**1. Introduction**

With the increasing popularity of electric vehicles (EVs), strict requirements are being established for the driving and charging safety of EVs. The insulation of EVs decreases due to rain, dampness, collision, and other reasons arising from the long-term exposure of EVs and charging equipment to the outdoor environment [1,2]. The DC system of EVs is connected to numerous power electronic devices, including the motor converter, battery charger, air conditioner, and DC–DC converter [3], and the overall connections may form a DC power microgrid system when EV is charging [4,5]. Insulation failure of any equipment affects the safety of the entire system. When the insulation resistance of the system decreases to below a threshold value, the vehicle sends warning signals. If the situation is serious, then the high-voltage system must be cut off and stopped for troubleshooting [6,7]. The DC insulation monitoring (DC-IM) function is thus required before charging by the DC charging pile and during the process of driving the EVs [8]. Various insulation-monitoring devices and embedded circuits have been designed and installed in DC charging piles, battery packs, high-voltage distribution boxes, and other equipment or embedded in the battery management system of EVs. DC-IM methods include balanced electric bridge [9], unbalanced electric bridge [10–14], high-voltage injection [15], differential amplification [16], and low-frequency small-signal injection [17,18]. The unbalanced electric bridge method can synchronously monitor positive and negative insulation resistances, has a low cost, and is easy to realize; thus, it has been widely used in EVs and charging piles.

Because the DC system of EVs connects various power electronic devices that contain many Y capacitors and parasitic capacitors, which make up the large ground capacitance (GC) of the system, GC, an unknown system parameter, seriously affects the monitoring accuracy and speed of DC-IM. Therefore, various solutions have been suggested in the literature. In [19,20], wavelet-transform and chaos theory detection methods were proposed to deal with interference signals. However, these methods are more suitable for a multi-branch DC system with the small-signal injection method than for systems with a large GC. The method based on the Kalman filter and Lyapunov equation proposed in [18] and [21] needs to be recursive step by step. Thus, obtaining the result takes a long time. The traditional sampling and comparison method is frequently used in current practical product applications. After initiating bridge conversion, sampling and calculation are performed only after GC is fully charged so that a stable voltage signal can be sampled. However, this method considerably slows down DC-IM and cannot meet the real-time requirements of EVs and the future development trend of EV safety.

This study proposes a method of unbalanced electric bridge DC-IM based on a three-point climbing algorithm. After switching the bridge, sampling is conducted for three times at equal intervals. The methods of filtering and automatic correction of sampling voltage are also proposed to reduce the result error caused by voltage ripple and sampling resolution. The calculation can predict the voltage value after the completion of GC charging. The method is simple and easy to implement. It does not need to wait for GC charging and multiple sampling, which can considerably increase the detection speed. Thus, the DC-IM period is fixed and unaffected by GC.

The rest of this paper is organized as follows. Section 2 analyzes the unbalanced electric bridge DC-IM with the existing GC. Section 3 proposes the novel method of the three-point climbing algorithm in order to avoid the impact on GC. Section 4 further optimizes the proposed method and describes the implementation method. In Section 5, The experimental data are exhibited to prove the theory. Finally, conclusions are included in Section 6. Some symbols used in the operation optimization are shown in Table 1.


**Table 1.** List of some symbols used in the operation optimization.
