The remarkable success of deep learning (DL) in predicting battery health has prompted interest in its application in recent years. While state-of-the-art DL models have achieved high accuracy in battery health prediction, they have not been widely adopted in industrial workflows, primarily due to their lack of interpretability and security. To address this issue, we propose a blockchain-based interpretable prediction algorithm for battery health prediction in electric vehicles (EVs) within the Internet of Vehicles (IoV). Specifically, the proposed method includes a platform architecture for a blockchain-based DL system, ensuring secure storage of user data during the prediction process. Notably, we develop a novel battery life prediction algorithm called BLP-Transformer, which leverages short-term relationships between degraded data and explains the impact of feature extraction on predicted results through the contribution of aggregated features based on a feature focusing mechanism. Experimental results demonstrate that the system is feasible for security and can provide accurate battery life prediction. In addition, the comparison study further highlights the superiority of the proposed algorithm in terms of robustness, prediction accuracy, and model interpretability.
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 4â9% daily electricity cost reductions for non-primary buildings, a smart building can achieve up to 60% of the daily profits. Further, EVs can save 50â69% in charging costs while performing V2X operations.
Irvylle Cavalcante, Jamilson JĂșnior, JĂŽnatas 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 energyâs selling price can be more attractive than the one offered by the retailersâmeaning 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.
Electric vehicles (EVs) and blockchain are the two fast-growing technologies in the current scenario. Both can solve several existing problems around us and can contribute significantly to the future. Features offered by blockchain are now taking Electric Vehicles to next level of security and safety. As EVs are a prominent option for solving global warming issues and reducing carbon footprinting to encourage sustainable development. The current challenges faced by EVs are the capacity to hold a charge, the availability of charging stations, and the time required for charging. The availability of charging stations and the time required for charging EVs are two major issues that are holding EVs back. In this paper, we are proposing a blockchain-based framework to address the issue of scheduling at charging stations. Blockchain ensures trust, privacy, secure transactions, and unbiased allocation of charging ports for charging. We also proposed a new charging scheduler algorithm inspired by the process scheduling algorithms used in scheduling CPUs. The quantitative analysis result shows that the proposed framework is efficient and can significantly reduce the waiting time of electric vehicles at charging stations.
Junwei Han, Pu Guo, Ye Yang, Wen Wang · 8 authors
Efficient aggregation of distributed charge-discharge loads is a key method to realize the value utilization of energy storage resources of electric vehicle power batteries. In order to adapt to the high-dimensional and complex trend brought by large-scale electric vehicles connected to the grid, the intelligent contract technology is introduced in this paper. Based on the whole-process modeling of charging and discharging behavior of electric vehicles, the boundary of electric vehicles convergent operation is established, and then the hierarchical scheduling optimization method of electric vehicles load is proposed. The effectiveness of the model was verified by analyzing three typical scenarios of disordered charging, ordered charging and V2G ordered charging and discharging.
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 stateâofâtheâart 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.
Muhammad Awais, Ayaz Ahmad, Sadiq Ahmad, Abdullah Shoukat
Electric vehicles (EVs) are getting more importance than traditional vehicles in today's era as they may lead to significant changes in society, so more research needs to be done on electric cars for their expansion. In most EV architecture, the charging station plays an important role. Due to the growth of the extended travel range of EVs, these will travel over different networks that different utility providers might serve. To mitigate this issue, we propose a solution based on the uniform Token generation concept. The concept of blockchain technology will be utilized for Token generation, as the blockchain technique is transparent and traceable; it does not require any third party for its operation. The proposed model will get information about the battery level of each EV through a communication network and based on the battery level. The corresponding best charging station will be assigned. When the EV starts to charge from the given charging station, a certain amount of tokens is transferred from the customer's wallet to that charging station's wallet.
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 EVsâ 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 CyberâPhysical 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 EVâs 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, VĂtor 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 ârefuelingâ 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 operatorsâ activity by optimizing the charging pointsâ 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.
Syed Muhammad Ahsan, Hassan Abbas Khan, and Naveed-ul-Hassan
Smart buildings are being built as a synergetic deployment of electric vehicles (EVs) and renewable energy sources. Smart charging of EVs and vehicle-to-everything (V2X) technologies are seen as way forward in this context in terms of achieving economic, technological, and environmental advantages. This paper proposes a framework for multi-objective techno-economic optimization for profit maximization of multiple inter-connected buildings (with bilateral contracts) and scheduling of EVs. The optimization problem is modeled as mixed integer linear programming problem, which is solved using CPLEX solver in ILOG optimization studio. The primary building owns the photovoltaic system coupled with storage and charging infrastructure for the fleet of EVs. The optimized charging of EVs at affordable rates using local resources at primary building assists the grid in managing the EVsâ load during peak hours. Results indicate that the primary building gains up to 62% daily profit after factoring in solar, storage, and charging station deployment costs. Additionally, secondary buildings (without solar, storage and charging facilities) earn up to 20% cost savings depending upon the nature of bilateral contracts with primary building. The results further suggest that fleet of EVs gains 35%â65% savings in charging cost based on lower charging rates and V2X operations with primary and secondary buildings.
H. Martins, H. Farias, G. Fenner, Camilo Albeto Sepulveda Rangel · 6 authors
This paper presents a comprehensive model for electric vehicle chargers (EVC) focused on scheduling loads in electric vehicle charging stations (EVCS). It also applies practical information from data gathered by Open Charge Point Protocol (OCPP) for validation of results. The scheduling strategy applies an evolutionary particle swarm optimization (EPSO) metaheuristic to set the hours for charge of the EV. The battery charging model combines the Kinetic Battery Model (KiBaM) and a Voltage Model (VM). A practical validation for the battery model accuracy is made with real data gathered by OCPP. The results showed a good operation for the framework in the EVCS in terms of economic cost and grid impact.. The results also showed a good performance for the battery model. Finally, the OCPP confirmed the results of the model with low errors in terms of performance.
As a representative of clean energy, photovoltaic is expected to become a major supplier of electricity in the future. The combination of electric vehicle (EV) battery and charging station provides a feasible way to promote the effective consumption of photovoltaic. However, the efficiency of mobile power supply is limited by information asymmetry and security problems, and it is urgent to optimize the distribution process. Firstly, the article introduces the energy blockchain to improve the security level of electricity transaction, and designs the photovoltaic-energy storage-charging supply chain. Secondly, based on the selected road network and the actual situation of EV mobile power emergency distribution, the distribution logistics network with 50 distribution points is built. Thirdly, taking the delivery time and comprehensive cost as objective functions, the mathematical model of emergency distribution route optimization for EV mobile power supply is established, and the adaptive NSGA-II algorithm is adopted for example analysis. Finally, the parameter variation of NSGA-II and comparison with two algorithms of GA and MOPSO are carried out to validate the feasibility and applicability of proposed method. The purpose of the research is to quickly and effectively select the optimal distribution route of mobile power supply from many roads by maximizing customer demands and reducing costs, so as to promote the photovoltaic consumption.
The main roles of an advanced Battery Management System (BMS) are to dynamically monitor the battery packs and ensure the efficiency and reliability of the Battery Energy Storage System (BESS). Estimating the State of Charge (SoC), State of Health (SoH), State of Power (SoP), State of Energy (SoE), State of Temperature (SoT), and State of Safety (SoS) depends on collecting, aggregating, and analyzing real-time data of the BESS. Based on the applications of BESSs and their sizes, there are several restrictions in accessing and sharing data between battery manufacturers, power grid operators, and electricity consumers, while building necessary communication infrastructure is required. To resolve such issues, a conceptual and technological Blockchain-based system is developed in this paper to securely share the real-time data collected from BESSs for monitoring and control purposes, i.e., state estimation. The proposed system benefits from integrating the Internet of Things (IoT) devices in a decentralized structure and connectivity of such IoT nodes, data privacy, and transparency and auditability. The proposed Blockchain-based system is capable of accurately estimating SoC, SoH, SoP, SoE, SoT, and SoS of BESSs in small-and grid-scales.
Summary In today's world, electric vehicles (EVs) play a significant role in transportation automation systems, and these vehicles are the replacement for fossil fuel usage vehicles. An EV generally depends on electric charges where the appropriate usage, charging, and energy management are the key constraints in EVs. To overcome these issues, proper energy management is essential in current EV management. In this paper, a novel blockchainâbased secure energy management has been proposed to provide efficient energy management in transportation automation. Primarily, the EVs have connected to the Internet of Things (IoT) sensors for collecting information like charging level, distance to be traveled, and location of the EVs. This information has been processed by an information center and transferred to the random forest classifier to identify the price of charging. Afterward, it can be transferred to the power scheduling algorithm for finding the nearest charging location (shortest distance) and time of charging to a specific EV. Finally, this information is stored in blocks to mitigate the misleading of EVs and to offer secure price transactions between the users and charging stations. The results manifest that the proposed scheme provides improved EV management with 94.5% of accuracy and maintains 10% lesser communication overhead as compared with existing stateâofâtheâart techniques.
S. Ramesh, J. Seetha, G. Ramkumar, Satyajeet Sahoo · 9 authors
The functioning of a solar hybrid power system is investigated in this research using a unique fuzzy control method. Turbines, solar photovoltaics, diesel engines, fuel cells, aqua-electrolyzes, and other autonomous generation products are used in the hybrid renewable energy system. Further energy storage components of the system include the batteries, turbine, and ultracapacitor. This research incorporates a supercapacitor hybrid energy storage system (HESS) into a solar hybrid power generating system, allowing the consumption and energy storage space and power output to be significantly increased. This studyâs approach incorporates a decentralized power generation system with a HESS while increasing electrical output in phases utilizing a dynamic reactive power compensation scheme and a conductance-fuzzy dual-mode control strategy. Due to a nonlinear behavior of photovoltaic (PV) devicesâ power output, maximum power point tracking (MPPT) methods must be used to create the greatest power. Infrequently developing atmospheric circumstances, traditional MPPT algorithms do not work adequately. Modeling is used to determine the microgridâs power output to the photovoltaic hybrid power generating organization, as well as the optimization method for each device in the network. The dynamic power factor correction scheme and also the conductance-fuzzy dual-mode control approach are primarily used in this study to optimize the solar hybrid renewable energy system.
In this paper, the idea of applying phase change materials (PCMs) as a method of energy use reduction in bitcoin mining will be investigated. The possible applications discussed include the implementation of PCMs in the mining equipment itself, the integration of PCMs into the mining warehouse envelope, and the use of PCMs in air conditioning systems. These applications aim to decrease energy requirements for warehouse climate control systems by decreasing their cooling load, and by increasing the efficiency of the miners by keeping them at a cooler operating temperature. This reduction in energy usage will help reduce bitcoinâs carbon footprint produced by fossil fuels electricity production.
Abstract The performance of lithiumâmetal batteries is severely hampered by uncontrollable dendrite growth and volume expansion on the metal anodes. Inspired by the âblockchainâ concept in data mining, here we utilize a conductive polymerâfilled metalâorganic framework (MOF) as the lithium host, in which polypyrrole (PPy) serves as the âchainâ to interlink Li âblocksâ stored in the MOF pores. While the Nârich PPy guides fast Li + infiltration/extrusion and serves as the nucleation sites for isotropic Li growth, the MOF pores compartmentalize bulk Li deposition for 3D matrix Li storage, leading to lowâbarrier and dendriteâfree Li plating/stripping with superb Coulombic efficiency. The asâfabricated lithiumâmetal anodes operate over 700 cycles at 5 mA cm â2 in symmetric cells, and 800 cycles at 1 C in full cells with a perâcycle capacity loss of only 0.017 %. This work might open a new chapter for Liâmetal anode construction by introducing the concept of âblockchainâ management of Li plating/stripping.
Abstract In the time of Industry 4.0, with the requirements for reducing CO2 in the atmosphere and realizing a circular economy in all aspects of life, the implementation of electric vehicles and IoT control systems was observed. The advantages and positive effects of implementing electric vehicles in the mining industry are discussed in the article. To achieve a circular economy and meet the criteria for sustainable development, a conceptual model is proposed for tracking the quantities of useful raw materials used in power batteries, as one of the main components of electric vehicles. To implement reliable and unambiguous communication among the various participants, blockchain technology is used.