Sizing of passive components in electric vehicle drive systems is a critical consideration, as it influences numerous factors, including cost, power density, performance, and efficiency. Traditionally, component sizing has been carried out using empirical design rules under worst-case considerations, which may result in over-sized designs. A systematic approach to sizing is essential to ensure effective hardware utilization without compromising system stability. An impedance-based stability analysis is presented to determine the optimal sizing of system components. Given the nonlinear nature of power converters, subsystems of the electric drive are linearized to obtain small-signal models, which are amenable to linear time-invariant (LTI) system analysis. Further, sensitivity of the overall system stability to parametric variations is studied by sweeping both control-stage and power-stage parameters. Additionally, critical points in the torque-speed regions are identified, and control-loop gain scheduling is explored as an alternative for lowering component size requirements. Results from time-domain simulations verify the correctness of the developed small-signal models. Detailed experimental studies are conducted to assess the stability of both the individual converter and the combined system. Experimental results closely match the simulations, validating the developed sizing and tuning methodology.
High-speed cornering is one of the representative driving scenarios for high-performance electric vehicles, where safety, maneuverability, and real-time control must be fulfilled. This study designs an autonomous cornering trajectory planning and tracking framework for high-performance electric vehicles, based on a continuous high-speed curvature motion planning method and a variable-constraint adaptive model predictive control approach. The proposed continuous high-speed curvature motion planning method provides a generalizable trajectory generation strategy for various racing corners, while considering vehicle dynamics constraints to plan feasible speed profiles. To address the limitations of linear control models in capturing vehicle nonlinear characteristics under extreme maneuvers, this study develops a variable-constraint adaptive model predictive control method. The core of this method lies in the integration of a lateral force intercept parameter into the prediction model, along with real-time updates of both this parameter and the tire cornering stiffness. Furthermore, the tire sideslip angle constraints are adaptively modified during the control process. To assess the limit-handling capabilities, the trajectory tracking performance is specifically evaluated under the aggressive U-turn maneuver. The overall effectiveness and superiority of the proposed approach are validated through co-simulations and driver-in-the-loop testing.
Due to the structural characteristics of the double windings and the low harmonic impedance, the dual three-phase permanent magnet synchronous motor (DTP-PMSM) suffers from high computational burden and increased stator current harmonics. To address the above issues, a simplified direct torque control (DTC) for DTP-PMSM based on vector error optimization is proposed. Firstly, the reference voltage vector is obtained through simple arithmetic operations based on the transformation of torque and flux amplitude, and a disturbance observer is constructed using the torque error to enhance parameter robustness. In the αβ plane, with the sector division, the basic voltage vector with the maximum amplitude is directly selected according to the phase of the reference voltage vector. In the xy plane, the harmonic voltages are classified into two types: one induced by torque control and the other caused by non-ideal factors. The harmonic voltage caused by non-ideal factors is compensated by the Adaptive Linear Neuron (Adaline), and the weight factor is iteratively updated by the Least Mean Square (LMS), eliminating the complex process of digital discretization. To achieve decoupling between harmonic and torque control, the harmonic voltage is synthesized by the simply constructed virtual vector in the xy plane, whose magnitude is zero in the αβ plane. Within one control period, the proposed method only uses one basic voltage vector and one virtual vector, and the duty cycle is determined by the principle of minimum voltage vector error, thereby effectively reducing the computational burden. Finally, experimental results on a DTP-PMSM drive platform validate the effectiveness of the proposed method.
In electromechanical actuator (EMA) systems, sensorless control methodologies offer a promising approach to simultaneously achieve cost-effectiveness and enhanced operational reliability. To broaden the applicability of the back electromotive force (BEMF) method at low speeds, an adaptive band-pass filtering-based extended state observer (ABPF-ESO) is designed. This observer embeds an adaptive band-pass filter into the internal model of a conventional linear ESO, enabling BEMF extraction with zero phase lag and adaptive bandwidth. Meanwhile, to decouple the rotor position error from the speed sign in traditional phase-locked loops (PLLs), a polarity correction strategy based on the sine difference formula for position error is introduced. This ensures robust observation when the motor reverses its rotational direction. Furthermore, to improve the dynamic response of the PLL under rapid variations in speed command and load torque, a third-order ESO-based PLL (TESO-PLL) structure integrating speed compensation is developed, which explicitly provides a method for dynamic parameter tuning. Finally, the effectiveness of the proposed method is verified through experiments on a Higale platform with a 0.2 kW SPMSM.
Vehicle-to-Grid (V2G) technology provides fast and flexible resources for grid frequency regulation (FR), yet its effectiveness is fundamentally constrained by the spatiotemporal uncertainty of electric vehicle (EV) mobility and user participation. To address this challenge, this paper proposes a capacityaware, prediction-constrained V2G frequency regulation control framework for large-scale EV aggregation. A statistical–neural hybrid model is developed to predict the available V2G frequency regulation capacity (FRC), where stratified statistical aggregation reduces communication overhead while an LSTM–Transformer network captures spatiotemporal dynamics without explicit probabilistic parameter identification. Based on the predicted FRC, an adaptive multi-mode control strategy is formulated as a dynamic constrained multi-objective optimization problem, jointly optimizing regulation power and incentive pricing to balance frequency tracking performance and operational cost. The control modes and update intervals are adaptively switched according to FR demand intensity and predicted capacity availability. Case studies using PJM market data demonstrate that the proposed approach achieves robust frequency stabilization with a demand–supply ratio above 94%, while reducing regulation costs by 15–30% compared with benchmark methods. The results validate the effectiveness of integrating prediction-constrained capacity awareness into V2G frequency regulation control.
High-speed maglev vehicles represent a transformative advancement in modern rail technology, offering unparalleled advantages in operational speed, safety metrics, and ride quality. However, this study identifies three critical challenges, which include multi-physics coupling effects encompassing vehicle-track-electromagnetic interactions, asymmetric unidirectional saturation in control inputs, and nonlinear disturbances from elastic track deformations. Conventional model-based controllers exhibit significant limitations in addressing these compounded uncertainties and asymmetric saturation constraints, often resulting in suboptimal stabilization and compromised safety margins. To address these challenges, this paper proposes a novel saturated proportional-derivative sliding mode control (PD-SMC) method integrated with back propagation neural network (BPNN) compensation. The proposed method does not rely on precise system modeling and effectively handles asymmetric unidirectional input saturation, ensuring system stability under structural and parametric uncertainties. Furthermore, the BPNN effectively mitigates nonlinear disturbances induced by track flexibility and external disturbances. The stability of the equilibrium point of all state variables in the levitation system is theoretically demonstrated through a rigorous Lyapunov analysis. Finally, simulation and experimental results validate the performance of the proposed method.
Traditional single-phase electric-drive-reconstructed onboard chargers (EDROCs) are widely adopted owing to their high integration density and accessibility. However, a bulky dc-link capacitor is usually required for power ripple suppression, and the incomplete cancellation of magnetomotive force leads to charging torque fluctuation, thus degrading the power density and reliability. To address these problems, this article proposes a novel EDROC topology under the criteria of minimum torque fluctuation. Two windings and the corresponding bridge arms of a symmetrical six-phase permanent magnet synchronous machine (SSPMSM) are first reconfigured as a series inductance-based active filter (AF) to suppress the power ripple. Different topologies with various winding selections are then evaluated to ensure standstill operation during charging. The optimal topology is subsequently identified for minimum torque fluctuation. An integrated control strategy combining bridgeless power factor correction (PFC) and AF current compensation is further developed. Finally, experimental results obtained from a 1kW laboratory prototype verify the effectiveness of the AF and the validity of the selected topology.
Lithium-ion batteries often exhibit significant cell-to-cell variability during capacity grading and pack integration, which can be further amplified at the pack level and affect system consistency and service life. Therefore, evaluating cell lifetime potential at the capacity grading stage or during very early cycling is critical for battery screening and consistency-based grouping. To address the limitations of traditional methods that rely on long-term aging tests, this study proposes an early lifetime assessment approach using capacity grading-stage single-cycle charge-discharge data. First, full-lifetime cycling data were divided into source-training and target-batch assessment sets, and multidimensional degradation-related features were extracted at several key state-of-health (SOH) nodes. Recursive Feature Elimination (RFE) was applied to select the most informative features. Subsequently, a Hierarchical Degradation Learning Network (HDL-Net) was developed, in which the Macro Degradation Structure Layer (MDSL) captures the global degradation trend, while the Adaptive Residual Refinement Layer (ARRL) models cell variability and local nonlinear characteristics. A Simulated Annealing Weight Optimizer (SAWO) was further incorporated as a lightweight residual distribution calibration module to refine the ARRL output. Experimental results show that the proposed method can effectively evaluate lifetime differences using capacity grading-stage single-cycle charge-discharge features, achieving an MAE of 11.015, an RMSE of 17.969, and an Acc±5% of 93.75%. Validation on two additional datasets from Tongji University further demonstrates the robustness and applicability of the proposed approach for battery screening and consistency-based grouping.
In this paper, a novel hybrid pole permanent magnet (HPPM) machine with combined Halbach-array and V-shape magnet arrangements is presented for special electric vehicles (EVs). The simplified magnetic equivalent circuit (MEC) is established to illustrate the flux-concentrating mechanism of the proposed hybrid pole design, and the design parameters are optimized so as to improve performance. Then, the contributions of harmonic components for different permanent magnets (PMs) are analyzed and researched, which reveals the mechanism of torque improvement and torque ripple suppression. Moreover, the electromagnetic performances of the proposed HPPM machine are obtained and compared with traditional PM machines, including back electromotive force (back-EMF), torque characteristics, and efficiency. The results show that the HPPM machine shows higher magnetic flux density and stronger torque output capability, and the torque density is achieved at 8.51N·m/kg to accommodate the specific needs of the special EVs. Finally, a 40kW 60-slot/8-pole prototype is manufactured and tested to verify the effectiveness of the proposed machine.
High-frequency operation, high resonant voltage stress, and harsh operating environments make wireless power transfer (WPT) systems highly susceptible to various faults. However, most existing fault diagnosis methods in electrical engineering are targeted at motors and power systems, and are not directly applicable to WPT systems with different operating conditions and circuit topologies. This paper proposes a multi-fault diagnosis method for dynamic wireless power transfer (DWPT) systems based on resonant capacitor voltage. By monitoring and calculating the DC and AC components of the resonant capacitor voltage to determine the system fault characteristics, the proposed method realizes the diagnosis and localization of inverter power transistor open-circuit, transmitting rail inter-turn short-circuit, and resonant capacitor breakdown faults, providing a basis for subsequent fault-tolerant control and targeted maintenance. After analyzing the impacts of different faults on DWPT system characteristics, this paper identifies the corresponding fault features. On this basis, a fault diagnosis method based on resonant capacitor voltage detection is proposed, and the diagnosis procedures for each fault are presented. The method is simple to implement, as it avoids the effects of dead time, leakage inductance, power transistor switching delays, parameter deviations, and sampling errors, front-end grid fluctuations, and vehicle lateral misalignment. It offers high real-time performance, reliability, and robustness, providing a brand-new solution for the online real-time fault diagnosis of DWPT systems. An experimental prototype is built for verification, and the results confirm the effectiveness and accuracy of the method in diagnosing all targeted faults.
In this paper, by using a nonlinear control strategy, we prove that the elimination of the fuel cell power converter in an electric vehicle powertrain is feasible. This strategy indirectly regulates the power delivered by the fuel cell through a nonlinear control implemented by a single DC-DC converter connected to a supercapacitor. This configuration allows for efficient adjustment of the operating point, in combination with an energy management strategy, to adapt to power demands. As a result, there is a reduction in the size of the powertrain and an increase in efficiency by eliminating the need for multiple DC-DC converters to directly control the fuel cell. Experimental test has been carried out, on a real electric vehicle powertrain, to validate the development.
At high speed, permanent magnet synchronous motors (PMSMs) exhibit increased stator iron loss due to the high fundamental electrical frequency. The resulting eddy-current reaction changes the magnitude and phase of the observed flux linkage, which may introduce angle estimation errors in position-sensorless control. Existing methods usually introduce an equivalent iron-loss resistance into the voltage model, but the eddy-current-induced flux attenuation and phase shift are not explicitly embedded into the active flux used for rotor-position estimation. To address this issue, a vector magnetic circuit (VMC) active-flux observer is proposed for high-speed PMSM sensorless control. The VMC flux-linkage relationship is reformulated into an active-flux-compatible form through linearization, allowing the eddy-current-induced flux attenuation and phase shift to be represented by the VMC active flux and the corresponding equivalent inductance. Considering the speed-dependent characteristic of the VMC model, a stationary-axis speed observer based on the mechanical model is also developed to improve transient performance and reduce steady-state speed error. Simulation and experimental results show that the proposed method reduces the angle estimation error by 48.9% compared with the conventional active-flux observer, confirming its effectiveness for high-speed PMSM sensorless control.
To effectively attenuate current ripple of the permanent magnet synchronous motor (PMSM), this paper proposes an enhanced active disturbance rejection control (EADRC) based on a dual error constraint observer with novel resonant controller (NRDEO). Within the proposed architecture, the dual error constraint observer (DEO) is designed to improve disturbance estimation performance by introducing an auxiliary error correction term on top of the conventional single error extended state observer. Meanwhile, a novel resonant controller (NRC), which is embedded in the disturbance estimation loop of the DEO, is developed to eliminate the resonant peak near the target frequency. With the direct current and alternating current disturbances accurately estimated, the feedback control law of EADRC is presented to effectively reject these disturbances. Furthermore, theoretical analysis comprehensively investigates the disturbance estimation and rejection capabilities, the convergence of the NRDEO, and the stability of the EADRC, which reveals that the proposed scheme can effectively enhance current dynamic performance and steady-state accuracy. Finally, the feasibility of the proposed scheme is evaluated on the PMSM experimental platform under various operational conditions.
This paper proposes a novel zero-sequence filter (ZSF) for open-end winding (OEW) permanent magnet (PM) motors and verifies its effectiveness in suppressing harmonics originating from non-ideal back electromotive force (BEMF). Although OEW configurations offer higher voltage utilization and advantages in high-speed operation, they are vulnerable to zero-sequence current (ZSC) harmonics due to the absence of a neutral point. These harmonics increase torque ripple, vibration, and power losses. To mitigate these effects, a distributed-winding-based ZSF structure that can be retrofitted and seamlessly integrated with the motor stator is proposed. The ZSF increases zero-sequence inductance without compromising motor output performance or requiring additional compensation control. The size and parameters of the filter can be determined based on mechanical constraints, and the resulting ZSC reduction can be analytically derived. A structurally compatible ZSF design with a conventional OEW PM motor is developed based on the slot and tooth configuration of a 4-pole, 12-slot combination. The proposed structure is magnetically equivalent to a ZSF with a toroidal core while maintaining mechanical compatibility with the motor winding layout. Finite element analysis (FEA) using Ansys Maxwell is conducted to evaluate the zero-sequence inductance and magnetic saturation characteristics across operating conditions. The results confirm that the designed filter achieves the target inductance and provides over 80% harmonic suppression at rated speed. Experimental validation is performed and the measured results demonstrate that the proposed integrated filter effectively suppresses ZSCs caused by BEMF harmonics.
The transition to electric mobility is essential to meet global climate targets, but it poses critical challenges to the stability of the power grid due to the high, concentrated demand from electric vehicles. This paper proposes a discrete-event optimisation model for the allocation and scheduling of electric vehicle charging, focusing on electric buses, which are characterised by large battery capacities and predictable schedules. The developed mixed-integer linear model introduces a detailed battery representation that captures nonlinear charging behaviour and incorporates key constraints such as power limits, time windows, charging station availability, time of use electricity tariff, and provides multiobjective optimisation for makespan and energy cost minimisation. Compared to traditional discrete-time approaches, the proposed formulation significantly reduces problem size while maintaining modeling accuracy. The proposed framework is validated using a real case study provided by Iveco S.p.A. company, involving a fleet of maximum fifty electric buses. The robustness of the model with respect to key parameters, together with the uncertainty analysis performed on simulated scenarios and the scalability analysis with an increasing number of electric buses, demonstrates the good performance of the proposed approach under different operational conditions.
Oil cooling is widely adopted to increase the power density of electric vehicle traction motors. However, part of the coolant often enters the air-gap through winding dripping, rotor splashing, and bottom accumulation. The air-gap oil produces strong viscous shear, increasing friction loss and causing noticeable torque fluctuation. This work introduces a rotor surface design that achieves active oil evacuation through droplet-scale transport mechanisms. The design is based on the observation that droplets experience force imbalances among centrifugal force, viscous stress, and surface tension when interacting with properly shaped microstructures. Two types of surface features are applied to the rotor. At the end region, small conical elements create a preferred path for outward motion by converting the combined effect of centrifugal force and surface tension into radial displacement. In the central region, shallow slots aligned with the circumferential flow generate an axial force component that gradually transports droplets toward the end. Together, these features form a continuous evacuation route and allow droplets to leave the air-gap more rapidly. Experiments confirm the proposed design reduces the amount of oil entering the air-gap, accelerates the oil evacuation, lowers residual oil volume, and decreases the associated viscous torque.
To address the problems of insufficient transient disturbance rejection capability and degraded steady-state current performance in surface permanent magnet synchronous motor (SPMSM) speed control, this paper proposes a finite-time switching active disturbance rejection control (FSADRC) method. The proposed method consists of a finite-time cascaded extended state observer (FCESO) and a switching control law. The FCESO is composed of a first-layer finite-time ESO and a second-layer linear ESO. The first-layer finite-time ESO and the second-layer linear ESO are designed to provide speed and disturbance estimations for the transient and steady-state stages, respectively, achieving both high disturbance rejection capability, rapid response, and high steady-state current performance. To suppress oscillations during the transition between transient and steady-state modes, a linear smooth switching mechanism is developed to ensure the continuity of the control signal. Experimental results demonstrate that the proposed FSADRC significantly improves transient disturbance rejection capability, convergence speed, and steady-state speed and current performance, thereby validating the effectiveness and robustness of the proposed FSADRC.
Fault detection and quantitative assessment are critical for ensuring the reliability of proton exchange membrane fuel cell. However, existing methods often suffer from limited generalization capability of data-driven models and insufficient robustness in fault assessment. To address these challenges, this study proposes a fault diagnosis framework that systematically fuses structural and mechanistic prior knowledge. First, a topology-aware graph network is constructed to simulate the information interaction of real system, and prior voltage equations are embedded into graph nodes to establish the reference model for residual-based fault detection. For online severity quantification, a multi-perspective metric based on improved dynamic time warping distance is developed, which comprehensively measures discrepancies in value deviation and fluctuation levels between reference and fault signals. Experiments on a typical commercial stack show that, compared with the latest methods, the proposed method increases the fault identification accuracy by 14.6% and 7.2% respectively. Furthermore, the proposed method exhibits superior capability in tracking fault evolution and robustness against false fluctuations.
To achieve a favorable trade-off between output capability and thrust cost for long-stroke rail applications, this article proposes a hybrid Halbach magnetic lead screw (HH MLS). The outer translator employs a Halbach array, while the inner rotor adopts a flux-focusing unit composed of axially magnetized permanent magnets and soft magnetic materials. An aluminum rotor core is used to maintain a favorable magnetic flux path while reducing rotor mass. A finite-element (FE) model is established to verify the operating principle and support the design optimization. Using thrust density and thrust cost as the optimization objectives, a sensitivity-based optimization is adopted to determine a favorable parameter combination. Comparative results with representative MLS topologies indicate that the proposed HH MLS achieves a balanced compromise between output capability and material-related cost, and its thrust-cost advantage becomes more pronounced as the stroke increases. An M1.6 screw-fastened axial-offset arc-segment PM assembly is adopted for prototype realization. Experimental results agree well with the FE predictions, validating the proposed HH MLS and the adopted manufacturing scheme.
Maintaining a stable anode pressure and hydrogen excess ratio (HER) in the hydrogen supply system (HSS) is critical towards the operational efficiency and reliability of a PEM fuel cell. In this paper, we present a novel multiple-input multiple-output (MIMO) double-loop controller based on an active inference control (AIC) approach for coordinated regulation of pressure and HER in the HSS. An HSS model is first established, incorporating a segmented anode model to approximate the stack’s distributed dynamics. Inspired by the neuroscientific free energy principle, the AIC is formulated in a variational Bayesian framework to address the pronounced nonlinearities inherent in the stack pressure and pump flow dynamics. The AIC serves as the outer-loop controller, in which a goal-directed conscious perception mechanism is designed based on a probabilistic generative model to infer system states and proactively predict their higher-order dynamics. By minimizing the free energy function, the AIC achieves robust and offset-free tracking of anode pressure and HER setpoints without requiring an accurate physical plant model, while dynamically generating reference values for the inner loop. The MIMO inner-loop controller employs a nonlinear state-feedback strategy to manage the well-established dynamics of the manifold pressure and pump motor response. Compared with existing controllers, our method mitigates transient overshoot in both anode pressure and HER, while concurrently reducing the respective root mean square errors by 88.1% and 38.2%. Hardware-in-the-loop experiments are conducted to validate the effectiveness of the proposed method.
