Optimization of Smart Electric Vehicle Charging Using Internet of Things and Hybrid Simulation for Enhanced Operations and Maintenance
Open Access Depositedhowever, existing approaches do not quantify how much better predictive maintenance performs in terms of system reliability, efficiency, and operational outcomes. This praxis presents a novel evaluation strategy that integrates Internet of Things (IoT) data and hybrid simulation methodologies to quantify the magnitude of performance improvements of predictive maintenance relative to reactive maintenance. By evaluating multiple reliability and operational metrics, this approach provides a comprehensive and data-driven method for measuring improvements in EV charging station operations. Despite the industry growth in EV charging stations, there are still issues with the steady operation of these stations. Many EV charging stations are experiencing issues with efficiency and maintenance in their operations. These issues lead to an increased failure rate in the operation of these EV charging stations, as well as limitations in their scalability. In response to these issues, a simulation framework based upon the Internet of Things (IoT) technologies and methodologies was created to provide an understanding of how the implementation of a predictive maintenance strategy could enhance the operations and maintenance of EV charging stations. Data from the existing EV charging stations was analyzed within MATLAB/Simulink to create models that could forecast the reliability and efficiency of the EV charging stations, and to create models for detecting anomalies within the system. These analyses were then integrated into an AnyLogic simulation platform that incorporated agent-based modeling (ABM), discrete-event simulation (DES), and system dynamics (SD) to evaluate and validate the systems performance across maintenance strategies such as reactive, preventive, and predictive scenarios. Metrics that are utilized to evaluate the performance of the systems include overall equipment effectiveness, uptime, MTBF, MTTR, failure rate and maintenance downtime - all of which are key performance indicators (KPI) of the systems. The statistical analysis of the simulation results indicated that implementing a predictive maintenance strategy resulted in a 15.54% increase in the OEE of the evaluated systems relative to reactive maintenance baseline, as well as a 27.97% reduction in maintenance-related downtime. Furthermore, analysis of the IoT system data revealed the significance of various data features in relation to both the efficiency and reliability metrics of the systems. The results of this integrated IoT data analytics and hybrid simulation framework provide a new model for evaluating the operations and performance of EV charging stations – one that is based upon the concept of OEE, rather than the uptime of the EV charging stations alone. It also indicates the predictive capabilities of the established framework. This research contributes a scalable decision-support methodology for EV charging manufacturers, network operators, and policymakers seeking to optimize maintenance strategies and infrastructure reliability through data-driven decision-making.
Existing Electric Vehicle (EV) evaluation tools and strategies are primarily used for data reporting and typically focus on single metrics such as uptime, which do not fully capture operational efficiency. From this perspective, it can readily be assessed that predictive maintenance is better than reactive or preventive maintenance
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