TY - JOUR
T1 - Assessing the accuracy of two steady-state temperature models for onboard passenger vehicle photovoltaics applications
AU - Patel, Neel
AU - Pieters, Bart Elger
AU - Bittkau, Karsten
AU - Sovetkin, Evgenii
AU - Ding, Kaining
AU - Reinders, Angèle H.M.E.
PY - 2024/7/9
Y1 - 2024/7/9
N2 - 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.
AB - 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.
KW - PV cell temperature
KW - VIPV
KW - temperature measurements
KW - temperature modeling
UR - http://www.scopus.com/inward/record.url?scp=85197769437&partnerID=8YFLogxK
U2 - 10.1002/pip.3832
DO - 10.1002/pip.3832
M3 - Article
SN - 1062-7995
VL - XX
JO - Progress in Photovoltaics: Research and Applications
JF - Progress in Photovoltaics: Research and Applications
IS - X
ER -