The optimization of traffic flow, enhancement of safety measures, and minimization of emissions in intelligent transportation systems (ITS) pivotally depend on the Vehicle License Plate Recognition (VLPR) technology. Challenges predominantly arise in the precise localization and accurate identification of license plates, which are critical for the applicability of VLPR across various domains, including law enforcement, traffic management, and both governmental and private sectors. Utilization in electronic toll collection, personal security, visitor management, and smart parking systems is commercially significant. In this investigation, a novel methodology grounded in the Kanade-Lucas-Tomasi (KLT) algorithm is introduced, targeting the localization, segmentation, and recognition of characters within license plates. Implementation was conducted utilizing MATLAB software, with grayscale images derived from both still cameras and video footage serving as the input. An extensive evaluation of the results revealed an accuracy of 99.267%, a precision of 100%, a recall of 99.267%, and an F-Score of 99.632%, thereby surpassing the performance of existing methodologies. The contribution of this research is significant in addressing critical challenges inherent in VLPR systems and achieving an enhanced performance standard.

In the context of increasing electric vehicle (EV) prevalence, the integration of renewable energy sources, particularly solar energy, into EV charging infrastructure has gained significant attention. This study investigates the economic viability of grid-connected photovoltaic (PV) systems for EV charging stations in Ngawi City, Indonesia, selected due to its substantial solar energy potential and ongoing renewable energy initiatives. Key factors influencing the economic feasibility of these systems include load requirements, renewable energy potential, system capacity, levelized cost of electricity, payback period, net present cost (NPC), and cost of energy (COE). A comprehensive techno-economic assessment was conducted to estimate the capital recovery time, incorporating both utilization costs and payback periods. The analysis utilized the Hybrid Optimization Model for Electric Renewables (HOMER) software, focusing on the application of PV energy in EV charging stations within Ngawi Regency. Findings indicate that a PV system-based generation approach can adequately meet the power needs of EV charging stations. Notably, this system is capable of generating surplus energy, which presents an opportunity for additional revenue, thus enhancing its economic attractiveness. The analysis determined that to produce an annual output of 562,227 kWh, a total of 1245 PV modules, each with a 370-watt capacity, are necessary. This off-grid PLTS system, relying exclusively on PV modules for electrical energy generation, can sufficiently supply a daily load of 342.99 kWh for an EV charging station. The study underscores the potential of solar-powered EV charging stations in contributing to sustainable urban development, reinforcing the integration of renewable energy into urban infrastructure.