Analysis of longitudinal-vertical coupling vibration of four hub motors driven electric vehicle under unsteady condition

The influence of hub motor unbalanced magnetic force (UMF) on the vibration of electric vehicle under steady state conditions has been known, but under unsteady conditions, the hub motor UMF will change with the vehicle operation condition, and there would exist complex coupling vibration for the hub motors driven electric vehicle. Here, the longitudinal-vertical coupling dynamics of the four hub motors driven electric vehicle under unsteady condition is studied. Integrating the motor electromagnetic excitation and electric vehicle dynamics, a longitudinal-vertical coupling dynamics model of the four hub motors driven electric vehicle is established. Based on the variable switching frequency field-oriented control model, analytical model of the UMFs acting on the motor stator and rotor parts under unsteady condition are developed. For model validation, a four hub motors driven electric vehicle has been tested, the accuracy of the longitudinal-vertical coupling dynamics model established in this paper was verified. Then, longitudinal-vertical coupling vibration characteristics of the four hub motors driven electric vehicle under road excitation and coupling excitation are analyzed. The results show that the longitudinal and vertical movement of the four hub motors driven electric vehicle is coupled by hub motor. In addition, under unsteady condition, the motor UMFs will cause vertical vibration of the electric vehicle body and hub motor stator, the vibration shows order characteristics including low order harmonic hfc and inverter switching frequency sideband harmonic k1fs±k2fc (fc = pn/60, fs is inverter switching frequency, k1 and k2 are positive integers, p is the number of pole pairs and n is motor speed.). The motor electromagnetic torque will cause longitudinal vibration of the electric vehicle body, the vibration shows order characteristics including harmonics fs±3fc and 2fs. The main harmonic of vehicle body pitch angle acceleration is 2fs.</p

Prediction and Diagnosis for Unsteady Electromagnetic Vibroacoustic of IPMSMs for Electric Vehicles Considering Rotor Step Skewing and Current Harmonics

Purpose: This study provides a detailed investigation on the prediction and diagnosis of unsteady electromagnetic vibroacoustic performance of IPMSMs for electric vehicles under typical unsteady operating conditions with consideration of rotor step skewing and current harmonics. Methods: Firstly, the control model considering the influence of PWM carrier modulation and rotor step skewing is established. Based on this, the currents of the IPMSM under unsteady operating conditions (driving condition and feedback braking condition) are obtained. Accordingly, the currents calculated through the control model are used as the excitation source of electromagnetic finite element. Then, the electromagnetic vibroacoustic performance under unsteady operating conditions is calculated through electromagnetic force subsection mapping and acoustic transfer vector (ATV) method. Moreover, the conditions where resonance vibroacoustic occurs are diagnosed. Finally, the results of prediction and diagnosis are fully verified by experiments of multiple physical fields. Results and Conclusions: The amplitude errors between prediction results and test results are less than 3.2%. The influence of current harmonics on electromagnetic vibroacoustic can be predicted. The frequency range and speed range of predicted peak vibroacoustic are consistent with the experimental results. The rotor step skewing can be used to weaken the vibroacoustic amplitude of IPMSMs under typical unsteady conditions in the full speed range. This study provides guidance for prediction and diagnosis for electromagnetic vibroacoustic performance of IPMSMs under typical unsteady operating conditions.</p

Evaluation of LFP Battery SOC Estimation Using Auxiliary Particle Filter

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