The state of charge (SOC) is one of the crucial states for battery management. The Kalman filter (KF) family algorithms are promising for SOC estimation. Based on the KF theory, a sufficiently accurate system model is the precondition for a better performance of the algorithm. Thus, we manage to improve the algorithm by estimating the battery model error. In this paper, the sources that may cause model errors are analyzed. Then, in order to estimate the unknown error term, the bias term characterizing the model error is adjoined to the original state vector to form a new state vector, and the KF is utilized to estimate the new state vector. It is a joint estimation algorithm for both SOC and the model error. Subsequently, by decoupling this joint estimation algorithm, a battery model error observer has been built. Finally, to verify the robustness of the proposed method against the battery model error, different types of errors such as open circuit voltage drift and voltage sensor drift are injected. The results indicate that the improved SOC estimation algorithm has better robustness and accuracy against the model mismatch compared with the standard KF algorithm.

Permanent magnet synchronous motors (PMSMs) with inverters are widely used in electric vehicles (EVs). However, current harmonics caused by the nonlinearity of the inverter generate torque ripples and give rise to torsional vibration in the vehicle driveline. This paper proposes a new compensation method to suppress the torque ripples. This method extracts the 6th-order harmonic component online from the d-axis and q-axis currents with the approximate Fourier transform, and adopts a harmonic current PI regulator to calculate compensation voltage, which is added to the voltage reference to compensate the nonlinearity of the inverter. After correcting the current distortion and improving the motor torque smoothness, the torsional vibration of the driveline caused by the motor pulsating torque is reduced. According to the simulation results, the 6th-order of motor torque ripple and the torsional vibration response is reduced about 26–28%, which confirms the validity of the proposed strategy. The proposed method does not need any additional hardware and can be implemented broadly in PMSM drives.

The state of charge (SOC) plays a critical role in battery management systems. This paper discusses the stability of the nonlinear SOC observer based on the extended Kalman filter. The model characterizing the lithium-ion battery nonlinearity is the basis of the stability analysis. After balancing the accuracy and the complexity of the models, the Thevenin battery model and the logarithmic fitting OCV (open circuit voltage) model are employed. The stability of the SOC observer is theoretically analyzed from two aspects: model parameters and system nonlinearity. Furthermore, the impact of system noises and nonlinear characteristics on the estimation is explored in a numerical way. For the estimation of SOC, the nonlinearity is mainly reflected in the OCV-SOC function. It is found out that the gradient variation of the OCV-SOC curve is not conducive to the estimation, especially when the gradient is small and the voltage noise is large. In order to improve the estimation performance, the role of matrices Q and R as the design parameters of the SOC observer is discussed. The results indicate that the observer is able to exhibit good stability and performance under appropriate settings.

In this paper, based on the research of the first generation of a hybrid electric city bus, principle researches have been offered on the parameters matching of the power-train and main components for the new generation of hybrid electric city bus. The vehicle system was designed integrated, and then simulation platform was developed and established. The dynamic and economy performance can be predicted based on the above work, results show that the designed hybrid electric city bus can meet the performance demands and validate rationality of the parameters matching.

The deadbeat predictive controller theoretically features a two-sample step response with the existence of one-sampling-period digital delay. Nevertheless, high sensitivity to high frequency perturbation is an important practical issue that restricts its application in reality. This paper proposes a novel deadbeat predictive flux linkage control method with disturbance suppression scheme for permanent magnet synchronous motor drives. The flux linkage prediction and voltage command calculation blocks are revised with additional state feedback and parallel integral path. Simulation results show that the proposed control scheme show immunity to different types of disturbance. Meanwhile, the steady-state error is eliminated even with mismatch of flux linkage parameter.