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This article proposes a current injection-based simultaneous stator winding and permanent magnet (PM) temperature tracking technique for dual three-phase permanent magnet synchronous machines (PMSMs). The estimation models involving winding and PM temperatures are derived for different control strategies with the cancelation of inductances. To improve the tracking performance, a Kalman filter is used for simultaneous winding and PM temperature estimation. Compared with the existing methods, the proposed simultaneous winding and PM temperature estimation technique is independent of the voltage-source nonlinearity effect and is applicable to various machine operating conditions. The main features of the proposed method are underscored by the cancelation of inductance and the elimination of the need for a lookup table. The test results on a laboratory dual three-phase PMSM are used to validate the proposed method under different speed and torque conditions.

For dual three-phase interior permanent magnet synchronous machines (DT-IPMSMs), open-phase fault (OPF) can result in significant average torque reduction and harmonics in the output torque and speed, which prevent the machines from a reliable and safe operation. Indeed, these adverse effects are mainly due to significant harmonics in the stator currents caused by OPF. This article investigates fault-tolerant control (FTC) of DT-IPMSM under OPF and proposes a computation-efficient FTC solution to maximize the average torque and minimize the fault-induced torque and speed ripples. In the proposed FTC, the open-phase model is first derived, and optimal stator currents are then derived to achieve maximized average torque and minimized fault-induced torque harmonics. The computation efficiency enables the proposed solution, the capability of FTC, under both the steady-state and transient conditions. Moreover, the proposed FTC can eliminate the harmonic current components in the torque contributing frame and, thus, reduce the harmonic losses, and nonlinear inductance maps are employed to consider magnetic saturation. The proposed FTC is compared with existing methods and evaluated with experiments on a laboratory DT-IPMSM under various operating conditions.

The accurate control of interior permanent magnet synchronous machine (IPMSM) and drive in electric vehicle applications is vital for achieving superior performance over a wide range of speeds and loads. Many control methods such as loss minimization and maximum efficiency (ME) have been developed to improve the efficiency of the motor–drive, by mainly considering the controllable fundamental losses. This article includes the effect of stator harmonic iron losses caused primarily by inverter sideband time harmonics that contribute to a significant amount of controllable electrical losses in IPMSMs. The sideband harmonic iron losses have been analytically modeled using a novel dq –axis model incorporating harmonic iron loss resistance. Subsequently, the harmonic iron losses have been included in an offline procedure used to determine optimal current advance angle for increased motor efficiency. The improved IPMSM losses and subsequently, the analytical efficiency models have been derived by considering varying motor parameters due to saturation and cross–saturation effects. The accuracy of the developed model and the improved ME control using the optimal current angle have been validated using numerical simulations and experimental investigations on a laboratory IPMSM.

This article proposes a closed-loop decoupled current control for torque ripple minimization (TRM) of permanent magnet synchronous machines (PMSMs) by using the speed measurements. In the proposed control, a decoupled scheme is developed to control the harmonic currents for TRM, in which the control of phase angle and magnitude is decoupled to simplify the controller design. The decoupled scheme consists of two PI controllers and one control rule: one PI is responsible for phase angle control, the other is responsible for magnitude control, and the control rule is responsible for coordinating the two PIs for TRM. The harmonic currents can produce additional loss, and thus this paper derives the optimal condition for TRM with minimized loss. With the derived condition, one can control q -axis current and calculate d -axis current from the derived condition, which can minimize the torque ripple with minimized loss and simplify the control structure. The proposed decoupled approach is evaluated with extensive experiments and comparative study on a laboratory PMSM drive.

Comprehensive parameters testing and analysis are critical to high-performance modeling and control of permanent magnet synchronous machines (PMSMs). In this article, a novel decoupled approach for dual three-phase PMSM parameter estimation including winding resistance, machine inductances, and PM flux linkage is proposed for comprehensive parameter testing. An improved machine model considering magnetic saturation and inverter nonlinearity is proposed at first, in which a quadratic equation is employed to model the nonlinear variation of machine inductances and inverter voltage distortion is also modeled. Thereafter, a novel decoupled estimation model is proposed to decouple multiparameter estimation into four simplified estimations using least squares method. This decoupled model can effectively reduce the cross influences between parameters and improve the computation efficiency. Moreover, it is capable of dealing with massive redundant measurements for accurate and computation-efficient parameter estimations, which is especially suitable for obtaining machine parameters over a wide operation range during machine testing, such as inductance maps under different operating conditions.

This article investigates efficiency optimization in a permanent magnet synchronous machine (PMSM) drive system, including the motor and the power converter. At first, the motor drive efficiency model based on the input and output power is proposed. In this model, the input power is calculated from the dc-link voltage and current measurements, and the output power is modeled and computed from the dq -axis voltages and currents considering temperature rise, magnetic saturation, cross-coupling effect, and inverter nonlinearity. The proposed efficiency model includes both the inverter and motor losses without the need of loss models, so it simplifies the efficiency calculation significantly. Based on this efficiency model, a novel gradient descent algorithm-based approach with a discrete search space and proper constraints is proposed to optimize the PMSM drive system efficiency, ensure a fast searching speed, and reduce the influence from measurement uncertainties. Compared with the existing approaches, the proposed approach is computationally efficient, does not require loss models, and is noninvasive as no signal injection is involved. The proposed approach is evaluated on a laboratory PMSM drive system with extensive experimental tests.

Accurate temperature information is crucial to dual three-phase permanent magnet synchronous machine (DT-PMSM) drives. Therefore, this article proposes two efficient models for permanent magnet temperature estimation of DT-PMSMs. The proposed models are derived through current injection in the reference frame that does not contribute to torque production. Through current injection, the proposed models can fully explore the two sets of machine equations to cancel winding resistance and machine inductances. To improve the estimation performance, inverter nonlinearity is compensated in the first model and cancelled in the second model. In comparison to existing methods, the proposed approach is computationally efficient and robust to parameter variation, magnetic saturation, and inverter nonlinearity. Moreover, the current injection will not affect the machine torque production and control performance. The proposed estimation approach is evaluated on a laboratory DT-PMSM under various operating conditions.

The reliability analysis of traction inverters is of great interest due to the use of new semi-conductor devices and inverter topologies in electric vehicles (EVs). Switching devices in the inverter are the most vulnerable component due to the electrical, thermal and mechanical stresses based on various driving conditions. Accurate stress analysis of power module is imperative for development of compact high-performance inverter designs with enhanced reliability. Therefore, this paper presents an inverter reliability estimation approach using an enhanced power loss model developed considering dynamic and transient influence of power semi-conductors. The temperature variation tracking has been improved by incorporating power module component parameters in an LPTN model of the inverter. A 100 kW EV grade traction inverter is used to validate the developed mathematical models towards estimating the inverter performance and subsequently, predicting the remaining useful lifetime of the inverter against two commonly used drive cycles.

This article proposes improved mathematical models for torque and system efficiency used toward obtaining accurate current advance angle for maximizing the efficiency of an interior permanent magnet (IPM) synchronous motor. First, improved torque and efficiency calculation procedure that consider the effects of parameter variations, such as inductance, stator resistance and PM flux linkage simultaneously, and motor and inverter losses, have been developed from a combination of analytical models and practical experiments. Subsequently, an offline search procedure has been utilized to determine the optimal current angle using the improved dq -axis-based models. The novelty of the efficiency model is that the method uses preliminary noninvasive experimental tests to consider the saturation and temperature effects simultaneously and successfully determine the relationship between stator and rotor temperatures by using only controller command voltages and currents. Experimental investigations are performed on a laboratory IPM for validating the developed control method through interpolation of improved look-up tables with the derived current angle values for varying speed, torque, and temperature conditions. The effectiveness of the proposed method in improving efficiency is also verified and compared with maximum efficiency and maximum torque per ampere methods using experimental sweep tests.

Few of the challenges with development of a single on–board motor for direct–drive electric vehicles include high torque density and low torque ripple. Therefore, in this paper, a 36–slot, 34–pole consequent pole six–phase permanent magnet synchronous machine (PMSM) has been optimized to address the aforementioned challenges for direct–drive application. Existing literature on optimization processes that rely solely on finite element models are restricted to three–phase machines only and also take longer computation time. Therefore, this paper proposes a novel optimization approach based on supervised machine learning for six–phase PMSM. In this approach, a non–conventional extended dual dq– frame model that accounts for higher order space harmonics in inductances and flux linkages has been developed and used for accurate computation of average torque and torque ripple of six–phase PMSM. Using the performance characteristics obtained from the extended dual dq –frame model for a set of initial design candidates, support vector regression algorithm is employed for supervised machine learning and increasing solutions in the design space. Furthermore, pareto front is used for selecting optimal machine models with maximum torque density and reduced torque ripple. Multi–objective trade–offs and comparison of initial and optimized designs based on average torque, torque ripple, efficiency and cost are performed.

This article proposes a novel dual dc injection-based maximum torque per ampere (MTPA) control algorithm for dual three-phase permanent magnet synchronous machines (PMSMs). The proposed analytical model takes magnetic saturation and temperature effects into account for achieving optimal current angle. In taking consideration of the two effects, a set of small currents are injected in the harmonic plane rather than the fundamental subspace, namely DQ 2, which does not interfere with average torque output. In such a way, a novel torque model only involving the command currents and voltages is derived to avoid machine parameters. Furthermore, an MTPA indicator is constructed by the proposed torque to current model to seek the MTPA angle, in which the optimal current angle is found when the indicator magnitude reaches the maximum. In particular, the gradient descent algorithm is employed to ensure the adaptivity and robustness of MTPA control, which offers high real-time capability, low computational cost and low complexity. The experiments validate the proposed MTPA strategy under various load, speed and temperature conditions.

Rotor magnet condition is important to maintain a stable permanent magnet synchronous motor (PMSM) operation. In this paper, Vold–Kalman filtering order tracking (VKF-OT) and dynamic Bayesian network (DBN) are employed for the real-time rotor demagnetization detection from the torque ripple. First, a torque ripple model of the PMSM considering electromagnetic noise is proposed, and the torque variation is studied to determine the effect of the demagnetization on the torque ripple, which indicates that it is feasible to detect the magnet demagnetization by analyzing the torque ripple. Then the torque is processed by wavelet transform to eliminate the electromagnetic disturbances. Second, the VKF-OT is introduced to track the order of the torque ripple of the PMSM to extract the torque ripple characteristics as the feature reflecting changes in magnet status. Third, the feature is employed to train the DBN for the rotor magnet demagnetization detection and prediction during motor operation. The proposed approach is a noninvasive and an online method that can be embedded in the physical motor controller. The validation results demonstrate that this method can detect the uniform demagnetization over a wide motor speed range.

This paper investigates open-phase fault modeling and fault-tolerant control (FTC) of dual three-phase permanent magnet synchronous machines (DT-PMSMs). A comprehensive fault model that considers both the permanent magnet torque and reluctance torque under the open-phase fault is proposed first. This model shows that under open-phase fault the average torque of DT-PMSM will decrease, while torque ripple will increase significantly. Then, a novel optimized FTC approach is developed based on the proposed 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. The proposed fault model and GA-based FTC are applicable to both surface-mounted and interior DT-PMSMs. However, existing approaches neglecting the reluctance torque are only applicable to surfaced-mounted DT-PMSMs. 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 approach is demonstrated with design examples, compared with existing one and validated with experiments on a laboratory interior DT-PMSM.

This paper investigates the use of the phase angle of the measured speed harmonics for efficient permanent magnet (PM) temperature estimation of permanent magnet synchronous machine (PMSM). Based on the mechanical model, a linear estimation model is derived to model the relationship between the PM temperature and the phase angle of the speed harmonic. This model shows that there is a linear relationship between the PM temperature and the tangent of the phase angle. Therefore, the PM temperature can be directly estimated from the phase angle of the speed harmonic using the derived model. Moreover, the derived estimation model does not involve machine parameters such as inductance and winding resistance, thus the proposed estimation approach is independent from machine parameter variation. The proposed approach is evaluated on a laboratory interior PMSM.

Emerging demands in sustainable energy and power conversion applications are calling for high frequency and high efficiency switching devices. Gallium nitride (GaN) as one type of wide band...

Torque ripple has been a critical issue for high-performance applications using permanent magnet synchronous machines (PMSMs). An efficient approach to minimize torque ripple is to control the stator current to follow an optimized current reference, which will produce an extra torque ripple to cancel the existing one. This paper proposes a multiple reference frame (MRF) based controller for torque ripple minimization (TRM), in which the measured speed ripple is explored as the feedback control signal. The proposed MRF-based controller consists of a TRM controller whose task is to find the optimal current reference and a current controller whose task is to control the actual current to follow the optimized current reference. In TRM controller, the control of reference current magnitude and phase angle is decoupled, and proportional integral (PI) controller is able to achieve TRM control. In current controller, MRF is adopted to convert harmonic current control into dc current control; thus, PI controller is able to achieve harmonic current control with the use of MRF. Compared with existing approaches, the proposed controller is capable of TRM under both steady-state and transient conditions. The proposed controller is experimentally evaluated on a laboratory PMSM drive system.

To develop a high-performance and reliable control for dual three-phase interior permanent magnet synchronous motor (IPMSM), accurate knowledge of machine parameters is of significance. This paper proposes an improved recursive least square (RLS) algorithm and a current injection-based parameter estimation method for dual three-phase PMSM with consideration of inverter nonlinearity and magnetic saturation. First, the vector space decomposition (VSD)-based dual three-phase PMSM model is established. The inverter nonlinearity model for dual three-phase PMSM is derived, and the cross saturation and the self-saturation of DQ1-axis inductances are modeled to improve the estimation accuracy. Finally, the machine parameters, including winding resistance, rotor flux linkage, and varying DQ1-axis inductances under different operating conditions, are estimated using the proposed current injection-based method with the RLS algorithm. Compared with existing methods, the proposed approach can achieve better estimation performance and is validated on a laboratory dual three-phase IPMSM under different temperature and operating conditions.

To prevent motor failures caused by thermal overloading, accurate knowledge of stator winding temperature is of significance. The proposed stator winding temperature tracking is a stator resistance-based estimation technique for dual three-phase permanent magnet synchronous machines. In the proposed approach, a dual DC signal-injection-based method is developed to determine winding temperature accurately, which is capable of cancelling the voltage-source inverter non-linearity effect with no interference with torque output. Meanwhile, the proposed method is not affected by magnetic saturation because of no inductance terms involved. The Kalman filter is employed to improve the performance of winding temperature estimation. Moreover, the proposed approach is independent of operating conditions and can be implemented in a standard motor drive with a minimum computational cost. The experimental results validate the proposed approach on a laboratory dual three-phase interior permanent magnet synchronous motor under various speeds and load conditions.

This paper investigates permanent magnet temperature (PMT) modeling and estimation for permanent magnet synchronous machines (PMSMs) by using the measured speed harmonic. First, a linear temperature model is derived to demonstrate that the magnitude of the speed harmonic decreases linearly with the increase of PMT. To achieve this linear model, the speed harmonic is induced by the injected harmonic currents satisfying certain conditions developed in this paper. To improve the estimation performance, PMT estimation is represented in a state–space model based on the derived temperature model, and the Kalman filter is applied to estimate the PMT from the measured speed harmonic. Compared with existing methods, the proposed approach has advantages in terms of simplicity in estimation and robustness to the variation of machine resistance and inductances. The proposed Kalman filter based modeling and estimation approach is evaluated with extensive experiments on a laboratory PMSM drive system under different speed and load conditions.

The 3-phase windings of interior permanent magnet synchronous machines (IPMSMs) used in electric vehicles (EVs) for traction can also be utilized for charging a battery. This integrated charging technology eliminates the on-board charger, leading to a significant reduction in the overall weight and cost of the EV. However, during integrated charging, the induced magnetic fields across the IPMSM windings are unbalanced, which could lead to addition of harmonic current components at the battery side, demagnetization, noise, and vibrations. This paper provides a novel design solution by implementing asymmetrical damper bars in the IPMSM rotor to mitigate unbalanced magnetic fields during integrated charging, and, thus, overcome the aforementioned issues. However, the bars introduced could affect the useful torque production of the machine during traction. Therefore, a magnetic equivalent circuit model based differential evolutionary algorithm is proposed and implemented to optimize the IPMSM rotor structure with dampers to achieve balanced magnetic fields during integrated charging operation and satisfactory traction performance. A comprehensive performance analysis of the optimally designed traction IPMSM equipped with integrated charging capability under both operating conditions is presented in this paper using finite element analysis.

This paper proposes a novel method of efficiency improvement in a vector controlled permanent magnet synchronous motors (PMSM) through system level maximum efficiency point determination using current angle as a control variable. Loss models for the inverter and the motor fundamental and harmonic losses, which are capable of being solved online using available terminal measurements in the system are initially developed. The loss models and dc-link power measurement are then used to seek the maximum efficiency angle for different operating conditions using a gradient descent optimization algorithm. The developed method is robust against changes in inductances due to saturation and cross saturation with loading conditions as well as temperature effects. The effectiveness of the developed method in improving the system efficiency is verified and compared with conventional maximum torque per ampere method. The proposed strategy has been validated on a laboratory interior PMSM, and the efficiency has been calculated for different speed and torque conditions. The experimental validations confirm the effectiveness of the proposed solution in improving the motor drive system energy efficiency.

Permanent magnet temperature (PMT) is crucial to high-performance control and condition monitoring of permanent magnet synchronous machines (PMSMs). This paper proposes a noninvasive PMT estimation approach based on the PMSM steady-state equation. First, a linear temperature model, dependent solely on the PMT, is derived from the steady-state equation and the PM thermal model. Thus, the PMT can be directly estimated from the measurements using the derived linear model. In order to improve the estimation performance, a linear state-space model is developed based on the derived model, and the Kalman filter is employed for PMT estimation. The inverter nonlinearity is considered and compensated in the proposed model to improve the estimation performance. Compared with the existing methods, the proposed approach is noninvasive and computationally efficient. More importantly, the derived model does not involve machine parameters such as winding resistance and self and mutual inductances, and thus, the proposed approach is independent of winding temperature rise, magnetic saturation, and cross-coupling effect. The proposed approach is evaluated with extensive experiments under various speed and load conditions.

This paper explores the use of speed harmonic for accurate and fast maximum-torque-per-ampere (MTPA) control of interior permanent magnet synchronous machines (IPMSMs). A novel MTPA angle calculation approach is proposed, in which the MTPA angle is directly calculated from the phase angle of the speed harmonic that is induced by an injected harmonic current. At first, the relation between the speed harmonic and machine parameters is derived from the IPMSM model, which shows that the phase angle of the induced speed harmonic can be utilized to cancel the machine parameters in existing MTPA equation. Thus, a new MTPA equation is derived, which does not involve machine parameters for MTPA angle calculation. Compared with existing methods, the proposed approach is independent from the machine and drive nonlinearities. Moreover, the MTPA angle is directly calculated from the derived MTPA equation, so the proposed approach is fast and computationally efficient. Thus, the proposed approach is suitable for both on-line applications and building off-line lookup tables of MTPA angles for IPMSMs. The proposed approach is experimentally evaluated on a laboratory IPMSM drive system.

When permanent magnet (PM) demagnetization in a PM synchronous machine (PMSM) occurs, the demagnetization characteristic information is usually coupled with other characteristics of the PMSM which may demonstrate similar features. Therefore, it is difficult to accurately detect the demagnetization by monitoring only one parameter. This paper explores the use of multi-sensor information, namely, acoustic noise and torque, for demagnetization detection through the information fusion technique. The acoustic noise and torque information are processed and analyzed using wavelet transforms for filtering and extracting features. Bayesian network-based multi-sensor information fusion is then proposed to detect the demagnetization ratio from the extracted features. The proposed approach is experimentally verified on a laboratory PMSM and compared with single-sensor dection methods.

Link prediction and node classification in social networks remain open research problems with respect to Artificial Intelligence (AI). Innate representations about social network structures can be effectively harnessed for training AI models in a bid to predict ties; and detect clusters via classification of actors with regard to a given social network. In this paper, we have proposed a distinct hybrid model: Representation Learning via Knowledge-Graph Embeddings and Convolution Operations (RLVECO), which hybridizes the strengths of Knowledge-Graph Embeddings (VE) and Convolution Operations (CO) in extracting and learning meaningful features from social graphs via Representation Learning (RL). RLVECO utilizes an edge sampling approach for exploiting features of a social graph via learning the context of each actor with respect to its neighboring actors.

Traditional proportional-integral (PI) based control has proven to be an influential control technique due to its advantage of easy implementation and robust performance for EV application. However, these controls are sensitive to plant parameters since the motor parameters change over time resulting in reduced performance and efficiency. This paper proposes a new vector control in permanent magnet synchronous motor (PMSM) with deep reinforcement learning (DRL) based controller in EV application. An advanced policy gradient algorithm is used to develop the DRL-based vector controller to overcome non-linearity in machine parameters and mitigate the decoupling inaccuracy issues in conventional control schemes. This contribution further evaluates the adaptive and dynamic performance against the conventional PI-based control and the research outlook is presented in the subsequent sections of this paper.

Permanent magnet synchronous machines (PMSMs) have become highly favored propulsion devices due to their exceptional merits, such as high efficiency and torque density. To operate the motor and drive in an efficient region, maximum torque per ampere control has drawn a great deal of interest among loss minimization strategies. Due to the presence of high nonlinearity and sensitivity to machine parameters, MTPA control strategy requires high robustness and reliability. This paper presents a comprehensive review of the present–day technological developments related to MTPA techniques for PMSM drives. The problems associated with MTPA techniques of PMSM, such as magnetic saturation effect, cross–coupling effect, temperature effect, are systematically summarized to outline the challenges in optimal current angle searching and stator current minimization. Additionally, in terms of machine parameter dependency, the MTPA techniques of PMSM are categorized into machine parameter–dependent and machine parameter independent. Furthermore, the recent advances of MTPA techniques are assessed, and the distinctive features of different methodologies are highlighted.

This paper investigates a novel current injection-based multi-parameter estimation technique for dual three-phase permanent magnet synchronous machine (PMSM) considering the magnetic saturation and temperature effects simultaneously. One set of currents is injected in harmonic subspace, which does not interfere with average torque production in the fundamental subspace to obtain the stator resistance under different temperature conditions. For each injection operation, the temperature effects on the resistance and permanent magnet flux linkage are considered and modelled. Meanwhile, this proposed method employs high order polynomial surface to fit the self-and cross-saturation effects. The analytical model achieved from the proposed estimation scheme satisfies both the light and heavy saturation regions. The proposed approach is validated through experimental studies on a laboratory dual three-phase interior PMSM.

This paper presents an implementation of wide bandgap devices in soft–switching inverter architectures to fully exploit the benefits of high switching frequencies and fast switching transients without the adverse effects of motor insulation degradation and elevated levels of electromagnetic interference. Circuit simulation tools and analytical loss models are utilized to validate the advantages of the auxiliary resonant commutated pole soft–switching converter architecture against the implementation of wide bandgap devices in a conventional two–level hard–switching inverter where techniques are employed to control the dV/dt of the semiconductor devices. In addition, it is found that high–frequency operation is possible without the penalty in efficiency since switching losses are minimized in soft–switching inverter topologies. This characteristic leads to increased inverter efficiency and reduced harmonic losses in the motor which makes the architecture attractive for future electric vehicle traction applications.

Due to the recent electrification trend in automotive industry, electric traction motors, specially permanent magnets synchronous motors (PMSMs), have gained significant attention from the scientific community. One of the critical aspects of powertrain is noise and vibration generation. In this paper, an overview on the different sources of noise and vibration in PMSMs is presented, with specific attention and details about the mechanisms of noise generation related to the electromagnetic parameters of the machine, along with the most commonly used methods of analysis for noise and vibration. Noise is split into different categories depending on the nature of its source, and several phenomena affecting electromagnetic-generated vibrations are listed and analyzed, as this type of vibration is recognized to be the most critical type in PMSMs used in vehicle applications.

Node classification and link prediction problems in Social Network Analysis (SNA) remain open research problems with respect to Artificial Intelligence (AI). Inherent representations about social network structures can be effectively harnessed for training AI models in a bid to detect clusters via classification of actors as well as predict ties with regard to a given social network. In this paper, we have proposed a unique hybrid model: Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN). Our proposition is designed for analyzing and extracting expressive feature representations from social network structures to aid in link prediction, node classification and community detection tasks. RLVECN utilizes an edge sampling technique for exploiting features of a given social network via learning the context of each actor with respect to its associate actors.

Several activities, comprising animate and inanimate entities, can be examined by means of Social Network Analysis (SNA). Classification tasks within social network structures remain crucial research problems in SNA. Inherent and latent facts about social graphs can be effectively exploited for training Artificial Intelligence (AI) models in a bid to categorize actors/nodes as well as identify clusters with respect to a given social network. Thus, important factors such as the individual attributes of spatial social actors and the underlying patterns of relationship binding these social actors must be taken into consideration. These factors are relevant to understanding the nature and dynamics of a given social graph. In this paper, we have proposed a hybrid model: Representation Learning via Knowledge-Graph Embeddings and Convolution Operations (RLVECO) which has been modelled for studying and extracting meaningful facts from social network structures to aid in node classification and community detection problems. RLVECO utilizes an edge sampling approach for exploiting features of a social graph, via learning the context of each actor with respect to its neighboring actors, with the aim of generating vector-space embeddings per actor which are further exploited for unexpressed representations via a sequence of convolution operations. Successively, these relatively low-dimensional representations are fed as input features to a downstream classifier for solving community detection and node classification problems about a given social network.

The interactions between humans and their environment, comprising living and non-living entities, can be studied via Social Network Analysis (SNA). Node classification, as well as community detection tasks, are still open research problems in SNA. Hence, SNA has become an interesting and appealing domain in Artificial Intelligence (AI) research. Immanent facts about social network structures can be effectively harnessed for training AI models in a bid to solve node classification and community detection problems in SNA. Hence, crucial aspects such as the individual attributes of spatial social actors, and the underlying patterns of relationship binding these social actors must be taken into consideration in the course of analyzing the social network. These factors determine the nature and dynamics of a given social network. In this paper, we have proposed a unique framework, Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN), for studying and extracting meaningful facts from social network structures to aid in node classification as well as community detection tasks. Our proposition utilizes an edge sampling approach for exploiting features of the social graph, via learning the context of each actor with respect to neighboring actors/nodes, with the goal of generating vector-space embedding per actor. Successively, these relatively low-dimensional vector embeddings are fed as input features to a downstream classifier for classification tasks about the social graph/network. Herein RLVECN has been trained, tested, and evaluated on real-world social networks.

Torque ripple has been a critical issue in permanent magnet synchronous machines (PMSMs). After PMSMs are designed, an efficient way for torque ripple minimization is to inject an optimized harmonic current into the machine to produce an additional torque ripple to cancel existing torque ripple resulted from the imperfection of machine design and manufacturing. This paper proposes a testing-based approach for optimal harmonic current design by using the speed harmonics. In particular, two testing solutions (TS), TS-A and TS-B, are proposed. In TS-A, two test signals are injected into the machine, and the optimal harmonic current is calculated from the test signals as well as the measured speed harmonics. In TS-B, two test signals are injected and adjusted to achieve the designed objective in terms of speed harmonics, and the optimal harmonic current is calculated from only the test signals. These two solutions can find the optimal harmonic current to minimize the torque ripple with minimal machine losses. Moreover, machine parameters are not required in the optimal harmonic current design, so the proposed solutions are not affected by the machine and drive nonlinearities. Compared with existing approaches, the proposed solutions have advantages in terms of computation efficiency and simple implementation. The proposed solutions are evaluated on a laboratory PMSM drive system.

This paper proposes a novel gradient descent based maximum torque per ampere (MTPA) control algorithm for interior permanent magnet synchronous machines (IPMSMs) by using the measured speed harmonics. The proposed approach does not require machine parameters and thus is not influenced by the machine and drive nonlinearities. Hence, the proposed approach can ensure a robust MTPA control under different loading conditions. Specifically, in the proposed approach, a small q -axis harmonic voltage is injected into the machine to induce a small harmonic component in the machine speed. Based on the PMSM torque equation, the mathematical relation between the induced speed harmonic and the output torque is derived, which shows that the magnitude of the induced speed harmonic is proportional to the output torque of an IPMSM. Therefore, the speed harmonic is explored to seek the MTPA angle, in which the MTPA angle is found when the speed harmonic magnitude is maximized. In particular, the gradient descent algorithm is employed to detect the MTPA angle, which is computationally efficient and converges quickly. The proposed approach is evaluated with both simulations and experiments based on a laboratory IPMSM drive system.

This paper explores the idea of detecting uniform permanent-magnet (PM) demagnetization by using acoustic noises in order to develop a reliable PM synchronous machine (PMSM) controller. A flux-based acoustic noise model is proposed to demonstrate that demagnetization will induce acoustic noise containing abnormal frequency. This paper will also analyze online PM demagnetization detection by using a back propagation neural network (BPNN) with acoustic noise data. First, seven objective and psychoacoustic indicators are proposed to evaluate the acoustic noise of healthy and demagnetized PMSMs under different speed and load conditions. Next, a novel BPNN-based PM demagnetization detection method is proposed. In this method, the PM demagnetization is detected by means of comparing the measured acoustic signal of PMSM with an acoustic signal library of seven acoustical indicators. The proposed PM demagnetization detection approach is experimentally evaluated. Unlike other approaches, this is a noninvasive method and is independent of internal motor parameters. The aforementioned seven indicators can process nonlinear signals and are used to comprehensively reflect noise quality.

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.

Permanent magnet (PM) temperature is critical to ensure high-performance and reliable control of permanent magnet synchronous machines (PMSMs) for electric vehicle (EV) applications. High-frequency (HF) voltage injection based approach has been shown to be capable of PM temperature estimation under all-speed range with simple implementation. This paper improves existing HF voltage injection based PM temperature estimation approach by considering the cross-coupling effect. The key to PM temperature estimation is the temperature-dependent HF resistance estimated from the injected HF voltage and the current response. It is found that the cross-coupling effect has a great influence on the HF resistance estimation. This paper firstly improves the HF voltage injection model by considering the cross-coupling effect. Then, a comparative numerical investigation is conducted to analyze the estimation errors induced by the cross-coupling effect. A novel HF resistance estimation approach is derived from the proposed improved model and the PM temperature is calculated from the HF resistance with a linear thermal model. The influence of inverter nonlinearity is also analyzed. Experimental investigations demonstrate that the proposed approach is able to improve the performance of PM temperature estimation.

A stator inter-turn fault occurring in one of the phases of a three-phase induction motor (IM) gives rise to high harmonics distortion in air-gap flux density, increased torque ripple, temperature rise in the stator windings, and mechanical vibrations due to varying magnetic forces and magnetic noise. The fault leads to a change in the electromagnetic field generated when compared to that during the normal motor operation. An incipient stator fault leads to variation of machine’s parameters, causing malfunction of the motor drive. Hence, it is of significant importance to detect the incipient fault before complete motor breakdown occurs. In this paper, a novel magnetic flux reference predictive method for control has been presented by using a harmonic compensation block in coordination with deep neural network (DNN) as a feedforward method to continue safe operation of motor after occurrence of incipient stator fault. This method takes into account both time and space harmonics discrepancies produced due to the fault. The proposed method has been implemented on a 7.5 hp IM using online observer of unhealthy conditions and compensated using DNN predictive methodology.

This paper investigates torque ripple minimization for permanent-magnet synchronous machines (PMSM), and proposes a novel analytical solution of optimal stator current design for torque ripple minimization. The proposed design is theoretically proven to be able to minimize the torque ripple with minimal machine losses. Moreover, the optimal stator current is computed from analytical expression, which is computationally efficient. Therefore, the proposed approach is applicable for torque ripple minimization under both transient state and steady state. However, existing approaches usually employ optimization algorithm to optimize the stator current, which is computationally complex and involves iterative computation, so their applicability is limited under transient state, because the optimal stator current must be adaptively updated with respect to operating conditions. Moreover, magnetic saturation is considered in the proposed approach by employing a novel linear model to model the relation between the inductance and the stator current under maximum torque per ampere (MTPA) control. In this way, the proposed analytical solution does not involve inductance, and thus, the influence of magnetic saturation can be effectively reduced. The proposed approach is validated on a laboratory PMSM drive system under both transient state and steady state.

Optimal stator current design has been widely investigated for torque ripple minimization of permanent magnet synchronous machines (PMSMs). The optimal current design requires accurate machine parameters including the permanent magnet flux and dq -axis inductances, which are varying during machine operation due to machine uncertainty. Therefore, this paper investigates how these machine parameter variations influence the optimal stator current design, and hence the torque ripple minimization performance. At first, torque ripple model-based analytical solution for optimal current design is introduced in this paper, which can theoretically reduce the torque ripple to zero. Then, machine parameter variations of a laboratory interior PMSM are tested and analyzed. It is found that the magnet flux under no load can be reduced by more than 10% from room temperature to the maximal operation temperature, and the inductance term ( $L_d$ - $L_q$ ) can be reduced by more than 50% from no load to full load. Afterwards, analytical equations are derived to quantify the resultant torque ripples due to the variations of magnet flux, dq -axis inductances, and the cogging torque. Finally, the numerical and experimental studies are conducted to investigate the resultant torque ripples under different percentages of parameter variations.

In permanent magnet synchronous machine, high-frequency (HF) signal injection has been extensively investigated for permanent magnet temperature (PMT) estimation, in which PMT is estimated from the temperature-dependent HF resistance. Existing studies require prior knowledge on the HF resistance and neglect the fact that PMT is temporally correlated. This paper proposes a state-space model for PMT estimation, in which PMT is modeled with a piecewise linear equation to explore the temporal correlation. The state-space model is nonlinear due to unknown model parameters, which is required to be known in existing studies. This paper proposes to use expectation maximization particle filter (EM-PF) for simultaneous PMT and model parameter estimation. After EM-PF estimation, the state-space model becomes linear, so Kalman filter is employed for online PMT estimation. The proposed EM-PF along with a Kalman-filter-based approach can explore the temporal correlation among PMTs to improve the estimation performance, which can be hardly achieved in existing studies regarding PMT as a time-independent parameter. It should be noted that EM-PF is for initial PMT and model parameter estimation, while Kalman filter is for online PMT estimation ensuring computation efficiency and real-time capability. Our approach is validated with both numerical and experimental investigations.

For permanent magnet synchronous machines (PMSMs), accurate magnet flux linkage information is critical for permanent magnet condition monitoring and drive performance improvement. This paper proposes a novel particle-filter-based magnet flux linkage estimation approach by using the harmonics in the machine speed. In the proposed approach, the harmonic current is first injected to change the speed harmonics, and the particle filter is then applied to estimate the magnet flux linkage from the speed harmonics. With a proper selection of injected harmonic current, it is capable of simultaneously estimating the magnet flux linkage and reducing the torque ripples as well as the speed ripples. The proposed approach is based on the machine mechanical equation, so it is not influenced by the magnetic saturation, the resistance variation, and the inverter nonlinearity. Specifically, at first, a novel state-space model is developed based on the machine mechanical equation, which models the relation between the magnet flux linkage and the speed harmonic. The state-space model is nonlinear, so the particle filter is employed for a magnet flux linkage estimation. Our particle-filter-based estimation approach is validated on a laboratory PMSM drive system under different loads, speeds, and temperatures.

To develop a reliable permanent-magnet synchronous machine (PMSM) controller for electric vehicle application, detection of permanent-magnet (PM) demagnetization conditions is of significance. This paper explores the use of torque ripple for online PM demagnetization fault diagnosis using continuous wavelet transforms (CWT) and grey system theory (GST). First, a torque-ripple-based rotor flux linkage detection model considering electromagnetic noises is proposed, which employs CWT filtering, wavelet ridge spectrum, and torque ripple energy extraction. This model is able to reveal the torque variation and eliminate the effect of electromagnetic interferences. Second, GST is employed to facilitate the detection of demagnetization ratios and torque ripple energy pulsations caused by demagnetization. Third, a current regulation strategy is proposed to minimize the torque ripples induced by PM demagnetization, which contributes to making the approach feasible to interior PMSM (IPMSM). Furthermore, the proposed real-time irreversible demagnetization detection approach can identify the demagnetization fault under different operating conditions. The proposed approach and current regulation strategy are experimentally verified on a down-scaled laboratory IPMSM.

Rotor magnet flux linkage is critical to monitor the state of the permanent magnet in a permanent magnet synchronous machine (PMSM). This paper proposes a novel online magnet flux-linkage estimation approach by using measured speed harmonics. The proposed approach is based on the PMSM's mechanical equation, so it is not influenced by magnetic saturation, inverter nonlinearity, and changes in machine parameters. The interaction of magnet flux linkage and harmonic current can produce torque harmonic, and thus speed harmonic. Therefore, we explore the use of speed harmonic for magnet flux-linkage estimation. At first, the torque and speed harmonic models are proposed. Then, the mathematical relationship between magnet flux linkage and speed harmonic is developed. Based on this model, a novel magnet flux-linkage estimation approach is proposed, which can eliminate the influence of the cogging torque and spatial harmonics in magnet flux-linkage estimation. The proposed approach is validated on a PMSM drive system under different loads and speeds.

Thermal overload protection techniques commonly use a simplified 1st order thermal model, which does not provide accurate stator winding temperature prediction and causes frequent undesirable stoppage of the motor in the processes. This paper proposes a higher order lumped parameter thermal network model to accurately predict stator winding temperature. First, the comparison of the predicted stator winding temperature from a 1st order thermal model with a higher order thermal model is performed. Second, a comparison between the actual and the predicted stator winding temperature rise is verified for a 20 hp totally enclosed fan-cooled copper rotor induction motor that has no rotor fins on its short-circuit ring to enhance heat dissipation compared with an aluminum rotor induction motor.

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.

Wide–band gap (WBG) power devices are gaining significant interest over conventional Si insulated gate bipolar transistor (IGBT) power devices for inverters in electric vehicle (EV) propulsion applications. Understanding motor–drive performance on a drive–cycle due to the influence of such emerging inverters is essential to design an optimal electric powertrain system which is superior in terms of cost, weight and efficiency when compared to the state–of–the–art. Specifically, this paper introduces a novel, hybrid approach that includes numerical and analytical simulations and analysis to consider drive–cycle load and switching characteristics of various power switches and harmonics generated by the inverter. A comprehensive list of performance indices including inverter and motor losses, current harmonics and torque ripple are initially selected and used in this approach. The impact of two–level IGBT, GaN, and SiC inverters on a surface permanent magnet machine are determined for a drive–cycle.

Wide band-gap (WBG) devices operating at higher switching frequencies have become attractive for EV traction inverter application in the recent years as they can be used to reduce harmonic distortion and switching loss. On the other hand, 800 V EV battery drivetrain systems have been recently deployed in commercially available in premium electric vehicles in order to achieve higher power for propulsion and faster charging. Given this scenario, this paper puts an effort to investigate and compare a 2-level inverter, a 3-level hybrid inverter and a nine-switch inverter for such an application. Motivation for investigating each of the inverter types is provided in the respective sections. This paper utilizes a novel hybrid modelling approach to get deeper insights into the power loss of inverter with device non-linearity and the harmonics, torque ripple and magnetic saturation in the motor due to different inverter types and their outputs. Cosimulation has been performed on each of the inverter types driving the same 100 kW surface-mounted permanent magnet motor on a WLTC drive cycle and results are discussed.

Due to high torque and power density, permanent magnet synchronous motor usually faces significant challenges for thermal stability. Moreover, it becomes difficult to keep thermal stability within the motor because the magnets that are embedded in the rotor or mounted on the rotor surface are susceptible to heat. Therefore, thermal investigation is one of the mandatory procedures during design phase of the permanent magnet synchronous motor to ensure thermal stability. In this paper, a universal lumped parameter thermal network model has been proposed for thermal design of all types of permanent magnet synchronous motors which can be used for wide range of winding configurations and any position of magnets in the rotor. The proposed analytical thermal model has been used to check the thermal performance of both newly designed interior and surface mounted permanent magnet synchronous motor prototypes for future electric vehicle application. Further, a numerical thermal model has been proposed also to check temperature distribution throughout all parts of both motors and for comparison.