# Blockchain Technology Applied in IoV Demand Response Management: A Systematic Literature Review
## 3 Current Perspectives and Research Efforts on Blockchain-Enabled IoV
### Electric Vehicles Charging Scheduling Using Blockchain  
To study the consequences of increased EV load and charging mechanisms, the accurate modeling of EV charging profiles is necessary [75]. The size and topology of the energy grid, the number and size of EVs, the mode, time and location of charging as well as the daily driving distance influence the above-mentioned charging profiles. As a consequence, the charging profiles of the drivers are increasingly coupled with charging schedules.  
[PERSON] et al. [76] proposed a centralized control architecture to handle the modeling and management of EV charging by reducing peak demand and increasing the number of EVs charged concurrently. A common finding in numerous studies on EV battery chargers is that EV battery workloads are commonly thought of as a static, with the actual system behavior of the batteries throughout the charging process being overlooked. In order to tackle the latter, [PERSON] [77] emphasized that a bi-directional energy flow is conceivable. EV batteries may be utilized in the grid in the manner of any other energy storage device, with the additional perk of mobility. The owners of EVs would be able to participate in energy market trading, recharge batteries when energy is cheap and discharge if the smart grid rewards them for their excess energy. This type of energy exchange and negotiation can allow the network to regulate demand (e.g., peak shaving) or offer additional storage in the case of excess renewable energy generation. Consumers will be able to pick where, when and which EV to charge, reducing the strain of the grid.  
Using the adaptable EV charging flow, [PERSON] [42] developed a model for smart charging of EVs, in which a software agent selects whether it should load a unit, in what sequence or whether it is better to sell energy to the market. The concept is based on blockchain technology, which makes interactions reliable and traceable, with the goal of decreasing or eliminating intermediaries in energy trade and lowering anxiety.  
Figure 5: Demand response program taxonomy.  
Furthermore, given the growing popularity of EVs and their unpredictable dynamic nature in terms of charging and route patterns, EV load might be difficult for energy distribution operators and utilities to manage [41]. Thus, [PERSON] proposed SMERCOIN, which is a real-time solution that integrates the concepts of priority and cryptocurrencies to encourage EV owners to charge on a renewable energy-friendly timeframe. Customers with a longer history of utilizing renewable energy are given priority in the system, which uses a rating system. By including cryptocurrency, the system may encourage users using both monetary and non-monetary techniques in a flat-rate manner. Similarly, [PERSON] [54] developed a distributed, privacy-preserved and incentive-compatible DR mechanism for IoV. In more detail, the authors suggested a low-cost consortium blockchain-enabled secure energy trade platform EVs, as well as an incentive system based on contract theory in order to encourage more EVs to join the DR program.  
Furthermore, a dependable solution is required to meet the future energy demands of urban and industrial customers, while also supporting the charging and discharging needs of EVs. Therefore, some research articles have been presented, studying the energy trading in the IoV for demand response management. For example, [PERSON] [39] proposed a blockchain-based secure energy trading scheme for demand response management between EVs and the service providers, while a double auction mechanism is proposed between EVs and SPs to maximizes social welfare with privacy preservation. Similar examples can also be found in [12; 46], in which the authors analyzed the energy in IoV-assisted smart cities, employing blockchain capabilities in order to select the most suitable charging station without sharing private information, and to balance the spatio-temporal dynamic demands of computing resource.