Method for evaluating service life loss of battery energy storage system based on revolving door algorithm

Abstract

The invention relates to a battery energy storage system operation life loss evaluation method based on a revolving door algorithm, and belongs to the technical field of electric power energy storage. According to the technical scheme, a white whale optimization algorithm is improved; based on the charge state change curve of the battery unit, searching for the global optimal gate width of the revolving door algorithm by using an improved whale optimization algorithm; on the basis of the global optimal gate width, extracting feature data points in the state-of-charge change curve of the battery unit by using the revolving door algorithm again; and based on the characteristic data points, obtaining the discharge depth of each battery unit in each half cycle, and further calculating the service life loss of the battery energy storage system. According to the method, the BESS operation life loss can be rapidly and accurately evaluated, the evaluation speed of the life loss is accelerated, the time required for evaluating the operation life loss is reduced, the method has accuracy and rapidity, the BESS can be promoted to be better applied to each link of a power system, and meanwhile, reference is provided for investment and regulation of the BESS.

Description

Technical Field

[0001] The present invention relates to a method for evaluating the operation life loss of a battery energy storage system based on a swing door algorithm, belonging to the technical field of power energy storage. Background Technique

[0002] Resource shortage and environmental pollution are two major problems that China is currently facing and urgently need to be solved. Therefore, the Chinese government has put forward the "dual carbon" goal, and adopted a series of measures such as building a new power system with new energy as the main body and accelerating the promotion of energy transformation to solve these two major problems. The battery energy storage system (BESS) is widely used in multiple power system scenarios such as reducing the peak-valley difference of load, regulating system frequency, suppressing the power fluctuation of new energy, and black start because of its ability to store electric energy and fast charge and discharge characteristics, and has become a crucial high-quality regulation resource in the power system.

[0003] However, the investment cost of BESS is relatively high and its service life is limited. Therefore, it is very important to quickly and accurately evaluate the operation life loss of BESS. Currently, the evaluation methods for the operation life loss of BESS mainly include two categories. One is to evaluate based on the health state of the battery, but the practicability of this type of method is poor, and it is necessary to stop the operation of BESS for evaluation, which will affect the safe operation of the entire power system. Therefore, it is difficult to be used in engineering practice. The second is to use the rain flow counting method to evaluate the operation life loss of BESS. Although this type of method has certain practical value, the complexity of the rain flow counting method is relatively high, so the required evaluation time is relatively long, and it is difficult to meet the rapidity requirements of the power system. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for evaluating the operation life loss of a battery energy storage system based on a swing door algorithm. By using an improved beluga optimization algorithm and a swing door algorithm, the SOC characteristic data points in the SOC change curve of the battery unit are effectively extracted, and the operation life loss of BESS can be quickly and accurately evaluated, which has very important practical value. At the same time, it speeds up the evaluation speed of life loss, reduces the time required for evaluating the operation life loss, meets the rapidity requirements of the power system, has both accuracy and rapidity, can promote the better application of BESS in all aspects of the power system, and also provides a reference for the investment and regulation of BESS, and solves the above technical problems existing in the prior art.

[0005] The technical solution of the present invention is as follows:

[0006] A method for evaluating the operation life loss of a battery energy storage system based on a swing door algorithm, comprising the following steps:

[0007] (1) The improved beluga whale optimization (IBWO) uses the Logistic chaotic initialization method and the Tent chaotic initialization method to determine the initial candidate solutions of the optimization individuals, so as to improve the diversity of the initial candidate solutions; a calculation method of a balance factor is designed according to the Sigmoid function to improve the ability of the optimization algorithm to balance between global exploration and local exploitation;

[0008] (2) Design the value function for searching the global optimal door width E in the swing door trending (SDT). Based on the SOC change curve of the battery units in the battery energy storage system, use the improved beluga whale optimization algorithm to search for the global optimal door width E of the swing door algorithm;

[0009] (3) Based on the global optimal door width E, use the swing door algorithm described above to process the SOC change curve of the battery unit, so as to extract the characteristic data points in the SOC change curve;

[0010] (4) According to the characteristic data points, obtain the discharge depth of each battery unit in each half cycle, and then calculate the operation life loss of the battery energy storage system.

[0011] In the step (1), the Logistic chaotic initialization method and the Tent chaotic initialization method are used to determine the initial candidate solutions of the optimization individuals. The calculation formula for the initial candidate solutions of the optimization individuals is as follows:

[0012] x i = x lb + α 1 Z Li ·(x ub - x lb ) + α 2 Z Ti ·(x ub - x lb ) (1)

[0013] In the formula: x i is the initial candidate solution of the i-th optimization individual, α 1 and α 2 are chaotic weight coefficients, and their values are 0.5 and 0.5 respectively. Z Li and Z Ti are the Logistic chaotic sequence and the Tent chaotic sequence respectively. x ub and x lb are the upper and lower boundaries of the optimization variables respectively.

[0014] The balance factor B designed according to the Sigmoid function in the step (1)f The calculation method is as follows:

[0015]

[0016] In the formula: B 0 is the initial value of the balance factor, and β is the adjustment parameter of the balance factor B f . After multiple experiments, the value of β is taken as -0.03, and n and n max are the current iteration number and the maximum iteration number respectively.

[0017] Based on the optimization result of the global optimal gate width in step (3), the rotation gate algorithm is used to process the SOC operating curve of the battery cell, so as to extract the characteristic data points representing the change trend of the SOC operating curve of the battery cell.

[0018] In step (4), according to the SOC characteristic data points, the discharge depth of each battery cell in each half cycle is obtained, and then the operation life loss of the battery energy storage system is calculated. The calculation method of the operation life loss of the battery energy storage system is as follows:

[0019] Regard the time duration between two adjacent SOC characteristic data points as one half cycle, and calculate the charge and discharge depth:

[0020] D i = |S F,i+1 - S F,i | (3)

[0021] In the formula: D i is the charge and discharge depth in the i-th half cycle period, and S F,i+1 and S F,i represent two adjacent SOC characteristic data points in the i-th half cycle period;

[0022] Calculate the maximum cycle number N i corresponding to the charge and discharge depth D max (D i ):

[0023]

[0024] In the formula: a m (m ∈ 0, 1, 2, 3, 4) are the polynomial fitting coefficients, and their values are 164000, -872500, 1827000, -1687000, 572900 respectively;

[0025] Calculate the equivalent cycle number in the i-th half cycle period:

[0026] N eq,i = 0.5 · N max (100%) / Nmax (D i ) (5)

[0027] Where: N max (100%) represents the maximum number of cycles of the battery cell at 100% charge-discharge depth, and N eq,i is the equivalent number of cycles lost by the battery cell in the i-th half-cycle period;

[0028] The equivalent number of cycles N lost by the battery energy storage system during the entire operation Bcy The calculation formula is as follows:

[0029]

[0030] Where: N B is the number of battery cells in the BESS, and N bcy,j is the equivalent number of cycles lost by the j-th battery cell during operation, and N c is the number of half-cycle periods experienced by the battery cell during operation, and N eq,i,j is the equivalent number of cycles lost by the j-th battery cell in the i-th half-cycle period;

[0031] The calculation formula for the percentage of battery energy storage system life loss is as follows:

[0032] L B = N Bcy / N max (100%). (7)

[0033] The present invention not only improves the traditional beluga whale optimization algorithm, but also uses the improved beluga whale optimization algorithm (IBWO) and the swing door trending (SDT) algorithm to extract the SOC characteristic data points in the state of charge (SOC) change curve of the battery cell. Based on this, a method for evaluating the operation life loss of the BESS is designed. Finally, the rapidity and accuracy of this method are verified by simulation.

[0034] Advantages of the present invention: By using the improved beluga whale optimization algorithm and the swing door algorithm, the SOC characteristic data points in the SOC change curve of the battery cell can be effectively extracted, and the operation life loss of the BESS can be evaluated quickly and accurately. It has very important practical value. At the same time, it speeds up the evaluation speed of life loss, reduces the time required for evaluating operation life loss, meets the rapidity requirements of the power system, has both accuracy and rapidity, can promote the better application of the BESS in all aspects of the power system, and also provides a reference for the investment and regulation of the BESS. Brief Description of the Drawings

[0035] Figure 1 is a flowchart of an embodiment of the present invention;

[0036] Figure 2 is an optimization flowchart of the improved beluga optimization algorithm according to an embodiment of the present invention;

[0037] Figure 3 is a flowchart for evaluating the operation life loss of BESS based on SOC characteristic data points according to an embodiment of the present invention;

[0038] Figure 4 is the extraction result of SOC characteristic data points of battery unit 1 according to an embodiment of the present invention;

[0039] Figure 5 is the extraction result of SOC characteristic data points of battery unit 2 according to an embodiment of the present invention;

[0040] Figure 6 is the extraction result of SOC characteristic data points of battery unit 3 according to an embodiment of the present invention;

[0041] Figure 7 is the extraction result of SOC characteristic data points of battery unit 4 according to an embodiment of the present invention;

[0042] Figure 8 is the extraction result of SOC characteristic data points of battery unit 5 according to an embodiment of the present invention;

[0043] Figure 9 is the extraction result of SOC characteristic data points of battery unit 6 according to an embodiment of the present invention;

[0044] Figure 10 is the extraction result of SOC characteristic data points of battery unit 7 according to an embodiment of the present invention;

[0045] Figure 11 is the extraction result of SOC characteristic data points of battery unit 8 according to an embodiment of the present invention;

[0046] Figure 12 is the extraction result of SOC characteristic data points of battery unit 9 according to an embodiment of the present invention;

[0047] Figure 13 is the extraction result of SOC characteristic data points of battery unit 10 according to an embodiment of the present invention. Detailed Description of the Embodiments

[0048] The present invention will be further described below with reference to the accompanying drawings through embodiments.

[0049] A method for evaluating the operation life loss of a battery energy storage system based on a rotation door algorithm includes the following steps:

[0050] (1) The improved beluga whale optimization (IBWO) uses the Logistic chaos initialization method and the Tent chaos initialization method to determine the initial candidate solutions of the optimization individuals, so as to improve the diversity of the initial candidate solutions; designs a calculation method of a balance factor according to the Sigmoid function to improve the ability of the optimization algorithm to balance between global exploration and local exploitation;

[0051] (2) Designs the value function for searching the global optimal door width E in the swing door trending (SDT), and based on the SOC change curve of the battery cells in the battery energy storage system, uses the improved beluga whale optimization algorithm to search for the global optimal door width E of the swing door algorithm;

[0052] (3) Based on the global optimal door width E, uses the swing door algorithm to process the SOC change curve of the battery cells to extract the characteristic data points in the SOC change curve;

[0053] (4) According to the characteristic data points, obtains the discharge depth of each battery cell in each half cycle, and further calculates the operation life loss of the battery energy storage system.

[0054] An embodiment of the present invention proposes a method for evaluating the operation life loss of a battery energy storage system based on the swing door algorithm, as Figure 1 shown in the flowchart of the embodiment of the present invention, and its implementation process includes the following detailed steps.

[0055] Step 1 designs the improved beluga whale optimization algorithm, uses the Logistic chaos initialization method and the Tent chaos initialization method to determine the initial candidate solutions of the optimization individuals, so as to improve the diversity of the initial candidate solutions; designs a calculation method of a balance factor according to the Sigmoid function to improve the ability of the optimization algorithm to balance between global exploration and local exploitation;

[0056] The designed IBWO optimization flowchart is as Figure 2 shown, and the specific optimization process is as follows:

[0057] 1) Define the IBWO parameters

[0058] Set the population size M and the maximum iteration number n of IBWO max ;

[0059] 2) Use the Logistic chaos initialization method and the Tent chaos initialization method to determine the initial candidate solutions of the optimization individuals;

[0060] Form the initial candidate solutions X = [x1 , x 2 ,..., x M , where x i (i ∈ 1, 2,... M) is the initial candidate solution of the i-th optimization individual, and its calculation formula is as follows:

[0061] x i = x lb + α 1 Z Li ·(x ub - x lb ) + α 2 Z Ti ·(x ub - x lb ) (8)

[0062] In the formula: α 1 and α 2 are chaotic weight coefficients, and their values are 0.5 and 0.5 respectively. x ub and x lb are the upper and lower boundaries of the optimization variable. Z Li and Z Ti are the Logistic chaotic sequence and the Tent chaotic sequence respectively. The calculation formulas of Z Li and Z Ti are as follows:

[0063]

[0064] In the formula: is the Logistic chaotic parameter, and its value is a random number within the range of (0, 4).

[0065]

[0066] In the formula: is the Tent chaotic parameter, and its value is a random number within the range of (0, 0.5) ∪ (0.5, 1).

[0067] 3) Judge the optimization process

[0068] IBWO judges the optimization process of the beluga individual according to the balance factor B f . When B f > 0.5, the beluga individual is in the global exploration stage. When B f ≤ 0.5, the beluga individual is in the local development stage; the designed balance factor B f The calculation formula is as follows:

[0069]

[0070] In the formula: B 0is the initial value of the balance factor, and β is the adjustment parameter of the balance factor B. After multiple experiments, the value of β is taken as -0.03, and n is the current iteration number. f After multiple experiments, the value of β is taken as -0.03, and n is the current iteration number.

[0071] 4) Global exploration process

[0072] The method for updating the position of the beluga individual in the global exploration process is as follows:

[0073]

[0074] Where: x i,j (n + 1) is the updated position of the i-th beluga in the j-dimensional space; p j (j = 1, 2,..., D) is a randomly selected integer; is the position of the i-th beluga in the p j -dimensional space; is the position of the randomly selected r-th beluga in the p 1 -dimensional space (r is a random integer between 1 and N); r 1 and r 2 are random numbers, and their value ranges are both (0, 1).

[0075] 5) Local exploitation process

[0076] The method for updating the position of the beluga individual in the local exploitation process is as follows:

[0077] x i (n + 1) = r 3 x b (n) - r 4 x i (n) + c f · L f · (x r (n) - x i (n)) (13)

[0078] Where: x i (n + 1) is the updated position of the i-th beluga; r 3 and r 4 are random numbers, and their value ranges are both (0, 1); x b (n) is the global optimal solution of all beluga individuals at the n-th iteration; x i (n) and x r (n) are the positions of the i-th beluga and the randomly selected r-th beluga at the n-th iteration, respectively; c f and L f are the random jump intensity and the Levy flight function that measure the Levy flight force, respectively, and their calculation formulas are as follows:

[0079] c f = 2r 4 ·(1 - n / n max ) (14)

[0080] L f = 0.05·u·σ / υ 1 / β (15)

[0081]

[0082] where: u and υ are random numbers conforming to the normal distribution; β 1 is a constant, taken as 1.5 in this study.

[0083] 6) Beluga whale falling process

[0084] The calculation method of the beluga whale's position during the falling process is as follows:

[0085] x i (n + 1) = r 5 x i (n) - r 6 x r (n) + r 7 σ s (17)

[0086] where: r 5 、r 6 and r 7 are random numbers, and their value ranges are all (0, 1); σ s represents the moving step length when the beluga whale exhibits the falling behavior, and its calculation formula is as follows:

[0087]

[0088] where: u b 、l b respectively represent the upper and lower boundaries of the optimization variable; c 2 represents the step factor, and its value is related to the falling behavior occurrence probability and population size. The calculation formula is as follows:

[0089] c 2 = 2W f ·N (19)

[0090] where: W f represents the probability of the beluga whale's falling behavior, and its calculation formula is

[0091] W f = 0.1 - 0.05·n / n max (20)

[0092] Step 2: Design the value function for searching the global optimal gate width E in the swing gate algorithm. Based on the SOC change curve of the battery cells in the battery energy storage system, use the improved beluga optimization algorithm to search for the global optimal gate width E of the swing gate algorithm;

[0093] The calculation steps of the swing gate algorithm are as follows:

[0094] 1) Initialization

[0095]

[0096] In the formula: t 0 and x 0 are the initial time and the corresponding data value respectively; t 1 and x 1 are the first time and the corresponding data value respectively; k 1d and k 2d are the initial values of the upper and lower fulcrum gate slopes respectively; E is the compression offset;

[0097] 2) Calculate the slope

[0098]

[0099] In the formula: t j and x j are the j-th time and the corresponding data value respectively; t k and x k are the k-th time and the corresponding data value respectively;

[0100] 3) Slope update

[0101]

[0102] 4) Data extraction

[0103] k 1d ≥k 2d (24)

[0104] If the formula (9) is satisfied, record the data value x j-1 of the previous time t j-1 as the feature data, and return to step 2), otherwise return to step 3);

[0105] The value function for searching the global optimal gate width E in the swing gate algorithm is as follows:

[0106]

[0107] In the formula: λ 1 and λ 2 are weights, and their values are 10 and 1.75 respectively, N 1 and N 2are the number of SOC data points of the battery cell and the number of SOC feature data points extracted by the SDT algorithm, respectively, s i is the SOC data value, y i is the SOC change trend value after linear interpolation processing of the SOC feature data points.

[0108] Step 3: Based on the global optimal gate width E of the rotation gate algorithm found by the improved beluga optimization algorithm, the rotation gate algorithm is used again to process the SOC change curve of the battery cell, and the feature data points in the SOC change curve can be extracted.

[0109] Step 4: According to the feature data points, the discharge depth of each battery cell in each half cycle is obtained, and then the operation life loss of the battery energy storage system is calculated. The process of calculating the operation life loss of the BESS according to the SOC feature data points is as shown in the appendix Figure 3 as follows, and the detailed calculation process is as follows:

[0110] 1) Regard the time duration between two adjacent SOC feature data points as one half cycle, and calculate the charge and discharge depth:

[0111] D i =|S F,i+1 -S F,i | (26)

[0112] In the formula: D i is the charge and discharge depth in the i-th half cycle period, S F,i+1 and S F,i represent two adjacent SOC feature data points in the i-th half cycle period;

[0113] 2) Calculate the maximum number of cycles N i corresponding to the charge and discharge depth D max (D i ):

[0114]

[0115] In the formula: a m (m ∈ 0, 1, 2, 3, 4) are the polynomial fitting coefficients, and their values are 164000, -872500, 1827000, -1687000, 572900 respectively;

[0116] 3) Calculate the equivalent number of cycles in the i-th half cycle period:

[0117] N eq,i =0.5·N max (100%) / N max (D i ) (28)

[0118] Where: N max (100%) represents the maximum number of cycles of the battery cell at 100% charge and discharge depth, N eq,i is the equivalent number of cycles lost by the battery cell during the i-th half-cycle period;

[0119] 4) The equivalent number of cycles N lost by the battery energy storage system during the entire operation process Bcy The calculation formula is as follows:

[0120]

[0121] Where: N B is the number of battery cells in the BESS, N bcy,j is the equivalent number of cycles lost by the j-th battery cell during operation, N c is the number of half-cycle periods experienced by the battery cell during operation, N eq,i,j is the equivalent number of cycles lost by the j-th battery cell during the i-th half-cycle period;

[0122] 5) The calculation formula for the percentage of battery energy storage system life loss is as follows:

[0123] L B = N Bcy / N max (100%) (30)

[0124] To further understand the present invention and verify the effectiveness of the proposed method for evaluating the operation life loss of a battery energy storage system based on the rotating door algorithm, a simulation is carried out using the SOC change curve of the battery cells of a battery energy storage system on a certain day. The scale of this battery energy storage system is 10 MW / 10 MWh, and it contains a total of 10 battery cells, with the scale of each battery cell being 1 MW / 1 MWh.

[0125] According to the SOC change curves of the 10 battery cells, the IBWO and SDT algorithms are used to extract the SOC characteristic data points of each battery cell. The extraction results of the SOC characteristic data points of the 10 battery cells are as shown in the appendix Figures 4 - 13 shown. The corresponding global optimal gate width, compression error, and compression ratio during the process of extracting the SOC characteristic data points of the 10 battery cells are shown in Table 1 below. It can be seen that the compression ratios and compression errors of the 10 battery cells are both small, thus verifying that the extracted SOC characteristic data points can characterize the change trend of the SOC curve of the battery cell.

[0126] Table 1 Corresponding global optimal gate width, compression error, and compression ratio during the process of extracting the SOC characteristic data points of 10 battery cells

[0127]

[0128] Based on the SOC characteristic data points, the operation life loss of the BESS is evaluated. The life loss evaluation results of 10 battery cells are shown in Table 2 below. Through calculation, it can be known that the equivalent cycle number loss of the BESS is 1.0454 times, and the life loss percentage is 0.0261%.

[0129] Table 2 Life loss evaluation results of 10 battery cells

[0130]

[0131] To verify the rapidity of the BESS life loss evaluation method designed by the present invention, the present invention is compared with the rainflow counting method. Using the rainflow counting method to evaluate the life loss of the BESS, the required time is 2.561 min, while the time required for the present invention to evaluate the life loss is only 1.154 s, thus verifying that the BESS life loss evaluation method designed by the present invention has rapidity.

Claims

1. A method for evaluating the operating life loss of a battery energy storage system based on a revolving door algorithm, characterized in that The following steps are involved: (1) The improved Beluga optimization algorithm, referred to as IBWO, uses the Logistic chaos initialization method and the Tent chaos initialization method to determine the initial candidate solutions of the optimization individuals to improve the diversity of the initial candidate solutions; a calculation method for the balance factor is designed based on the Sigmoid function to improve the ability of the optimization algorithm to balance between global exploration and local development; (2) Design a value function for searching the global optimal gate width E in the revolving door algorithm (SDT). Based on the SOC change curve of the battery cells in the battery energy storage system, use the improved Beluga optimization algorithm to search for the global optimal gate width E of the revolving door algorithm. (3) Based on the global optimal gate width E, the revolving door algorithm is used to process the SOC change curve of the battery cell to extract characteristic data points in the SOC change curve; (4) Based on the characteristic data points, the discharge depth of each battery cell in each half cycle is obtained, and then the operating life loss of the battery energy storage system is calculated.

2. According to claim 1, a method for evaluating the operating life loss of a battery energy storage system based on a revolving door algorithm is characterized in that: In the step (1), the Logistic chaos initialization method and the Tent chaos initialization method are used to determine the initial candidate solution of the optimal individual. The calculation formula of the initial candidate solution of the optimal individual is as follows: x i =x lb +α1Z Li ·(x ub -x lb )+α2Z Ti ·(x ub -x lb ) (1) Where: x i is the initial candidate solution of the i-th optimization individual, α1 and α2 are the chaos weight coefficients, whose values ​​are 0.5 and 0.5 respectively, Z Li and Z Ti are Logistic chaotic sequence and Tent chaotic sequence respectively, x ub and x lb are the upper and lower bounds of the optimization variable respectively.

3. According to claim 1, a method for evaluating the operating life loss of a battery energy storage system based on a revolving door algorithm is characterized in that: In step (1), a balancing factor B is designed according to the Sigmoid function. f The calculation method is as follows: Where: B0 is the initial value of the balance factor, β is the balance factor B f After many tests, the value of β was taken as -0.03, n and n max are the current number of iterations and the maximum number of iterations respectively.

4. According to claim 1, a method for evaluating the operating life loss of a battery energy storage system based on a revolving door algorithm is characterized in that: In the step (3), based on the optimization result of the global optimal gate width, the SOC operating curve of the battery cell is processed using a rotating door algorithm, so as to extract characteristic data points representing the change trend of the SOC operating curve of the battery cell.

5. According to claim 1, a method for evaluating the operating life loss of a battery energy storage system based on a revolving door algorithm is characterized in that: In step (4), the discharge depth of each battery cell in each half cycle is obtained according to the SOC characteristic data points, and then the operating life loss of the battery energy storage system is calculated. The calculation method of the operating life loss of the battery energy storage system is as follows: The time between two adjacent SOC characteristic data points is regarded as a half cycle, and the charge and discharge depth is calculated: D i =|S F,i+1 -S F,i | (3) Where: D i is the charge and discharge depth in the i-th half cycle, S F,i+1 and S F,i represents two adjacent SOC characteristic data points in the i-th half cycle; Calculate the charge and discharge depth D in the i-th half cycle i The corresponding maximum number of cycles N max (D i ): Where: a m (m∈0,1,2,3,4) are the polynomial fitting coefficients, whose values ​​are 164000, -872500, 1827000, -1687000, 572900 respectively; Calculate the equivalent number of cycles in the i-th half cycle: N eq,i =0.5·N max (100%) / N max (D i ) (5) Where: N max (100%) indicates the maximum number of cycles of the battery cell at 100% charge and discharge depth, N eq,i is the equivalent number of cycles lost by the battery cell in the i-th half cycle; The equivalent number of cycles N during which the battery energy storage system loses power during the entire operation process Bcy The calculation formula is as follows: Where: N B is the number of battery cells in BESS, N bcy,j N is the equivalent number of cycles lost by the jth battery cell during operation, c N is the number of half-cycles that the battery cell undergoes during operation. eq,i,j is the equivalent number of cycles lost by the jth battery cell in the i-th half cycle; The formula for calculating the battery energy storage system life loss percentage is as follows: L B =N Bcy / N max (100%) (7)

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