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.
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.
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.
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.
Haya R. Hasan, Khaled Salah, Ahmad Mayyas, Ahmad Musamih · 7 authors
Greenhouse gas emissions and carbon footprints have surged dramatically, with the transportation industry being a major contributor. While the UN aims to adopt zero-emission vehicles by 2040, the demand for electric vehicles (EVs) raises sustainability concerns about sourcing earth metals for batteries. Digital passports have emerged to track a product’s lifecycle, composition, certifications, origin, and recyclability. However, existing EV battery passport systems lack sufficient traceability, immutability, auditability, and are prone to manipulation due to their centralized nature. In this paper, we propose a decentralized blockchain-based digital passport for EV batteries using composable Non-Fungible Tokens (NFTs) to ensure traceable, tamper-proof records that promote transparency across the supply chain. Our solution enables standardized data sharing and interoperability while integrating off-chain storage for efficiency. We evaluate four cathode chemistries using ten sustainability Key Performance Indicators (KPIs) to compute an overall sustainability score per battery. We present a system architecture, smart contracts, and supporting algorithms are presented, with testing and validation in a simulated environment. A cost and security analysis confirms affordability and resilience against known attacks. Our approach offers practical value for sustainability assessments, regulatory audits, carbon footprint tracking, and circular economy initiatives by providing immutable, component-level provenance and real-time KPI updates. Limitations include simulation-based validation, lack of stakeholder testing, and scalability challenges in live deployments. While we address some of these, via Layer 2 scaling and modular smart contract design, further research is required to validate adoption in real-world EV supply chains. The smart contract code is made publicly available on GitHub.
Karim Moawad, Ahmad Musamih, Assia Chadly, Ahmad Mayyas · 8 authors
The urgency to combat climate change and reduce greenhouse gas emissions has led to increased global demand for Lithium-ion (Li-ion) batteries. Such batteries are widely used in portable electronics and electric vehicles. However, their adoption encounters challenges related to mining ethics, supply chain transparency, sustainability, and waste management. This paper proposes a blockchain-based solution that addresses these challenges in the Li-ion battery supply chain. Using the ERC-721 standard for Non-fungible tokens (NFTs), we tokenize all items/materials in the supply chain, ensuring data management, transparency, and ownership control. We integrate the Ethereum blockchain with the Interplanetary File System (IPFS) to handle NFT metadata and large-sized files, reducing storage costs and network congestion. We develop ten smart contracts (SCs) to facilitate various Li-ion supply chain functionalities, managing items/materials data and ownership. By leveraging NFTs, our solution promotes circular economy principles by facilitating secondary market trading, asset reuse, and sustainable recycling practices. We introduce a structured decision framework that empowers stakeholders to navigate operational and ethical challenges effectively. The effectiveness and practicality of the solution are demonstrated through system architecture, sequence diagrams, algorithms, and testing results. Furthermore, we assess our proposed solution’s affordability, efficiency, security, and generalizability across different industries.
Karim Moawad, Ammar Hummieda, Ahmad Musamih, Khaled Salah · 5 authors
Lithium-ion batteries (LIBs) have become a cornerstone of modern technology, where they serve as the power source for a wide range of applications, including electric vehicles and renewable energy storage systems. However, rapid production growth has introduced challenges regarding end-of-life management, particularly with waste disposal, resource recovery, and environmental sustainability. Inefficient recycling often leads to valuable materials like cobalt, lithium, and nickel being discarded in landfills, which exacerbates resource scarcity and poses environmental and health risks. To address these issues, there is a critical need for more efficient, transparent, and accountable systems for the collection, recovery, and recycling of LIBs. In this paper,A blockchain and Non-Fungible Token (NFT)-based solution is proposed to enable circular recycling and material recovery. This system improves transparency, traceability, and accountability throughout the battery lifecycle. The smart contracts (SCs) source code is made publicly available on GitHub.
Abstract The classical pathway of mass production followed a linear model with trashed products and wasted remaining materials at the final stage of their life cycle. Smart approaches of manufacturing and product life cycle management aim for Circular Economy (CE) models to implement sustainable business models to overcome imbalances between resource supply and demand of goods. Non-Fungible Token (NFT) solutions together with smart contracts seem to have the potential to realise such new sustainable business models in the context of CE. The study demonstrates how NFT technology can become an integral part of smart product life cycle management for batteries of e-cars. The research highlights how circular business models can be developed and implemented in the e-car sector around the life cycle management of batteries as well as how NFT technology can contribute to sustainable conceptualisation for battery recycling.
Widespread use of lead acid batteries (LABs) is resulting in the generation of million tons of battery waste, globally. LAB waste contains critical and hazardous materials, which have detrimental effects on the environment and human health. In recent times, recycling of the LABs has become efficient but the collection of batteries in developing countries is not efficient, which led to the non-professional treatment and recycling of these batteries in the informal sector. This paper proposes a blockchain-enabled architecture for LAB circularity, which ensures authentic, traceable and transparent system for collection and treatment of batteries. The stakeholders-battery manufacturers, distributors, retailers, users, and validators (governments, domain experts, third party experts, etc.)-are integrated in the circular loop through a blockchain network. A mobile application user interface is provided to all the stakeholders for the ease of adoption. The batteries manufactured and supplied in a geographical region as well as the recycled materials at the battery end-of-life are traced authentically. This architecture is expected to be useful for the battery manufacturers to improve their extended producer responsibility and support responsible consumption and production.
As electric vehicles (EV) become more prevalent and advances in electric vehicle electronics continue, vehicle-to-grid (V2G) techniques and large-scale scheduling strategies are increasingly important to promote renewable energy utilization and enhance the stability of the power grid. This study proposes a hierarchical multistakeholder V2G coordination strategy based on safe multi-agent constrained deep reinforcement learning (MCDRL) and the Proof-of-Stake algorithm to optimize benefits for all stakeholders, including the distribution system operator (DSO), electric vehicle aggregators (EVAs) and EV users. For DSO, the strategy addresses load fluctuations and the integration of renewable energy. For EVAs, energy constraints and charging costs are considered. The three critical parameters of battery conditioning, state of charge (SOC), state of power (SOP), and state of health (SOH), are crucial to the participation of EVs in V2G. Hierarchical multi-stakeholder V2G coordination significantly enhances the integration of renewable energy, mitigates load fluctuations, meets the energy demands of the EVAs, and reduces charging costs and battery degradation simultaneously.
Syed Muhammad Danish, Aroosa Hameed, Ali Ranjha, Gautam Srivastava · 5 authors
The increased charging demand resulting from the rapid development of electric vehicles (EVs) poses various challenges to the stable operation of the distribution network and smart grid. Due to the stochastic EV charging behaviour, the high charging demand at the charging stations (CSs) elevates the load curve which may lead to a spatially imbalanced load demand. As such, forecasting the highly stochastic EV charging load considering an individual EV's unique charging behaviour can result in maintaining the safe operation of the grid and distribution network. Therefore, in this work, we propose Block-FeDL, a blockchain-based Federated Learning (FL) approach for EV charging load forecasting considering the private and sensitive charging information of each EV user. Thereafter, we use a Bidirectional Long Short Term Memory (BiLSTM) model within the FeDL for predicting the EV charging load patterns at each client. Moreover, instead of using a centralized server for global model aggregation, we use blockchain technology, where the model aggregation is performed in a decentralized manner and the local model parameters shared by the FL clients can be validated and securely recorded. Lastly, the results show that the Block-FeDL outperform the second-best baseline method by 95%, 96% and 77% in terms of mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) for forecasting the EV charging load.
Kuo-Yang Wu, Tzu-Ching Tai, Bo-Hong Li, Cheng‐Chien Kuo
Under net-zero objectives, the development of electric vehicle (EV) charging infrastructure on a densely populated island can be achieved by repurposing existing facilities, such as rooftops of wholesale stores and parking areas, into charging stations to accelerate transport electrification. For facility owners, this transformation could enable the showcasing of carbon reduction efforts through the self-use of renewable energy while simultaneously gaining charging revenue. In this paper, we propose a dynamic energy management system (EMS) for a solar-and-energy storage-integrated charging station, taking into consideration EV charging demand, solar power generation, status of energy storage system (ESS), contract capacity, and the electricity price of EV charging in real-time to optimize economic efficiency, based on a real-world situation in Taiwan. This study confirms the benefits of ESS in contracted capacity management, peak shaving, valley filling, and price arbitrage. The result shows that the incorporation of dynamic EMS with solar-and-energy storage-integrated charging stations effectively reduces electricity costs and the required electricity contract capacity. Moreover, it leads to an augmentation in the overall operational profitability of the charging station. This increase contains not only the revenue generated from electricity sales at the charging station but also the additional income from surplus solar energy sales. From a comprehensive cost–benefit perspective, introducing this solar-and-energy storage-integrated EMS can increase facility owners’ net income by 1.25 times compared to merely installing charging infrastructure.
Imran Hussain, Hafiz Ashiq Hussain, Nasim Ullah, Stanislav Mišák
Conventional centralized optimization and management approaches may not work well in an emerging and distributed energy system with a high penetration of electric vehicles and green energy sources. The usage of blockchain technology is growing as a strong competitor as it can provide this kind of market with a transparent, secure, and efficient transactional platform. Nevertheless, most energy systems usually depend on complex mathematical optimization, which is poorly incorporated into blockchain applications. Moreover, time-sensitive message dissemination requirements, resource-intensiveness, high computational load, and communication overhead of the traditional blockchain consensus mechanisms make it difficult to connect with real-time vehicular networks. Here, we employ Proof of Intelligence (PoI), a novel prosumer-centric blockchain consensus mechanism to develop a comprehensive model of trust based on commitments of supply and demand through the application of peer-to-peer energy exchange with effective and dynamic integration of renewable sources and electric vehicles both in the day ahead and real-time energy trading platforms. Additionally, the PoI smart contract is developed to seamlessly incorporate mathematical optimization with an increased level of security, scalability, throughput, and low confirmation latency of transactions achieved through the reduced effort involved in finding and confirming the optimal solution in comparison with conventional blockchain consensus mechanisms.
Alia Al Sadawi, Eiman ElGhanam, Mohamed S. Hassan, Ahmed Osman
With the increasing investments in on-the-move electric vehicle (EV) charging solutions and wireless charging lanes (WCLs), coordination of the energy requirements of mobile EVs becomes essential to ensure load balancing while maximizing demand coverage. This necessitates the development of online and mobility-aware algorithms for assigning EV-to-charging lanes. In this work, a decentralized, blockchain-based EV assignment and energy allocation system is presented. The objective of system is to coordinate the charging requirements of mobile EVs among the available WCLs within a network of EV chargers in an Internet of Electric Vehicles (IoEVs). This blockchain-based system offers higher security, transparency and immutability over traditional rule-based coordination schemes. It also offers an integrated end-to-end framework that handles user registration, authentication, lane activation and energy reporting. This is in addition to its main functionality of establishing a real-time and load-balanced EV-to-WCL assignment process that addresses the EV energy requirements within constraints of traveling distance and remaining EV energy. The proposed system is tested on the Ethereum blockchain and its security and transparency are both validated accordingly.
Alessandro Neri, Maria Angela Butturi, Henrique L. Sauer, Francesco Lolli · 6 authors
The growing demand for electric vehicles necessitates an efficient and sustainable life-cycle management of lithium-ion batteries. This work examines existent literature on digital battery passports, crucial for high-quality data for decision-making purposes, and distributed ledger technologies as transparent and efficient enablers. An hybrid BWM-TOPSIS approach is employed to rank various platforms for digital passport implementation in an automotive company. The analysis identifies Hedera as the most suitable ledger, followed by IOTA and EOS. Future research directions include empirical validation of the findings and exploring collaborative decision-making models to enhance the robustness of the selection process.
Yubao Zhang, Xin Chen, Yi Gu, Zhicheng Li · 5 authors
With the growing prevalence of electric vehicles (EVs) and advancements in EV electronics, vehicle-to-grid (V2G) techniques and large-scale scheduling strategies have emerged to promote renewable energy utilization and power grid stability. This study proposes a multi-stakeholder hierarchical V2G coordination based on deep reinforcement learning (DRL) and the Proof of Stake algorithm. Furthermore, the multi-stakeholders include the power grid, EV aggregators (EVAs), and users, and the proposed strategy can achieve multi-stakeholder benefits. On the grid side, load fluctuations and renewable energy consumption are considered, while on the EVA side, energy constraints and charging costs are considered. The three critical battery conditioning parameters of battery SOX are considered on the user side, including state of charge, state of power, and state of health. Compared with four typical baselines, the multi-stakeholder hierarchical coordination strategy can enhance renewable energy consumption, mitigate load fluctuations, meet the energy demands of EVA, and reduce charging costs and battery degradation under realistic operating conditions.
Syed Muhammad Ahsan, Hassan Abbas Khan, Sarmad Sohaib, Anas Hashmi
The operation of smart buildings (with solar, storage and suitable power routing infrastructure) can be optimized with the addition of parking stations for electric vehicles (EVs) with vehicle-to-everything (V2X) operations including vehicle-to-vehicle (V2V), vehicle-to-building (V2B) and vehicle-to-grid (V2G) operations. In this paper, a multi-objective optimization framework is proposed for the smart charging and discharging of EVs along with the maximization of revenue and savings of smart building (prosumers with solar power, a battery storage system and a parking station) and non-primary/ordinary buildings (consumers of electricity without solar power, a battery storage system and parking station). A mixed-integer linear program is developed to maximize the profits of smart buildings that have bilateral contracts with non-primary buildings. The optimized charging and discharging (V2X) of EVs at affordable rates utilizing solar power and a battery storage system in the smart building helps to manage the EV load during on-peak hours and prevent utility congestion. The results indicate that in addition to the 4–9% daily electricity cost reductions for non-primary buildings, a smart building can achieve up to 60% of the daily profits. Further, EVs can save 50–69% in charging costs while performing V2X operations.
Irvylle Cavalcante, Jamilson Júnior, Jônatas Augusto Manzolli, L.A.L. de Almeida · 7 authors
In the present day, it is crucial for individuals and companies to reduce their carbon footprints in a society more self-conscious about climate change and other environmental issues. In this sense, public and private institutions are investing in photovoltaic (PV) systems to produce clean energy for self-consumption. Nevertheless, an essential part of this energy is wasted due to lower consumption during non-business periods. This work proposes a novel framework that uses solar-generated energy surplus to charge external electric vehicles (EVs), creating new business opportunities. Furthermore, this paper introduces a novel marketplace platform based on blockchain technology to allow energy trading between institutions and EV owners. Since the energy provided to charge the EV comes from distributed PV generation, the energy’s selling price can be more attractive than the one offered by the retailers—meaning economic gains for the institutions and savings for the users. A case study was carried out to evaluate the feasibility of the proposed solution and its economic advantages. Given the assumptions considered in the study, 3213 EVs could be fully charged by one institution in one year, resulting in over EUR 45,000 in yearly profits. Further, the economic analysis depicts a payback of approximately two years, a net present value of EUR 33,485, and an internal rate of return of 61%. These results indicate that implementing the proposed framework could enable synergy between institutions and EV owners, providing clean and affordable energy to charge vehicles.
Wenshuai Ma, Junjie Hu, Yao Li, Zhuoming Fu · 6 authors
Abstract With global concerns about carbon emissions, the proportion of renewable energy generation worldwide is increasing, and the demand for flexible resources in power systems is growing. In recent years, as a clean means of transportation, the number of electric vehicles has increased, and the optimal scheduling of electric vehicles has become a research hotspot. The rise of artificial intelligence, blockchain, and other innovative technologies has enriched research on optimal scheduling of electric vehicles. To reveal the latest developments in electric vehicle optimal scheduling studies, this paper summarises the application of state‐of‐the‐art technologies, including deep learning, deep reinforcement learning, and blockchain technology in the optimal scheduling of electric vehicles. Moreover, the advantages and disadvantages of various technical applications are highlighted. Finally, considering the shortcomings and developmental status of applications of the above three technologies, some suggestions for future research directions are proposed.
Nowadays, EVs are rapidly increasing in popularity, and are accepted as the vehicles of the future all over the world. The most important components are their battery and charging systems. The energy capacity of EVs’ batteries has a significant potential to supply different energy requirements. Therefore, EVs must be designed in accordance with bidirectional power flow, and Electric Vehicle Supply Equipment (EVSE) should be upgraded as Electric Vehicle Power Exchange Equipment (EVPE). This power exchange infrastructure can be called Vehicle-to-Anything (V2X). V2X will also be the key solution for energy grids of the future that will turn into a much larger and smarter system with the help of emerging digitalization technologies, such as Artificial Intelligence (AI), Distributed Ledger Technology (DLT), and the Internet of Things (IoT). This study introduces a multi-layer Cyber–Physical Power Systems (CPPS) framework to explore the potential of V2X technologies allowing bidirectional charging. In addition, the impact of e-mobility is discussed from the V2X perspective. V2X has the potential to provide more practical use of electric vehicles and to bring advantages to the user in terms of both economy and comfort, thus accelerating the transformation of e-mobility and making it easier to accept.
The gradual transition from a traditional transportation system to an intelligent transportation system (ITS) has paved the way to preserve green environments in metro cities. Moreover, electric vehicles (EVs) seem to be beneficial choices for traveling purposes due to their low charging costs, low energy consumption, and reduced greenhouse gas emission. However, a single failure in an EV’s intrinsic components can worsen travel experiences due to poor charging infrastructure. As a result, we propose a deep learning and blockchain-based EV fault detection framework to identify various types of faults, such as air tire pressure, temperature, and battery faults in vehicles. Furthermore, we employed a 5G wireless network with an interplanetary file system (IPFS) protocol to execute the fault detection data transactions with high scalability and reliability for EVs. Initially, we utilized a convolutional neural network (CNN) and a long-short term memory (LSTM) model to deal with air tire pressure fault, anomaly detection for temperature fault, and battery fault detection for EVs to predict the presence of faulty data, which ensure safer journeys for users. Furthermore, the incorporated IPFS and blockchain network ensure highly secure, cost-efficient, and reliable EV fault detection. Finally, the performance evaluation for EV fault detection has been simulated, considering several performance metrics, such as accuracy, loss, and the state-of-health (SoH) prediction curve for various types of identified faults. The simulation results of EV fault detection have been estimated at an accuracy of 70% for air tire pressure fault, anomaly detection of the temperature fault, and battery fault detection, with R2 scores of 0.874 and 0.9375.
Francesco Lo Franco, Vincenzo Cirimele, Mattia Ricco, Vítor Monteiro · 6 authors
Electric car-sharing (ECS) is an increasingly popular service in many European cities. The management of an ECS fleet is more complex than its thermal engine counterpart due to the longer ”refueling“ time and the limited autonomy of the vehicles. To ensure adequate autonomy, the ECS provider needs high-capacity charging hubs located in urban areas where available peak power is often limited by the system power rating. Lastly, electric vehicle (EV) charging is typically entrusted to operators who retrieve discharged EVs in the city and connect them to the charging hub. The timing of the whole charging process may strongly differ among the vehicles due to their different states of charge on arrival at the hub. This makes it difficult to plan the charging events and leads to non-optimal exploitation of charging points. This paper provides a smart charging (SC) method that aims to support the ECS operators’ activity by optimizing the charging points’ utilization. The proposed SC promotes charging duration management by differently allocating powers among vehicles as a function of their state of charge and the desired end-of-charge time. The proposed method has been evaluated by considering a real case study. The results showed the ability to decrease charging points downtime by 71.5% on average with better exploitation of the available contracted power and an increase of 18.8% in the average number of EVs processed per day.