## 2. SOH Definition and Computation
For example, charging or discharging with a Constant Current (CC) in a large voltage range (contained at least the peak voltage) and the CC should be small enough to sufficiently show the voltage plateau [28]. Furthermore, all the peaks on the IC curve lie within the voltage plateau region of the _V-Q_ (Voltage-Capacity) curve [29], which is relatively flat and more sensitive to measurement noise. Hence, effective and robust algorithms of obtaining the IC and DV curve need to be developed. [PERSON] et al. [30] proposed a probability density function (PDF), which has an equivalence performance with ICA/DVA for predicting the SOH. [PERSON] et al. [31] presented an algorithm to obtain the DV curve according to the center least squares used the location interval between two influence points or the transformation parameter of the DV curve, as a result, the error was within 2.5%. [PERSON] et al. [32] proposed a simple and robust smoothing method based on Gaussian filter to reduce the noise on IC curves. [PERSON] et al. [33] presented a SOC based ICA/DVA method to address the question, that is, the conventional cell terminal voltage based ICA/DVA methods are sensitive to the changed resistance and polarization during battery aging processes. (3) Methods based on data-driving  
This type of methods is aimed to find degradation mechanism of a battery by analyzing the characteristic data without considering its electrochemical reaction and failure mechanism [34]. The data that can reflect the characteristics of the battery SOH and its evolution is called health factor [35]. This method will establish the relationship between the experiment data and battery degradation to diagnose and estimate the SOH of the battery [36]. Since the SOH of the battery is bound to be reflected by its external charge and discharge characteristics, thus this type of method does not consider the internal characteristics of the battery [37]. [PERSON] et al. [38] proposed a data-driven approach to trace SOH on the fly by using current, voltage and temperature while leveraging their historical distributions. When it is used under actual EV driving conditions, the average error is less than 2.18%. [PERSON] et al. [23] put forward a SOH prediction method based on the health factor constructed through OCV of the battery. It can effectively track the SOH. However, it takes a long time to get the OCV value and is hard to implement online. [PERSON] et al. [39] and [PERSON] et al. [40] used voltage drop of same time and the same voltage drop in each cycle life test as health factors respectively. This method needs to work under constant current discharge conditions to achieve the health factor. [PERSON] et al. [41] proposed a health factor characterized by increment of Ohmic internal resistance, increment of polarization internal resistance and the reduction of polarization capacitance to realize online SOH prediction. The main problem of this type of methods is that the battery has to be discharged or has to work under specific operating conditions, such as under cycle life test conditions, to obtain the health factor. It is known that additional discharging is detrimental to the battery and that, when the battery is actually used, the operating conditions are dynamic. Aforementioned, a novel SOH estimation method adopting the relative features of the terminal voltage drop as the health factor is proposed. The battery only needs to be charged to get the health factor and the operating conditions are not restricted.