Bart Elger Pieters

Outdoor data are essential to study the reliability of PV modules and systems. Each electrical performance measure is dependent on the conditions the measurement is conducted at and, therefore, needs to be considered in the context of dynamically changing outdoor conditions. In this paper, we introduce a statistical model designed to analyze PV outdoor data. This model uses a timeseries of current‐voltage (IV) characteristics, alongside meteorological data, including plane‐of‐array irradiance ( ) and module temperature ( ). The model aims to utilize all available information to predict the respective performance measure as well as its uncertainty at arbitrary conditions and times. First, to ensure its quality and relevance, a suitable filtering approach is applied to the IV curves, and data from nine modules from five locations (Arizona USA, Germany, India, Italy, and Saudi Arabia) observed for over 2 years. Following this, we utilize the extended solar cell parameters (ESPs), a descriptive model for IV characteristics using 10 parameters. The ESPs, then, undergo a principal component analysis (PCA), which transforms the EPSs into a set of uncorrelated principal components (PCs). Individual Gaussian process regressions (GPRs) are then trained on these principal components (PCs). Once the GPRs are trained, the model is capable of reproducing and predicting the complete IV characteristics at any given time , for specified values of and . This prediction includes an assessment of its standard deviation, which is derived from data noise and the distance from the observations. This model serves as a versatile tool for various applications, such as analyzing acclimatization effects, degradation trends, seasonal variations, and the performance ratio (PR) of PV modules or systems.

We present an analysis of the performance data of a monitored PV system onboard a light commercial electric vehicle during parking and driving conditions in the Hannover region of Germany. The PV system's nominal power is 2180 W_P with flat silicon modules on the vehicle's roof, rear, left, and right sides and other electronic components needed to charge the vehicle's high‐voltage (HV) battery. The analysis indicated that after 488.92 h of operation, the modules mounted on the vehicle roof produced 133.32 kWh of electricity during parking at the best possible orientation compared to 15.4, 30.67, and 22.99 kWh for the modules mounted on the rear, left, and right sides, respectively. During the trips, after 31.99 h of operation, 6.12, 0.68, 1.08, and 1.86 kWh of electricity were produced by the modules on the roof, rear, left, and right sides, respectively. The overall system efficiency was in the 60%–65% range. The aggregated usable electricity reaching the HV battery after multiple conversion stages generated by the system at the two parking locations was 129.39 kWh. PV electricity generated at the two parking locations enabled a range extension of approximately 530 km, which is 30% of the total distance driven during the measurement period between April and July 2021.

Nonuniformity of irradiation in photovoltaic (PV) modules causes a current mismatch in the cells, which leads to energy losses. In the context of vehicle‐integrated PV (VIPV), the nonuniformity is typically studied for the self‐shading effect caused by the curvature of modules. This study uncovers the impact of topography on the distribution of sunlight on vehicle surfaces, focusing on two distinct scenarios: the flat‐surface cargo area of a small delivery truck and the entire body of a commercial passenger vehicle. We employ a commuter pattern driving profile in Germany and a broader analysis incorporating random sampling of various road types and locations across 17,000 km² in Europe and 59,000 km² in the United States using LIDAR‐derived topography and OpenStreetMap data. Our findings quantify irradiation inhomogeneity patterns shaped by the geographic landscape, road configurations, urban planning, and vegetation. The research identifies topography as the primary factor affecting irradiation distribution uniformity, with the vehicle's surface orientation and curvature serving as secondary influencers. The most significant variation occurs on vertical surfaces of the vehicle in residential areas, with the lower parts receiving up to 35% less irradiation than the top part of the car. These insights may be used to improve the design and efficiency of vehicle‐integrated photovoltaic systems, optimizing energy capture in diverse environmental conditions.

Not-yet-known not-yet-known not-yet-known unknown Outdoor data is essential to study the reliability of PV modules and systems. Each electrical performance measure is dependent on the conditions the measurement is conducted at and, therefore, needs to be considered in the context of dynamically changing outdoor conditions. In this paper, we introduce a statistical model designed to analyze PV outdoor data. This model uses a timeseries of current-voltage ( IV) characteristics, alongside meteorological data, including plane-of-array irradiance ( G POA ) and module temperature ( T Mod ). The model aims to utilize all available information to predict the respective performance measure as well as its uncertainty at arbitrary conditions and times. First, to ensure its quality and relevance, a suitable filtering approach is applied to the IV curves, G POA and T Mod data from 9 modules from 5 locations (Arizona USA, Germany, India, Italy, Saudi Arabia) observed for over two years. Following this, we utilize the Extended Solar cell Parameters (ESPs), a descriptive model for IV characteristics using 10 parameters. The ESPs, then, undergo a principal component analysis (PCA), which transforms the EPSs into a set of uncorrelated principal components (PCs). Individual Gaussian process regressions (GPRs) are then trained on these principal components (PCs). Once the GPRs are trained, the model is capable of reproducing and predicting the complete IV characteristics at any given time t, for specified values of G POA and T Mod . This prediction includes an assessment of its standard deviation, which is derived from data noise and the distance from the observations. This model serves as a versatile tool for various applications, such as analyzing acclimatization effects, degradation trends, seasonal variations, and the performance ratio (PR) of PV modules or systems.

In applications that utilize detailed solar resource assessments with high‐resolution topography data, calculating the topographic horizon is critical for accurate shading calculations. In particular, the horizon calculation significantly influences the time needed to model solar irradiation in integrated photovoltaic applications. The new approximate horizon algorithm was developed to balance accuracy and computation time. This study evaluates the algorithm's performance in modeling vehicle‐ and building‐integrated photovoltaics, considering the impact of surface orientation and elevation. It is demonstrated that the proposed horizon algorithm achieves the same level of accuracy four times faster than previously known approaches for vehicle‐integrated applications. Moreover, for building‐integrated applications, the proposed approach performs better at elevations higher than 10 m on facades and roofs. Finally, the impact of maximum sampling distance on irradiation for high‐ and low‐resolutions topography is studied.

Partial shading can significantly impair the efficiency of thin-film solar cells. When exposed to partial shading, cells within the array tend to become reverse biased, leading to thermal runaway events and the emergence of hotspots. In Cu(In,Ga)Se2 (CIGS) solar cells such hotspots are also associated with so-called worm-like defects. Both theoretical and experimental studies have shown that in CIGS, a positive-feedback loop leads to instability and thermal runaway events. However, we observe an inconsistency between published simulation results and recently published experimental work. In a recent experimental study, it was shown that under certain conditions, a hotspot develops within 1ms, showing signs of melting of the CIGS in an area with a 5μm radius. However, in published simulation results, the time for such high temperatures to develop is in the order of seconds, a discrepancy of three orders of magnitude. In this work, we argue that this discrepancy is explained by the size of the seed defect, demonstrating that the origin of these experimentally observed, fast-developing hotspots is likely microscopic defects. To this end, we developed an electro-thermal finite element model, with very high temporal and spatial resolution. We demonstrate that, assuming a seed defect with a 10nm radius, we can reproduce the experimental results with respect to the size of the defect and the time it took to develop.

This study uncovers the impact of geographic variability on the distribution of sunlight on vehicle surfaces, focusing on two distinct scenarios: the flat-surface cargo area of a small delivery truck and the entire body of a commercial passenger vehicle. We employ a commuter pattern driving profile in Germany and a broader analysis incorporating random sampling of various road types and locations across Europe and the USA using LIDAR-derived topography and OpenStreetMap data. Our findings reveal significant irradiation inhomogeneity patterns shaped by the geographic landscape, road configurations, urban planning, and vegetation. The research identifies topography as the primary factor affecting irradiation distribution uniformity, with the vehicle’s surface orientation and curvature serving as secondary influencers. These insights hold immense potential for improving the design and efficiency of vehicle-integrated photovoltaic systems, presenting a foundational knowledge base from which to optimize energy capture in diverse environmental conditions.

We assess the accuracy of two steady‐state temperature models, namely, Ross and Faiman, in the context of photovoltaics (PV) systems integrated in vehicles. Therefore, we present an analysis of irradiance and temperature data monitored on a PV system on top of a vehicle. Next, we have modeled PV cell temperatures in this PV system, representing onboard vehicle PV systems using the Ross and Faiman model. These models could predict temperatures with a coefficient of determination (R²) in the range of 0.61–0.88 for the Ross model and 0.63–0.93 for the Faiman model. It was observed that the Ross and Faiman model have high errors when instantaneous data are used but become more accurate when averaged to timesteps of greater than 1000–1500 s. The Faiman model's instantaneous response was independent of the variations in the weather conditions, especially wind speed, due to a lack of thermal capacitance term in the model. This study found that the power and energy yield calculations were minimally affected by the errors in temperature predictions. However, a transient model, which includes the thermal mass of the vehicle and PV modules, is necessary for an accurate instantaneous temperature prediction of PV modules in vehicle‐integrated (VIPV) applications.