Performance Evaluation Method of Battery Clusters at Multiple Scales

Abstract

The present invention discloses a method for evaluating the performance of a battery cluster at multiple scales, including obtaining the available capacity parameter, charge and discharge capacity parameter, temperature rise characteristic parameter, self-discharge parameter, and remaining life of the battery cells in the battery cluster; and obtaining the performance parameter of the battery cluster according to the weighted sum of the available capacity parameter, charge and discharge capacity parameter, temperature rise characteristic parameter, self-discharge parameter, and remaining life. The present invention evaluates the performance of the battery cluster based on multiple scales, enabling comprehensive evaluation of the various performances of the battery cluster, improving the accuracy of the battery cluster performance evaluation, and providing accurate data and indicators for further scheduling and analysis.

Description

Technical Field

[0001] The present invention belongs to the technical field of performance evaluation of battery clusters, and particularly relates to a method for evaluating the performance of battery clusters at multiple scales. Background Art

[0002] With the rapid development of the new energy industry, energy storage technology, as a key technology to support the grid connection of renewable energy, improve the efficiency, safety, reliability and economy of traditional power, and support distributed energy, energy Internet, regional energy management systems and electric vehicles, is of great significance for changing the traditional power supply mode, realizing energy transformation, high-proportion access of renewable energy, ensuring energy security, and achieving the goals of energy conservation and emission reduction.

[0003] Batteries have been widely used in the energy storage field. In large-scale energy storage applications, in order to meet the energy and capacity requirements, a battery system generally consists of multiple battery clusters connected in parallel; each battery cluster is composed of multiple battery packs connected in series, and the battery cluster is also equipped with contactors and circuit breakers to achieve electrical protection of the battery cluster; each battery pack is composed of multiple single cells connected in parallel and in series.

[0004] As an electrochemical system, a battery cluster undertakes functions such as demand control, peak shaving and valley filling, and power following during actual operation and maintenance. Therefore, the performance of the battery cluster has a direct impact on the safety and life of the internal battery cells, the control strategy of the battery cluster, the response and life of electrical components, and even the cost and economy of the overall energy storage power station. Therefore, the accurate evaluation of the battery cluster performance is the basis and fundamental purpose of the control strategy and operation and maintenance plan of the energy storage project.

[0005] Patent CN113589189A proposes a method for predicting the health status of lithium batteries based on the characteristics of charge and discharge data. By establishing a long-term and short-term memory network model, training, validating and testing the battery health index data set, the health status of the actual battery system can be predicted. At the same time, an improved genetic algorithm is also introduced in the algorithm to optimize the network model parameters and improve the prediction accuracy.

[0006] Patent CN113657360A proposes a method for predicting the health status of battery cells according to the actual charge and discharge current and voltage data of the battery cells. By dividing the voltage data into intervals, calculating the amount of charge change according to the current in different intervals, and obtaining the health status parameters of the lithium battery according to the prediction model trained by Gaussian process regression.

[0007] Patent CN113625172A proposes an analysis method for the influencing factors of the operation benefit of a lithium battery energy storage system. Taking the battery health degree, the system energy loss rate, and the depth of discharge as the key indicators affecting the operation benefit, the core factors are found through the control variable method to guide the operation and maintenance work of the battery energy storage system and bring economic benefits.

[0008] The health status prediction method mentioned in Patent CN113589189A requires formulating relatively accurate battery health evaluation indicators and accumulating a large amount of battery data related to the health status, which is difficult to adapt to the situation where the battery cluster is quickly put into operation for health assessment.

[0009] For the health status parameter prediction method proposed in Patent CN113657360A, under the actual working conditions of the battery cells, the voltage of the battery cells changes frequently, and it is difficult to divide the voltage range of the battery cells to accurately predict the corresponding change in the amount of electricity.

[0010] The method for analyzing the influencing factors of the operation benefit of a battery energy storage system proposed in Patent CN113625172A requires relatively accurate preliminary prediction of key information such as the default working conditions, theoretical losses, and cycle times of the battery system, which will be greatly affected by errors in actual operation.

[0011] As an electrochemical device for energy storage and supply in the power grid, the performance indicators of the battery system for energy storage have always been a concern for both the user side and the power grid side. How much energy a storage system can provide, how many loads it can carry, and under what environmental conditions it can work often mean the reliability of the entire local power grid and are also the basis for realizing various precise dispatching and rapid response, bringing economic benefits to the power station.

[0012] Currently, the vast majority of performance evaluations for energy storage systems focus on the health status evaluation of the battery system, which mainly involves the battery capacity and the cycle life of the battery; however, in actual applications of energy storage systems, load capacity, environmental adaptability, dynamic working condition response ability, etc. are all indicators concerned by the upper control unit and the power grid, and there is less evaluation and discussion on these indicators. Summary of the Invention

[0013] The technical problem to be solved by the present invention is to overcome the defect of low accuracy in the performance evaluation of energy storage batteries in the prior art and provide a performance evaluation method for battery clusters at multiple scales.

[0014] The present invention solves the above technical problem through the following technical solutions:

[0015] The present invention provides a performance evaluation method for battery clusters at multiple scales, including the following steps:

[0016] Obtain the available capacity parameter, charge and discharge capacity parameter, temperature rise characteristic parameter, self-discharge parameter, and remaining life of the battery cells in the battery cluster;

[0017] Obtain the performance parameter of the battery cluster according to the weighted sum of the available capacity parameter, charge and discharge capacity parameter, temperature rise characteristic parameter, self-discharge parameter, and remaining life.

[0018] Among them, the performance parameter Property cluster =α 1 ·K Health +α 2 ·K discrepancy +α 3 ·K load +α 4 ·K heat +α 5 ·K ICR , K Health represents the remaining life, K discrepancy represents the available capacity parameter, K load represents the charge and discharge capacity parameter, K heat represents the temperature rise characteristic parameter, K ICR represents the self-discharge parameter, 0 < α i < 1, and

[0019] Preferably,

[0020] Among them, C cluster represents the capacity of the battery cluster, Soc i represents the Soc value of the single battery cell in the battery cluster, C i represents the capacity of the single battery cell in the battery cluster, and Ncluster represents the number of single battery cells in the battery cluster.

[0021] Preferably,

[0022]

[0023] Among them, P cluster = f(T, Soc), P cluster represents the maximum charge and discharge power of the battery cluster, P i represents the maximum charge and discharge power of the single battery cell in the battery cluster, Ncluster represents the number of single battery cells in the battery cluster, and T represents the ambient temperature.

[0024] Preferably,

[0025] Among them, T cluster represents the temperature rise characteristic of the battery cluster in actual application. Characterize the temperature rise characteristics of the individual cells in the battery cluster, and Ncluster represents the number of individual cells in the battery cluster.

[0026] Preferably,

[0027] Among them, k ICR Characterize the self-discharge k value of the cells in the battery cluster, and OCV 1 Characterize the open-circuit voltage of the cells at the initial stage of standing, and OCV 2 Characterize the open-circuit voltage of the cells at the end of standing, and t 1 Characterize the relative time at the initial stage of standing, and t 2 Characterize the relative time at the end of standing, and k cluster Characterize the expected normal self-discharge k value of the battery cluster, and Ncluster represents the number of individual cells in the battery cluster.

[0028] Preferably, obtaining the remaining life includes:

[0029] Conduct an aging test on cell samples of the same type as the cells to obtain the coupling relationship between the remaining life and the current rate, ambient temperature, depth of discharge, and number of cycles.

[0030] Preferably, obtaining the charge and discharge capacity parameters includes:

[0031] Conduct a charge and discharge power capacity test on cell samples of the same type as the cells, and obtain the actual charge and discharge capacity according to the actual extreme operating conditions of the battery cluster.

[0032] Preferably, the actual extreme operating conditions include the total current of the battery cluster at the cut-off voltage of the cells and the historical average operating conditions when the highest cut-off temperature is reached.

[0033] Preferably, obtaining the temperature rise characteristic parameters includes:

[0034] Conduct a temperature rise characteristic test on cell samples of the same type as the cells under different operating conditions in a thermostatic and humid environment chamber to obtain the temperature rise characteristic T heat .

[0035] Preferably, the performance evaluation method further includes:

[0036] According to the environmental settings of the battery cluster, set α corresponding to the environment i .

[0037] The positive and progressive effects of the present invention are as follows: The present invention evaluates the performance of the battery cluster based on multiple scales, enabling comprehensive evaluation of the various performances of the battery cluster, improving the accuracy of battery cluster performance evaluation, and providing accurate data and indicators for further scheduling and analysis. Description of the Drawings

[0038] Figure 1 Flow chart of the performance evaluation method of the battery cluster at multiple scales according to a preferred embodiment of the present invention. Detailed implementation manners

[0039] The present invention will be further described below by way of a preferred embodiment, but the present invention is not limited to the scope of the described embodiments.

[0040] This embodiment provides a performance evaluation method for a battery cluster at multiple scales. Referring to Figure 1 , the performance evaluation method for the battery cluster at multiple scales includes the following steps:

[0041] Step S1: Obtain the available capacity parameter, charge and discharge capacity parameter, temperature rise characteristic parameter, self-discharge parameter, and remaining life of the battery cells of the battery cluster.

[0042] Step S2: Obtain the performance parameter of the battery cluster according to the weighted sum of the available capacity parameter, charge and discharge capacity parameter, temperature rise characteristic parameter, self-discharge parameter, and remaining life.

[0043] Among them, the performance parameter Property cluster =α 1 ·K Healt h + α 2 ·K discrepancy +α 3 ·K load +α 4 ·K heat +α 5 ·K ICR , K Health represents the remaining life, K discrepancy represents the available capacity parameter, K load represents the charge and discharge capacity parameter, K heat represents the temperature rise characteristic parameter, K ICR represents the self-discharge parameter, 0 <α i <1, and

[0044] In specific implementation, obtaining the remaining life includes: performing an aging test on a battery cell sample of the same model as the battery cell to obtain the coupling relationship between the remaining life and the current magnification, ambient temperature, discharge depth, and number of cycles.

[0045] The remaining service life of a single battery cell is determined by the cycle service life of the battery cell and also depends on the actual use conditions of the battery cell, which directly affects the available capacity of the battery cluster. Usually, the methods for estimating the remaining life of a battery cell include methods such as cycle calendar life tables and particle filters. By directly processing the voltage and current data of the battery cell, the actual remaining service life estimate of the battery cell can be obtained.

[0046] Obtain charge-discharge capacity parameters, including: performing charge-discharge power capacity tests on cell samples of the same model as the cell, and obtaining the actual charge-discharge capacity according to the actual extreme conditions of the battery cluster. Among them, the actual extreme conditions include the total current of the battery cluster at the cut-off voltage of the cell and the historical average conditions when the highest cut-off temperature is reached.

[0047] The battery cluster outputs power externally to drive the load. In addition to being limited by the rated power of the battery cluster, the charge-discharge capacity of the battery cluster is also related to factors such as the state of charge of the battery cluster and the external environmental temperature, that is:

[0048]

[0049] Among them, P cluster = f(T, Soc), P cluster represents the maximum charge-discharge power of the battery cluster, P i represents the maximum charge-discharge power of the single cell of the battery cluster, Ncluster represents the number of single cells in the battery cluster, and T represents the environmental temperature.

[0050] In addition to being affected by the remaining service life of the cells, the actual available capacity of the battery cluster is also affected by the consistency of the cells in the battery cluster. Excessive consistency differences often cause the battery cluster to reach the safety operating voltage threshold of the single cell due to the highest voltage cell and the lowest voltage cell first in the actual operating conditions, resulting in the battery cluster being unable to continue to output power. Therefore, the available capacity of the battery cluster is determined by adding the minimum value of the chargeable amount of all cells and the maximum value of the dischargeable amount, that is:

[0051]

[0052]

[0053] Among them, C cluster represents the capacity of the battery cluster, Soc i represents the Soc value of the single cell of the battery cluster, C i represents the capacity of the single cell of the battery cluster, and Ncluster represents the number of single cells in the battery cluster.

[0054] Obtain the temperature rise characteristic parameters, including: performing temperature rise characteristic tests on cell samples of the same model as the cell under different conditions in a thermostatic and humid environment chamber to obtain the temperature rise characteristic T heat . The obtained temperature rise characteristic T heat is used as the input. In actual applications, the temperature rise characteristic of the battery cluster is T cluster . Then the calculation formula for the temperature rise characteristic parameters is:

[0055] Among them, T cluster characterizes the temperature rise characteristics of the battery cluster in actual applications, and characterizes the temperature rise characteristics of the single cells in the battery cluster.

[0056] Since a battery is an electrochemical system, when the external temperature is too high, the operating conditions are too severe, or the battery system heat design is unreasonable, it will cause the battery cluster temperature to be too high, thus affecting the service life of the battery. In severe cases, it will also lead to safety problems such as thermal runaway. The temperature rise of a single cell can often be regarded as the heat exchange between the heat generated by the internal resistance and electrochemical reactions in the cell and the ambient temperature outside the metal shell of the cell, so as to model the temperature field; while the temperature rise characteristics of the battery cluster are often also determined by the cell structure of the battery cluster. The arrangement of the cells, the layout of the heat dissipation structure, the impedance of the connecting devices, etc., are all factors that need to be considered in the temperature field modeling of the battery cluster.

[0057] Among them, characterizes the k of the single cells in the battery cluster ICR , k ICR characterizes the self-discharge k value of the cells in the battery cluster, OCV 1 characterizes the open-circuit voltage of the cell at the initial stage of static state, OCV 2 characterizes the open-circuit voltage of the cell at the end stage of static state, t 1 characterizes the relative time at the initial stage of static state, t 2 characterizes the relative time at the end stage of static state, k cluster characterizes the expected normal self-discharge k value of the battery cluster.

[0058] Under the influence of factors such as manufacturing defects, excessive parallel internal circulation, overcharging and over-discharging, and cyclic aging of the cells, there is also a probability that the cells will have phenomena such as internal short circuits and self-discharge. These electrochemical phenomena will also have a direct impact on the performance of the battery cluster, such as the charge-discharge efficiency of the ratio of the charged amount to the discharged amount. The self-discharge phenomenon is generally evaluated by the k value after standing for a period of time. At the same time, the voltage and current data of the operating conditions can also be evaluated by comparing the special values of healthy cells and internally short-circuited cells to screen out internally short-circuited cells.

[0059] The performance evaluation method of this embodiment further includes: setting α corresponding to the environment according to the environment set for the battery cluster i .

[0060] In some specific application scenarios, Property cluster = α 1 ·K Health + α 2 ·K discrepancy + α 3 ·K load + α4 ·K heat +α 5 ·K ICR ,

[0061] α in this calculation expression i (i ∈ [1, 5]) can be adjusted according to the profit focus of the actual project. For example, compared with the energy storage of a conventional power station, the offshore PV energy storage system is often in an island state, and electrical appliances, air conditioners, communication equipment, etc. on the island all require the energy storage unit to supply power. Then, in this application scenario, the attention degree to the self-discharge parameter of the battery cluster is usually higher, that is, the corresponding weight α i is higher.

[0062] For example, for an island PV energy storage system, assume α 1 = 0.4, α 2 = 0.2, α 3 = 0.05, α 4 = 0.05, α 5 = 0.3.

[0063] Then when the system is just built, due to consistency factors such as process differences, K Health = 1, K discrepancy = 0.99, K load = 1, K heat = 1, K ICR = 1. It is calculated that Property cluster = 0.998.

[0064] After running for a period of time, affected by factors such as seawater erosion, day-night temperature difference, and long-term static state, K Health = 0.95, K discrepancy = 0.9, K load = 0.98, K heat = 1, K ICR = 0.8. It is calculated that Property cluster = 0.899.

[0065] In this application scenario, compared with the health degree K Health = 0.95 of a conventional energy storage system, obviously Property cluster = 0.899 can more accurately reflect the performance of the energy storage system.

[0066] The battery cluster usually only shows the total cluster voltage, total cluster current, and cluster power output at the grid connection port of the power grid. It is often difficult to directly identify the state and various performances of the battery cluster from the grid side. Therefore, the battery cluster needs to evaluate its own performance and send information, so as to guide the grid side in the operation and maintenance of the battery cluster.

[0067] In addition to the health state that is usually concerned about, the power following characteristic is also very important during the operation of an actual power station for the performance of a battery cluster. Moreover, the ability to output power often also depends on the remaining life of the battery cluster, charge and discharge capabilities, etc. At the same time, the electrochemical performance of the battery cluster, such as self-discharge and other characteristics, also directly determines the performance and safety of the battery cluster. Therefore, when evaluating the performance of a battery cluster, it is necessary to evaluate the different performances of the battery cluster separately at multiple scales so as to obtain the comprehensive performance index of the battery cluster.

[0068] Although the specific implementation manners of the present invention are described above, those skilled in the art should understand that this is only an example. The protection scope of the present invention is defined by the appended claims. Without departing from the principle and essence of the present invention, those skilled in the art can make various changes or modifications to these implementation manners, but these changes and modifications all fall within the protection scope of the present invention.

Claims

1. A method for evaluating the performance of a battery cluster at multiple scales, characterized in that, it includes the following steps: Obtain the available capacity parameter, charge and discharge ability parameter, temperature rise characteristic parameter, self-discharge parameter of the battery cluster, and the remaining life of the battery cells of the battery cluster; Obtain the performance parameter of the battery cluster according to the weighted sum of the available capacity parameter, the charge and discharge ability parameter, the temperature rise characteristic parameter, the self-discharge parameter, and the remaining life; Among them, the performance parameter Property cluster = α 1 ·K Health + α 2 ·K discrepancy + α 3 ·K load + α 4 ·K heat + α 5 ·K ICR , k Health represents the remaining life, K discrepancy represents the available capacity parameter, K load represents the charge and discharge capacity parameter, K heat represents the temperature rise characteristic parameter, K ICR represents the self-discharge parameter, 0 < α i < 1, and Among them, T cluster characterizes the temperature rise characteristic of the battery cluster in actual application, characterizes the temperature rise characteristic of the single cell of the battery cluster, and Ncluster represents the number of the single cells in the battery cluster; Among them, k of the i-th single battery cell representing the battery cluster ICR , k ICR represents the self-discharge k value of the battery cells of the battery cluster, OCV 1 represents the open-circuit voltage of the battery cell at the initial stage of static state, OCV 2 represents the open-circuit voltage of the battery cell at the end stage of static state, t 1 represents the relative time at the initial stage of static state, t 2 represents the relative time at the end stage of static state, k cluster represents the expected normal self-discharge k value of the battery cluster, and Ncluster represents the number of the single battery cells in the battery cluster.

2. The method for evaluating the performance of a battery cluster at multiple scales according to claim 1, characterized in that, Among them, C cluster represents the capacity of the battery cluster, and Soc i represents the Soc value of the single cell in the battery cluster, and C i represents the capacity of the single cell in the battery cluster, and Ncluster represents the number of the single cells in the battery cluster.

3. The method for evaluating the performance of a battery cluster at multiple scales according to claim 1, characterized in that, Among them, P cluster = f(T, Soc), P cluster represents the maximum charge and discharge power of the battery cluster, i represents the maximum charge and discharge power of the single battery cell of the battery cluster, Ncluster represents the number of the single battery cells in the battery cluster, and T represents the ambient temperature.

4. The method for evaluating the performance of a battery cluster at multiple scales according to claim 1, characterized in that, Obtaining the remaining life includes: Conducting an aging test on battery cell samples of the same model as the battery cells to obtain the coupling relationship between the remaining life and the current rate, ambient temperature, depth of discharge, and number of cycles.

5. The method for evaluating the performance of a battery cluster at multiple scales according to claim 1, characterized in that, Obtaining the charge and discharge ability parameter includes: Conducting a charge and discharge power ability test on battery cell samples of the same model as the battery cells, and obtaining the actual charge and discharge ability according to the actual extreme conditions of the battery cluster.

6. The method for evaluating the performance of a battery cluster at multiple scales according to claim 5, characterized in that, The actual extreme conditions include the total current of the battery cluster when the battery cell reaches the cut-off voltage and the historical average conditions when the highest cut-off temperature is reached.

7. The method for evaluating the performance of a battery cluster at multiple scales according to claim 1, characterized in that, Obtaining the temperature rise characteristic parameter includes: Perform the temperature rise characteristic test of the battery cells under different working conditions on the battery cell samples of the same model as the battery cells in a temperature and humidity controlled environmental chamber to obtain the temperature rise characteristic T heat .

8. The method for evaluating the performance of a battery cluster at multiple scales according to claim 1, characterized in that, The performance evaluation method further includes: α corresponding to the environment set according to the battery cluster i .

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