The hybrid power system has been widely adopted in the unmanned aerial vehicle (UAV) to meet various flight profiles. In this paper, an optimal sizing strategy for fuel cell (FC)-Li-ion battery-supercapacitor (SC) based hybrid power system in the UAV is proposed. The required energy from the FC is optimized based on the optimal efficiency range. In addition, using the frequency-based power split algorithm, a multi-objective function, minimizing the weight and economic cost, for the battery-SC subsystem is constructed. The PSO intelligent optimization algorithm is used to find the optimal sizing for the system. Finally, some simulations have been carried out to verify the correctness of the proposed algorithm.

With a large number of constant power loads (CPLs) connected to the DC microgrid (MG), the injected negative impedance drastically deteriorates the stability of the DCMG. To address this issue, a stabilization strategy for the DCMG with CPLs fed by the four-switch Buck-Boost (FSBB) converters is proposed in this paper. Due to the adopted DC machine technique, a virtual capacitor is paralleled to the output port of the FSBB converter to support the bus voltage in the presences of CPL power changes. The stability of the system is analyzed based on the Middlebrook impedance stability criterion, and the effectiveness of the proposed control strategy is verified by MATLAB/Simulink simulation.

Compared to single fuel cell powertrain, the multi-source hybrid system addresses the slow dynamic response and inability to recover and recycle braking energy capability inherent to fuel cell system. In aviation, hybrid systems enhance energy management efficiency and enable rapid power response to dynamic demands through the synergistic integration of multiple power sources. Concurrently, the energy management strategy coordinates the operational states of all sources in real time and optimally allocates power loads, thereby enhancing system operational stability and extending service life. Integrating Equivalent Consumption Minimization (ECM) principle, an Optimized Parasitism-Predation Algorithm (OPPA) is proposed. Compared with the traditional PPA, the optimized algorithm features two key enhancements. First, it integrates gradient information analysis and adopts a probabilistic acceptance mechanism for suboptimal solutions, thereby significantly expediting the local search process. Second, certain fixed parameters within the algorithm are adaptively adjusted over time, enabling the algorithm to effectively achieve the exploration-exploitation trade off. Simulation results demonstrate that the proposed strategy effectively balances multiple performance metrics within the model. When benchmarked against the State Machine Strategy and the Equivalent Consumption Minimization Strategy (ECMS), it outperforms in enhancing system efficiency and stability while concurrently reducing system costs.

The LLC resonant converter (LLC)possesses the features of high efficiency, high power density and galvanic isolation, which makes it a promising candidate for the energy storage converter in DC microgrid applications. In DC microgrid, power electronic loads are widely used, such as motor drivers. The negative impedance characteristics of these constant power loads (CPL) will affect the stability of the source converter. In this paper, a control method is proposed for the LLC resonant converter in the energy storage system. By adopting the nonlinear disturbance observer (NDO), the negative impedances caused by the power electronics loads are compensated. In addition, a reduced-order model for the LLC resonant converter is used to simplify the controller design. A Lyapunov based stability analysis is carried out, and the simulation results of using Matlab/Simulink are given to show the advantages of the proposed control method.

Compared to single-stack fuel cell systems, multi-stack fuel cell systems (MFCS) can increase fault tolerance through redundancy. In this paper, an improved energy management control strategy (EMS) based on hierarchical is proposed for power allocation of fuel cells and the battery based on a multi-stack fuel cell hybrid power system. This strategy aims to maintain the state of charge (SOC) of a battery within a healthy range and reduce hydrogen consumption. At the overall level, power distribution is carried out between the battery and fuel cell; At the local level, power distribution is then applied to individual fuel cells. Fuel cell weighting factors were used and range reduction control was applied to SOC. The simulation results show that when the SOC is high, low, or within the expected range, comparing the improved state machine strategy with the traditional state machine strategy, the SOC of the improved state machine strategy changes faster to the expected range, and the hydrogen consumption is also reduced by 11.550g, 9.200g, and 9.710g, respectively. Thus, the economy and durability are improved.

Electrochemical impedance spectroscopy (EIS) has broad application prospects in the field of structural analysis and performance optimization of proton exchange membrane fuel cells (PEMFC), and the establishment of an equivalent impedance model based on EIS can intuitively and accurately characterize the internal characteristics of fuel cells. In this paper, an EIS diagram is drawn based on experimental data, and an equivalent impedance model that can well characterize PEMFC is proposed, and compared with commonly used equivalent models. The correspondence between each topology in the model design and the actual PEMFC electrochemical process is explained, and the electrochemical reaction principle of the catalytic layer is explained based on the model. The fitting analysis shows that the accuracy (chi-square error) of the designed model on the experimental data is improved by about 58.16%. This model can guide the process of EIS image analysis and establishing equivalent circuit models, and provide guiding ideas for related mechanism research.

Highly accurate speed prediction technology is of great significance for online implementation of energy management strategy (EMS). Due to the complex and variable driving conditions, the accuracy of conventional speed prediction method is yet to be improved by driving pattern adaption. This paper proposes a vehicle speed prediction method based on long- and short-term memory neural network (LSTM) with driving pattern recognition and integrates it in energy management framework based on model predictive control (MPC). First, similar samples belonging to the same driving pattern are selected offline to train a more efficient and targeted LSTM. Then an online speed prediction algorithm based on driving pattern recognition is proposed. The results show that root mean square error (RMSE) of the whole driving cycle is reduced by 39% compared to conventional LSTM. Meanwhile, fuel economy and fuel cell system (FCS) durability are improved, which proves the effectiveness of the proposed method. And the real-time applicability of the proposed predictive EMS is verified.

As the different driving modes have a great influence on the performance of fuel cell hybrid electric heavy trucks, it's vital to study a driving mode-consensus energy management strategy. In order to improve the economy and durability of the system and further enhance the real-time performance of energy management strategy (EMS) in different driving conditions, in this paper, a multi-dimension fuzzy control energy management strategy based on a model predictive control framework (MPC) was raised. In the offline phase, the proposed strategy selects the appropriate parameters for each mode by an evaluation function which give full consideration to fuel cell hydrogen consumption, battery consumption, and degradation of fuel cells and batteries. Meanwhile, in the online phase, the proposed strategy dynamically matches the weighting coefficients of objective functions and optimal Markov transfer probability matrix (TPM) by multi-dimensional fuzzy optimization of parameters to better adapting to changing driving patterns overtime. The simulation results demonstrate that compared to traditional MPC-EMS proposed strategy can maintains a more stable battery SoC and reduce the total consumption function by 8.75%, hydrogen consumption by 22.65%, batteries degradation functions by 6.56% and the fuel cells degradation functions by 47.22% under driving cycle1. Moreover, the robustness of the proposed method is verified by testing it under two different driving cycles. The proposed method under CWTVC still shows better performance. Therefore, the proposed strategy can effectively improve the economy and durability of fuel cell hybrid electric heavy trucks system.

With the advantages of light weight, small size, simple structure, high efficiency, and high power density, permanent magnet synchronous motors have become the main choice of major new energy vehicle enterprises. However, under complex vehicle driving conditions, the electromagnetic parameters of permanent magnet synchronous motors change due to temperature changes, cross-coupling effects, and magnetic saturation of the core, which affect the performance of the motor control system. Therefore, fast and accurate identification of motor electromagnetic parameters is required to ensure the performance and reliability of the motor control system. By summarizing the literature, this paper analyzes the reasons for electromagnetic parameter transformation, compares the advantages and disadvantages of various electromagnetic parameter identification algorithms, points out the potential future development direction of identification algorithms, and provides ideas for future research on parameter identification algorithms.

Fuel cell has received more and more attention in the field of green aviation in recent years due to their zero-emission and pollution-free advantages. In this paper, a flight mission model of a distributed electric propulsion aircraft is established, and an energy management strategy based on an improved multi-dimensional coupled state machine is designed by considering factors such as multi-mission profile power requirements and failure emergency measures for the aviation electric propulsion hybrid power system. The strategy is based on a state machine and divides the operation of the distributed electric propulsion aircraft into four states: climb, cruise, descent, and malfunction. The overall operation test of the aircraft model is carried out based on the four states, and the research results indicate that compared with the load-following mode control strategy, the proposed strategy increases the average fuel cell (FC) efficiency by 3.83%. In addition, the proposed strategy saves 7.95% of hydrogen and can effectively ensure flight safety in the event of a malfunction.

The low economy of fuel cell hybrid electric vehicles is a big challenge to their wide usage. In this article, a road, health, and price-conscious optimal cost minimization strategy based on a decision-making framework was developed to decrease their overall cost. First, an online applicable cost minimization strategy was developed to minimize the overall operating costs of the vehicles, including the hydrogen cost and degradation costs of the fuel cell and battery. Second, a decision-making framework composed of the driving pattern recognition-enabled, prognostics-enabled, and price prediction-enabled decision makings, for the first time, was built to recognize the driving pattern, estimate the health states of power sources, and project future prices of hydrogen and power sources. Based on these estimations, optimal equivalent cost factors were updated to reach the optimal results on the overall cost and charge sustaining of a battery. The effects of driving cycles, degradation states, and pricing scenarios were analyzed.

This paper presents a vehicle speed prediction method based on the self-learning multi-step Markov Chain. By estimating the transition probability matrices with the online measured data, the proposed method can better adapt to the novel driving conditions. Through three representative case studies, its effectiveness and the advantages over the conventional Markov predictor under multiple driving scenarios are verified. Simulation results show that in dealing with the newly encountered driving conditions, the proposed approach can reduce the average prediction error by 25.70% compared to the conventional Markov predictor. Besides, the maximum online computation time of the proposed method is 7.021ms, indicating its real-time practicality.