Battery discharge strategy evaluation method under pulse working condition

Abstract

The invention discloses a battery discharge strategy evaluation method under a pulse working condition, and belongs to the technical field of battery management systems. The method aims at evaluating the influence of pulse working conditions on battery performance and providing an optimal discharging strategy of the battery on the premise of ensuring the safety of the battery. The method is characterized by comprising the following steps: comparing parameters such as discharge capacity, discharge energy, discharge power, maximum discharge temperature and the like of a battery through offline test data of the battery under different pulse working conditions, and revealing a correlation degree level between pulse parameters (discharge current amplitude, discharge pulse width, duty ratio and the like) and battery performance indexes by applying a grey correlation degree analysis method; a discharge strategy analysis method based on an MYCIN uncertain factor is adopted, battery performance under different pulse parameters is compared and analyzed, and a solution with theoretical depth and engineering practicability is provided for battery system optimization in a pulse load scene.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of battery management system, and particularly relates to a battery discharge strategy evaluation method under pulse working conditions. BACKGROUND

[0002] High-rate pulse discharge (HRPD) technology has become the core technology of new-generation high-power energy storage and energy conversion due to its fast response capability and ultra-high power density, and is widely used in national defense, aerospace, new energy and industrial fields. In the field of new energy vehicles, HRPD technology is driving the revolution of 800V high-voltage platforms: the solid-state battery of ET7 supports 5C discharge, achieving 2.9 seconds to break 100; the two-way fast charging system of DriveOne makes the vehicle feedback 200kWh of electric energy to the power grid in 5 minutes, with an efficiency of 92%. In the field of unmanned aerial vehicles, HRPD enables high-maneuvering flight and high-energy weapon systems, and the micro reconnaissance and attack integrated unmanned aerial vehicle uses CIGS thin-film solar cells and HRPD hybrid power supply to support 20 times / mission of continuous high-speed dive attack.

[0003] Considering that the output characteristics of the battery system will change under different pulse conditions, in order to evaluate the influence of different pulse discharge conditions on the performance of the battery system, the application provides a battery discharge strategy evaluation method under pulse working conditions. For the discharge characteristics of high-power lithium batteries under different pulse working conditions, in order to let the battery discharge more electric quantity and energy while appropriately reducing the battery temperature and prolonging the service life of the battery, the grey correlation analysis method is used to analyze the uncertainty of the battery under specific index parameters, and the MYCIN uncertain factor is used to intuitively evaluate the influence of different pulse working conditions on the battery, and to provide guiding suggestions for the next step of the working mode of the battery system.

[0004] MYCIN uncertain factor is used to intuitively evaluate the influence of different pulse working conditions on the battery, and to provide guiding suggestions for the next step of the working mode of the battery system. SUMMARY

[0005] (I) Invention purpose

[0006] The purpose of the application is to provide a battery discharge strategy evaluation method under pulse working conditions, which provides a solution with both theoretical depth and engineering practicability for battery system optimization under pulse load scenarios.

[0007] (II) Technical solution

[0008] A method suitable for evaluating the battery discharge strategy under pulse working conditions, which comprises the following steps:

[0009] Step 1. Initialization, in which the following parameters are set by the operator in the evaluation method:

[0010] Set the correlation coefficient ρ, 0<ρ<1;

[0011] Set the number of battery characteristic quantities n, n is an integer;

[0012] Set the battery performance related factors m, m is an integer;

[0013] Set the number of pulse working conditions K;

[0014] Step two. Import the discharge voltage, discharge current, discharge time and temperature values of the battery under different pulse working conditions, and establish a battery operating parameter database under different pulse working conditions;

[0015] Step three. Calculate the battery discharge capacity, discharge energy, maximum discharge temperature and the like under different pulse working conditions, and establish a characteristic quantity matrix X0=(X0(1), X0(2), …, X0(n)) representing the performance of the battery, wherein X0(1), X0(2), …, X0(n) are all K×1 order matrices, X0(1) represents the battery discharge capacity under K different pulse working conditions, X0(2) represents the battery discharge energy under K different pulse working conditions, X0(3) represents the maximum discharge temperature of the battery under K different pulse working conditions, and the like;

[0016] Step four. According to the battery discharge pulse working conditions, establish a pulse working condition and battery performance related factor matrix X i =(X i (1), X i (2), …, X i (m))(i=1, 2, …, K), wherein X i (1), X i (2), …, X i (m) are all K×1 order matrices, X i (1) represents the battery discharge current amplitude under K different pulse working conditions, X i (2) represents the battery discharge pulse width under K different pulse working conditions, X i (3) represents the battery discharge intermittent time under K different pulse working conditions, and the like;

[0017] Step five. According to the overall demand of the system, the battery capacity and energy are greater than the threshold values I1 and I2 respectively, and the maximum temperature of the battery discharge process is less than the threshold value I3 and the like system requirements, design the index matrix I n =(I1, I2, …, I n ) of battery capacity, energy, maximum discharge temperature and the like, I n is an n×1 order matrix;

[0018] Step six. Establish an intuitionistic fuzzy decision matrix D=(d ij ) K×n , wherein d ij =<u ij ,v ij >, uij denotes the degree of support for the indicator I j (j = 1, 2,..., n), v ij denotes the degree of opposition to the indicator I j (j = 1, 2,..., n), u ij ∈ [0, 1], v ij ∈ [0, 1], u ij +v ij ≤ 1;

[0019] Step seven. Calculate the score function s ij and its average value

[0020]

[0021] Step eight. Calculate the q-order uncertainty of the indicator I j according to formula (3):

[0022]

[0023] wherein r ij is the grey mean correlation degree,

[0024] is the minimum value of the absolute value of the difference between the score function s ij and its average value ;

[0025] is the maximum value of the absolute value of the difference between the score function s ij and its average value ;

[0026] is the absolute value of the difference between the score function s ij and its average value ;

[0027] q is the order of the uncertainty model of the indicator I j ;

[0028] ρ is the correlation coefficient, generally 0.5.

[0029] Step nine. Calculate the reliability of the indicator I j according to formula (4):

[0030] CF(e j ) = 1 - DOI(I j ) j = 1, 2,..., n (4)

[0031] wherein CF(e j ) denotes the evidence e jthe trust degree of the evidence e

[0032] Step ten. Calculate the index I according to the following formula j the substantial uncertainty factor matrix under the following formula:

[0033]

[0034] CF T (h i / e j ) represents the trust degree of the evidence e j under the condition that the trust degree of the evidence e j is CF(e j ), to the hypothesis h i .

[0035] Step eleven. Calculate the uncertainty factor fusion degree under multiple indexes according to the following formula:

[0036]

[0037] Step twelve. According to the calculation result of formula (6), the larger the value is, the higher the comprehensive score of the battery discharge strategy is, that is, the discharge strategy is considered to be better.

[0038] (Three) effective income

[0039] The beneficial effects of the present application are: according to the offline test data of the battery under different pulse working conditions, the correlation degree level between the pulse parameters (discharge current amplitude, discharge pulse width, duty cycle, etc.) and the battery performance index is revealed, the discharge strategy analysis method based on the MYCIN uncertainty factor is adopted, the performance of the battery under different pulse parameters is compared and analyzed, and a solution with theoretical depth and engineering practicability is provided for the optimization of the battery system under the pulse load scene. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 . The flow chart of the optimal discharge strategy evaluation method of the battery under the pulse working condition DETAILED DESCRIPTION

[0041] The present application will be further described below in combination with the drawings and examples.

[0042] Example 1

[0043] The flow chart of the optimal discharge strategy evaluation method of the battery under the pulse working condition of the present application is shown in Figure 1 . The method steps are as follows:

[0044] Step one. Initialization:

[0045] The correlation coefficient p is set to 0.5; the number of battery characteristic quantities n is set to 3; the battery performance related factors m are set to 3; and the number of pulse working conditions K is set to 20;

[0046] Step two. Ten batteries are discharged under 20 different pulse working conditions respectively, and the discharge voltage, discharge current and discharge temperature value are collected. The battery pulse working conditions are designed as follows: the discharge current amplitude is constant, the battery discharge pulse width and the battery discharge intermittent time are constant, the battery discharge current amplitude is set to 300A, 330A, 375A, 405A and 450A, the battery discharge pulse width is set to 5s and 6s, and the battery discharge intermittent time is set to 30s and 5s. According to the principle of orthogonal design, 20 different pulse working conditions are designed and recorded as A i (i = 1, 2, …, 20). In each pulse working condition, the discharge voltage, discharge current and discharge temperature value are collected once every second, and a database of 10 batteries under 20 different pulse working conditions is established;

[0047] Step three. The average values of discharge capacity, discharge energy and maximum discharge temperature of the 10 batteries under different pulse working conditions are calculated, and the characteristic quantity X0 = (X0(1), X0(2), X0(3)) representing the battery performance is established, wherein X0(1) represents the average value of the discharge capacity of the 10 batteries under 20 different pulse working conditions, X0(2) represents the average value of the discharge energy of the 10 batteries under 20 different pulse working conditions, and X0(3) represents the average value of the maximum discharge temperature of the 10 batteries under 20 different pulse working conditions. In this embodiment, X0 is shown in the following table;

[0048]

[0049]

[0050] Step four. According to the battery discharge pulse working condition, the pulse working condition and the battery performance related factor matrix X i = (X i (1), X i (2), X i (3)) is established, wherein X i (1) represents the discharge current amplitude of the battery under 20 different pulse working conditions, X i (2) represents the discharge pulse width of the battery under 20 different pulse working conditions, and X i (3) represents the discharge intermittent time of the battery under 20 different pulse working conditions. In this embodiment, X i is shown in the following table;

[0051]

[0052] Step five. According to the overall demand of the system, the battery capacity and energy are greater than 95% and 80% of the rated value respectively, and the maximum battery discharge temperature is less than 50℃, the index matrix I of the designed battery capacity, energy and maximum discharge temperature is designed n =(14.25, 27.2, 50);

[0053] Step six. Intuitionistic fuzzy decision matrix D=(d ij ) K×n is established, where d ij =<u ij ,v ij >, u ij represents the support degree of index I j (j=1, 2, …, n), v ij represents the opposition degree of index I j , u ij ∈[0, 1], v ij ∈[0, 1], u ij +v ij ≤1, in this embodiment, the intuitionistic fuzzy decision matrix D under different pulse working conditions is shown in the following table;

[0054]

[0055] Step seven. The score function s ij and its average value

[0056]

[0057]

[0058] Step eight. The 2nd order uncertainty of index I j is calculated according to formula (3), DOI(I1)=0.113; DOI(I2)=0.126; DOI(I3)=0.115;

[0059] Step nine. The confidence of index I j is calculated according to formula (4), CF(e1)=0.887; CF(e2)=0.875; CF(e3)=0.885;

[0060] Step ten. The substantial uncertainty factor matrix under different pulse working conditions of index I j is calculated according to formula (5):

[0061]

[0062]

[0063] Step eleven. Calculate the uncertainty factor fusion degree of multiple indexes under different pulse conditions according to formula (6):

[0064]

[0065] Step twelve. According to the calculation results of formula (6), the greater the value, the higher the comprehensive score of the battery discharge strategy, that is, the better the discharge strategy. The evidence fusion information values in the above table are sorted from large to small. In consideration of the battery discharge capacity, discharge energy and maximum temperature during discharge, the test scheme A3>A4>A7>A8>A2>A5>A1>A6>A 10 >A9>A 13 >A 14 >A 17 >A 18 >A 11 >A 12 >A 15 >A 16 >A 19 >A 20 , that is, in consideration of the battery capacity, energy and maximum temperature during discharge, the battery performance is optimal under the condition of discharge amplitude 300A, discharge pulse width 5s and discharge intermittent time 5s, and the battery performance is worst under the condition of discharge amplitude 450A, discharge pulse width 6s and discharge intermittent time 5s.

[0066] The above is a further detailed description of the present application in combination with specific embodiments, and the specific implementation of the present application cannot be limited to these descriptions. For ordinary skilled persons in the technical field to which the present application belongs, without departing from the concept of the present application, a number of simple deductions or substitutions can be made, which should be considered as belonging to the protection scope of the present application.

Claims

1. A method for evaluating a battery discharge strategy under pulse duty cycle, comprising: Comprising the following steps: Step one. Initialization, set the following parameters: Set the correlation coefficient ρ, 0 < ρ < 1; Set the number of characteristic quantities representing battery characteristics n, n is an integer; Set the battery performance related factors m, m is an integer; Set the number of pulse working conditions K; Step two. Import the discharge voltage, discharge current, discharge time and temperature value of the battery under different pulse working conditions, and establish the battery operation parameter database under different pulse working conditions; Step three. Calculate the battery discharge capacity, discharge energy and maximum discharge temperature under different pulse working conditions, and establish the characteristic quantity matrix X0=(X0(1), X0(2), …, X0(n)) representing the performance of the battery; Wherein, X0(1), X0(2), …, X0(n) are K×1 order matrix; X0(1) represents the battery discharge capacity under K different pulse working conditions; X0(2) represents the battery discharge energy under K different pulse working conditions; X0(3) represents the maximum discharge temperature of the battery under K different pulse working conditions; Step four. According to the battery discharge pulse working condition, the pulse working condition and the battery performance related factor matrix X are established i = (X i (1), X i (2), …, X i (m)), i = 1, 2, …, K; wherein X i (1), X i (2),..., X i (m) are each Kxl matrices; X i (1) represents the amplitude of the battery discharge current under K different pulse conditions; X i (2) represents the discharge pulse width of the battery under K different pulse conditions; X i (3) represents the battery discharge intermittent time under K different pulse conditions; Step five. According to the overall demand of the battery system, the battery capacity, energy is greater than the threshold I1, I2, respectively, the maximum temperature of the battery discharge process is less than the threshold I3 and other system requirements, the battery capacity, energy, maximum discharge temperature index matrix I is designed n =(I1, I2, …, I n ), where I n is an n x 1 order matrix; Step six. Establishing the intuitionistic fuzzy decision matrix D = (d ij ) K×n ; where d ij = <u ij , v ij >, u ij represents the degree of support for the index I j (j = 1, 2, …, n), v ij represents the degree of opposition to the index I j , u ij ∈ [0, 1], v ij ∈ [0, 1], u ij + v ij ≤ 1; Step seven. Calculate the score function s according to formula (1) and (2) respectively ij and the average value s ij = u ij -v ij (1) Step eight. Calculate the index I according to formula (3) j qth order uncertainty of : wherein r ij is the grey mean correlation degree, the score function s ij the difference from its mean value the minimum of the absolute value of the difference the score function s ij the difference from its mean value the maximum value of the absolute value of the difference the score function s ij the difference from its mean value the absolute value of the difference q is the order of the uncertainty model of index I j the order of the uncertainty model of index I ρ is the correlation coefficient, generally 0.5; Step nine. Calculate the index I according to formula (4) j the confidence level under CF(e j ) = 1 - DOI(I j )j = 1,2,...,n (4) where CF(e j ) denotes the confidence of the evidence e j . Step ten. Calculate the index I according to the following formula j The substantial uncertainty factor matrix is as follows: Among them, CF T (h i / e j ) Characterization in the presence of evidence e j Evidence e j The trust level is CF(e) j Under the condition of ), for hypothesis h i The level of trust is genuine. Step eleven. Calculate the uncertainty factor fusion degree under multiple indexes according to the following formula: Step twelve. According to the calculation result of formula (6), sort, the larger the value, the higher the comprehensive score of the battery discharge strategy, that is, the better the discharge strategy.

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