To improve forecasting accuracy for photovoltaic (PV) power output, this paper proposes a hybrid method for forecasting the short-term PV power output. First, by introducing the noise level, an improved complementary ensemble empirical mode decomposition (EEMD) with adaptive noise (ICEEMDAN) is developed to determine the ensemble size and amplitude of the added white noise adaptively. ICEEMDAN can change PV power output with non-symmetry into intrinsic mode functions (IMFs) with symmetry. ICEEMDAN can enhance the forecasting accuracy for PV power by IMFs with physical meaning (not including spurious modes). Second, the selection method of relative modes (IF), which is determined by the comprehensive factor, including the shape factor, crest factor and Kurtosis, is introduced to adaptively classify the IMFs into groups including similar fluctuating components. The IF can avoid the drawbacks of threshold determination by an empirical method. Third, the modified particle swarm optimization (PSO) (MPSO) is proposed to optimize the hyper-parameters in the support vector machine (SVM) by introducing the piecewise inertial weight. MPSO can improve the global and local search ability to make the particles traverse the global space and strengthen the performance of local convergence. Finally, the proposed method (ICEEMDAN-IF-MPSO-SVM) is used to forecast the PV power output of each group individually, and then, the single forecasting result is reconstructed to obtain the desired forecasting result for PV power output. By comparison with the other typical methods, the proposed method is more suitable for forecasting PV power output.

Accurate short-term photovoltaic (PV) power forecasting is crucial for ensuring the stability and efficiency of modern power systems, particularly given the intermittent and nonlinear characteristics of solar energy. This study proposes a novel hybrid forecasting model that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the jellyfish search (JS) optimization algorithm, and a bidirectional long short-term memory (BiLSTM) neural network. First, the original PV power signal was decomposed into intrinsic mode functions using a modified CEEMDAN method to better capture the complex nonlinear features. Subsequently, the fast Fourier transform and improved Pearson correlation coefficient (IPCC) were applied to identify and merge similar-frequency intrinsic mode functions, forming new composite components. Each reconstructed component was then forecasted individually using a BiLSTM model, whose parameters were optimized by the JS algorithm. Finally, the predicted components were aggregated to generate the final forecast output. Experimental results on real-world PV datasets demonstrate that the proposed CEEMDAN-JS-BiLSTM model achieves an $R^2$ of 0.9785, a MAPE of 8.1231%, and an RMSE of 37.2833, outperforming several commonly used forecasting models by a substantial margin in prediction accuracy. This highlights its effectiveness as a promising solution for intelligent PV power management.

Passenger fuel cell vehicles (FCVs) require high-gain DC/DC converters to achieve voltage matching between the low-power fuel cell (FC) stack (50–200 V) and the vehicle DC bus (400–800 V). To address the challenges in existing step-up DC/DC converters in relation to balancing the requirements of high voltage gain, wide input voltage range, low input current ripple and voltage stress, the common ground of input–output, and high efficiency in passenger FCV applications, this paper proposes three types of high-gain DC/DC converters based on an interleaved structure, incorporating quadratic Boost, quasi-Z source, and switched-inductor impedance networks. These designs effectively balance the scenario requirements of passenger FCVs. Meanwhile, taking one of the proposed converters (Interleaved-Quadratic Boost; I-QB) as an example, its steady-state performance such as voltage gain is analyzed and compared in detail with existing voltage step-up DC/DC converters. Furthermore, a scaled-down SiC-based experimental platform is constructed. Steady-state experiments validate the converter’s maximum voltage step-up capability of ten times, wide input voltage range of 30–80 V, input current ripple of less than 0.3 A, and low voltage stress on devices (≤U_o/2), thereby confirming the feasibility of these converters and the correctness of the performance analysis. The dynamic experimental results indicated that under input voltage step changes of 50–80 V and 100–50% load step changes, the I-QB converter exhibits a minor voltage overshoot with settling time under 200 ms. The prototype achieves a peak efficiency of 94.2%, confirming these converters’ suitability for passenger FCV powertrains.