With the pervasiveness of electric vehicles and an increased demand for fast charging, stationary high-power fast-charging is becoming more widespread, especially for the purpose of serving pure electric buses (PEBs) with large-capacity onboard batteries. This has resulted in a huge distribution capacity demand. However, the distribution capacity is limited, and in some urban areas the cost of expanding the electric network capacity is very high. In this paper, three battery energy storage system (BESS) integration methods—the AC bus, each charging pile, or DC bus—are considered for the suppression of the distribution capacity demand according to the proposed charging topologies of a PEB fast-charging station. On the basis of linear programming theory, an evaluation model was established that consider the influencing factors of the configuration: basic electricity fee, electricity cost, cost of the energy storage system, costs of transformer and converter equipment, and electric energy loss. Then, a case simulation is presented using realistic operation data, and an economic comparison of the three configurations is provided. An analysis of the impacts of each influence factor in the case study is discussed to verify the case results. The numerical results indicate that the appropriate BESS configuration can significantly reduce the distribution demand and stationary cost synchronously.

Owing to the influence of ground capacitance in electric vehicles, in the traditional unbalanced electric bridge DC insulation monitoring (DC-IM) method, the voltage of positive and negative electric bridges changes slowly. To calculate the insulation resistances, sampling should be conducted once the voltage of the bridge becomes stable, that will inevitably extend the monitoring cycle. To reduce the monitoring cycle, this study proposes a three-point climbing algorithm, namely, three-bridge voltage sampling with equal sampling intervals, to predict the evolution of the bridge voltage curve. However, due to the existence of sampling errors, the insulation resistances calculated by sampling values will deviate from the actual values. Then, this article also proposes the filter and correction methods of three sampled voltages to improve monitoring accuracy. Through experimental data, the influences of different parameters on the results are verified, and comparisons with the traditional method are shown in the back. The conclusion is that compared with the traditional method, the proposed method can monitor insulation resistance more quickly and ensure fixed monitoring cycles under different ground capacitance values and keep the similar monitoring accuracy.

In order to reduce the recharging time of electric vehicles, the charging power and voltage are becoming higher, which has led to a huge distribution capacity demand and load fluctuation, especially in pure electric buses (PEBs) with large onboard batteries. Based on one actual direct current (DC) fast-charging station, a two-step strategy for the suppression of the peak charging power was developed in this paper, which combined charging optimization and a battery energy storage system (BESS) configuration. A novel charging strategy was proposed, with the PEBs fast-charging during operating hours and normal charging at night, based on a new charging topology. Then, a charging sequence optimization model was established, according to the operation characteristics analysis of the DC fast-charging station. The particle swarm optimization (PSO) algorithm is applied to optimize the charging sequence, which is disordered at present. Linear programming is used to configure the battery energy storage system in order to further decrease the peak charging power and satisfy the distribution capacity constraint. The two-step strategy was simulated by the dataset from the real station. The results show that the distribution capacity demand, charging load fluctuation, electricity cost, and size of the BESS were significantly decreased.

The balance control of neutral-point voltage (NPV) is important in the three-level converter. In this paper, this problem is studied by taking the VIENNA circuit as an example. The deviation of NPV is essentially caused by mismatch between the charging and discharging time of two series capacitors by the neutral-point current flowing into or out of the DC side. The unbalanced NPV will lead to unbalanced voltage stress of two capacitors and power switches and may cause overvoltage damage to both and also increase the total harmonic distortion (THD) and harmonic components in the AC current. In this paper, by analyzing the role and effect of a small-voltage vector on NPV, a control strategy based on the selection method of dynamical adjustment for a small vector is proposed. By judging the fluctuation of NPV, different small vectors are dynamically selected to act to adjust the NPV. For verification, the proposed strategy is compared with the traditional zero-sequence voltage injection (ZSV-J) method through simulation and experiment. Compared with ZSV-J, the THD of AC current is decreased by about 27.2%, the efficiency is increased by about 1.66%, and the adjustment speed of NPV is increased by about 50%. Therefore, the feasibility and advantages of the strategy are verified.