To improve the heat transfer capability and the temperature uniformity of automotive thermoelectric generators, this study proposes a novel heat exchanger integrating vapor chambers and ring fins. A fully coupled multiphysics numerical model is established to predict the thermoelectric generator's performance and evaluate the maximum thermal stress within thermoelectric modules. The Taguchi method is employed to systematically optimize key parameters, including exhaust temperature, exhaust mass flow rate, the number of vapor chambers, ring fin thickness, and the number of ring fins, to maximize net power. The results show that the proposed thermoelectric generator raises the hot side temperature by 82.91 K and improves axial temperature uniformity. Strong interaction effects are observed among the key parameters, indicating that adjusting a single parameter can not achieve system-level optimization. The Taguchi method identifies the optimal parameter combination as: the exhaust temperature of 673 K, the exhaust mass flow rate of 30 g/s, 12 vapor chambers, the ring fin thickness of 1 mm, and 9 ring fins, under which the net power reaches 326.78 W, nearly ten times higher than that of the baseline configuration. Mechanical analysis under varied exhaust conditions confirms that the maximum thermal stress with the optimal parameter combination remains well below the allowable limit. This work provides an effective strategy for enhancing the performance of thermoelectric generators.

More-electric/all-electric aircraft impose stringent requirements on the safe operation capability of aircraft high-voltage DC power supply systems.The doubly salient electro-magnetic machine,with its robust and reliable rotor structureand ability to regulate flux under fault conditions,exhibits potential application advantages as an aircraft high-voltage DC generator.This paper first introduces the structure and operating principle of the oil-cooled doubly salient electro-magnetic machine,and analyzes the influence patterns of armature winding open-circuit and short-circuit faults on electromagnetic characteristics and loss distribution.Subsequently,based on a lumped-winding thermal conduction equivalent model,a three-dimensional temperature field finite element analysis model for oil-cooled motors is estab-lished to investigate the temperature rise characteristics under normal and fault conditions.Finally,using the winding's extreme temperature rise as a safety threshold,the fault-tolerant output capability of the machine under different faults is analyzed and evaluated through thermal limit constraints analysis and relevant verification experiments;thereby,providing theoretical and practical foundations for fault-tolerant operation in aircraft high-voltage DC power supply systems.

The nonlinear relationship between the suspension force and the field current affects the suspension performance of the bearingless doubly salient electromagnetic (BDSEM) motor under magnetic saturation. At the first of this paper, the suspension principle and the characteristics of the nonlinear suspension force of the BDSEM are analyzed. Secondly, the influence of magnetic saturation on the suspension performance of the motor is revealed, and the variable gain suspension control method is proposed to achieve the stable suspension of the motor under magnetic saturation. Thirdly, the effectiveness of the proposed suspension control method is verified and analyzed by simulation. Finally, a 12/8-pole BDSEM prototype and suspension experimental platform are developed, and the experimental results show that the proposed method significantly improves the suspension performance of the motor under magnetic saturation.

Variable flux memory machines (VFMMs) can perform flexible online magnetization state (MS) regulation via d-axis magnetizing current injection. However, the ultra-short duration and large amplitude of the magnetizing current, combined with significant parameter perturbation disturbances during MS manipulations, impose stringent demands on current tracking accuracy and system robustness. Conventional linear active disturbance rejection control (LADRC) suffers from inherent phase lag that impairs current tracking accuracy, whereas the error-based LADRC (ELADRC) improves dynamic response but leaves the critical need for enhanced disturbance rejection. The above demerits make the conventional LADRC unsuitable for VFMMs. To address these limitations, the compensation function ELADRC (CF-ELADRC) is proposed for VFMM drive systems, which is an inherently straightforward and robust current control strategy that offers faster dynamic response and exceptional anti-disturbance performance. Experimental results demonstrate that the proposed CF-ELADRC substantially outperforms both LADRC and ELADRC in terms of magnetizing current tracking accuracy and steady-state recovery rate during flux regulation operations, thereby confirming both the effectiveness and theoretical correctness of the proposed CF-ELADRC.

The cross-coupling between X-axis and Y-axis suspension forces affects the stable suspension of the bearingless doubly salient electromagnetic motor (BDSEM), and magnetic saturation further exacerbates this influence. In this article, a suspension force rotating vector control method of the BDSEM considering magnetic saturation is proposed. First, the influences of magnetic saturation on the direction of suspension forces are analyzed. Second, a mathematical model of suspension forces considering magnetic saturation is derived, and the decoupling of suspension forces is achieved based on coordinate transformation. Third, simulations are conducted to verify the decoupling effectiveness of the suspension force, and the proposed method is compared with the traditional method. Finally, a prototype is developed, and the experimental platform is also established. Experimental results demonstrate the feasibility of the proposed control method.

Due to the structural constraints of the salient-pole rotor and narrow air gap in reluctance machines, conventional winding direct cooling methods cannot be directly applied, limiting a further improvement in power density. To address this challenge, this article proposes a novel direct oil cooling solution employing an in-slot sleeve (ISS) structure. The in-slot portion is enclosed by the ISS, while the electric machine ends are sealed with the end sleeve. These two components are combined through the stator end plate to form a complete enclosed cooling channel. This arrangement separates the windings from both stator and rotor components without occupying the air gap space, enabling direct winding cooling. This article employs a 45 kW ISS direct oil-cooled doubly salient electromagnetic machine (DSEM) as the test case. A refined thermal network model is developed, accounting for loss distribution characteristics of the DSEM, and the influence of the ISS on winding cooling performance is analyzed. An experimental platform validates prolonged operation at 3 L/min flow rate, 65 degrees C oil temperature, and 22.1 A/mm2 current density, confirming the accuracy of the thermal model. Furthermore, thermal simulation results demonstrate that the maximum sustainable current density reaches 27.1 A/mm2 at this flow rate.

Under high-speed and heavy-load conditions, conventional control methods for doubly salient electromagnetic generator (DSEG) systems face the problem of diminished effectiveness or even ineffectiveness due to core saturation and commutation shift. To address this issue, an equivalent synchronous boost control method is proposed to improve the output performance of high-speed DSEG systems. The three-phase full-bridge active rectifier (AR) is equivalently operated as a synchronous boost converter, where the upper-bridge MOSFETs are chopper-controlled as the equivalent boost switch, and all MOSFETs are conducted during the boost off-state according to phase-current directions to realize synchronous rectification. Based on the derived conduction modes, the DSEG can achieve improved performance with an appropriate duty cycle. In addition, switching frequency and bus capacitance are shown to affect the control performance. The proposed method is suitable for high-speed operation and does not require rotor position information. Experimental results demonstrate that, compared with the diode-rectifier-based method, the proposed method increases output power by up to 43% under limited field current, and reduces total loss by up to 23% under closed-loop control. Moreover, under high-speed and heavy-load conditions, it outperforms conventional AR-based methods such as angular position control. The distributions of current, loss, and efficiency under different parameter settings are also analyzed.

Hybrid-variable-flux PM machine (HVF-PMM) can achieve higher flux regulation (FR) flexibility and wider high-efficiency operation regions, as it integrates both variable magnetization state (MS) and variable leakage flux (VLF) properties. However, introducing the VLF property via leakage bridge designs inevitably increases the difficulty of remagnetizing low-coercive-force (LCF) magnets, thereby leading to an oversized inverter rating. To address this issue, this article proposes an inverter-rating-reduction (IRR) design methodology that intentionally avoids difficult-to-remagnetize regions of the LCF magnets while maintaining the required performance. To achieve this, multioperating-mode field-circuit coupled models are established, in which the re- and demagnetizing currents are constrained to have equal amplitudes. Consequently, the inverter rating can be reduced by balancing the re- and demagnetizing currents. First, the machine configuration, FR principles, and the underlying mechanism by which the leakage bridges influence the magnetizing currents are introduced and analyzed. Then, the IRR methodology is clarified and detailed, followed by sensitivity-based optimization to obtain the optimal design. Furthermore, a comparative study of the electromagnetic characteristics of the HVF-PMM before and after the optimization is conducted, showing a 28.8% reduction in inverter current rating owing to the balanced magnetizing currents. Finally, a prototype of the HVF-PMM is manufactured and tested, and the results validate the effectiveness and practicality of the developed design methodology.

The magnetic saturation in the bearingless doubly salient electromagnetic motor (BDSEM) results in significant nonlinearity in suspension force characteristics, which leads to difficulty in suspension force control. In this paper, a suspension force control method based on the neural network compensator is proposed to make the BDSEM achieve stable suspension under magnetic saturation conditions. Firstly, the influence of magnetic saturation on the relationship between suspension forces and suspension currents, and the variables influencing suspension forces are analyzed. Secondly, a simplified neural network compensator is constructed to compensate for the relationship between the suspension forces and currents. Thirdly, a simulation model of the suspension system is built and the proposed method is compared with the traditional method. Finally, experiments are conducted, and the feasibility of the proposed control method is verified.

The effect of the field current and commutation angle parameters on the average torque of a doubly salient electromagnetic motor (DSEM) is highly mutually coupled at high-speed operation. Hence, it is hard for the conventional methods to realize the simultaneous adjustment of field current and commutation angle to fully use the torque capability of the DSEM at high-speed operation. In this article, a commutation angle closed-loop (CAC) based average torque control method is proposed. First, the effect of the field current and commutation angle parameters on the average torque is analyzed. The characteristics of optimal commutation angle parameters are derived, and the CAC method is proposed to adjust the commutation angle parameters by aligning the phase current intersections with phase back electromotive force (EMF) intersections adaptively. Then, the relationship between the field current and average torque is investigated under the proposed CAC method. On this basis, the variable-step perturbation and observation algorithm and average torque control are proposed to adjust the field current by maximizing the average torque. Finally, the proposed CAC-based average torque control method is verified on the high-speed DSEM drive system. The experimental results indicate that the proposed control method can adaptively obtain the optimal commutation angle parameters and field current simultaneously and realize the maximum torque operation of the high-speed DSEM drive system.

In the suspension control process of the bearingless doubly salient motor (BDSM), the rotor is prone to oscillation, making it difficult to achieve a stable suspension state. Therefore, obtaining a dynamic suspension force model that accounts for actual operating conditions is key to achieving stable suspension. To address this issue, this paper proposes a suspension force linearization method based on the motor’s inductance characteristics. This method transforms the BDSM into a multi-input multi-output (MIMO) dynamic model, with its dynamic behavior described through state-space equations. On this basis, a block diagram of the suspension control system incorporating the BDSM dynamic model is constructed, laying a theoretical foundation for system stability analysis and the improvement of the motor's suspension performance. Finite element simulation results substantiate the accuracy of the proposed motor dynamic model.

Stimuli-responsive hydrogels hold immense promise for biomedical applications, but conventional gelation processes often struggle to achieve the precision and complexity required for advanced functionalities such as soft robotics, targeted drug delivery, and tissue engineering. This study introduces a class of 3D-printable magnetic hydrogels with tunable stiffness, adhesion, and magnetic responsiveness, prepared through a simple and efficient “one-pot” method. This approach enables precise control over the hydrogel’s mechanical properties, with an elastic modulus ranging from 43 kPa to 277 kPa, tensile strength from 93 kPa to 421 kPa, and toughness from 243 kJ/m3 to 1400 kJ/m3, achieved by modulating the concentrations of acrylamide (AM) and Fe3O4 nanoparticles. These hydrogels exhibit rapid heating under an alternating magnetic field, reaching 44.4 °C within 600 s at 15 wt%, demonstrating the potential for use in mild magnetic hyperthermia. Furthermore, the integration of Fe3O4 nanoparticles and nanoclay into the AM precursor optimizes the rheological properties and ensures high printability, enabling the fabrication of complex, high-fidelity structures through extrusion-based 3D printing. Compared to existing magnetic hydrogels, our 3D-printable platform uniquely combines adjustable mechanical properties, strong adhesion, and multifunctionality, offering enhanced capabilities for use in magnetic actuation and hyperthermia in biomedical applications. This advancement marks a significant step toward the scalable production of next-generation intelligent hydrogels for precision medicine and bioengineering.

The sudden drop in suspension force caused by excitation loss in the bearingless doubly salient electromagnetic (BDSEM) motor, leads to requirement for fault-tolerant control. In this article, the suspension force characteristics and suspension current control of the 12/8-pole BDSEM motor under the excitation loss are analyzed. The biased-magnetic field compensation (BFC) current control strategy for BDSEM motor is proposed to achieve stable suspension force under excitation loss without additional hardware. Finally, the simulation results verify the effectiveness of the proposed method.

Inductance parameters are crucial for position-sensorless control of doubly salient electromagnetic machine (DSEM) at low-speed operation. However, the inherent temporal coupling between inductance detection and current control will limit the phase current injection interval, consequently degrading the torque capability. This paper proposes an online inductance identification method for DSEM based on decoupled inductive sensing. Firstly, the connection configuration of sensing winding is elaborated, followed by theoretical analysis of inductance characteristics. The voltage expression of sensing winding is derived through electromagnetic field modeling, and a finite element model (FEM) is established. Subsequently, the detection principle and system configuration employing decoupled inductive sensing are presented, achieving real-time inductance identification. Finally, comprehensive validation through finite element analysis and system simulation confirms the correctness and effectiveness of the proposed method.

Large deflection analysis is crucial for understanding and predicting the mechanical performance of flexible beam structures, which can be used to analyze metamaterial unit cells simplified into flexible beam structures. This paper investigates the large deflection behavior of an inclined cantilever beam with its freedom end subjected to a dead load. Firstly, considering the geometric nonlinearity of the beam and the influence of boundary conditions, a mathematical model of the beam is established and solved. Secondly, equations for the deflection curve and strain energy are derived, expressed in the semi-analytical form of elliptic functions. Then, a program is developed using Riemann integration combined with the bisection method to iteratively obtain the final calculation results. Finally, the calculation results of this paper are compared with those obtained by the nonlinear finite element method, thereby validating the accuracy of the proposed algorithm.

The high-performance control of the spacecraft attitude is significant for successfully executing diverse tasks. To realize this goal, a velocity-free adaptive neural-fuzzy predefined-time attitude controller is presented for the spacecraft with uncertain inertia, exogenous disturbances, and input saturation. Firstly, an improved predefined-time stable system is established, featuring an adjustable convergence time to enhance the flexibility of the controller design. Utilizing the robust approximation ability of the neural-fuzzy network, a state observer and a nonsingular sliding mode controller are developed to achieve accurate state measurements, improve strong robustness, and eliminate singularity issues. Subsequently, a modified anti-saturation method is designed via the Gaussian function and auxiliary compensation system to resolve the input saturation problem. Based on the Lyapunov theorem, the predefined-time stability of the whole system is confirmed. Finally, through comparative simulations and numerical analysis, it can be concluded that: 1) the system state converges within a predefined time related to only a single parameter, and the actual convergence time is adjustable; and 2) compared to existing control schemes, the proposed control scheme demonstrates superior anti-disturbance ability, avoids potential singularities, achieves faster convergence, and eliminates input saturation.

Dual three-phase (DTP) electric machines are increasingly favored in industrial applications and automotive electric drive systems. Different machine types are used in different scenarios due to their unique advantages. However, the corresponding parametric models developed for different machine types increase development and calibration costs, and they are poorly compatible with each other. In this context, this article introduces a unified mathematical model that combines the electromagnetic characteristics of different DTP machines by introducing the concept of a "Faraday reference frame" for the use of permanent magnet synchronous machines (PMSMs), reluctance synchronous machines (RSMs), externally excited synchronous machines (EESMs), and induction machines (IMs). A unified state-space model of DTP machines is developed. The model adapted to different machine types is discussed. Furthermore, the unified flux and torque observers are built on the basis of the proposed model. The proposed observers are validated in a circuit-level simulation and an electromagnetic-level co-simulation with finite element analysis (FEA) modeling of different machines. By comparing the observer output with the machine output, the model can observe the flux at s alpha/s beta subplane with an estimation error less than 4.9% NRMSE. The algorithm is validated at the physical level in an experimental environment, achieving torque estimation accuracy greater than 99.38% under steady-state operating conditions.

The bearingless doubly salient electromagnetic machine, as an emerging bearingless reluctance machine, the mechanism of its suspension force generation is still unclear. In this article, first, an analytical model (AM) that considers the rotor eccentricity is established. Second, the suspension force is divided into ten components to identify its key constituents. The reasons for variations of inductances with the radial displacement are explained according to the AM, and hence the mechanism of the suspension force generation is revealed. Third, an analytical method for suspension forces is proposed based on the inductance variations, and the effect of the advanced angle control on the suspension force is illustrated. Besides, a decoupling control based on the analytical method is proposed to reduce the suspension force ripples. Finally, the effectiveness of the AM and the decoupling control is experimentally verified. The proposed analytical method is also suitable to examine radial forces in other electrical machines.

The saturation state of the bearingless doubly salient electromagnetic motor (BDSEM) changes the gain relationship between current and suspension forces, which makes the suspension control system of the BDSEM unstable. In this paper, a suspension control method for the BDSEM based on the back propagation (BP) neural network is proposed. Firstly, the influence of magnetic saturation on the suspension control system and its mechanism are analyzed. Secondly, suspension forces characteristics are analyzed based on the finite element simulation under saturation conditions, and the data set is provided for the suspension forces modeling. Thirdly, the suspension control method based on neural network is introduced to control the nonlinear suspension forces with different field current. Finally, the simulation verification confirms the feasibility of the proposed control method under the BDSEM saturation conditions.

The conventional position sensorless control for the doubly salient electromagnetic machine (DSEM) introduces the extra voltage sensor, which reduces the reliability of the motor drive system. Hence, a position sensorless control method based on non-conduct phase current injection is proposed in this paper. In the proposed method, the constant duty cycle voltage pulse is injected into the non-conduct phase and the response current amplitude is compared to the pre-set commutation threshold to determine the current commutation point. To solve the inherent delay of current sampling, an online compensation strategy by adjusting the commutation threshold is proposed. The position sensorless control method in this paper only needs current sensors to perform commutation without the requirement of voltage sensors.