Affected by the magnetic saturation effect and unmodeled dynamics, the parameters of the synchronous reluctance motor (SynRM) are highly nonlinear and time-varying. The resulting unreasonable current loop reference command severely restricts the maximum efficiency and high control performance of SynRM. Therefore, an adaptive non-singular terminal sliding mode control scheme for SynRM drive system is proposed to improve the dynamic performance and robustness. Firstly, an analytical model of flux linkage and inductance that satisfies the energy conversion mechanism is proposed to estimate the required parameters of the control system in real time. Secondly, a novel fast finite time adaptive-gain reaching law is proposed to shorten the arrival time while reducing the chatter near the sliding mode surface. Then, a non-linear disturbance observer is designed to estimate the total disturbance of the system. The asymptotic stability of the system is proved by Lyapunov's theorem. The experimental results demonstrate that the system has satisfactory dynamic performance and robustness.

To reveal the penetration characteristics of high-frequency harmonics of the traction network to the three-phase 380 V power system in the traction substation (TSS), the harmonic equivalent circuit model and harmonic penetration mathematical model of the two-phase to three-phase Scott-T transformer are established. Through the power quality measurement and harmonic analysis, the results indicate that the high-frequency harmonics in the traction network will severely penetrate the three-phase 380 V power system, which will cause almost the same degree of harmonic distortion, verifying the correctness of the harmonic penetration model. To filter high-frequency harmonics in TSS, a novel structure of high-pass filter (HPF) is proposed that features with high-pass characteristic at high frequencies while high-impedance at fundamental frequency. Furthermore, a set of three-phase experimental device is developed, and a long-term filtering experiment in the TSS is performed. The experimental results show that the novel HPF experimental device can effectively filter out high-frequency harmonics of the three-phase 380 V power system, and have almost no loss and reactive power. The feasibility and effectiveness of the suppression scheme based on the novel HPF are verified.

Severe thermal runaway is induced in the majority by short-circuit faults inside battery packs in vehicular applications. In this article, the challenging issue of interpretable soft short-circuit fault diagnosis for lithium-ion battery packs is investigated comprehensively. By using the streaming differential matrices from improved virtual voltage measurement, a full-dimensional statistical analysis scheme considering the random occurrence of a soft short-circuit is proposed to characterize the fault signatures. On this basis, an eigenvalue-decomposition-based fault detection indicator is designed, and the quantitative threshold correlation between the designed fault indicator and the original fault signature is derived such that the interpretable fault detection control limit self-commissioning can be achieved. Furthermore, the detected fault can be localized by evaluating the eigenvalue contributions to the designed fault detection indicator. Finally, a test platform with commercial lithium-ion phosphate cells in a compact battery pack is constructed to validate the approach. Experiments conducted with the World Light Vehicle Test Cycle confirm that millivolt-level short-circuit fault diagnosis can be achieved within minutes, and the fault detection and localization procedures are complementary such that the false alarm rate is minimized. It is robust against both the momentary and cumulative short-circuit conditions even with a maximum 2-C current within almost 83% state-of-charge range.

Classical speed controller is challenged by nonlinear factors such as discontinuities in the stator iron cores, fluctuations in excitation current, and variations in the suspension air gap, leading to speed fluctuations, especially during low-speed operation of maglev train. To address these issues and enhance control performance, a robust speed controller is proposed in this paper, which inherently integrates the advantages of sliding-mode control (SMC) and extended state observer (ESO). Specifically, an adaptive sliding-mode reaching law is developed that directly incorporates ESO, eliminating the need for a separate disturbance observer to compensate the control signal. This integration enables real-time disturbance updates, reducing the chattering effect typically associated with traditional exponential reaching laws and improving dynamic performance under significant disturbances. Subsequently, the mathematical model of the DC excited linear synchronous motor (LSM) is established, and a speed controller is designed using forward Euler’s discretization scheme with the adaptive sliding-mode reaching law. Finally, a DC excited linear synchronous motor experimental platform is built to validate the proposed method. Comparative experiments demonstrate the superior dynamic performance and fluctuation suppression capabilities of the proposed method.

The synchronous reluctance machine (SynRM) has gained increasing attraction for future electric drive systems. The characterization of its magnetic and current dynamics is essential for high-performance model-driven control, which still remains a promising problem. In this article, a dynamic characterization scheme for SynRM with dual axis hybrid excitation self-comissioning is proposed to characterize the magnetic and current dynamics. Inspired by separating dual-axis current variables, a compact magnetic circuit model is investigated with reciprocity analytics. Furthermore, an improved current dynamic model is derived with resulting analytical apparent and incremental inductances. On this basis, the characterized parameters involved in the magnetic circuit and current dynamic models can be accurately identified by combining the designed dual axis hybrid excitation method with effective parameter identification algorithm. The effectiveness and practicability of the proposed scheme are verified based on the development platform for commercial SynRM drive. This simple dynamic characterization scheme fits for online implementation on standard drives. Compared with the existed work, 38.2 ${\%}$ improvement of normalized modeling accuracy for magnetic circuit can be achieved. In the meanwhile, the normalized modeling errors of apparent inductances for current dynamics are strictly less than 6.91 ${\%}$ .

For mechanism analysis and high-performance control of synchronous reluctance machine (SynRM), accurate and reliable parameter identification of nonlinear magnetic model is always required. However, the accuracy and robustness of traditional heuristic algorithms are restricted by incomplete individual performance evaluation and single population evolution mechanism. In this paper, we propose a self-adaptive synergistic optimization (SSO) algorithm for extracting the parameters of the model. A novel synergistic-performance evaluation is first established to classify candidates automatically. Then, a self-organized mechanism is proposed to select optimal evolution strategies designed for classified candidate solutions. Around the current best candidate, the exploration is guaranteed in priority. Meanwhile, a self-adaptive mechanism is introduced to select other candidates to construct more promising evolutionary direction. Thus, achieving a good balance between exploration and exploitation. The parameter estimation performance of SSO algorithm is evaluated through standard datasets of SynRM magnetic model obtained by the finite element analysis. Comprehensive experiment results demonstrate the competitiveness and effectiveness of the proposed SSO algorithm compared with other algorithms, especially in terms of the accuracy and robustness. According to these superiorities, it can be concluded that the proposed algorithms are promising parameter identification methods for SynRM nonlinear magnetic model.