The stratospheric airship is a kind of aircraft that completely relies on the cycle of photovoltaic energy systems to achieve long duration flight. The accurate estimation of the operating temperature of solar cell modules on stratospheric airship is extremely important for the design of photovoltaics system (PV system), the output power calculation of PV system, and the calculation of energy balance. However, the related study has been rarely reported. A support vector machine prediction method based on particle swarm optimization algorithm (PSO-SVM) was established to predict the operating temperature of solar cell modules on stratospheric airship. The PSO algorithm was used to dynamically optimize the SVM’s parameters between the operating temperature of the solar cell modules and the measured data such as atmospheric pressure, solar radiation intensity, flight speed, and ambient temperature. The operating temperature data of the two sets of solar cell modules measured in the flight test were used to verify the accuracy of the temperature prediction model, and the prediction results were compared with a back propagation neural network (BPNN) method and the simulation results calculated by COMSOL Multiphysics of COMSOL, Inc., Columbus, MA, USA. The results shown that the PSO-SVM model realized the accurate prediction of the operating temperature of solar cell modules on stratospheric airship, which can guide the design of PV system, the output power calculation of PV system, and the calculation of energy balance.

Current stratospheric airships generally employ photovoltaic cycle energy systems. Accurately calculating their power generation is significant for airships’ overall design and mission planning. However, the power generation of solar arrays on stratospheric airships is challenging to model and calculate due to the dynamic nature of the airships’ flight, resulting in continuously changing radiation conditions on the curved surface of the airships. The power generated by the airship solar array was modeled herein through a combination of the flight attitude, spatial position, time, and other influencing factors. Additionally, the model was modified by considering the variation in photovoltaic conversion efficiency based on the radiation incidence angle, as well as the state of charge and power consumption of the energy storage battery pack. This study compared the measurement data of power generation in real flight tests with the calculation results of the model. The comparison showed that the results of the calculated model were highly consistent with the actual measured data. An average normalized root-mean-square error of 2.47% validated the accuracy of the newly built model. The generalizability and rapidity of the model were also tested, and the results showed that the model performed well in both metrics.

High-energy-density lithium batteries play a crucial role in the lightweight design of stratospheric airship systems. This paper conducts an in-depth experimental study of the equivalent circuit model of soft-pack batteries, with a focus on how parameter identification methods affect model accuracy. To this end, first-order RC, second-order RC, and third-order RC equivalent circuit models were constructed, and model parameters under different temperature and current conditions were obtained through constant-current intermittent discharge experiments. During the parameter identification process, special consideration was given to the impact of sampling time on voltage measurements and the interdependent constraints among models. Additionally, the effects of current, temperature, and SOC (state of charge) variations on ohmic resistance and polarization resistance–capacitor parameters were analyzed. The experimental results show that the root mean square error (RMSE) of battery terminal voltage calculated using parameter identification methods that account for these factors is significantly lower than when these factors are not considered. By comparing the voltage calculation accuracy and operational efficiency of the three models, the second-order RC model was determined to be the preferred choice due to its simple structure, high computational efficiency, and superior accuracy.

Stratospheric airships are long‐endurance flight platforms that utilize renewable energy systems. The solar array in these systems cannot generate electricity at night; only an energy storage battery pack can be used to power the entire system. Therefore, accurate modeling and estimation of the output characteristics and operating conditions of battery packs have become the focus of current research. During the actual flight of an airship, uneven temperature distribution causes changes in the output characteristics and differences in the cell voltage of the battery packs. To address this problem, this study establishes an output characteristic model, series–parallel structure coupled with a temperature field model (SPTM), for a battery pack based on the series–parallel structure of a single cell. In addition, the influence of temperature distribution was considered in the model. Then, a flight verification test was conducted, and the test data and simulation data were compared. The average absolute error of the SPTM was maintained at approximately 0.61 V, which accurately reflected the output characteristics of the battery pack. The simulation model was then used to study the cell voltage difference under changes in the temperature distribution and demonstrate the relationship between the cell voltage difference and temperature difference.