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1. Longitudinal Control Strategy for Connected Electric Vehicle with Regenerative Braking in Eco-Approach and Departure

   https://doi.org/10.3390/app13085089  

   Bautista-Montesano, Rolando; Galluzzi, Renato; Mo, Zhaobin; Fu, Yongjie; Bustamante-Bello, Rogelio; Di, Xuan (April 2023, Applied Sciences)

   The development of more sustainable urban transportation is prompting the need for better energy management techniques. Connected electric vehicles can take advantage of environmental information regarding the status of traffic lights. In this context, eco-approach and departure methods have been proposed in the literature. Integrating these methods with regenerative braking allows for safe, power-efficient navigation through intersections and crossroad layouts. This paper proposes rule- and fuzzy inference system-based strategies for a coupled eco-approach and departure regenerative braking system. This analysis is carried out through a numerical simulator based on a three-degree-of-freedom connected electric vehicle model. The powertrain is represented by a realistic power loss map in motoring and regenerative quadrants. The simulations aim to compare both longitudinal navigation strategies by means of relevant metrics: power, efficiency, comfort, and usage duty cycle in motor and generator modes. Numerical results show that the vehicle is able to yield safe navigation while focusing on energy regeneration through different navigation conditions. 

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2. EMI Mitigation, Material Characterization, Transformer Equivalent Circuit Modeling

   Vahdani, Reza (May 2025, Missouri S&T Library)

   Modern high-frequency electronic systems demand precise characterization and modeling techniques to ensure signal integrity and electromagnetic compatibility. This thesis presents three core studies focused on real-world challenges in high-speed and power electronics: EMI mitigation using 3D printed absorbers, wideband liquid dielectric charac- terization, and accurate transformer modeling. The first study demonstrates a targeted approach to mitigating electromagnetic in- terference (EMI) in a commercial router. By using holography imaging to identify radiation hotspots, custom absorber structures were designed with commercially available materials, fabricated via 3D printing, and applied directly to emission sources. Radiated emission tests in a reverberation chamber showed up to 9 dB reduction in EMI, validating the method’s effectiveness for practical applications. The second study proposes a wideband method (up to 30 GHz) to characterize liquid dielectrics using a differential microstrip line and the Djordjevic-Sarkar model. Dielectric constant (DK) and dissipation factor (Df) were extracted by comparing S-parameters of air and liquid-immersed conditions between measurement and simulation results. Experi- mental validation with PAO4 and DC-15 liquids showed strong agreement with traditional cavity resonance methods, with DK deviations under 1.5%. The third study addresses transformer modeling across 100 kHz to 30 MHz by developing an equivalent circuit model that accurately captures winding effects, parasitic capacitance, and leakage inductance. The approach incorporates an optimized coupling coefficient based on physical parameters. Validation with air-core inductors demonstrated high model accuracy, with an average relative error of 2.2%. 

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3. Derivation and Validation of a Common-Mode Model for a Neutral Point Clamped Dual Active Bridge

   https://doi.org/10.1109/ECCE50734.2022.9947783  

   Olson, Ryan; Shafei, Ahmad El; Li, Tianchen; Cuzner, Robert; Nasiri, Adel; Zhao, Yue; Ma, Zhuxuan (October 2022, 2021 IEEE Energy Conversion Congress and Exposition (ECCE))

   This paper applies a common-mode modeling approach for a Silicon Carbide (SiC) based medium voltage neutral point clamped (NPC) dual active bridge (DAB) with a 2 level Full-Bridge (2L-FB) stage utilizing an electromagnetic interference (EMI) characterization testbed. A common-mode equivalent circuit model (CEM) for the system is derived, which accurately captures the effect of cross-mode coupling behavior between differential-mode and common-mode caused by circuit asymmetries, such as baseplate capacitance of multi-chip power modules or windings in the transformer. This cross-mode coupling effect is required to accurately model EMI at the higher frequencies of the conducted emissions standards. The derived CEM shows close agreement when compared to the mixed-mode simulation, verifying the model's efficacy. Additionally, baseplate current was shown to be minimized by tying the neutral point of the converter to the heatsink, where this result can be explained through the CEM. The CEM of the NPC DAB will be validated through empirical measurements on an EMI characterization testbed. The testbed features a copper ground plane and custom-built LISNs that can handle the unfiltered harmonic and EMI content of power electronic converters. 

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4. Accelerating Electric-magnetic machine simulation using the Fourier Neural Operator (FNO)

   Guo, Zhengke; Gu, Xianfeng David; Chen, Shikui (August 2025, American Society of Mechanical Engineers (ASME))

   Electrical machines traditionally rely on Finite Element Analysis (FEA) to evaluate or simulate their properties by solving the associated partial differential equations (PDEs). However, FEA is computationally costly, which limits its capability for rapid design iteration and real-time simulations. While recent surrogate models such as Physics-Informed Neural Networks (PINNs) have shown promise, they often suffer from slow convergence and scalability issues in complex geometries. In this paper, we propose the use of the Fourier Neural Operator (FNO) as a resolution-invariant surrogate model to significantly reduce the computation time required for FEA-based PDE solutions in electric machines. Previous research has demonstrated the FNO’s ability to learn mappings for time-sequence problems by approximating operators between function spaces. Building on this, we present a methodology to directly predict the later-state electromagnetic fields of a rotating interior permanent magnet (IPM) motor based on its earlier-stage data by approximating the underlying operator that governs these transitions. Our framework enables full-geometry modeling without relying on segmentation, preserving accuracy while dramatically improving computational efficiency. The model was trained and validated on an FEA dataset with multiple boundary conditions and motor configurations, demonstrating strong generalization across different designs and resolutions. Experimental results show that the proposed FNO method achieves a significant reduction in computational time compared to traditional FEA simulations while maintaining an acceptable level of accuracy. This study highlights the potential of neural operators for accelerating electromagnetic simulations, enabling faster design iterations and offering new possibilities for real-time and optimization-based applications in electric machine design. 

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5. Position Sensor Offset Error Quantification in Extremum-Seeking Self-Healing Pmsm Drives

   https://doi.org/10.1109/ECCE58356.2025.11260333  

   Dissanayake, Ramitha K; Kuruppu, Sandun S (October 2025, IEEE)

   Adaptive control is a powerful tool in motor control as it can be used in the optimal operation of motors. Position sensor offset error (PSOE) is a critical and challenging issue in permanent magnet synchronous machines (PMSMs) and the extremum seeking control (ESC) based fault diagnosis and mitigating methods are available to drive non-salient PMSMs at the optimal operating point despite the presence of PSOE. This study proposes a method to quantify the PSOE in ESC-driven nonsalient PMSM as it is essential to quantify the PSOE for fault severity assessment. Comprehensive simulation and experimental results are provided to validate and demonstrate the effectiveness of the proposed method. 

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