Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm² single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated.

Conventional approaches to forecasting of real-time thermal ratings (RTTRs) provide only single-point estimates with no indication of the size or distribution of possible errors. This paper describes weather-based methods to estimate probabilistic RTTR forecasts for overhead lines which can be used by a system operator within a chosen risk policy with respect to the probability of a rating being exceeded. Predictive centers of weather conditions are estimated as a sum of residuals predicted by a suitable auto-regressive model and temporal trends fitted by Fourier series. Conditional heteroscedasticity of the predictive distribution is modelled as a linear function of recent changes in residuals within one hour for air temperature and wind speed or concentration of recent wind direction observations within two hours. A technique of minimum continuous ranked probability score estimation is used to estimate predictive distributions. Numerous RTTRs for a particular span are generated by a combination of the Monte Carlo method where weather inputs are randomly sampled from the modelled predictive distributions at a particular future moment and a thermal model of overhead conductors. Kernel density estimation is then used to smooth and estimate the percentiles of RTTR forecasts which are then compared with actual ratings and discussed alongside practical issues around the use of RTTR forecasts.

Reasonable and effective control of a cathode air supply system is conducive to improving the dynamic response, operating efficiency, and reliability of fuel cell systems. This paper proposes a novel data-driven adaptive oxygen excess ratio (OER) control strategy based on online parameter identification for fuel cell systems. The proposed control scheme employs a second-order active disturbance rejection controller (ADRC) derived from the proportional-integral-derivative tuning rule to effectively deal with model uncertainties and external disturbances. Online parameter identification continuously translates the cathode air supply system into the second-order model, enabling the real-time adaptation of controller parameters to varying operating conditions. Simulation results demonstrate that the OER control strategy proposed significantly improves voltage stability and system efficiency under dynamic conditions compared to traditional methods. The innovation of this paper is that, based on consideration of the nonlinear slow time-varying characteristics of a PEMFC and the frequent disturbance of load current, adaptive control under system dynamic conditions can be considered. Combining the parameter identification scheme, an adaptive online self-tuning scheme is designed for the identified system model, which avoids the tediousness of a complex modeling process and has promotion value in practical applications.

Liquid hydrogen is regarded as a key energy source and propellant for lunar bases due to its high energy density and abundance of polar water ice resources. However, its low boiling point and high latent heat of vaporization pose severe challenges for storage and management under the extreme lunar environment characterized by wide temperature variations, low pressure, and low gravity. This paper reviews the strategies for siting and deployment of liquid hydrogen storage systems on the Moon and the technical challenges posed by the lunar environment, with particular attention for thermal management technologies. Passive technologies include advanced insulation materials, thermal shielding, gas-cooled shielding layers, ortho-para hydrogen conversion, and passive venting, which optimize insulation performance and structural design to effectively reduce evaporation losses and maintain storage stability. Active technologies, such as cryogenic fluid mixing, thermodynamic venting, and refrigeration systems, dynamically regulate heat transfer and pressure variations within storage tanks, further enhancing storage efficiency and system reliability. In addition, this paper explores boil-off hydrogen recovery and reutilization strategies for liquid hydrogen, including hydrogen reliquefaction, mechanical, and non-mechanical compression. By recycling vaporized hydrogen, these strategies reduce resource waste and support the sustainable development of energy systems for lunar bases. In conclusion, this paper systematically evaluates passive and active thermal management technologies as well as vapor recovery strategies along with their technical adaptability, and then proposes feasible storage designs for the lunar environment. These efforts provide critical theoretical foundations and technical references for achieving safe and efficient storage of liquid hydrogen and energy self-sufficiency in lunar bases.