Intelligent lithium battery current distribution processing method

Abstract

The invention provides an intelligent lithium battery current distribution processing method, which comprises the following steps that S1, an FSU firstly communicates with a charging ammeter through a DTU to obtain a current direction, and whether a battery is charged or discharged is judged; s2, if in the charging state, the FSU obtains online battery pack parameters and carries out fuzzy processing and membership processing; training to obtain weights, and forming a charging rule base; performing result aggregation according to a weight calculation method to obtain defuzzified output, namely a charging current limiting distribution value Ichglmt of each battery pack; the BMS and dc / dc of the battery pack execute the calculated charging current limiting distribution value Ichglmt; and S3, in the discharge state, the process is similar to that in the step S2. According to the invention, charging and discharging current limiting value distribution of the intelligent lithium battery can be carried out, so that synchronous emptying and full charging of each battery pack are realized, the peak load shifting efficiency is higher, and the stability of the system is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of batteries, and particularly to an intelligent lithium battery current distribution processing method. Background Art

[0002] Existing battery packs perform power distribution in an average distribution manner. Each battery pack evenly distributes the power of the load or the switching power supply. When the power of the load or the switching power supply changes, the first battery pack (the closest to the FSU with the smallest line resistance) is used as a buffer to absorb the power change.

[0003] In this way, the difference in the battery power between battery packs increases (the SOC difference widens). In addition, since all battery packs use the same power, heat accumulation occurs in the battery packs stacked in the middle, resulting in serious heating of the DC / DC, which may cause the battery pack to limit the current in advance. After the load / power changes, it cannot bear the load, resulting in the early end of peak shaving and valley filling.

[0004] In addition, since the BMS cannot accurately estimate the SOC, all charge and discharge stop points are executed according to the single-cell voltage. Taking the LFP battery cell as an example, the discharge stop point is 2.5V, and the charge stop point is 3.6V. Only adjusting the power distribution through the SOC will result in the actual power distribution not meeting the expectations. Summary of the Invention

[0005] The present invention provides an intelligent lithium battery current distribution processing method, which solves the defect of unreasonable current limiting distribution in the prior art during charging and discharging.

[0006] The technical solution of the present invention is realized as follows:

[0007] An intelligent lithium battery current distribution processing method includes the following steps:

[0008] S1. First, the FSU communicates with the charging meter through the DTU to obtain the current direction and determine whether the battery is charging or discharging;

[0009] S2. If in the charging state, the FSU obtains the number n of online battery packs and the cell temperature T cell [i], remaining power SOC[i], health state SOH[i], buck-boost unit temperature T dc / dc [i] and the voltage V of the single cell with the largest voltage in the battery pack max [i];

[0010] Respectively for T cell [i], SOC[i], SOH[i], T dc / dc [i] and V max[i]Perform fuzzification and membership degree processing, fuzzify into a fuzzy set, and set the initial membership function with the membership degree range being (0, 1);

[0011] Use the fuzzy logic Tool box toolbox in Matlab for modeling and simulation. Refer to the Mamdani fuzzy system, conduct logic training, and cycle-train its membership function to obtain the weights during charging. The obtained weights form the charging rule base;

[0012] Substitute the membership degree into the charging rule base, perform result aggregation according to the weight calculation method, and obtain the defuzzified output, that is, the charging current limiting distribution value I of each battery pack chglmt , and the specific formula is as follows:

[0013]

[0014] and represent the weights during charging, and P supply represents the available power;

[0015] Screen out the battery pack with the largest charging current limiting distribution value, and record the largest charging current limiting distribution value as I chglmt (max), increase the charging current limiting distribution value of this battery pack, and preferentially allocate the charging to this battery pack;

[0016] The BMS and dc / dc of the battery pack execute the calculated charging current limiting distribution value I chglmt , and complete the current distribution;

[0017] S3. In the discharge state, the FSU obtains the number of online battery packs n, and the cell temperature T of each battery pack cell [i], remaining power SOC[i], health state SOH[i], buck-boost unit temperature T dc / dc [i] and the voltage V of the single cell with the minimum voltage in the battery pack min [i];

[0018] Perform fuzzification and membership degree processing on T cell [i], SOC[i], SOH[i], T dc / dc [i] and V min [i] respectively, fuzzify into a fuzzy set, and set the initial membership function with the membership degree range being (0, 1);

[0019] Use the fuzzy logic toolbox in Matlab for modeling and simulation. Refer to the Mamdani fuzzy system, conduct logic training, and cycle-train its membership function to obtain the weights during discharge. The obtained weights form the discharge rule base;

[0020] Substitute the membership degree into the discharge rule base, and perform result aggregation according to the weight calculation method to obtain the defuzzified output, that is, the current limiting allocation value I of the discharge current assigned to each battery pack dshlmt , and the specific formula is as follows:

[0021]

[0022] and represents the weight during discharge, and P need represents the power required by the load;

[0023] Select the battery pack with the largest current limiting allocation value of the discharge current. The largest current limiting allocation value of the discharge current is denoted as I dshlmt (max), increase the current limiting allocation value of the discharge current of this battery pack, and preferentially use this battery pack for discharge;

[0024] The BMS and dc / dc of the battery pack execute the current limiting allocation value I of the discharge current of each battery pack dshlmt , and complete the current allocation.

[0025] Preferably, perform defuzzification processing on T cell [i], SOC[i], SOH[i], T dc / dc [i] and V max [i] / V min [i]. Fuzzify T cell [i] into negative large, negative medium, negative small, zero, positive small, positive medium and positive large, abbreviated as {NB, NM, NS, O, PS, PB, PB}. The initial setting of the membership function of this fuzzy set is [0, 0, 0.25, 1, 0.87, 0.6, 0]. In this way, perform defuzzification processing on SOC[i], SOH[i], T dc / dc [i] and V max [i]V min [i].

[0026] Preferably, increase the current limiting allocation value of the charging current of this battery pack by 5A; increase the current limiting allocation value of the discharge current of this battery pack by 5A.

[0027] Advantages of the present invention: Through communication, the present invention obtains information such as the cell temperature, SOC of each battery pack, and the device temperature of the DC / DC. During the charging and discharging process of the battery pack, fuzzy expert logic is used to combine the cell temperature inside the battery pack and the states of the BMS+DC / DC to allocate the charging and discharging power of the battery, so that the battery pack can be fully charged and discharged as much as possible at the same time. At the same time, considering charging at the maximum single-cell voltage and discharging at the minimum single-cell voltage for power distribution, and considering that under different SOHs, it affects battery heating and total capacity, which are all incorporated into the fuzzy expert logic for consideration; and the current control function is integrated into the FSU of the intelligent lithium battery. The FSU is responsible for monitoring the system load and at the same time allocating the charging and discharging current limiting values of the intelligent lithium battery, so as to achieve the synchronous emptying and full charging of each battery pack (close to simultaneous full charging and full discharging), thereby making the peak shaving and valley filling more efficient and the system more stable. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0029] Figure 1 It is the system architecture diagram of the intelligent lithium battery in the present invention;

[0030] Figure 2 It is the internal block diagram of the FSU in the present invention;

[0031] Figure 3 It is the flowchart of a method for processing intelligent lithium battery current distribution in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0033] The following is an explanation of the English and English abbreviations involved:

[0034] MATLAB is a commercial mathematical software produced by MathWorks in the United States, which is used in the fields of data analysis, wireless communication, deep learning, image processing and computer vision, signal processing, quantitative finance and risk management, robotics, control systems, etc. MATLAB includes a main package with hundreds of internal functions and more than thirty toolboxes. Fuzzy Logic Toolbox is one of them.

[0035] The Mamdani fuzzy inference system is a classic type of fuzzy inference system first applied to industrial control systems by Ebrahim Mamdani. The Mamdani fuzzy system consists of three parts: a fuzzification operator, a fuzzy inference mechanism, and a defuzzification operator, all of which are related to the division of the system universe of discourse. Mamdani-type fuzzy inference can perform inference calculations from input to output through a set of previously mastered inference rules, thus establishing an accurate identification system.

[0036] DTU (Data Transferunit) is a wireless terminal device specifically used to convert serial port data into IP data or convert IP data into serial port data for transmission through a wireless communication network.

[0037] The application of FSU (Field Supervision Unit) in battery monitoring refers to the intelligent dynamic environment monitoring unit of communication base stations, which is mainly used to monitor the status and performance of batteries.

[0038] BMS (Battery Management System) is used to intelligently manage and maintain each battery cell, monitor the status of the battery, and prevent the battery from overcharging and over-discharging to extend the service life of the battery.

[0039] Refer to Figures 1-3 , an intelligent lithium battery current distribution processing method, includes the following steps:

[0040] S1, the FSU first communicates with the charging meter through the DTU to obtain the current direction and determine whether the battery is charging or discharging;

[0041] S2, if in the charging state, the FSU obtains the number of online battery packs n, and the cell temperature T cell [i], remaining power SOC[i], health status SOH[i], buck-boost unit temperature T dc / dc [i] and the voltage of the single cell with the largest voltage in the battery pack V max [i];

[0042] Respectively for T cell [i], SOC[i], SOH[i], Tdc / dc [i] and V max [i] perform fuzzification and membership degree processing; Fuzzify T cell [i] into negative large, negative medium, negative small, zero, positive small, positive medium and positive large, abbreviated as {NB, NM, NS, O, PS, PB, PB}, and the initial setting of the membership function of this fuzzy set is [0, 0, 0.25, 1, 0.87, 0.6, 0]. In this way, for SOC[i], SOH[i], T dc / dc [i] and V max [i] perform gelatinization processing and set the initial membership function, and the membership degree range is (0, 1);

[0043] Use the fuzzy logic Tool box toolbox in matlab for modeling and simulation. Refer to the Mamdani fuzzy system, perform logical training, and cycle through training its membership function to obtain the weights during charging, and the obtained weights form the charging rule base;

[0044] Substitute the membership degree into the charging rule base, perform result aggregation according to the weight calculation method, and obtain the defuzzified output, that is, the charging current limiting allocation value I of each battery pack chglmt , and the specific formula is as follows:

[0045]

[0046] and represent the weights during charging, and P supply represents the available power;

[0047] Screen out the battery pack with the largest charging current limiting allocation value, and record the largest charging current limiting allocation value as I chglmt (max), increase the charging current limiting allocation value of this battery pack (such as increasing by 5A), and preferentially allocate the charging to this battery pack. Without this process, after the power increases, the battery pack with the smallest contact internal resistance with the FSU will absorb the charging current, which will increase the imbalance between the batteries;

[0048] The BMS and dc / dc of the battery pack execute the calculated charging current limiting allocation value I chglmt , and complete the current allocation;

[0049] S3. If in the discharge state, the FSU obtains the number n of online battery packs, and the cell temperature T of each battery pack cell [i], remaining power SOC[i], health status SOH[i], buck-boost unit temperature T dc / dc [i] and the voltage V of the single cell with the smallest voltage in the battery pack min [i];

[0050] Fuzzify T cell [i] into negative large, negative medium, negative small, zero, positive small, positive medium, and positive large, abbreviated as {NB, NM, NS, O, PS, PB, PB}. The initial setting of the membership function of this fuzzy set is [0, 0, 0.25, 1, 0.87, 0.6, 0]. In this way, for SOC[i], SOH[i], T dc / dc [i] and V min [i] are fuzzified, and the initial membership function is set with the membership degree range of (0, 1);

[0051] Modeling and simulation are carried out using the fuzzy logic toolbox in Matlab. Referring to the Mamdani fuzzy system, logical training is performed, and the membership function is cyclically trained to obtain the weights during discharge, and the obtained weights form the discharge rule base;

[0052] Substitute the membership degree into the discharge rule base, and perform result aggregation according to the weight calculation method to obtain the defuzzified output, that is, the discharge current limiting allocation value I dshlmt allocated to each battery pack, and the specific formula is as follows:

[0053]

[0054] and represent the weights during discharge, and P need represents the power required by the load;

[0055] Screen out the battery pack with the largest discharge current limiting allocation value, and record the largest discharge current limiting allocation value as I dshlmt (max). Increase the discharge current limiting allocation value of this battery pack (such as increasing by 5A), and give priority to using this battery pack for discharge. Without this process, after the power increases, the battery pack with the smallest contact internal resistance with the FSU will increase the discharge current, which will increase the imbalance between the batteries;

[0056] The BMS and dc / dc of the battery pack execute the calculated discharge current limiting allocation value I dshlmt of each battery pack to complete the current allocation.

[0057] The present invention obtains information such as the cell temperature, SOC, and device temperature of the DC / DC of each battery pack through communication. During the charging and discharging of the battery pack, fuzzy expert logic is used to combine the cell temperature inside the battery pack and the states of the BMS + DC / DC to allocate the charging and discharging power of the battery, so that the battery pack can be fully charged and discharged as much as possible at the same time. At the same time, it is considered that during charging, the power is allocated according to the maximum single-cell voltage, and during discharging, the power is allocated according to the minimum single-cell voltage. Also, considering that under different SOHs, it affects battery heating and total capacity, it is incorporated into the fuzzy expert logic for consideration; and the current control function is integrated into the FSU of the intelligent lithium battery. The FSU is responsible for monitoring the system load and at the same time allocating the charging and discharging current limiting values of the intelligent lithium battery, so as to achieve the synchronous emptying and full charging of each battery pack (close to simultaneous full charging and full discharging), thereby making the peak shaving and valley filling more efficient and the system more stable.

[0058] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent lithium battery current distribution processing method, characterized in that: The following steps are involved: S1, FSU first communicates with the charging meter through DTU to obtain the current direction and determine whether the battery is charging or discharging; S2, if in charging state, FSU obtains the number of online battery packs n and the cell temperature T of each battery pack cell [i], remaining charge SOC[i], health status SOH[i], buck-boost unit temperature T dc / dc [i] and the voltage of the largest single cell in the battery pack V max [i]; T cell [i], SOC[i], SOH[i], T dc / dc [i] and V max [i] Perform fuzzy processing and membership processing, fuzzify into fuzzy sets, and set the initial membership function with the membership range of (0, 1); Use the fuzzy logic tool box on matlab to perform modeling and simulation, refer to the Mamdani fuzzy system, perform logic training, and loop train its membership function to obtain the weights during charging. The obtained weights form the charging rule base. The membership degree is brought into the charging rule library, and the results are aggregated according to the weight calculation method to obtain the defuzzified output, that is, the charging current limit allocation value I of each battery pack. chglmt , the specific formula is as follows: and Represents the weight during charging, P supply Represents the power available; The battery pack with the largest charging current limit allocation value is selected, and the largest charging current limit allocation value is recorded as I chglmt (max), increase the charging current limit allocation value of the battery pack and give priority to allocating charging to the battery pack; The battery pack's BMS and dc / dc execution calculate the charging current limit distribution value I chglmt , complete the current distribution; S3, in the discharge state, the FSU obtains the number of online battery packs n and the cell temperature T of each battery pack cell [i], remaining charge SOC[i], health status SOH[i], buck-boost unit temperature T dc / dc [i] and the voltage of the smallest single cell in the battery pack V min [i]; T cell [i], SOC[i], SOH[i], T dc / dc [i] and V min [i] Perform fuzzy processing and membership processing, fuzzify into fuzzy sets, and set the initial membership function with the membership range of (0, 1); Use the fuzzy logic toolbox on matlab to perform modeling and simulation, refer to the Mamdani fuzzy system, perform logic training, and loop train its membership function to obtain the weights during discharge. The obtained weights form the discharge rule base. The membership degree is brought into the discharge rule library, and the results are aggregated according to the weight calculation method to obtain the defuzzified output, that is, the discharge current limit allocation value I allocated to each battery pack. dshlmt , the specific formula is as follows: and Represents the weight during discharge, P need Represents the power required by the load; The battery pack with the largest discharge current limit allocation value is selected, and the largest discharge current limit allocation value is recorded as I dshlmt (max), increase the discharge current limit allocation value of the battery pack, and give priority to using the battery pack for discharge; The battery pack's BMS and dc / dc execution calculate the discharge current limit distribution value I for each battery pack dshlmt , completing the current distribution.

2. The intelligent lithium battery current distribution processing method according to claim 1, characterized in that: T cell [i], SOC[i], SOH[i], T dc / dc [i] and V max [i] / V min [i] Perform fuzzy processing and transform T cell [i] is fuzzy as negative large, negative medium, negative small, zero, positive small, positive medium and positive large, abbreviated as {NB, NM, NS, O, PS, PB, PB}. The initial setting of the membership function of this fuzzy set is [0, 0, 0.25, 1, 0.87, 0.6, 0]. In this way, SOC[i], SOH[i], T dc / dc [i] and V max [i] / V min [i] Gelatinization is performed.

3. The intelligent lithium battery current distribution processing method according to claim 1, characterized in that: Increase the charging current limit distribution value of the battery pack by 5A; increase the discharging current limit distribution value of the battery pack by 5A.

Images