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.

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.

For the automated analysis of I/V-characteristics of solar cells and modules, descriptive parameters are essential. In particular with the rise in machine-learning techniques and the related increase data volumes, there is a need for good, general purpose, descriptive parameters. The most commonly used descriptive parameters for I/V are the standard solar cells parameters, consisting of V oc , I sc , V mpp , and I mpp . Also other representations may be considered, such as one diode model parameters corresponding to a particular I/V. However, these representations are very coarse and cannot distinguish or represent many common (non-ideal) features of an I/V (e.g. an S-shape). In this work we propose an extended set of solar cell parameters, which are well defined, and easy to determine. We evaluate the effectiveness of the extended solar cell parameters by reconstructing the I/V from the extracted parameters. This allows one to “measure” information loss. We compare the accuracy of our parameters with other commonly used curve models for I/V, namely the one diode model, and the Karmalkar-Haneefa model. The models are applied to a large set of I/V (about 2.2 million curves), covering a wide range of technologies and conditions. We demonstrate our extended solar cell parameters consistently provide an accurate description of nearly all I/V in these datasets. Furthermore, we present our I/V analysis tool which we use to process these datasets. This tool is fast and capable of extracting the extended solar cell parameters, as well as parameters for the one diode model and the Karmalka-Haneefa model. Finally, we exemplary show how the extended solar cell parameters may be used to detect partial shading in outdoor data, by training a simple random-forest classifier based on extended solar cell parameters.

We present an analysis of the performance data of a monitored PV system onboard a light commercial electric vehicle during parking and driving conditions. The PV system’s nominal power is 2180 WP with flat silicon modules on the vehicle’s roof, rear, left, and right sides, as well as other electronic components needed to charge the vehicle’s high-voltage (HV) battery. The analysis indicated that after 488.92 hours of operation, the modules mounted on the vehicle roof produced 133.32 kWh of electricity during parking 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 hours 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.

Photovoltaics (PV) in onboard vehicle applications adds weight to an electric vehicle (EV), increasing the overall energy consumption. Although the added PV system weight (1.5– 40 kg) is small compared to the vehicle weight (1500–2200 kg), the power generated by PV (55–700 W) is also very small com- pared to the power needed (up to 80–285 kW) to propel an EV, making the effect of additional PV system weight on energy con- sumption a non-trivial topic to analyze. We present a method to study the impact of vehicle onboard PV weight on the energy balance of EVs for different Vehicle-added PV (VAPV) and vehi- cle-integrated PV (VIPV) configurations with eight different PV technologies, using data from vehicle onboard measurement campaigns and simulations. Simulations are carried out for the driving phase of two electric cars (medium and large passenger cars). Our method calculates the energy consumption attributa- ble to the added PV system weight (0.05–1.4 Wh/km) and PV energy yield (0.12–3.12 Wh/km) for a selection of trips. The re- sults of these simulations are expressed through a newly intro- duced parameter called “onboard PV yield factor”, where posi- tive values indicate a net energy gain and negative values indicate a net energy loss of the onboard PV system. Our results show that the onboard PV yield factor for a VAPV configuration can range between -69.1 and 86.9 %, and for a VIPV configuration, between 77.2 and 89.7 %.