Researchers from Indonesia’s Sebelas Maret University have investigated the use of nano-enhanced phase-change materials (NePCMs) for the passive cooling of PV modules used in electric bicycle charging stations. The study combines a systematic meta-analysis, experimental evaluation, and artificial neural network (ANN)-based forecasting.
“In tropical regions such as Indonesia, ambient temperatures frequently exceed 30 C while solar irradiance levels reach approximately 4.76 kWh/m²/day, creating conditions that intensify thermal accumulation in PV modules,” the team said. “As a consequence, thermal management has become a critical aspect for maintaining stable PV performance, particularly in solar-powered charging infrastructure.”
They added that, unlike previous studies that primarily focused on individual experimental evaluations, their work integrated quantitative evidence synthesis to guide material selection before experimental implementation. They said that the findings provided a structured foundation for designing advanced PV–PCM hybrid cooling systems specifically for solar-powered public electric vehicle charging station (PEVCS) infrastructure.
Phase change materials (PCMs) can absorb, store, and release large amounts of latent heat over defined temperature ranges. They have often been used at the research level for PV module cooling and the storage of heat.
The PRISMA-based systematic meta-analysis began with a pool of 371 publications, from which 30 articles were selected for review. The team assessed the impact of different nanoparticle types, concentrations, and PCM matrices on cooling performance. The analysis found that nano-enhanced PCMs increased thermal conductivity by an average of 26.94% and reduced PV operating temperatures by 16.33 C. These findings guided the selection of a bio-based soy wax PCM containing 5 wt% silicon nanoparticles for experimental validation.
In the experimental validation phase, the researchers built a laboratory-scale PV-assisted electric bicycle charging station using a 50 W monocrystalline PV module integrated with bio-based soy wax containing 5 wt% silicon nanoparticles. The module was connected to a lithium-ion battery, solar charge controller, inverter, and electric bicycle charging load. The prototype was tested outdoors in Indonesia throughout January 2026.
The tests showed that the nano-enhanced phase-change material (NePCM) reduced the average PV module temperature by 6.8 C while maintaining stable electrical output, with average power generation of 29.27 W.
The 720 hours of operational data were then used to develop an artificial neural network (ANN) model, with 80% of the data allocated for training and 20% for testing. The model was used to forecast both PV electricity generation and electricity consumption. The generation model achieved strong performance, with a coefficient of determination (R²) value of 0.997, a mean absolute error (MAE) of 0.61 W, and a root mean square error (RMSE) of 1.06 W, indicating close agreement between predicted and measured PV output.
By contrast, the electricity consumption model showed lower accuracy, achieving an R² value of 0.307, an MAE of 54.19 W, and an RMSE of 71.94 W.
The researchers explained that the relatively low R² value, which is a statistical measure that indicates how closely a model’s predictions match observed data, indicated that electricity consumption was influenced by additional external factors not fully captured by the current input dataset, including user behavior patterns and variations in charging schedules. They added that future forecasting models could benefit from incorporating additional variables related to user activity, temporal charging patterns, and operational demand characteristics.
The researchers concluded that, although the improvements in PV performance were moderate, the consistent thermal regulation provided by the NePCM demonstrated the feasibility of passive cooling strategies based on nano-enhanced phase-change materials. They noted that combining thermal management systems with intelligent forecasting models could become an important element in the development of adaptive PV energy systems.
Their findings were presented in “Enhancing photovoltaic efficiency in electric bicycle charging systems: The role of nano-enhanced phase change materials and predictive neural network modeling,” published in Next Energy.
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