Monitoring critical temperatures in permanent magnet synchronous motors is crucial for improving working reliability. Aiming at resolving the difficulty in online temperature estimation, an accurate and simple five-node lumped parameter thermal network (LPTN) is proposed and the mathematical model of the LPTN is built. Both radial and axial heat transfer paths inside the motor are considered to model the complete thermal circuit. In addition, an innovative parameter identification method based on multiple linear regression is applied to identify the parameters of the LPTN model. The parameters in the state equation are identified instead of the data of the motor, which are strongly dependent on the material and geometrical parameters. Finally, an open-loop estimation scheme based on the state equation and Kalman filter algorithm is adopted to predict the motor temperature online. The model performances are validated by extensive experiments under varying speed and torque conditions in terms of the accuracy and robustness. The results indicate that the temperature estimation error is within the range of ±5 °C in most cases and the proposed model can quickly follow the load variation. Besides, the online temperature estimation scheme and parameter identification method are easy and convenient to implement in an embedded system, which is feasible in automobile applications.

Two problems can cause control performance degradation on permanent magnet synchronous motor (PMSM) systems, namely, fluctuation of PMSM parameters and the time delay between current sampling and command value update. In order to reduce the influence of these problems, a new current-predictive control strategy is proposed in this article for medium- and high-speed PMSM. This strategy is based on the discrete mathematical model of PMSM. This new control strategy consists of two main steps: First, an integrator is applied to calculate current compensation value; second, the predictive current value is obtained through deadbeat-current predictive method. The stability of predictive control system is also proved in the article. With this deadbeat-current predictive control scheme, the real current can reach the desired value within one control-step. Based on this new current control method, Luenberger observer and phase-locked loop position tracker is applied in this article. Experimental results for 0.4 kW surface-mounted PMSM confirm the validity and excellent performance for parameters fluctuation of new current predictive control.

The total control range of asymmetric dual three-phase permanent magnet synchronous motor (ADT_PMSM) is divided into three different segments by voltage modulation range and current control dimensions and respective control strategies to get the injected voltages in harmonic subspace for three segments are presented in this paper. The three segments are sinusoidal current modulation region, sinusoidal voltage modulation region and overmodulation region. In sinusoidal current modulation region, resonant controller is adopted in the harmonic subspace to calculate the injected voltages, which can compensate the effect of six-phase voltage source inverter (VSI) dead time for two sets of three-phase windings and takes the pole correction into consideration. In sinusoidal voltage modulation region, open loop control strategy in harmonic subspace is adopted. Overmodulation region 1 and 2 are defined in overmodulation region, and the harmonic voltages which are injected into harmonic subspace to extend the modulation index are calculated based on superposition principle and VSD theory. In order to achieve smooth transition from different regions, a novel space vector pulse width modulation (SVPWM) technique for ADT_PMSM is proposed. The experimental results demonstrate the validity and feasibility of the suggested control approach.

The modeling of conduction and switching losses for insulated gate bipolar transistors (IGBTs) and free-wheeling diodes (FWDs) in automobile applications is becoming increasingly important, especially for the improvement of the system efficiency and the reliability prediction. The traditional modeling of conduction and switching losses based on the space vector pulse width modulation (SVPWM) is not applicable in practice due to the complex curve-fitting and the computation demands. In this paper, a simple and practical losses model for IGBTs and FWDs is proposed based on the SVPWM algorithm. Firstly, the traditional power losses model is introduced briefly. Then, the piecewise linear switching losses model and the conduction losses model based on the equivalent three-order harmonic model of the duty cycle are proposed. The comparison of experimental results between the traditional model and the proposed model is presented in the experiment validation. Furthermore, the power analyzer is adopted to measure the inverter losses, and the chips losses are further validated when other extra losses are considered. The proposed model shows good modeling accuracy with the large benefit of smaller measurement and lower computation requirements.

Active-short-circuit and locked-rotor modes are common abnormal operations in new energy vehicles. The IGBT junction temperature measurement for these two operating conditions is a challenging problem due to the unexpected large current and the asymmetric operation of semiconductor chips. In addition, different cooling flow rates have a significant influence on the heat dissipation, which will also have an impact on the building of the thermal model. Based on these difficulties, a modified Foster thermal network under active-short-circuit and locked-rotor modes has been presented considering different cooling conditions. The power loss models of the semiconductor chip under abnormal conditions are developed and a modified Foster thermal network based on the NTC temperature sensor is proposed. The model can be adapted to different cooling conditions since the thermal impedance fluctuates slightly at different cooling flow rates. The proposed thermal model is verified with inverter application under active-short-circuit and locked-rotor modes and the experimental performance shows good accuracy compared with the infrared camera measurement results.

Intelligent vehicles are constrained by road, resulting in a disparity between the assumed six degrees of freedom (DoF) motion within the Visual Simultaneous Localization and Mapping (SLAM) system and the approximate planar motion of vehicles in local areas, inevitably causing additional pose estimation errors. To address this problem, a stereo Visual SLAM system with road constraints based on graph optimization is proposed, called RC-SLAM. Addressing the challenge of representing roads parametrically, a novel method is proposed to approximate local roads as discrete planes and extract parameters of local road planes (LRPs) using homography. Unlike conventional methods, constraints between the vehicle and LRPs are established, effectively mitigating errors arising from assumed six DoF motion in the system. Furthermore, to avoid the impact of depth uncertainty in road features, epipolar constraints are employed to estimate rotation by minimizing the distance between road feature points and epipolar lines, robust rotation estimation is achieved despite depth uncertainties. Notably, a distinctive nonlinear optimization model based on graph optimization is presented, jointly optimizing the poses of vehicle trajectories, LPRs, and map points. The experiments on two datasets demonstrate that the proposed system achieved more accurate estimations of vehicle trajectories by introducing constraints between the vehicle and LRPs. The experiments on a real-world dataset further validate the effectiveness of the proposed system.