The existing evaluation methods and indexes of noise of electric motors cannot comprehensively reflect all the physical characteristics of noise of hub permanent-magnet synchronous motors (HPMSM) and human subjective sensations. In this paper, a method of the sound quality (SQ) evaluation of HPMSM for electric vehicles is proposed. The method is divided into three steps. In the first step, a noise objective evaluation of HPMSM with seven acoustical objective indexes, including loudness, roughness, A-weighted sound pressure level (A-W SPL), tonality, sharpness, articulation index (AI), and fluctuation strength, is made. In the second step, a noise subjective evaluation of HPMSM by using the grade evaluation method is made. The subjective annoyances (SA) of noise samples of HPMSM are obtained. In the last step, the SQ evaluation model of HPMSM by using the BP neural network theory is established. The average error rate of the proposed model is only 3.97%. By means of weight analysis, a comparison between the relative importance of acoustical objective parameters is found out. The main original contribution of this paper is that the proposed method can comprehensively reflect all the physical sound characteristics of HPMSM as well as their influence on human subjective sensations.

To accurately and quickly predict the open-circuit air-gap magnetic field in interior permanent magnet synchronous machines (IPMSMs), an analytical model based on magnetic equivalent circuit (MEC) method is proposed. The analytical model can provide radial and tangential components of open-circuit magnetic field in the slotless air gap. The radial component is obtained from the MEC model, and the tangential component is obtained from boundary conditions of macroscopic equations. Then, a complex relative air-gap permeance is applied to take the effect of slots into account. As a result, an accurate solution of both radial and tangential components of the flux density in the slotted air gap is obtained. Additionally, the no-load back electromotive force (EMF) and cogging torque are calculated based on the open-circuit air-gap magnetic field. All the analytical results are verified by the finite element (FE) analysis and they are well matched. In the end, a direct measurement experiment of open-circuit air-gap magnetic field is proposed to verify the validity of both radial and tangential open-circuit air-gap magnetic fields.

State of charge (SOC) estimation of lithium batteries is one of the most important unresolved problems in the field of electric vehicles. Due to the changeable working environment and numerous interference sources on vehicles, it is more difficult to estimate the SOC of batteries. Particle filter is not restricted by the Gaussian distribution of process noise and observation noise, so it is more suitable for the application of SOC estimation. Three main works are completed in this paper by taken LFP (lithium iron phosphate) battery as the research object. Firstly, the first-order equivalent circuit model is adapted in order to reduce the computational complexity of the algorithm. The accuracy of the model is improved by identifying the parameters of the models under different SOC and minimum quadratic fitting of the identification results. The simulation on MATLAB/Simulink shows that the average voltage error between the model simulation and test data was less than 24.3 mV. Secondly, the standard particle filter algorithm based on SIR (sequential importance resampling) is combined with the battery model on the MATLAB platform, and the estimating formula in recursive form is deduced. The test data show that the error of the standard particle filter algorithm is less than 4% and RMSE (root mean square error) is 0.0254. Thirdly, in order to improve estimation accuracy, the auxiliary particle filter algorithm is developed by redesigning the importance density function. The comparative experimental results of the same condition show that the maximum error can be reduced to less than 3.5% and RMSE is decreased to 0.0163, which shows that the auxiliary particle filter algorithm has higher estimation accuracy.

To improve the heat dissipation performance of power batteries in electric racing cars in the Formula Student Electric China (FSEC), a battery cooling system was researched. A battery thermal model and a temperature experimental platform were established. The thermal model was verified by comparing the results of the ANSYS/Workbench simulations with the experimental results, and the maximum error was 7.2%. Based on the FSEC dynamic conditions, the cooling demand was analyzed according to the heat transfer theory. Then, an orthogonal method was used to optimize the position of the cooling fan and the arrangement of the cells, and a parameterized battery simulation model based on ANSYS/Icepak was established. The simulation results show that the maximum temperature difference was optimized by 38.35%. The results of the simulation were in good agreement with the experimental results, and the maximum error was less than 2 °C. This indicates that this design can ensure a car battery system that has a good heat dissipation performance in the FSEC; thus, the intended goal was achieved.

The brake energy recovery technology has become a key technology for electric vehicles which can significantly increase the energy economy of the electric vehicle. By-wire control of friction braking is the basic requirement for regenerative braking. For commercial vehicles with the pneumatic brake system, this article presents a novel electronic pneumatic brake system actuated by a linear motor. The electronic pneumatic brake system was modeled based on Matlab/Simulink and the accuracy of the model was verified by bench test data. Based on the analysis of model characteristic, PID and fuzzy PID controller are designed. The control effect is verified by the hardware-in-loop simulation platform of the electronic pneumatic brake system. Experimental results show that the overall performance of fuzzy PID control is better than PID control. The research can provide a friction braking force control solution for the braking energy recovery technology of an electric bus or truck using a pneumatic brake system.