A battery energy storage system capacity estimation method suitable for data centers

Abstract

The application provides a battery energy storage system capacity estimation method suitable for a data center, acquires voltage, current and temperature data of four stages of discharging, standing after discharging, charging and standing after charging of the data center battery energy storage system, analyzes the correlation of average value, variance, sample entropy and capacity by using a Pearson correlation coefficient, selects a discharging voltage variance with relatively large correlation as an evaluation parameter of the capacity, and then calculates the capacity of the remaining batteries by using an SOH evaluation method, so that SOH estimation of the battery energy storage system is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of intelligent operation and maintenance of battery energy storage systems, and particularly relates to a battery energy storage system capacity estimation method suitable for data centers. BACKGROUND

[0002] As an important part of new energy storage, battery energy storage is increasingly favored by people due to its large energy density, flexible installation, compact structure and other characteristics. When applied in data centers, a large number of battery cells are connected in series and parallel to form a battery energy storage system. However, as the operation time of the battery energy storage system gradually increases, the voltage, capacity and temperature of individual battery cells gradually differ due to changes in material activity and operating environment, leading to gradually increasing inconsistency between the batteries and reducing the service life of the system. In order to ensure reliable power supply for data centers, the uninterruptible power supply is composed of a battery system, and long-term idling makes it difficult to grasp the health status. Therefore, considering the consistency between the batteries and the reliability of power supply, it is very important to evaluate the SOH of the battery energy storage system in data centers. The conventional method needs to estimate the SOH of each battery in the system. Since the SOH estimation method is generally complex and the calculation process is repeated multiple times, the computational complexity is greatly increased. Therefore, it is imperative to explore easier signals to evaluate the capacity consistency of the battery. SUMMARY

[0003] To overcome the shortcomings of the prior art, the application provides a battery energy storage system capacity estimation method suitable for data centers. The voltage, current and temperature data of a battery unit in the data center battery energy storage system during discharge, rest after discharge, charging and rest after charging are obtained. The correlation between the average value, variance, sample entropy and capacity of the data is analyzed using the Pearson correlation coefficient. The discharge voltage variance with greater correlation is selected as the capacity evaluation parameter. The capacity of the remaining battery units in the battery energy storage system is calculated by the SOH evaluation method proposed in the application, thereby realizing SOH estimation of the battery energy storage system.

[0004] To achieve the above purpose, the technical scheme adopted by the application is as follows:

[0005] A battery energy storage system capacity estimation method suitable for data centers. The voltage, current and temperature data of a battery unit in the data center battery energy storage system during discharge, rest after discharge, charging and rest after charging are obtained. The correlation between the average value, variance, sample entropy and capacity of the data is analyzed using the Pearson correlation coefficient. The discharge voltage variance with greater correlation is selected as the capacity evaluation parameter. The capacity of the remaining battery units in the battery energy storage system is calculated by the SOH evaluation method proposed in the application, thereby realizing SOH estimation of the battery energy storage system.

[0006] Further, the calculation of the capacity of the other battery units in the battery energy storage system includes:

[0007] The battery unit SOH is characterized by a relative capacity, which is the ratio of the difference between the current capacity and the end-of-life capacity and the difference between the rated capacity and the end-of-life capacity:

[0008] (1)

[0009] wherein, is the current capacity, is the rated capacity, is the capacity at the end of the life of the battery;

[0010] The capacity calculation formula of different battery units in the same battery energy storage system is:

[0011] (2)

[0012] wherein, is the minimum capacity of the battery in the battery energy storage system known, is the maximum capacity of the battery in the battery energy storage system known, is the capacity of any one battery unit, is the relative state of health of the battery unit .

[0013] According to the voltage, current and temperature data, and the parameter in linear relationship with the capacity, the capacity of any one battery unit is obtained through equivalent calculation:

[0014] (3)

[0015] wherein, is a function in linear relationship with the capacity.

[0016] Further, taking the variance of the discharge voltage as an evaluation index, the capacity of the remaining battery units is estimated under the condition that the capacity of any two battery units in the known battery energy storage system is known, and the formula is as follows:

[0017] (4)

[0018] wherein, the subscripts and are the labels of any two battery units, is the variance.

[0019] Beneficial effects:

[0020] The present application only needs to obtain the voltage data in the battery discharge stage, and can accurately evaluate the capacity of the remaining batteries in the system. The required data is easy to obtain, the overall calculation cost is low, the dependence on hardware is also low, and it is very suitable for practical application. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1a , Figure 1b , Figure 1c is a voltage, current and temperature schematic diagram of the discharging phase; wherein, Figure 1a is a voltage of the discharging phase, Figure 1b is a current of the discharging phase, Figure 1c is a temperature of the discharging phase;

[0022] Figure 2a , Figure 2b is a voltage and temperature schematic diagram of the discharging rest phase; wherein, Figure 2a is a voltage of the discharging rest phase, Figure 2b is a temperature of the discharging rest phase;

[0023] Figure 3a , Figure 3b , Figure 3c is a voltage, current and temperature schematic diagram of the charging phase; wherein, Figure 3a is a voltage of the charging phase, Figure 3b is a current of the charging phase, Figure 3c is a temperature of the charging phase;

[0024] Figure 4a , Figure 4b is a voltage and temperature schematic diagram of the charging rest phase; wherein, Figure 4a is a voltage of the charging rest phase, Figure 4b is a temperature of the charging rest phase. DETAILED DESCRIPTION

[0025] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0026] A battery energy storage system capacity estimation method suitable for a data center of the present application, by acquiring voltage, current and temperature data of four phases of discharging, resting after discharging, charging and resting after charging of the data center battery energy storage system, using Pearson correlation coefficient to analyze the correlation of data average value, variance, sample entropy and capacity, selecting the discharging voltage variance with larger correlation as the capacity evaluation parameter, and then calculating the capacity of the remaining batteries through the SOH evaluation method proposed in the present application, thereby realizing the SOH estimation of the battery energy storage system.

[0027] When the battery SOH is represented by relative capacity, it is the ratio of the difference between the current capacity and the end-of-life capacity, and the difference between the rated capacity and the end-of-life capacity.

[0028] (1)

[0029] wherein, is the current capacity, is the rated capacity, is the capacity at the end of the battery life.

[0030] Based on the above definitions, the present application defines the capacity calculation method of different battery units in the same system as follows

[0031] (2)

[0032] wherein, is the minimum capacity of the battery known in the battery energy storage system, is the maximum capacity known in the system, is the capacity of any one battery unit, is the relative state of health of the battery unit .

[0033] Since it is difficult to measure the battery capacity, starting from the easily measured data such as voltage, current and temperature, the parameters linearly related to the capacity are explored, and then the capacity of any one battery unit is obtained through equivalent calculation.

[0034] (3)

[0035] wherein, is a function linearly related to the capacity.

[0036] The present application takes the variance of the discharge voltage as the evaluation index, estimates the capacity of the remaining batteries under the condition that the capacity of any two batteries in the known battery energy storage system is known, and the formula is as follows:

[0037] (4)

[0038] wherein, the subscripts and are the labels of any two batteries, is the variance.

[0039] The present application will be described in detail below with specific embodiments.

[0040] The four batteries in the data center are discharged by constant power discharge cycle: the battery is discharged at a power of 15W until the voltage is 3.2V; then it is static for a period of time without discharge current; then the battery is charged at 2A (constant current) until 4.2V, at which time it is switched to constant voltage mode and continues to charge the battery until the charging current drops below 0.01A; then the battery is static for a period of time without charging current.

[0041] The capacity of the four-section battery is shown in Table 1. After the same charge-discharge cycle, the capacity of the same period after attenuation is selected for analysis.

[0042] Table Capacity of four-section battery

[0043]

[0044] The voltage, current and temperature data of the four stages of discharge, rest after discharge, charge and rest after charge are obtained, as shown in Figure 1a , Figure 1b , Figure 1c , Figure 2a , Figure 2b , Figure 3a , Figure 3b , Figure 3c , Figure 4a , Figure 4b .

[0045] In order to obtain the parameters linearly related to the capacity, the voltage, current and temperature of the four stages of battery charge and discharge are calculated respectively, and the corresponding average value, variance and sample entropy are calculated, and the results are shown in Tables 2 to 5.

[0046] Table 2 Average value, variance and sample entropy of the first battery parameter

[0047]

[0048] Table 3 Average value, variance and sample entropy of the second battery parameter

[0049]

[0050] Table 4 Average value, variance and sample entropy of the third battery parameter

[0051]

[0052] Table 5 Average value, variance and sample entropy of the fourth battery parameter

[0053]

[0054] The Pearson correlation coefficients of the calculated average value, variance and sample entropy and the capacity are shown in Table 6. It can be seen that the Pearson correlation coefficients of the discharge voltage variance, the discharge current variance and the capacity are 0.9905 and 0.9902 respectively, which are larger in values among the numerous parameters and have almost linear relationship with the capacity. Therefore, the discharge voltage variance and the discharge current variance are preferred as the indexes for evaluating the capacity. The present application is based on the analysis of the constant power discharge mode, and the discharge voltage and the discharge current are both variable. Under the constant current discharge mode, the discharge current is a fixed value. Therefore, in order to improve the adaptability of the evaluation indexes, the discharge voltage variance is mainly used as the main evaluation index in the present application.

[0055] Table 6 Pearson correlation coefficients of different parameters and the capacity

[0056]

[0057] As shown in Table 7, when the capacities of the second battery and the fourth battery are known, the relative errors of the first battery and the third battery estimated by the method of the present application are 0.0406 and 0.0199 respectively, and the errors are within 5%, which can be applied to the actual operation system.

[0058] Table 7 Capacity estimation and error

[0059]

[0060] Those skilled in the art can easily understand that the above description is only preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement and improvement within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A capacity estimation method for battery energy storage systems suitable for data centers, characterized in that: By acquiring voltage, current, and temperature data of a battery cell in the data center battery energy storage system during four stages—discharge, post-discharge rest, charging, and post-charging rest—and using Pearson correlation coefficient analysis to analyze the correlation between the average value, variance, sample entropy, and capacity of the data, the discharge voltage variance with a high correlation is selected as the capacity evaluation parameter. Then, the capacity of other battery cells in the battery energy storage system is calculated, thereby realizing the SOH estimation of the battery energy storage system. The calculation of the capacity of other battery cells in the battery energy storage system includes: The battery cell's SOH is characterized by its relative capacity, which is the ratio of the difference between the battery's current capacity and its end-of-life capacity to the difference between its rated capacity and its end-of-life capacity. (1) In the formula, For the current capacity, For rated capacity, This is the capacity at the end of the battery's life. The formula for calculating the capacity of different battery cells in the same battery energy storage system is as follows: (2) In the formula, The smallest known battery capacity in a battery energy storage system. This is the largest known capacity in a battery energy storage system. It refers to the capacity of any single battery cell. For battery cells The relative state of health; Based on voltage, current, and temperature data, as well as parameters linearly related to capacity, the capacity of any single battery cell can be obtained through equivalent calculations. (3) In the formula, It is a function that is linearly related to the capacity.

2. The capacity estimation method for a battery energy storage system suitable for data centers according to claim 1, characterized in that: Using the variance of discharge voltage as an evaluation index, the capacity of the remaining battery cells in a battery energy storage system is estimated given the capacities of any two battery cells. The formula is as follows: (4) In the formula, the subscript and For any two battery cells, Let Variance be the variance.

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