Search

The growing adoption of electric vehicles (EVs) and the expansion of their charging infrastructure offer substantial potential to advance sustainable mobility in smart cities. Nevertheless, the highly interconnected design of EV charging stations (EVCSs) renders this infrastructure vulnerable to multiple cybersecurity threats, such as false data injection, man-in-the-middle attacks, malware infections, and denial-of-service attacks. Moreover, financial threats—including fraudulent billing and credit card data theft—pose significant risks to EV users. In this study, we introduce a blockchain network built on Hyperledger Fabric tailored for EVCSs to address these vulnerabilities. The proposed network employs smart contracts to securely and transparently handle essential operations, including authentication, charging session oversight, and payment confirmation. By identifying and preventing unauthorized data alterations or access attempts, the blockchain framework enhances the robustness of EVCS networks. A before-and-after comparative evaluation of the implemented blockchain network illustrates its effectiveness in countering prevalent cyberattacks targeting EVCS infrastructure. The evaluation encompasses performance indicators derived from the Hyperledger Caliper benchmarking tool, revealing the solution’s minimal latency suitable for real-time applications and its capacity to scale in line with expanding EV infrastructure demands. Implementing this blockchain-augmented security solution is anticipated to boost user confidence and dependability in EVCS systems. 

The usefulness of Hybrid Energy Storage Systems (HESSs) has been demonstrated across various application areas, including Charging Stations (CSs), grid services, and microgrids. HESSs combine two or more individual Energy Storage Systems (ESSs) to merge the advantages of each technology and enhance overall system performance. In this study, we introduce a novel power management controller, termed the Hybrid Controller, designed for efficient charging and discharging of the HESS while accounting for its State of Charge (SoC) and the fluctuating supply and load conditions. The Hybrid Controller improves HESS utilization by minimizing energy exchange with the grid, thereby prioritizing and maximizing the use of available Photovoltaic (PV) power. The effectiveness of the proposed approach was evaluated through simulations using several performance indicators, namely Autarky, charge/discharge cycles, and Self-Consumption (SC). For this purpose, we defined 24 scenarios across different locations in Germany. 

The rapid expansion of electric vehicle (EV) adoption has led to a swift growth in charging infrastructure, accompanied by a rising incidence of malfunctions in charging equipment. To enhance the management and maintenance of these facilities, this study introduces an approach using the XGBoost algorithm, specifically tailored for direct current (DC) EV charging stations. A comprehensive operation and maintenance framework is developed, grounded in thorough condition evaluation. This framework incorporates the current functional state of the stations alongside patterns in user charging behavior. Key considerations include drivers’ traveling and recharging preferences, prevailing road congestion levels, and the condition of charging hardware. The model is trained on a dataset compiled from records, live surveillance information, and ambient external conditions. Employing the XGBoost technique, a robust evaluation model assesses the operational health of charging stations. Drawing from these evaluation outcomes and integrating relevant fault-related metrics, a dedicated risk evaluation framework is formulated. By using the energy not charged (ENC) metric, the system performs ongoing risk monitoring for each facility, assigns risk-priority rankings, and establishes a sequence for necessary interventions. An advanced optimization model for DC station maintenance is then constructed, balancing financial efficiency with system reliability objectives, ultimately generating an effective maintenance schedule across stations. Case study findings indicate that this combined condition assessment and strategic maintenance plan markedly enhances overall system dependability and delivers superior economic returns from upkeep efforts, all while satisfying established performance criteria.

As electric vehicles (EVs) steadily gain popularity on public roads, commercial EV charging has become a routine business service. As this trend continues, charging station operators are actively exploring strategies to boost the profitability of their services or to distribute limited resources more effectively. Dynamic pricing has proven effective for increasing revenue in markets with varying customer demand. This paper introduces a Markov Decision Process (MDP)-based framework for dynamic pricing that maximizes either revenue or utilization for charging station operators. The proposed method is implemented through a Monte Carlo Tree Search (MCTS) algorithm. It is tested in simulation across multiple scenarios derived from a real-world dataset of EV charging sessions. Results demonstrate that the approach delivers near-optimal pricing decisions within milliseconds, even for large-scale instances, delivering substantial improvements in revenue or utilization compared to a conventional flat-rate baseline across various parameter settings.