State of health (SOH) is critical to the efficient and reliable use of lithium-ion batteries (LIBs), especially in electric vehicle (EV) applications. Recently, electrochemical impedance spectroscopy (EIS)-based technique has been proven to be effective for SOH estimation of LIB. However, existing EIS-based methods failed to consider the impact of ambient temperature and battery state of charge (SOC), leading to the limited flexibility of these methods under dynamic environments. In this work, a novel EIS-based method is proposed for battery SOH estimation considering variations of temperature and SOC. An equivalent circuit model (ECM) is first introduced, in which the solid electrolyte interface (SEI) resistance and charge transfer resistance are employed to map their relationship with SOH under variant temperature and SOC. Subsequently, a probabilistic model, taking charge transfer resistance, temperature, and SOC as input variables, is developed for LIB SOH estimation. Experimental study indicates that the estimation error of the proposed method is around 4% when simultaneously considering the temperature and SOC effects. Moreover, the estimation error can reach 1.29% under certain conditions (e.g., 80% SOC at 30 °C). Both results of estimation error are better than the existing EIS-based methods, which indicates that the proposed method is more flexible for SOH estimation with higher precision.

To develop a high-performance and reliable permanent-magnet synchronous machine (PMSM) drive for electric vehicle (EV) applications, accurate knowledge of the PMSM parameters is of significance. This paper investigates online estimation of PMSM parameters and voltage source inverter (VSI) nonlinearity using current injection method in which magnetic saturation is also considered. First, a novel dc component-based current injection model considering VSI nonlinearity is proposed, which employs the dc components of dq-axis currents and voltages for PMSM parameter and VSI-distorted voltage estimation. This method can eliminate the influence of rotor position error on VSI nonlinearity estimation. Second, a simplified linear equation is employed to model the cross- and self-saturation of the dq-axis inductances during current injection, which can facilitate the estimation of the inductance variations induced by magnetic saturation. Third, a novel current compensation strategy is proposed to minimize the torque ripples caused by current injection, which contributes to making our approach applicable to both surface and interior PMSMs. Therefore, the proposed online parameter estimation approach can estimate the winding resistance, rotor flux, VSI-distorted voltage, and the varying dq-axis inductances under different operating conditions. The proposed approach is experimentally validated on a down-scaled laboratory interior PMSM prototyped for direct-drive EV powertrain.

This paper investigates torque ripple minimization for permanent-magnet synchronous machine (PMSM), and proposes a closed-loop fuzzy-logic-based current controller by using the magnitude of the speed harmonic as the feedback control signal. The speed harmonic can be obtained from machine speed measurement, so the proposed approach does not require accurate machine parameters and is not influenced by the nonlinearity of the machine and drive. The torque harmonic can produce the speed harmonic of the same order, so their relation is investigated, which shows that the magnitude of the speed harmonic is proportional to the magnitude of the torque harmonic of the same order, so it can be used as a measure of torque harmonic for torque ripple minimization. Then, the torque harmonic model is developed to facilitate the design and analysis of the current controller. Afterward, a novel fuzzy-logic-based current controller is proposed to minimize the dominant torque harmonics. The proposed current controller is evaluated on a laboratory PMSM drive system under different load conditions and operation speeds.

Vector space decomposition (VSD) model has been widely utilized for dual three-phase permanent magnet synchronous machine (dual-PMSM) control, in which two DQ frames, DQ1 and DQ2, are introduced to facilitate controller design. Existing studies show that harmonic current injection in DQ2 frame can increase the output torque for a given peak phase current, which is referred as maximum torque per peak current (MTPPC) control. However, the injected harmonic current will induce a small dc torque and the harmonic torque. This paper firstly proposes a comprehensive torque model considering the harmonics in PM flux linkages, inductances and stator currents to investigate the induced torque components, which are neglected in existing approaches. These torque components are then considered in the harmonic current design to improve MTPPC control performance. The harmonic current design results in a multi-objective optimization problem, and genetic algorithm is employed to optimize the harmonic current to maximize the output torque with minimal torque harmonic. Compared with existing approaches, the proposed approach is applicable to both surfaced-mounted and interior dual-PMSMs. Experimental investigations on a laboratory interior dual-PMSM demonstrate that the output torque of test motor can be increased by more than ten percent with a negligible increase in torque ripple.

This paper investigates open-phase fault modeling and fault tolerant control (FTC) of dual three-phase permanent magnet synchronous machines. A comprehensive fault model that considers both the permanent magnet torque and reluctance torque under the open-phase fault is proposed at first. This model shows that under open-phase fault the average torque of dual three-phase PMSM will decrease, while torque ripple will increase significantly. Then, a novel optimized FTC approach is developed based on the proposed fault model, in which genetic algorithm (GA) is applied to optimize the stator currents to maximize the average torque and minimize the torque ripple under open-phase fault condition. The proposed fault model and GA-based FTC are applicable to both surface-mounted and interior dual three-phase PMSMs. However, existing approaches are only applicable to the surfaced-mounted dual three-phase PMSMs because the reluctance torque has been neglected. Moreover, the proposed approach is simple in implementation as it employs the original control structure, and it is capable of smooth switching between the healthy operation and FTC without inducing noticeable torque pulses. The proposed GA-based FTC is demonstrated with design examples, compared with existing approach and validated with experimental tests on a laboratory interior dual three-phase PMSM.

Torque ripple modeling and minimization for interior permanent magnet synchronous machines (IPMSMs) requires accurate information of the inductances, which vary nonlinearly due to magnetic saturation. However, existing approaches fail to consider the magnetic saturation and, thus, their performance are limited under different load conditions. Therefore, this paper improves the torque ripple model by considering magnetic saturation, and employs this model for the optimal current design to improve the performance of torque ripple minimization for IPMSMs under different load conditions. At first, numerical studies are performed to analyze and understand how magnetic saturation affects the torque ripples in IPMSMs. Then, a novel torque ripple model for IPMSMs is developed, in which the inductance term is replaced by exploring the machine electrical model. This improved torque ripple model is computationally efficient and it can provide fast and accurate torque ripple prediction. Based on this model, a genetic algorithm (GA)-based optimal stator current design approach is proposed to minimize the torque ripple in IPMSMs. The proposed GA-based approach can adaptively optimize the stator current under different load conditions, which can guarantee the robust performance of torque ripple minimization under different saturation levels. The proposed approach is validated through an experimental test on a laboratory IPMSM drive system.