Emerging topics such asenvironmental protection and energy utilization have pushed research and development of electric vehicles and hybrid electricvehicles (EVs/HEVs). In the last few decades, numerous technologies have been developed for EV/HEVimportance. In this article, key research topics in the area of EVs/HEVs, namely electric machines, electrochemical energy sources, wireless charging infrastructure, and latest EV/HEV models are covered. This paper aims to consolidate the key emerging technologies in this field and provide the readers a blueprint to begin their own journeys.

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 magnet 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 magnetic saturation. The key to PM temperature estimation is the temperature-dependent HF resistance which is estimated from the injected HF voltage and the HF 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.

The implementation of traditional current state observers for single current sensor (SCS) based permanent magnet synchronous machine (PMSM) control use continuous-time domain analysis and Euler or Tustin approximation for discretization. However, stability problem occurs at low sampling-to-fundamental frequency ratio condition with Euler approximation method and heavy computation burden cannot be ignored with Tustin method. To overcome these limitations, a discrete-time adaptive observer is proposed for SCS control in PMSM drives. First, commonly adopted Luenberger observers designed with Euler and Tustin methods are reviewed and analyzed. Then, a novel hybrid discretization (HY) method is proposed to design a discrete-time adaptive Luenberger observer with improved discretization accuracy while maintaining computational efficiency. In the proposed HY method, the nonlinear part of the PMSM model is discretized using the accurate Runge–Kutta discretization method, while the linear part is discretized using the computationally-efficient Euler approximation method. This HY method achieves a balance between simplicity and accuracy, resulting in a highly effective discretization of the observer. Moreover, the speed-adaptive gain is designed to guarantee stability and dynamic performance over a wide speed range. Experimental results have been performed on a laboratory interior PMSM drive to confirm the effectiveness of the proposed method.

A 3-phase permanent magnet synchronous machine (PMSM) drive system usually requires at least two current sensors for phase current measurements to achieve high performance control. When there is only one current sensor working in the drive system, accurate current observers are critical to ensure the control performance. In this paper, a novel adaptive current observer based single current sensor (SCS) control strategy is proposed. In the proposed approach, an augmented current state observer is built based on the PMSM dynamic equations with the consideration of machine parameter variation and system uncertainties. To ensure a good SCS control performance in the time-varying speed condition, a new computationally efficient methodology towards a desired pole-placement for the adaptive observer gain design is also proposed. More specifically, the proposed observer gains can be calculated analytically for the desired dynamic performance. Compared with existing methods, the proposed current observer has a straightforward gain design, and it further reduces current estimation errors and improves the robustness against machine parameter variation and system uncertainty. The proposed SCS control approach is validated with extensive experimental tests on a laboratory interior PMSM under both steady-state and transient conditions.

High-performance current and speed control are required to obtain smooth output torque, current tracking, and speed tracking in permanent-magnet synchronous motor (PMSM) drives. The motor speed and stator current control rely on multiple nonlinear motor parameters, which play a crucial role in shaping the performance of PMSM. Moreover, tuning the speed and current controller parameters using the conventional control technique depends on these PMSM parameters, also variation of these parameters will have a decisive influence on the dynamic performance of PMSM. To enhance the robustness of vector control and tracking methodology against PMSM parameter uncertainties and load disturbances, a novel artificial intelligence (AI)-based advanced speed and current control technique for PMSM is proposed in this article. Subsequently, the methodology for designing and training the suggested Dense Neural Network (DNN) controllers are elicited. The proposed controllers can handle the inevitable fluctuation and non-linearity in motor parameters at different load points and drive conditions. The proposed DNN scheme is validated in terms of settling time, dynamic responsiveness, tolerance to parameter fluctuations, and overall robustness. A comparative analysis is conducted against adaptive proportional-integral (API) control applied to the same PMSM within the OPAL-RT real-time simulator (RTS). The viability of the proposed control scheme is substantiated through simulation, Software-In-the-Loop (SIL) and Hardware-In-the-Loop (HIL) testing with an RTS and an automotive-grade controller board across diverse conditions.

Power electronics and motor drive components existing in a conventional electric vehicle (EV) drivetrain employed to propel the EV can also be used to charge the battery under level 3 fast charging capacity. This integrated battery charging technology can be realized by employing the winding inductances of the three-phase interior permanent magnet synchronous motor (IPMSM) used for propulsion as line inductors while charging the EV's battery pack. The difference in the d- and q-axis inductances in the IPMSM offers unbalanced Thevenin impedances as viewed from stator terminals. This causes the voltages across the stator windings to become unbalanced when balanced three-phase currents synchronized with the grid utility voltages are forced into the three armature phases during charging. This paper first presents a case study employing a conventional laboratory IPMSM to illustrate the unbalanced phase winding impedance during an “emulated integrated charging” operation. Thereafter, the authors derive motivation to design and analyze an IPMSM with dampers in the rotor for propulsion and mitigation of the saliency effect, that is, the effect of unequal armature phase impedance at standstill during integrated charging. A novel design approach is presented and employed to design an IPMSM with dampers. Performance of the designed IPMSM with dampers is compared to that of the conventional IPMSM under both integrated charging and traction conditions. The proposed design is also validated experimentally using a laboratory IPMSM with dampers.