized renewable energy (DRE) is paving the way for fair energy access across Sub-Saharan Africa. • ii. DRE systems are all about empowering communities, promoting energy democracy, and building resilience against climate change. • iii. Unfortunately, there are policy, financial, and technical hurdles that hold back the widespread adoption of DRE in the area. • iv. Various case studies showcase a range of DRE strategies and creative financing solutions. • v. For a successful shift to sustainable energy, integrated policy reforms and regional collaboration are essential.

Arvind Singh, Rahul Kumar, Mohit Bajaj, B. Hemanth Kumar · 6 authors

The increasing integration of renewable energy into smart grids introduces challenges of demand-supply imbalance, peak load stress, and cyber-physical vulnerabilities. Existing demand response (DR) frameworks often lack scalability, privacy-preserving data sharing, and secure transaction mechanisms, which limit user participation and grid resilience. To address these challenges, this study proposes GridSyncNet, a blockchain-enabled multi-agent deep reinforcement learning framework for real-time demand response. The framework integrates federated learning to enhance decentralized forecasting accuracy, blockchain consensus to ensure transparent and tamper-proof energy trading, and actor–critic based DRL agents to dynamically optimize load scheduling and energy dispatch across prosumers. Extensive simulations demonstrate that GridSyncNet outperforms benchmark models such as OD-CNN, D-FCAS, and USTCF. Specifically, it achieves a 98.2 % demand response efficiency, 30.6 % reduction in carbon emissions, and 97.4 % forecasting accuracy. Comparative analysis with multi-agent DRL (MADRL) approaches further confirms that GridSyncNet provides superior scalability, privacy, and security in decentralized environments. The proposed framework contributes to the design of secure, resilient, and sustainable energy management systems, offering practical insights for accelerating the transition toward net-zero energy communities. By combining blockchain, federated learning, and multi-agent reinforcement learning, GridSyncNet establishes a comprehensive pathway for trustworthy and adaptive smart grid operations. • A multi-agent deep reinforcement learning framework for adaptive DR in smart grids. • Decentralized peer-to-peer energy trading to transparent, secure energy trading. • Renewable energy utilization 89 %, CO 2 reduction 30.6 % & forecasting accuracy 92.4 %. • Federated learning & improved system resilience against cyber threats for DSM. • Optimize load balancing, peak shaving & cost efficiency for distributed grid agents.

Ali Farhani

The exponential growth of the electric vehicle (EV) industry, driven by decarbonization goals and energy transition policies, has intensified the need for sustainable and transparent supply chains. Lithium-ion batteries (LIBs), the cornerstone of EVs, pose complex life cycle challenges related to ethical sourcing, environmental degradation, traceability gaps, and inefficient end-of-life (EOL) management. Addressing these multifaceted issues requires an integrated technological approach. This study proposes a unified framework leveraging Digital Product Passports (DPPs) and blockchain technology to enable real-time, tamper-proof tracking of battery materials, components, and performance metrics throughout their lifecycle.The paper further integrates machine learning, with a focus on reinforcement learning (RL), to optimize logistics and predictive maintenance based on dynamic supply chain data. To ensure privacy and regulatory compliance in data sharing, the framework incorporates zkSNARKs—a zero-knowledge proof system that preserves confidentiality while maintaining verifiability across distributed networks. This triadic approach promotes lifecycle transparency, supports circular economy goals through efficient material reuse and recycling, and reduces the total cost of ownership (TCO) for EV stakeholders.The proposed solution addresses critical industry challenges—such as counterfeit components, low recycling efficiency, and supply chain opacity—while offering scalable applications in adjacent sectors like consumer electronics and renewable energy. The integration of DPPs, blockchain, and AI-based optimization establishes a resilient, interoperable infrastructure that enables enhanced sourcing, sustainability, and collaborative innovation in the evolving EV ecosystem.

Anargul Belgibayeva, Аrtem Аrtyukhov, Viera Kubičková, Miroslava Čukanová · 7 authors

Type of the article: Research Article AbstractDecentralization and renewable energy have gained significant global attention due to their potential to enhance energy security, promote sustainability, and democratize energy access. This study aims to provide a comprehensive bibliometric analysis of research trends, key contributors, and thematic developments in the field of the decentralization of energy sources and their renewability. The research methodology involves a bibliometric analysis based on data extracted from the Scopus database, covering publications from 1973 to 2025. The analysis reveals exponential growth in research output, particularly after 2014, with over 3,700 publications recorded in 2023 alone. Citation trends indicate that foundational studies on decentralized microgrids and distributed energy systems remain highly influential, while recent works on blockchain-based energy trading and AI-driven energy management are gaining prominence. The study identifies China (11.7% of total publications), the United States (6.5%), and India (5.7%) as the leading contributors, with significant research activity also observed in European countries. Additionally, journals such as Applied Energy, Renewable Energy, and Energies serve as the primary publication platforms in this domain. Thematic analysis highlights a shift from bioenergy and land-use studies toward smart grids, energy storage, artificial intelligence, and decentralized finance for energy markets. Furthermore, co-authorship and international collaboration have increased significantly, with 25% of papers involving multi-country research efforts. Keyword analysis indicates growing research interest in emerging topics such as hydrogen energy, demand-side management, and digitalization in decentralized energy systems. These findings underscore the increasing interdisciplinary nature of decentralized energy research, integrating technological, economic, and policy dimensions. AcknowledgmentThis study was prepared as part of the project IZURZ1_224119/1 (Swiss National Science Foundation).

K Sujit, Komala Chowdenahally Ramaswamy, Siva Ramkumar M, Jayant Giri · 5 authors

Research and development in the vehicle industry have emphasized the potential for advancing electric transportation that is highly efficient, secure, and sustainable. The electric vehicle (EV), powered by renewable energy sources and equipped with high-efficiency electric motors and controls, offers a practical, dependable, and ecologically friendly urban transportation system. EVs operate using a battery that is equipped onboard. Practical and dependable system operation relies heavily on managing and monitoring batteries. Nevertheless, the market for electric vehicles has experienced a decline in growth due to their limited lifespan and high price. To enhance the system's efficiency and lifespan, substantially improving the battery management aspect is imperative. In this research, the Internet of Things (IoT), machine learning (ML), and Blockchain (BC) technologies are used to develop an energy-efficient EV battery management system (BMS). The IoT sensors are attached to the electric vehicles to collect data such as the charging level, the distance that must be driven, and the position of the electric vehicles. This information was saved and processed by a database, then inputted to the LightGBM classifier to determine the cost of charging. After that, it was processed by the power scheduling approach (PSA) to determine the space and time of charging that is closest to a particular electric vehicle and the charging site. At last, this information is saved in blocks to prevent electric vehicles from being misrouted and ensure that pricing transactions between users and charging stations are conducted securely using BC. The results demonstrate that the research model provided enhanced EV-BMS with an accuracy rate of 96.52% and that it retains a communication overhead that is 12% lower compared to the other models.

Oluwaseun O. Tooki, Olawale Popoola

The current hike in electricity demand, deterioration of electrical grids, and climatic conditions have necessitated the push for technology to enhance energy efficiency, optimize energy usage, and minimize greenhouse gas emissions. The Transactive Energy System (TES) is a highly favoured technology designed to provide solutions for optimizing energy usage since it incorporates economic and dynamic control mechanisms to balance the amount of energy generated and supplied. Cost savings present a clear advantage of TES for consumers, translating to reduced bills, and the platform enables customers with Distributed Energy Resources to trade their excess energy, transforming consumers into prosumers. However, energy trading in TES comes with challenges such as maintaining a dynamic balance between supply and demand, as well as issues of privacy, trust, and resilience. Blockchain Technology (BT)-based TES can address these challenges due to its reliability, transaction transparency, and robust encryption methods. However, BT has its shortcomings that need to be addressed. Therefore, this research analyzes the opportunities, limitations, challenges, and complexities of implementing blockchain-based energy trading platforms within a decentralized TES. This review adopted a systematic approach, known as the Preferred Reporting Items for Systematic reviews and Meta-Analyses, to provide in-depth insights into the review purpose, methodology, findings, recommendations, and future research directions. It was observed from the review that certain challenges underscore the necessity for standardization in BT-based TES implementation. Moreover, it was discovered that decentralizing the TES energy trading infrastructure promotes energy democracy and that adopting fast computing techniques will facilitate digital and intelligent operations in TES. It was also found that the Directed Acyclic Graph-based distributed ledger may soon replace generic blockchain, as it can simultaneously process large micro-transactions in P2P networks. It is observed that implementing a peer rating mechanism in the energy trading network will enhance participants' commitment to their reputational standing in the market, while adapting analytical modelling for performance evaluation of this energy solution could equally be encouraged.

Rashmi Sharma, Himani Garg, Chitvan Agrawal

With the growing prevalence of electric vehicles (EVs), electrical grids face increasing strain due to heightened demand and potential overload during charging. This paper proposes a tokenized Ethereum-based framework that enables charging point operators (CPOs) and stations (CSs) to manage EV charging requests while ensuring grid stability through time flexibility (adjustable durations) and power flexibility (dynamic load modulation). Smart contracts automate peer-to-peer trading of charging parameters like energy needs and time limits, shifting loads to off-peak hours and adjusting prices based on real-time grid capacity. Simulations reveal that EV users who adopted time- and power-flexible charging experienced a 42% increase in participation compared to those using rigid, fixed-rate systems. Two scenarios were tested: 1) requests every 15 minutes on a 33 kW grid, where smart charging achieved a 71% efficiency improvement over uncontrolled charging and increased acceptance rates from 38% to 70%; and 2) consecutive requests to the same CSs, where acceptance rates rose from 23% to 43%, with smart charging reducing peak-to-valley load differences by 43–50%, flattening demand profiles. The system, developed on Ethereum using Remix IDE and MetaMask and tested on the Sepolia testnet, demonstrates higher electricity sales, improved grid stability, and enhanced flexibility in time, power, and cost. Tokenization incentivizes participation through rewards, allows users to bid for priority slots via proof-of-stake (PoS), and ties reputation metrics to token costs. Practical Byzantine Fault Tolerance (PBFT) ensures fault tolerance, while dynamic pricing and monetized flexibility create scalable EV-grid synergy, balancing supply-demand mismatches and attracting investors.

Qingwen Wei, Shuping Dang, Zhihui Ge, Xiangcheng Li · 5 authors

Direct acyclic graph (DAG)-based ledgers and distributed consensus algorithms have been proposed for use in the Internet of Things (IoT). The DAG-based ledgers have many advantages over single-chain blockchains, such as low resource consumption, low transaction fee, high transaction throughput, and short confirmation delay. However, the scalability of the DAG consensus has not been comprehensively verified on a large scale. This paper explores the scalability of DAG consensus within the low-to-high load regime (L2HR) using the tangle model, where L2HR characterizes the transition from a phase of low network load to another phase of high network load. In particular, we determine the average number of tips in the tangle in L2HR when adopting the uniform random tip selection (URTS) and rigorously prove that using the tangle model, the average number of tips at the end of L2HR converges to a constant. We also analyze the probability that a transaction in L2HR becomes an abandoned tip, the approximate average time required for the network load to transition from low load regime (LR) to high load regime (HR), and the average time required for a tip being approved for the first time in L2HR. All analytics are verified by numerical simulations.

Meselu Tegenie Mellaku, Yibeltal T. Wassie, Pernille Seljom, Muyiwa S. Adaramola

The economy of East Africa (EA) is striving for a structural transformation with a strong focus on expanding the manufacturing sector. However, challenges related to modern and reliable energy supply have hindered the sector's growth performance across the region. This systematic review explores the potential, opportunities, and challenges to integrating decentralized renewable energy solutions to bridge the energy supply-demand gap in the EA's manufacturing sector. It also provides up-to-date insights into the extent of integration of decentralized renewable energy technologies in the EA manufacturing sector. Relevant data and information for the review were retrieved from 46 references, including databases and web-based sources. The findings highlight that the EA region possesses abundant untapped solar, wind, and bioenergy resources that can close the sector's energy supply-demand gap. The review also reveals that renewable energy solutions are becoming increasingly techno-economically competitive with conventional energy sources for hybrid and stand-alone applications in the manufacturing sector. However, several challenges impede the integration of decentralized renewable energy technologies in the sector, including a lack of enabling regulatory frameworks, limited financing options, limited access to renewable technologies , and a lack of skilled labor. Nonetheless, international initiatives aimed at supporting developing countries in combating climate change can help overcome the region's financial and technological constraints by facilitating technology transfer, capacity building, and offering affordable financing options. Furthermore, the ambition of East African nations to expand their manufacturing sectors presents a stimulating opportunity to accelerate the integration of decentralized renewable energy technologies into the sector.