BMS system optimization method

Abstract

The invention provides a BMS (Battery Management System) optimization method, which comprises the following steps of: S1, acquiring a charging rate, a discharging rate and a self-discharging rate of a battery cluster in a charging and discharging process, and judging and positioning a serial number of an abnormal battery by adopting a triple standard deviation method; the problem that the discharge characteristics of the batteries in the same cluster are inconsistent due to internal and external factors in the long-time operation process of the batteries is solved, the abnormal batteries are rapidly positioned, and the batteries are subjected to electricity supplementing operation, so that the abnormal batteries can return to the health state, and the overall service life of the battery cluster is prolonged; in a planned curve charging and discharging mode, a specific algorithm is adopted for charging, a battery lack of electricity is quickly charged, the charging and discharging efficiency of the system is not influenced, and meanwhile the system yield is increased; according to the method, the big data processing capability of the BMS system in the existing topological structure is solved, the recognition processing capability of the BMS system on abnormal batteries is ensured, and the stability and reliability of the BMS system are also improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power equipment, and more specifically, to a BMS system optimization method. Background Art

[0002] In the BMS system of industrial and commercial storage, the battery charging and discharging status is controlled by the BMS system. The BMS system serves as the safety protection center of the battery system and also shares the scheduling of the overall peak-valley arbitrage model. The efficiency of battery charging and discharging affects the benefits of the entire system. For charging and discharging control technology, the general traditional BMS system controls the battery charging and discharging by judging the threshold of the SOC value or by the single-cell charging and discharging cut-off voltage. For battery charging technology, the general traditional method first detects whether the battery inside the Pack needs to be charged by the extreme difference method, then disassembles the equipment, and finally uses a special charging instrument to charge the battery inside the Pack or replace the new battery. However, the existing BMS system has the following shortcomings:

[0003] First: For battery recharging technology, it is not possible to provide protective recharging for abnormal batteries in advance, that is, to identify and locate abnormal batteries in advance, and the recharging has already occurred under the condition of low battery. Recharging the battery under low battery condition will not have ideal effect, and the battery will have to be replaced in the end.

[0004] Second: The typical working mode of the industrial and commercial storage system is the planning curve. If the power replenishment strategy is implemented under static conditions, the charging and discharging time will be extended, resulting in a decrease in the system yield.

[0005] Third: With the increase of battery clusters, the number of batteries increases exponentially. As the processing data of the BMS system main controller increases, the processing efficiency will decrease, which may lead to misjudgment or untimely data processing. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a BMS system optimization method for improving the overall operating efficiency and stability of the BMS system in view of the shortcomings of the above technical solutions.

[0007] The present invention provides a BMS system optimization method, the method comprising the following steps:

[0008] S1, obtaining the charge and discharge rate and self-discharge rate of the battery cluster during the charge and discharge process, and using the triple standard deviation method to determine and locate the serial number of the abnormal battery;

[0009] S2, obtaining the serial number of the abnormal battery, when the battery is low on power, using PID adjustment to replenish power for the low-power battery in real time.

[0010] In the BMS system optimization method of the present invention, step S1 includes the following steps:

[0011] S11, obtaining the charging rate of the battery cluster in a static state, and calculating the average value and standard deviation of the charging and discharging rate, wherein the average value of the charging rate is expressed as The average value of the charging rate is calculated as follows: The standard deviation of the charging rate is calculated as follows:

[0012]

[0013] Among them, n represents the battery serial number, x i Expressed as charging rate, S c is the standard deviation of the charging rate.

[0014] In the BMS system optimization method of the present invention, step S1 includes the following steps:

[0015] S12, obtaining the discharge rate of the battery cluster in a static state, and calculating the average value and standard deviation of the discharge rate; the average value of the discharge rate is expressed as The average value of the discharge rate is calculated as follows: The standard deviation of the discharge rate is calculated as follows: Among them, n represents the battery serial number, x i Expressed as the discharge rate, Expressed as the average value of the discharge rate.

[0016] In the BMS system optimization method of the present invention, step S1 includes the following steps:

[0017] S13, obtaining the self-discharge rate of the battery cluster in a static state, and calculating the average value and standard deviation of the self-discharge rate; the average value of the self-discharge rate is The calculation formula is as follows: The standard deviation of the self-discharge rate is calculated as follows: Among them, x v It is expressed as the self-discharge rate, and n is the battery serial number.

[0018] In the BMS system optimization method of the present invention, step S1 includes the following steps:

[0019] S14, set the threshold of charging rate to k c , obtained by three times the standard deviation And the charging rate is related to the threshold k c For comparison, if the charging rate is within the threshold k c If the battery is within the range, the serial number of the abnormal battery is obtained.

[0020] In the BMS system optimization method of the present invention, step S1 includes the following steps:

[0021] S15, set the threshold of discharge rate to k uc , obtained by three times the standard deviation Avruc+3×Suc; and compare the discharge rate with the threshold kuc. If the discharge rate is within the threshold k uc If the battery is within the range, the serial number of the abnormal battery is obtained.

[0022] In the BMS system optimization method of the present invention, step S1 includes the following steps:

[0023] S17, let the threshold of self-discharge rate be k sc , obtained by three times the standard deviation Avrsc+3×Ssc; and compare the self-discharge rate with the threshold ksc. If the self-discharge rate is within the threshold k sc If the battery is within the range, the serial number of the abnormal battery is obtained.

[0024] In the BMS system optimization method of the present invention, step S2 includes the following steps:

[0025] S21, when the charge and discharge rate of the abnormal battery is higher than that of the normal battery, the abnormal battery number is set to m, and the average voltage of the normal battery is calculated, where the average voltage is Calculate the voltage target value S of the m-size battery v , where S v =Avr1, real-time sampling of the current voltage value X of battery m K , and calculate the voltage target value S v With the current voltage value X K The difference is E k =S v —X K , connect the battery number m that needs to be recharged, and set the voltage of the recharge power source to the voltage target value S v ; Through PID algorithm P out =K p ×E k Perform a charging operation on the abnormal battery m.

[0026] The BMS system optimization method of the present invention solves the problem that the discharge characteristics of the battery are inconsistent with those of the same cluster due to internal and external factors during long-term operation of the battery, and quickly locates the abnormal battery and performs power replenishment operation on the battery so that the abnormal battery can return to a healthy state, thereby extending the overall life of the battery cluster; it solves the problem that in the planned curve charge and discharge mode, a specific algorithm is used for power replenishment to quickly replenish the low-power battery without affecting the system charge and discharge efficiency, and at the same time increases the system yield; it solves the problem of the BMS system's processing ability for big data in the existing topology structure, ensuring that the BMS system not only has the ability to identify and process abnormal batteries, but also improves the stability and reliability of the BMS system. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic diagram of the flow chart of the BMS system optimization method of the present invention;

[0028] Figure 2 It is the physical connection diagram of power replenishment in the BMS system optimization method. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] like Figure 1 As shown, Figure 1 1 is a flow chart of an embodiment of a BMS system optimization method of the present invention. A BMS system optimization method is provided, the method comprising the following steps:

[0032] In step S1, the charge and discharge rates and self-discharge rates of the battery cluster during the charge and discharge process are obtained, and the serial number of the abnormal battery is determined and located using the triple standard deviation method;

[0033] In step S2, the serial number of the abnormal battery is obtained, and when the battery is low on power, the low-power battery is replenished in real time using a PID adjustment replenishment method.

[0034] In one embodiment, the step S1 comprises the following steps:

[0035] In step S11, the charging rate of the battery cluster in a static state is obtained, and the average value and standard deviation of the charging and discharging rate are calculated. The average value of the charging rate is expressed as The average value of the charging rate is calculated as follows: The standard deviation of the charging rate is calculated as follows:

[0036]

[0037] Among them, n represents the battery serial number, x i Expressed as charging rate, S c is the standard deviation of the charging rate, and x is the average value of the charging rate.

[0038] In one embodiment, the step S1 comprises the following steps:

[0039] In step S12, the discharge rate of the battery cluster in a static state is obtained, and the average value and standard deviation of the discharge rate are calculated; the average value of the discharge rate is expressed as The average value of the discharge rate is calculated as follows: The standard deviation of the discharge rate is calculated as follows: Among them, n represents the battery serial number, x i Expressed as the discharge rate, Expressed as the average value of the discharge rate.

[0040] In one embodiment, the step S1 comprises the following steps:

[0041] In step S13, the self-discharge rate of the battery cluster in a static state is obtained, and the average value and standard deviation of the self-discharge rate are calculated; the average value of the self-discharge rate is The calculation formula is as follows: The standard deviation of the self-discharge rate is calculated as follows: Among them, x v It is expressed as the self-discharge rate, and n is the battery serial number.

[0042] In one embodiment, the step S1 comprises the following steps:

[0043] In step S14, the threshold value of the charging rate is set to k c , obtained by three times the standard deviation And the charging rate is related to the threshold kc For comparison, if the charging rate is within the threshold k c If the battery is within the range, the serial number of the abnormal battery is obtained.

[0044] In one embodiment, the step S1 comprises the following steps:

[0045] In step S15, the threshold value of the discharge rate is set to k uc , obtained by three times the standard deviation And the discharge rate and the threshold k uc For comparison, if the discharge rate is within the threshold k uc If the battery is within the range, the serial number of the abnormal battery is obtained.

[0046] In one embodiment, the step S1 comprises the following steps:

[0047] In step S17, the threshold of the self-discharge rate is set to k sc , obtained by three times the standard deviation And the self-discharge rate is related to the threshold k sc For comparison, if the self-discharge rate is within the threshold k sc If the battery is within the range, the serial number of the abnormal battery is obtained.

[0048] In one embodiment, the step S2 comprises the following steps:

[0049] In step S21, when the charge and discharge rate of the abnormal battery is higher than that of the normal battery, the abnormal battery number is set to m, and the average voltage of the normal battery is calculated, where the average voltage is Calculate the voltage target value S of the m-size battery v , where S v =Avr1, real-time sampling of the current voltage value X of battery m K , and calculate the voltage target value S v With the current voltage value X K The difference is E k =S v —X K , connect the battery number m that needs to be recharged, and set the voltage of the recharge power source to the voltage target value S v ; Through PID algorithm P out =K p ×E k Perform a charging operation on the abnormal battery m. Where Kp is the gain and Ek is the error.

[0050] Specifically, taking 16 batteries in series as an example, the self-discharge rate of the battery in a static state is counted respectively. The charge and discharge rate of the battery in the range of SOC 30% to 80% is shown in Table 1:

[0051] Table 1 Battery charge and discharge rate table in the range of 30% to 80% at 1C

[0052]

[0053] Taking 16 batteries in series as an example, the self-discharge rate of the batteries in a static state is counted respectively. The self-discharge rate table of the batteries 30 minutes after the charge and discharge is completed is shown in Table 2:

[0054] Table 2 Battery self-discharge rate 30 minutes after charge and discharge

[0055]

[0056] Taking 16 batteries in series as an example, the self-discharge rate of the batteries in a static state is counted respectively. The 1C charge and discharge rate table of the batteries in the range of 30% to 80% is shown in Table 3:

[0057] Table 3 Battery charge and discharge rate table at 1C in the range of 30% to 80%

[0058]

[0059] Taking 16 batteries in series as an example, the self-discharge rate of the batteries in a static state is counted respectively. The self-discharge rate table of the batteries 30 minutes after the charge and discharge is completed is shown in Table 4:

[0060] Table 4 Battery self-discharge rate 30 minutes after charge and discharge

[0061]

[0062] The average value and standard deviation of the charge and discharge rate and the self-discharge rate are calculated according to Table 1 and Table 2 respectively;

[0063] Among them, the standard deviation calculation formula is:

[0064] ; S is the standard deviation, x i is each data point, x is the mean of the data, and n is the number of data points.

[0065] In Table 1, the average value of the 1C charging rate is calculated as Battery serial number n=16,x i yes

[0066] Table 1 Charging rate data, assuming the charging rate standard deviation is S c .in,

[0067]

[0068] In Table 1, the average value of the 1C discharge rate is calculated as n=16,x i is the discharge rate data in Table 1, assuming the standard deviation of the discharge rate is S uc ,in,

[0069]

[0070] In Table 2, the average value of the battery self-discharge rate is calculated as n=16,

[0071] x i is the self-discharge rate data in Table 2, assuming the standard deviation of the self-discharge rate is S sc ;

[0072] in,

[0073] , in calculation table 1, the threshold of 1C charging rate is set as k c ,but

[0074]

[0075] ; In calculation table 1, the threshold value of 1C discharge rate is set to k uc ,but

[0076]

[0077] ; In calculation table 2, the threshold value of the battery self-discharge rate is set to k sc ,but

[0078]

[0079] ; Get the serial number of the abnormal battery;

[0080] For Table 1, the value of 1C charging rate is calculated according to the threshold k in the previous step. c By comparison, the value of battery number 1 is 5, which is not within the threshold range (2.625-4.8125), and the values ​​of battery numbers 2-16 are 4, which are within the threshold range (3.3125-4.8125), so N1=1.

[0081] For Table 1, the value of the 1C discharge rate is calculated according to the threshold k in the previous step. uc By comparison, the value of battery number 1 is 6, which is not within the threshold range (2.625 to 5.625), and the values ​​of battery numbers 2 to 16 are 4, which are within the threshold range (2.625 to 5.625), so N2 = 1;

[0082] For Table 2, the value of the battery self-discharge rate is calculated according to the threshold k in the previous step. scBy comparison, the value of battery number 1 is 3, which is not within the threshold range (-1.89372 to 2.45625), and the value of battery numbers 2 to 16 is 0.1, which is within the threshold range (-1.89372 to 2.45625), so N3 = 1;

[0083] According to the above, the battery number of abnormal self-discharge rate is N1, the battery number of abnormal charge rate is N2, and the battery number of abnormal discharge rate is N3. It is calculated that N1=N2=N3;

[0084] According to the data in Table 3 and Table 4, the battery number of abnormal self-discharge rate is G1, the battery number of abnormal charging rate is G2, and the battery number of abnormal discharge rate is G3. It is calculated that G1=G2=G3;

[0085] During the charging and discharging process, the charging and discharging cut-off voltage of the abnormal battery will be earlier than that of the normal battery, so the abnormal battery needs to be recharged or discharged. Recharge can be performed in the following way: the positive pole of battery 1 is connected to the equalizing resistor and the positive power supply through relay 1, and the negative pole of the battery is connected to the negative power supply and the next battery through relay 2. The other fifteen batteries are connected in the same way. The overall connection diagram is as follows: Figure 2 The algorithm for charging abnormal batteries is as follows:

[0086] According to the first step, the charge and discharge rate of abnormal batteries is higher than that of normal batteries. According to Table 5, assuming that the bad battery number is 1, the average voltage Avr1 of normal batteries is calculated first, that is, the average voltage Avr1 of batteries 2 to 15; By opening the equalizing resistor of battery No. 1 and discharging battery No. 1, the voltage of battery No. 1 can be made close to the average voltage through the charging PID algorithm. At the end of charging, the battery cluster as a whole can be charged to the charging cut-off voltage and stopped, thereby increasing the charging capacity of the battery cluster. Table 6 shows the battery voltage without discharge compensation. It can be seen that the bad battery reaches the charging cut-off voltage first, causing the entire battery cluster to stop charging.

[0087] Table 5 Battery charging voltage value at a certain time

[0088] Battery serial number 1 2 3 4 5 6 7 8 Real-time voltage V 3.355 3.310 3.310 3.310 3.310 3.310 3.310 3.310 Battery serial number 9 10 11 12 13 14 15 16 Real-time voltage V 3.310 3.310 3.310 3.310 3.310 3.310 3.310 3.310

[0089] Table 6 Battery voltage value at charging cut-off voltage (before optimization)

[0090]

[0091] Table 7 Battery voltage value at charging cut-off voltage (after optimization)

[0092]

[0093]

[0094] The voltage target value S of battery No. 1 is calculated by Table 5 v =Avr1=3.310, the current value of battery No. 1 is obtained by real-time voltage sampling X k =3.355; the difference between the target voltage and the current value is E k =S v -X k =3.310-3.355=0.045; the proportional control formula is: P out =K p ×E k .

[0095] In the BMS system, the battery discharge energy is related to the consumption resistance R and the total discharge time T. The consumption resistance is fixed in hardware, so controlling the battery discharge time can control the battery discharge energy. In this algorithm, the battery discharge time can be set by starting and stopping the timer in the BMS controller. The minimum timing interval of the timer is 10ms, and the discharge time is controlled by the counter N T Therefore, K p =N T ×R;P out =N T ×R×E k ; At the initial moment, according to the empirical value, the discharge is about 2000ms. At this time, since only the abnormal battery is discharging and the other 15 normal batteries are charging, the target value will converge quickly, and the voltage of the abnormal battery can be reduced to about the target value; at this time, resample and calculate the difference between the abnormal battery voltage value and the average value of the other 15 batteries. If the difference is within 10mv, the adjustment is terminated. If the average voltage is not reached, the first two steps are repeated, and the timer is turned on to discharge the abnormal battery, but the proportional coefficient value needs to be reset. Repeat the above steps until the average value of the abnormal battery voltage and the normal voltage difference is within 10mv, then the charging algorithm adjustment is terminated.

[0096] The charging process during discharge is as follows:

[0097] According to the conclusion that the charge and discharge rate of abnormal batteries is higher than that of normal batteries, according to Table 8, assuming that the bad battery number is 1, the average voltage Avr2 of normal batteries is calculated first, that is, the average voltage Avr2 of batteries 2 to 16;

[0098] By turning on the charging module of battery No. 1 and charging battery No. 1, the voltage of battery No. 1 can be made close to the average voltage through the discharge PID algorithm. At the end of discharge, the battery cluster can be discharged to the discharge cut-off voltage and stop, thereby increasing the discharge capacity of the battery cluster. Table 9 shows the battery voltage without charging compensation. It can be seen that the bad battery reaches the discharge cut-off voltage first, causing the entire battery cluster to stop discharging.

[0099] Table 8 Battery discharge voltage value at a certain moment

[0100] Battery serial number 1 2 3 4 5 6 7 8 Real-time voltage V 3.255 3.310 3.310 3.310 3.310 3.310 3.310 3.310 Battery serial number 9 10 11 12 13 14 15 16 Real-time voltage V 3.310 3.310 3.310 3.310 3.310 3.310 3.310 3.310

[0101] Table 9 Battery voltage value at discharge cut-off voltage (before optimization)

[0102]

[0103] Table 10 Battery voltage value at discharge cut-off voltage (after optimization)

[0104]

[0105]

[0106] The voltage target value S of battery No. 1 is calculated by Table 10 v =Avr2=3.310, and the current value of battery No. 1 is obtained by real-time voltage sampling X k =3.255; the difference between the target voltage and the current value is calculated as E k =S v -X k =3.310-3.255=0.055; the proportional control formula is: P out =K p ×E k ;

[0107] In the BMS system, the abnormal battery needs to be recharged at this moment. The battery number that needs to be recharged is connected through the relay module, and the recharge power supply voltage is set to the battery voltage target value S v The battery charging energy is related to the charging time T, so controlling the battery charging time can control the battery charging energy. In this algorithm, the battery charging time can be set by starting and stopping the timer in the BMS controller. The minimum timing interval of the timer is 10ms, and the charging time is controlled by the counter N T ,When the charging time is reached, disconnect the relay module between the charging power source and the battery that needs to be charged. The charging calculation formula is as follows

[0108] K p =N T

[0109] Pout =N T ×E k

[0110] At the initial moment, according to the empirical value, N T =200, that is, charging for about 2000ms. At this time, since only the abnormal battery is replenishing power and the other 15 normal batteries are discharging, the abnormal battery charging target value will quickly converge to the average value of the normal batteries, and the voltage of the abnormal battery can rise to about the target value;

[0111] At this time, resample and calculate the difference between the abnormal battery voltage and the average value of the other 15 batteries. If the difference is within 10mv, the adjustment is terminated. If it does not reach the average voltage, the abnormal battery is charged and the timer is turned on to charge the abnormal battery, but the proportional coefficient value needs to be reset.

[0112]

[0113] Repeat the above steps until the average value of the abnormal battery voltage and the normal voltage difference is within 10mv, then the discharge and power replenishment algorithm adjustment is terminated.

[0114] By analyzing the battery charging, discharging and self-discharge rates, abnormal batteries can be sensed in advance, rather than discovering battery problems at the end of the battery life, so that battery health parameters can be predicted in advance; it has a synchronous repair function, that is, when the battery health status is abnormal or when the battery is low on power, the battery can be recharged in time, so that the abnormal battery characteristics are consistent with the normal battery performance, improving the overall charging and discharging capacity of the battery cluster, and avoiding the need to disassemble the battery for recharging; the identification of abnormal batteries occupies a large amount of controller resources. Through distributed computing, not only can the use of BMS system resources be reduced, but also resource management of BMS system data can be performed, improving the overall operating efficiency and stability of the Industrial and Commercial Storage BMS system.

[0115] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0116] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0117] Therefore, the above is only a preferred specific implementation mode of the present invention, and the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention. The protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A BMS system optimization method, the method comprising the following steps: S1, obtaining the charge and discharge rate and self-discharge rate of the battery cluster during the charge and discharge process, and using the triple standard deviation method to determine and locate the serial number of the abnormal battery; S2, obtaining the serial number of the abnormal battery, when the battery is low on power, using PID adjustment to replenish power for the low-power battery in real time.

2. The BMS system optimization method according to claim 1, characterized in that: The step S1 comprises the following steps: S11, obtaining the charging rate of the battery cluster in a static state, and calculating the average value and standard deviation of the charging rate, wherein the average value of the charging rate is expressed as The average value of the charging rate is calculated as follows: The standard deviation of the charging rate is calculated as follows: Among them, n represents the battery serial number, x i Expressed as charging rate, S c is the standard deviation of the charging rate, Expressed as the average value of the charging rate.

3. The BMS system optimization method according to claim 2, characterized in that: The step S1 comprises the following steps: S12, obtaining the discharge rate of the battery cluster in a static state, and calculating the average value and standard deviation of the discharge rate; the average value of the discharge rate is expressed as The average value of the discharge rate is calculated as follows: The standard deviation of the discharge rate is calculated as follows: Among them, n represents the battery serial number, x i Expressed as the discharge rate, Expressed as the average value of the discharge rate.

4. The BMS system optimization method according to claim 3, characterized in that: The step S1 comprises the following steps: S13, obtaining the self-discharge rate of the battery cluster in a static state, and calculating the average value and standard deviation of the self-discharge rate; the average value of the self-discharge rate is The calculation formula is as follows: The standard deviation of the self-discharge rate is calculated as follows: Among them, x v It is expressed as the self-discharge rate, and n is the battery serial number.

5. The BMS system optimization method according to claim 4, characterized in that: The step S1 comprises the following steps: S14, set the threshold of charging rate to k c , obtained by three times the standard deviation And the charging rate is related to the threshold k c For comparison, if the charging rate is within the threshold k c If the battery is within the range, the serial number of the abnormal battery is obtained.

6. The BMS system optimization method according to claim 5, characterized in that: The step S1 comprises the following steps: S15, set the threshold of discharge rate to k uc , obtained by three times the standard deviation And compare the discharge rate with the threshold kuc. If the discharge rate is within the threshold k uc If the battery is within the range, the serial number of the abnormal battery is obtained.

7. The BMS system optimization method according to claim 6, characterized in that: The step S1 comprises the following steps: S17, let the threshold of self-discharge rate be k sc , obtained by three times the standard deviation The self-discharge rate is compared with the threshold ksc. If the self-discharge rate is within the threshold k sc If the battery is within the range, the serial number of the abnormal battery is obtained.

8. The BMS system optimization method according to claim 7, characterized in that: The step S2 comprises the following steps: S21, when the charge and discharge rate of the abnormal battery is higher than that of the normal battery, the abnormal battery number is set to m, and the average voltage of the normal battery is calculated, where the average voltage is Calculate the voltage target value S of the m-size battery v , where S v =Avr1, real-time sampling of the current voltage value X of battery m K , and calculate the voltage target value S v With the current voltage value X K The difference is E k =S v —X K , connect the battery number m that needs to be recharged, and set the voltage of the recharge power source to the voltage target value S v ; Through PID algorithm P out =K p ×E k Perform a charging operation on the abnormal battery m.

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