In this work, a modeling and experimental study of a new thermoelectric cooler–thermoelectric generator (TEC-TEG) module is investigated. The studied module is composed of TEC, TEG and total system heatsink, all connected thermally in series. An input voltage (1–5 V) passes through the TEC where the electrons by means of Peltier effect entrain the heat from the upper side of the module to the lower one creating temperature difference; TEG plays the role of a partial heatsink for the TEC by transferring this waste heat to the total system heatsink and converting an amount of this heat into electricity by a phenomenon called Seebeck effect, of the thermoelectric modules. The performance of the TEG as partial heatsink of TEC at different input voltages is demonstrated theoretically using the modeling software COMSOL Multiphysics. Moreover, the experiment validates the simulation result which smooths the path for a new manufacturing thermoelectric cascade model for the cooling and the immediate electric power generation.

This paper presents a novel yokeless and segmented armature (YASA) axial-flux in-wheel motor with amorphous magnetic material (AMM) stator cores for a solar-powered electric vehicle. Although this new axial-flux in-wheel motor has many advantages such as high efficiency, shorter axial length, and high power density, its working condition is complicated. In-wheel motors are usually operated in electromagnetic, thermal, and other multiphysics environments. Increasing the performance requirements of in-wheel motors, such as power density, efficiency, and reliability, requires a multiphysics design approach. The focus of this paper is on the analysis of electromagnetic characteristics, losses, temperature distribution, mechanical behavior and other characteristics of the axial-flux in-wheel motor. The back electromotive force (EMF) and electromagnetic torque of the motor with harmonic current are obtained by the 3-D finite element method (FEM). The permanent magnet (PM) eddy-current losses when using different PM shapes are studied. The equivalent thermal model of the tape-wound AMM stator segments and the windings are established, and the temperature distribution of the motor is obtained. The mechanical behavior of the stator segments and the rotor disks when the motor is eccentric and axially offset is analyzed, and the structural strength of the motor is evaluated. Finally, a prototype of the motor is fabricated, and the electromagnetic performance and temperature of the motor are tested to verify the accuracy of the multiphysics design approach.

Solar cars are energy-sensitive and affected by many factors. In order to achieve optimal energy management of solar cars, it is necessary to comprehensively characterize the energy flow of vehicular components. To model these components which are hard to formulate, this study stimulates a solar car with the digital twin (DT) technology to accurately characterize energy. Based on the hybrid modeling approach combining mechanistic and data-driven technologies, the DT model of a solar car is established with a designed cloud platform server based on Transmission Control Protocol (TCP) to realize data interaction between physical and virtual entities. The DT model is further modified by the offline optimization data of drive motors, and the energy consumption is evaluated with the DT system in the real-world experiment. Specifically, the energy consumption error between the experiment and simulation is less than 5.17%, which suggests that the established DT model can accurately stimulate energy consumption. Generally, this study lays the foundation for subsequent performance optimization research.

Vehicle intelligence and networking have manifested the significance of the embedded controller area network (CAN) bus. However, the lack of message encryption and identity (ID) authentication leaves electric control units (ECUs) exposed to cyber attacks. To identify the potential attacks on the CAN, intrusion detection systems (IDSs) are required with consideration of their computational burden and their application in vehicles. Therefore, we propose a lightweight ECU identification scheme. Explicitly, the proposed method records the periodic intervals of frames and calculates accumulated clock offsets with the recursive least square (RLS) algorithm; meanwhile, the empirical rules (ERs) are adopted to eliminate the noises. Then, the ECU fingerprints have been formulated with the derived clock skew, clock offsets, as well as their expectations. Furthermore, to accurately identify the attackers in the masquerade attacks, a double-verified attacker identification approach is proposed, in which the data dependency and intra–inter-class algorithm are, respectively, utilized for better executability. Finally, we have tested the proposed method with an actual vehicle, and the results manifest that the proposed method could identify the abnormal ECUs with an identification accuracy of at least 98%, and its execution time is less than 3 ms.

As the mainstream powertrain of electric vehicles, permanent magnet motors are facing the challenge of durability and thermal failure. Therefore, the real-time rotor temperature monitoring plays a critical role, however, it is hard to online measure with sensors. To this end, a rotor temperature estimation method based on the lumped-parameter thermal networks and dual H infinity filters is proposed. Firstly, the lumped-parameter thermal network of three nodes, such as the stator, rotor and bearing, is numerically formulated to determine the power loss. Accordingly, the discretized state-space expressions are specified for the time-step iterative solution. Then, to address the uncertainty of model parameters, the dual H infinity filters are used in the rotor temperature estimation process. Finally, the simulation and experimental tests are performed to validate the effectiveness and the real-time executability of the proposed method. The test results show that the proposed method can well track the actual temperature tendency with estimation errors of less than 7.5 °C. Compared with the existing methods, the worst-case estimation accuracy has been improved by at least 25 %; besides, the proposed method presents good robustness against the parameter uncertainty; meanwhile, the higher estimation convergence is made in the face of huge model deviations.