With the increasing deployment of fuel cell systems in heavy-duty applications, high operating cost remains a critical challenge, with performance inconsistency in multi-stack architectures being a major contributing factor. This study presents a novel bi-level energy management strategy tailored for heavy-duty multi-stack fuel cell power systems. The upper-layer framework employs a model predictive control-based multi-objective optimization to minimize operating cost, while the lower-layer framework optimizes power allocation across fuel cell stacks to mitigate performance deviations and enhance consistency. The simulation results demonstrate that, across an 1800s driving cycle, the proposed Cost-Consistency-oriented energy management strategy achieves an 11.25% reduction (1.75μV) in voltage degradation for aged stack compared to healthy stack. Furthermore, compared to two benchmark methods, the proposed strategy reduces power fluctuations in aged stacks by 20.4% and 5.5% respectively, while achieving operating cost reductions of 1.3% and 20.2%. These results validate the bi-level architecture's capability in simultaneously optimizing economic feasibility and performance consistency for multi-stack fuel cell systems, which provides a scalable solution for heavy-duty applications.

To address the limitations of conventional Hall sensor interpolation methods in PMSM vector control, including dynamic response lag and discontinuous estimation, this study proposes a rotor position and velocity estimation method based on Hall vector filter phase-locked loop. The approach converts three-phase Hall signals into rotating vectors through coordinate transformation, extracts fundamental sinusoidal components using a synchronous frequency tracking filter (SFTF), and achieves closed-loop observation of rotor position/speed through phase-locked loop (PLL) synchronization, effectively suppressing the Hall signal deviation and improving the estimation accuracy. Furthermore, an improved PLL architecture with open-loop feedforward compensation is developed to resolve the direct-current (DC) offset error in conventional quadrature PLL under variable-speed conditions. Experimental verification demonstrates the proposed method's superior performance over conventional interpolation approaches in dynamic response speed, position estimation accuracy, and anti-interference capability, confirming its engineering viability.

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.

Abstract There are a lot of flammable materials in the textile workshop, and once a fire occurs, it will cause property damage and casualties. At present, smoke detection in textile workshops mainly relies on temperature‐sensing smoke sensors with low detection rate and poor real‐time performance, which cannot meet the task of smoke detection in complex environments. Therefore, this paper proposes an improved mixed Gaussian and YOLOv5 smoke detection algorithm for textile workshops. In order to reduce the interference of static background in smoke detection, an improved gaussian mixture algorithm is used to extract suspected smoke areas in video by using the dynamic characteristics of smoke. Then, an adaptive attention module is added to the feature pyramid infrastructure of the YOLOv5 target detection network to improve the multi‐scale target recognition ability. In addition, the focal loss function is used to reduce the impact of background and foreground class imbalances on the detection results. The experimental results show that the detection accuracy of the proposed method is 94.7%, and the average detection speed is 66.7 FPS. By comparing with the existing state‐of‐the‐art algorithms, the detection capability of this method has been significantly improved. At the same time, it has high real‐time performance and detection accuracy in smoke detection in textile workshops.

In this paper, an improved Model Reference Adaptive Controller is designed to synchronize speed of two BLDMs of a dual channel hoist. Due to ability of disturbance rejection is enhanced by proposed control method, both channels with different load could track the dynamic respond of reference model. Speed synchrony during transient state is much better improved which significantly narrows the position error finally. Stability of method is proven based on Lyapunov theorem, and effectiveness is verified by simulation.

There are many kinds of disturbances in the actual permanent magnet synchronous motor (PMSM) control system, including load sudden change, friction and others, which affect its speed control performance. The unknown torque disturbance and load torque in the PMSM system are considered to total disturbance using disturbance compensation control strategy in this paper, the extended state observer (ESO) is used to estimate the total disturbance in real time, and the feedforward compensation is applied to the speed loop controller to suppress the system disturbance. The trapezoidal wave disturbance and square wave disturbance are introduced in the PMSM system for simulation of the disturbance estimation and speed response, the estimation effects of total disturbance through output torque and input q-axis current are compared. The results show that the disturbance compensation control based on ESO can effectively suppress the unknown system disturbance, improve the speed tracking accuracy and enhance the robustness of the system.

This paper proposes a generalized static fault diagnosis method, which is suitable for multi-phase inverter used in motor drives. The short or open circuit fault diagnosis is based on a simple monitoring network just consisted of two current limit resistors and two photocouplers. Therefore the proposed method could keep the system safety under the condition of only weak current passing through the network. According to the rules in fault diagnosis, the faults could be detected and classified by the output signals of monitoring network. Different with the past approaches, the aforementioned method requires that the fault self-test must be completed before system running. And the output ports of monitoring network can be directly connected with sample device and farther sent to the computer/micro-controller by communication. Besides, the monitoring network has the feasibility to be applied in any multi-phase system without structural change. In the end, simulation and experimental results of short and open circuit faults are provided to validate the effectiveness of the proposed diagnosis.

The fuel supply system plays an important role in combustion engines, and thus a higher reliability is required. A novel fault diagnosis and prevention strategy are presented in this paper. The diagnosis model is built up based on deviation calculation by introducing the flow sensor principle, which take reference flow rate and measured flow rate as criteria. The estimation function between the fuel flow rate, outlet pressure and pump rotating speed is obtained, which could be used to estimate the fuel flow rate. The estimation method can provide the flow rate to the entire system and then keep the fuel supply system working reliably even when the sensors are in failure. Experimental results show the effectiveness of the proposed strategy in terms of accurate fault detection and robust prevention in the fuel supply system.

As an important part of multiple-function and intelligent single-phase grid-connected Photovoltaic (PV) system, fault ride-through (FRT) has attracted extensive attention in the recent years. According to the continuously updated grid-connected requirements, the inverters should remain connected with grid under grid faults even the grid voltage dips to zero, and provide reactive current injection to support the grid recovery. In this paper, a contrastive evaluation of two intuitive and simple flexible power control methods, which are implemented in two different reference frame, are presented in the case of zero-voltage ride-through (ZVRT) operation. Only the dq-rotating reference frame power control method provides an acceptable ability of ZVRT. In addition, the performance is verified in both the simulation and experiment results.

The paper proposes an advanced communication control strategy aiming at the problem of torque ripple during the commutation process of Brushless DC motor (BLDCM). By commutating in advance, the slope of off-phase current and conduction-phase current are adjusted. By comparing and analyzing the equilibrium equation of three-phase current in case of different commutation leading angle, a conclusion can be drawn from the analysis that the decaying rate of off-phase current is fundamentally the same as the increasing rate of conduction-phase current, and the torque ripple is minimum, when commutates 60 electrical degree in advance. Besides, for suppressing the freewheeling current of three-phase bridge inverter circuit caused by commutating in advance, a topology is proposed. The theoretical analysis shows that, the proposed method can reduce the torque ripple during the commutation process, and improve the control precision of BLDCM system.

A single stage high gain Buck-Boost inverter (BBI) for photovoltaic application is proposed in this paper. The proposed topology is with simple circuit and low switch count. The three-winding tapped inductor is applied to attain high gain feature. One-cycle control (OCC) is adopted to obtain superior source ripple rejection. The paper presents the detail operation principle of the proposed topology, and the derivation of the OCC control law. The proposed topology and the control scheme are verified by simulation.

Conduction losses, which are proportional to the square of rms inductor current, account for an important part of total losses for a dual active bridge (DAB) converter. A minimum rms inductor current control strategy based on Dual Phase Shift (DPS) modulation is proposed to lower the conduction losses and improve efficiency of the converter over a wide input/output voltage range. Based on the steady-state mathematical model of rms inductor current and transmission power, the minimum rms current trajectory (MCT) composed of phase-shift and PWM duty cycle is derived. The proposed control strategy make the DAB converter operate along MCT in the steady state to improve efficiency and realize fast regulation of output voltage in dynamic process. Simulation results verify that, compared with Single Phase Shift (SPS) control, significant reduction of rms current at light load, zero rms current at no-load and high dynamic performance have been achieved.

In this paper, a power-on self-test scheme for buck-converter-based brushless DC motor drives is proposed to ensure the motor operating in safe operation area (SOA). At the beginning, the DC link capacitor between the buck converter and inverter will be charged. After that, it is used to provide current during inverter testing procedure. Generally, the faults are identified by detecting the maximum value of the discharging current. Based on the proposed fault-detection scheme, brushless DC motor can run safely by detecting the failures of inverter and three-phase windings of motor in advance. Compared with the traditional fault-detection schemes, only one current sensor with low accuracy is needed for proposed approach. In addition, potential components damage can be prevented since inverter is powered by capacitor instead of power supply. The feasibility of the proposed scheme is confirmed by simulation results.

This paper presents a novel fault diagnosis approach for three-phase full-bridge inverter, which is the most popular used in the brushless DC motor (BLDCM) system and the permanent magnet synchronous motor (PMSM) system. The short or open circuit fault diagnosis of the six power switches of the inverter is accomplished by only four simple resistor divider networks and one sampling resistor. By comparing the divided voltages with the reference voltage, the faults are detected and identified under a set of special judgment rules. The proposed method is designed to be used before system running. Compared with the existed methods, the proposed approach works with only weak current passing, which is more safe. Finally, the effectiveness is verified by simulation.