BMS management system, and prediction system and prediction method for battery working time

Abstract

The invention discloses a BMS management system, and a prediction system and prediction method of battery working time. Belongs to the technical field of battery management. The invention discloses a BMS management system and a prediction system and method for battery working time. The BMS management system comprises a battery pack, an equalization module, a switch module, a monitoring module and a control module. Wherein the control module obtains the SOC of each battery based on the charging and discharging information of each battery, generates a plurality of pairwise paired balance groups based on the SOC of each battery, and connects the balance groups to the corresponding equalization circuits through the control switches so as to equalize the batteries in the balance groups. In the technical scheme provided by the invention, the common equalization circuit comprising a plurality of different equalization levels is innovatively designed, which is different from the previous method that equalization circuits of different levels are configured for each battery and equalization is started as required.

Description

Technical Field

[0001] The present application relates to the field of battery management technology, and in particular to a BMS management system, a battery operating time prediction system and a prediction method. Background Art

[0002] As a commonly used energy storage technology, batteries have been widely used in many fields such as power grid load balancing, vehicle driving, and industrial production. In order to ensure the safety and service life of battery production, the smallest single battery is currently designed to be relatively small in terms of voltage, capacity, and load capacity. In order to meet the actual needs of various fields, multiple small batteries are usually combined into multiple battery packs by connecting them in series to increase the voltage and in parallel to increase the current, and then these battery packs are integrated into a complete energy storage system.

[0003] The performance of battery cells is affected by multiple factors such as manufacturing process and application environment, so there are certain differences. After multiple charge and discharge cycles, the performance differences between battery cells will further expand, which will not only lead to a decline in the overall performance of the battery, but also shorten its service life, and thus have a negative impact on the performance and service life of the entire battery pack. Taking the charging process as an example, when a single cell in the battery pack reaches the upper capacity limit, it can no longer be charged, so the overall performance of the battery pack will be limited by this smallest capacity battery.

[0004] In order to solve this problem, a balancing circuit is currently commonly used to balance the load of the battery. Take two batteries in series as an example. When one battery is fully charged and the other is not yet fully charged, the balancing circuit will transfer the current of the fully charged battery to the other battery to achieve a balance between the charging states of the two. However, in the existing balancing circuit, the key is to be equipped with an energy storage element. Due to the voltage difference between different batteries, different levels of energy storage elements are required for balancing. At the same time, since the aging rate of each battery is inconsistent and difficult to predict, if a balancing circuit is set for any two batteries, the circuit structure will be too complicated. In addition, even if a balancing circuit is set for two batteries, the energy storage element on the balancing circuit may not be able to adapt to the voltage difference between the two batteries. Therefore, in a complex battery pack, the balancing capacity of each battery is often insufficient, which further affects the energy storage performance of the battery pack. Summary of the invention

[0005] The content of this application is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this application is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0006] As a first aspect of the present application, in order to solve the technical problems mentioned in the above background technology section, the present application provides a BMS battery management system, including:

[0007] A battery pack is composed of several batteries connected in series and parallel;

[0008] The equalization module is provided with a plurality of equalization circuits of different equalization levels;

[0009] A switch module is provided with a plurality of control switches, and the control switches control the connection between the battery and the equalization circuit;

[0010] Monitoring module, used to monitor the charging and discharging information of each battery;

[0011] The control module is respectively connected with the battery pack, the balancing module, the switch module and the monitoring module by signals;

[0012] The control module obtains the SOC of each battery based on the charge and discharge information of each battery, generates a plurality of balancing groups paired in pairs based on the SOC of each battery, and connects the balancing groups to corresponding balancing circuits using control switches to balance the batteries in the balancing groups.

[0013] In the technical solution proposed in the present application, unlike the previous practice of configuring different levels of balancing circuits for each battery and starting balancing when needed, a common balancing circuit containing multiple different balancing levels is innovatively designed. When it is detected that there are batteries in the battery pack that need to be balanced, the two batteries that need to be balanced are accurately connected only through the switch module, and then the balancing operation is performed. This solution cleverly utilizes the characteristics that the aging rates of each battery in the battery pack are inconsistent, but the number of batteries that actually need to be balanced is relatively small. By adopting this common balancing circuit design, the overall complexity of the balancing circuit in the battery pack is effectively reduced. At the same time, this solution can also flexibly adapt to the balancing needs of each battery in the battery pack, ensuring the performance and stability of the battery pack.

[0014] Furthermore, the monitoring module includes:

[0015] The original information monitoring unit is used to monitor the voltage, current and internal resistance of each battery;

[0016] The SOC calculation unit calculates the SOC value of each battery in real time based on the Kalman filter method.

[0017] In the technical solution disclosed in this application, a Kalman filter method is adopted to achieve real-time high-precision calculation of the SOC (State of Charge) value of each battery. This method is not only highly accurate, but also can continuously revise the prediction method as the battery aging process progresses, thereby ensuring the continuous accuracy of the SOC value calculation.

[0018] Although this solution uses a balancing module and a switch module to accurately control the process of each battery connecting to the balancing circuit, in fact, the number of balancing circuits still needs to be moderate and should not be set too much. If it is assumed that each battery can be connected to any balancing circuit, then each battery needs to be equipped with a switch connected to the balancing circuit. However, too many balancing circuits will inevitably make the design of the switch module too complicated. In order to more effectively reduce the number of balancing circuits in the balancing module, this application specifically proposes the following technical solutions:

[0019] Furthermore, the control module includes:

[0020] An information acquisition unit, used to obtain the SOC value of each battery and establish a first SOC sequence of the battery pack;

[0021] An information updating unit, which obtains the rate information of the SOC of each battery and establishes a second SOC sequence of the battery pack;

[0022] The balancing recording unit obtains the connection information of each balancing circuit and obtains the number of idle balancing circuits;

[0023] A model building unit, based on the first SOC sequence, the second SOC sequence and the idle number of the balancing circuit, establishes a particle swarm balancing model of the battery pack to solve the division method of the balancing group and the corresponding relationship between each balancing group and the balancing circuit;

[0024] Execution unit: According to the corresponding relationship between the balancing group and the balancing circuit, the switch module is controlled to connect the balancing group to the corresponding balancing circuit.

[0025] In the technical solution proposed in this application, the first SOC sequence, the second SOC sequence and the number of idle equalization circuits are combined to construct a particle swarm model. The model can comprehensively consider the SOC value of each battery and their rate of change, so as to accurately solve the optimal equalization solution. This solution aims to minimize the demand for the number of equalization circuits while achieving the best equalization efficiency.

[0026] Although the first SOC sequence and the second SOC sequence can indicate the current charging status of each battery and the charging status in the future, it is difficult to accurately find the correlation between the charging of each battery. Therefore, it is difficult to accurately construct a particle swarm equilibrium model. To address this problem, this application provides the following technical solutions:

[0027] Furthermore, the first SOC sequence H1 is: H1 = S1, S2, S3, ... S i …S n ; The second SOC sequence H2 is: H2 = V1, V2, V3, ... V i ,…Vn ;

[0028] Among them, S i represents the SOC value of the ith battery, V i represents the rate of change of the SOC of the i-th battery, i represents the index of the battery, and n represents the total number of batteries;

[0029] The model building unit preprocesses H1 and H2 to obtain battery distribution information;

[0030] A plane rectangular coordinate system is established with H1 as the ordinate and H2 as the abscissa to obtain the battery distribution diagram M.

[0031] In the technical solution provided in the present application, a plane rectangular coordinate system is established with H1 as the vertical coordinate and H2 as the horizontal coordinate to obtain a battery distribution map, so the battery distribution map can more intuitively reflect the differences in charging and discharging of each battery, thereby better establishing a particle swarm equilibrium model.

[0032] In practice, the number of idle cells in the balancing circuit is generally not equal to the number of cells that need to be balanced. Therefore, in order to ensure that the two are equal, the number of cells that need to be balanced needs to be adjusted. If the SOC difference is directly used as the basis for pairing, it is difficult to establish a particle swarm model from the overall perspective of the battery pack, which may easily lead to poor active balancing of the battery and make it impossible to fully charge all the batteries. To address this problem, the present application provides the following technical solutions:

[0033] Furthermore, the construction process of the particle swarm equilibrium model includes the following steps:

[0034] Step 1: Extract the center point of the battery from the battery distribution map M;

[0035] Step 2: Take the center point as the reference point, and the time when the battery at the reference point is fully loaded as the reference time. Draw a dividing line on the battery distribution diagram M according to the reference time. The batteries on the right side of the dividing line need positive load, and the batteries on the left side of the dividing line need negative load.

[0036] Step 3: Taking the dividing line as the reference, translate w1 positions to the right to generate the first dividing line; translate w2 positions to the left to generate the second dividing line;

[0037] Step 4: Construct a particle swarm model based on w1, w2 and R.

[0038] In the technical solution provided in the present application, a reference point is established based on a reference time, and then a dividing line is established, and then the dividing line is diffused to the right and to the left to generate a first dividing line and a second dividing line; and then the balancing mode selected based on this solution can take into account the overall balancing effect of the battery pack, so as to try to make all batteries reach a full load state.

[0039] Furthermore, step 1 includes the following steps:

[0040] Step 11: Extract all data points from the battery distribution map M, each data point is expressed as (V i , S i ) means, i=1,2,…n;

[0041] Step 12: For each data point i, use the Gaussian kernel function and bandwidth h to calculate the density estimate of data point i;

[0042]

[0043] Wherein, exp is: exponential function, used to calculate weight; (V, S) is the point in the battery distribution diagram M where density needs to be calculated:

[0044] Step 13: Calculate the density estimation value of each point, obtain the division method of the battery distribution map R to obtain the density grid map, map the density value of each point to the density grid map, and obtain the density map U;

[0045] Step 14: Obtain the area with the largest density value in the density map U of the battery, take the area as the central area, and extract the central point from the central area.

[0046] In this solution, the Gaussian kernel function is used to calculate the center point, so no matter how the data points are distributed, the Gaussian kernel function will smooth them in the same way, thereby obtaining an isotropic density estimation result, and then accurately locating the position of the center point; in this way, when locating the center point in this solution, it is not actually locating a specific battery, but finding the center point related to the SOC distribution of all batteries. Although the center point is a virtual point, it can best reflect the future load expectations of the batteries in the battery pack.

[0047] In the existing particle swarm construction algorithm, the balancing circuit and each battery are generally used as the basis for selection to assign the best battery group to each balancing circuit. This solution places too much emphasis on the matching relationship between the battery group and the balancing circuit, which easily leads to the final result falling into a local optimal solution and failing to find a global solution.

[0048] Furthermore, step 4 includes the following steps:

[0049] Step 41: Establish a feasible solution matrix L;

[0050] In each element of the feasible solution matrix L, a is w1 and b is w2; in the feasible solution matrix L, each element represents a battery group division scheme, and for each battery group division scheme, an optimal balancing group division method can be generated;

[0051] Step 42: Setting the objective function f(x);

[0052]

[0053] Among them, α is the first parameter, β is the second parameter, U is the normalized value of the remaining balancing circuit, T is the normalized value of the remaining working time of the battery, Q1 is the maximum charge and discharge capacity of all batteries under the current charging scheme, and Q2 is the theoretical charge capacity of all batteries when they are fully charged;

[0054] Step 43: Set the number of honey sources and the iteration threshold;

[0055] Step 44: Use a greedy selection method to replace the old nectar source with a new nectar source; repeat the above steps for m consecutive iterations until the best nectar source is found.

[0056] In the technical solution provided in the present application, the values ​​of w1 and w2 are used as the sorting basis when constructing the feasible solution matrix L. In this way, when updating the nectar source, the movement relationship between the first boundary line and the second boundary line can be well measured, and the best solution can be found quickly and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The illustrative embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application.

[0058] In addition, throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the components and elements are not necessarily drawn to scale.

[0059] In the attached picture:

[0060] Figure 1 It is a structural diagram of the BMS battery management system.

[0061] Figure 2 Schematic diagram of connecting the battery to the balancing circuit through the switch module.

[0062] Figure 3 Schematic diagram of the connection between the battery and the balancing circuit.

[0063] Figure 4 It is a scatter plot of the battery distribution map;

[0064] Figure 5 A schematic diagram of drawing the first dividing line o, the second dividing line q and the separation line in the battery distribution diagram. DETAILED DESCRIPTION

[0065] Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0066] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0067] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments.

[0068] Example 1, reference Figure 1 , a BMS battery management system, including: a battery pack, a balancing module, a switch module, and a control module. Among them, the battery pack is composed of a number of batteries connected in series and parallel. There is no specific restriction on the series and parallel connection of batteries inside the battery pack, but it is flexibly configured according to actual needs and the existing battery pack design. But it is clear that the battery pack in this solution is composed of multiple batteries, and all batteries must maintain a synchronous working state during the charging or discharging process. Therefore, once any battery in the battery pack is fully discharged or charged, the entire battery pack will stop working, showing a typical short board effect.

[0069] To address this problem, a balancing module is introduced to achieve balanced management of each battery in the battery pack. Specifically, the present application designs multiple balancing circuits with different balancing levels. These balancing circuits are all active balancing circuits, and components such as capacitors or inductors are configured on the circuits (the specific configuration method is not detailed here). When these balancing circuits are connected to two batteries with different SOCs, the two batteries can be balanced so that they can complete the charging or discharging process synchronously.

[0070] As for the specific design scheme of the equalization circuit, since it belongs to the scope of the prior art, its design structure will not be described in detail here. However, it is worth mentioning that all the equalization circuits in this scheme are active equalization circuits, and their core function is to connect two batteries with different residual power through the equalization circuit, so as to realize the synchronous charging or discharging of the two batteries.

[0071] In this solution, the balancing module includes multiple balancing circuits, but these balancing circuits are not directly connected to the two batteries, but are flexibly connected to any two batteries through the switch module. Specifically, the switch module is composed of multiple control switches, such as Figure 2As shown, it can control the connection state between the left pin and any pin on the right. In this way, any two batteries can be connected to the required balancing circuit using the control module. Figure 2 Only the connection of the positive pole of the battery to the switch module is shown. For the circuit structure on the other side, only mirror copying is required.

[0072] For ease of understanding, Figure 3 A circuit diagram example of a balancing module for balancing 3 batteries is provided.

[0073] The monitoring module is used to monitor the charge and discharge information of each battery. Various sensors are set in each battery of the battery pack to obtain the voltage, current, and internal resistance of each battery, so as to obtain the charge and discharge information of each battery. In this application, active balancing needs to be achieved. The most critical part is to obtain the SOC value of the battery. The SOC value cannot be measured directly and needs to be directly calculated using the detected data. Specifically:

[0074] The monitoring module includes: an original information monitoring unit and a SOC calculation unit. The original information monitoring unit is used to monitor the voltage, current and internal resistance of each battery; the SOC calculation unit calculates the SOC value of each battery in real time based on the Kalman filter method.

[0075] The Kalman filter method is an existing technology. For ease of understanding, the general method of the Kalman filter method is provided below:

[0076] Determine the state equation and observation equation: The state equation describes how the battery state changes over time, usually including influencing factors such as current and temperature.

[0077] The observation equation describes how to estimate the battery state by measuring battery parameters such as voltage and current.

[0078] Set the initial state of the system, including the initial value of SOC.

[0079] Set the initial variance matrix of the system to represent the uncertainty about the initial state.

[0080] According to the state equation and the control quantity (such as current, temperature, etc.) at the current moment, the battery state and variance matrix at the next moment are predicted.

[0081] Based on the observed values ​​and predicted values, the Kalman gain matrix is ​​used to update the state and variance matrix of the system. The observed values ​​are usually obtained by measuring the battery voltage, current and other parameters.

[0082] By repeating the prediction and update steps, a more accurate SOC estimate can be obtained. As new data is continuously observed, the Kalman filter algorithm can update the SOC estimate in real time.

[0083] The control module obtains the SOC of each battery based on the charge and discharge information of each battery, generates a plurality of balancing groups paired in pairs based on the SOC of each battery, and connects the balancing groups to corresponding balancing circuits using control switches to balance the batteries in the balancing groups.

[0084] Specifically: the control module includes: an information acquisition unit, an information update unit, a balancing recording unit, a model building unit, and an execution unit. The information acquisition unit is used to obtain the SOC value of each battery and establish the first SOC sequence of the battery pack; the information update unit is used to obtain the rate information of the SOC of each battery and establish the second SOC sequence of the battery pack;

[0085] Because the SOC calculation unit can calculate the SOC value of each battery and the growth rate of the SOC in real time, the growth rate of the SOC selects the growth rate in the most recent unit time, and the length of the unit time is set according to the requirements. In this application, it is set to 1 second, so the required first SOC sequence and second SOC sequence can be directly obtained. The balancing recording unit obtains the connection information of each balancing circuit and obtains the idle number of the balancing circuit.

[0086] A model building unit, based on the first SOC sequence, the second SOC sequence and the idle number of the balancing circuit, establishes a particle swarm balancing model of the battery pack to solve the division method of the balancing group and the corresponding relationship between each balancing group and the balancing circuit;

[0087] Execution unit: According to the corresponding relationship between the balancing group and the balancing circuit, the switch module is controlled to connect the balancing group to the corresponding balancing circuit.

[0088] The execution unit is a specific execution element. When the corresponding relationship is known, it only needs to control the corresponding control switch in the switch module to be closed or opened. For this reason, the execution unit is not described here.

[0089] Furthermore, the first SOC sequence H1 is: H1 = S1, S2, S3, ... S i …S n ; The second SOC sequence H2 is: H2 = V1, V2, V3, ... V i ,…V n ; Among them, S i represents the SOC value of the ith battery, V i represents the rate of change of the SOC of the i-th battery, i represents the index of the battery, and n represents the total number of batteries.

[0090] The above is the representation of the first SOC sequence and the second SOC sequence.

[0091] The model building unit preprocesses H1 and H2 to obtain battery distribution information;

[0092] A plane rectangular coordinate system is established with H1 as the ordinate and H2 as the abscissa to obtain the battery distribution diagram M.

[0093] The battery distribution diagram M is the information that the model building unit needs to calculate after receiving the first SOC sequence and the second SOC sequence. The battery distribution diagram M contains the distribution information of the battery in SOC. In fact, because the charging power and discharging power of the battery pack remain basically unchanged during use, the change rate of the SOC of each battery is basically equal and will not change. Therefore, the batteries in the battery distribution diagram M will actually follow the law of normal distribution, concentrated near the change rate of the average SOC, and then gradually extend upward or downward as the charging and discharging proceeds. Figure 4 shown.

[0094] Further: The construction process of the particle swarm equilibrium model includes the following steps:

[0095] Step 1: Extract the center point of the battery from the battery distribution map M.

[0096] Step 1 includes the following steps:

[0097] Step 11: Extract all data points from the battery distribution map M, each data point is expressed as (V i , S i ) means, i=1,2,…n;

[0098] Step 12: For each data point i, use the Gaussian kernel function and bandwidth h to calculate the density estimate of data point i;

[0099]

[0100] Wherein, exp is: exponential function, used to calculate weight; (V, S) is the point in the battery distribution diagram M where density needs to be calculated:

[0101] Step 13: Calculate the density estimate of each point, set the smallest cell to obtain a density grid map, map the density value of each point to the density grid map, and obtain the density map U;

[0102] The smallest cell here is the difference between the smallest SOC and the SOC rate. The specific situation is set according to the accuracy. If lower accuracy is required, the cell is set smaller, otherwise it is set larger.

[0103] Step 14: Obtain the area with the largest density value in the density map U of the battery, take the area as the central area, and extract the central point from the central area.

[0104] The central area is the grid with the largest and equal density value, and the center point is the geometric center of the area.

[0105] Step 2: Take the center point as the reference point, and the time when the battery at the reference point is fully loaded as the benchmark time. Draw a dividing line on the battery distribution diagram M according to the benchmark time. The batteries on the right side of the dividing line require positive load, and the batteries on the left side of the dividing line require negative load.

[0106] In step 1, the center point is calculated. Although there is not necessarily a battery at the center point, it can be used as an ideal battery position. Using the SOC value and SOC rate of the center point as a benchmark, the time it takes to fully charge the point can be calculated, and then the benchmark time can be obtained. Then, all points that are fully charged within the benchmark time can be obtained to obtain a dividing line, which is a straight line.

[0107] Specifically, because the SOC needs to be charged to the maximum value (100%), and then there is a reference time t, the following equation can be obtained for each point:

[0108] S0=S+tV, where S0 and t are constants, S0 is the maximum value that needs to be reached for charging, and t is the reference time. After a simple transformation of the equation, we can get: S=tV-S0; thus, S=tV-S0 is the function expression of the dividing line.

[0109] For example: as shown in point (10, 2), when the SOC reaches 20, it means that charging is complete, and the reference time is 5 seconds; the point to the right of point (10, 2) that can reach 20 within 5S is: (5, 3), and the point to the left of point (10, 2) that can reach 20 within 5S is: (15, 1). In this way, it can be seen that (15, 1), (10, 2), and (5, 3) are all on a straight line, and correspondingly, the dividing line is also a straight line.

[0110] Step 3: Taking the dividing line p as the reference, translate w1 positions to the right to generate the first dividing line; translate w2 positions to the left to generate the second dividing line.

[0111] like Figure 5 As shown, although there are some differences between the batteries between the first dividing line o and the second dividing line q, the difference in the expected charging time is generally small. Therefore, for the batteries between the first dividing line and the second dividing line, it is not necessary to consider whether balancing is required.

[0112] The batteries on the right side of the first dividing line and the batteries on the left side of the second dividing line need to be actively balanced, and the batteries on both sides can be paired in pairs. Generally speaking, the batteries on the right charge faster, so they are distributed in the upper right corner. Conversely, the batteries on the left charge slower, so they are distributed in the lower left corner. By pairing the two in pairs for active balancing, the SOC information of the two batteries can be moved closer to the inside of the first dividing line and the second dividing line.

[0113] Step 4: Construct a particle swarm model based on w1, w2, u and R.

[0114] Step 4 includes the following steps:

[0115] Step 41: Establish a feasible solution matrix L;

[0116] In each element of the feasible solution matrix L, a is w1, b is w2, and the minimum number of batteries to the right of the first dividing line or to the left of the second dividing line is z; in the feasible solution matrix L, each element represents a battery group division scheme; in the feasible solution matrix L, each element represents a battery group division scheme, and for each battery group division scheme, an optimal balancing group division method can be generated.

[0117] The key to the particle swarm algorithm is the construction method of the feasible solution matrix L. In this scheme, the arrangement of the elements in the feasible solution matrix L is based on the values ​​of w1 and w2. For example, (1,1,u) means that if w1 and w2 are both equal to 1, the minimum number of batteries on the right side of the first dividing line or on the left side of the second dividing line is z, z<u.

[0118] In this case, z batteries need to be paired. When the batteries that need to be paired are known, how to reasonably select a suitable balancing circuit is an existing technology and will not be further elaborated here. In this solution, the battery farthest from the center is mainly selected for active balancing. Therefore, in practice, each time w1 and w2 are taken, a distribution scheme for the balancing circuit can be obtained, and from top to bottom, w1 gradually increases, and from left to right, w2 gradually increases. At the same time, z will gradually decrease. The relationship between the elements in the feasible solution matrix L reflects the implicit state of the gradual change of w1 and w2, and thus the optimal solution can be found with a smaller number of iterations.

[0119] Step 42: Setting the objective function f(x);

[0120]

[0121] Among them, α is the first parameter, β is the second parameter, U is the normalized value of the remaining balancing circuit, T is the normalized value of the remaining working time of the battery; Q1 is the maximum charge and discharge capacity of all batteries under the current charging scheme, and Q2 is the theoretical charge capacity of all batteries when they are fully charged.

[0122] The objective function provided in this application is f(x), and the value of the objective function needs to be as large as possible during the iteration process. In the objective function, α and β are parameter items, which are used to adjust the first item and the second item in the objective function to have the required weight influence. It indicates the relationship between the number of remaining balancing circuits and the remaining working time of the battery after the current balancing circuit allocation method is adopted. If the two are closer, the value will be larger. In this way, when allocating balancing circuits, all balancing circuits will not be allocated immediately, but will be gradually introduced during the charging and discharging process to ensure that there are a certain number of free balancing circuits in the middle and late stages of charging and discharging to play a balancing role; It is used to ensure that the balancing effect can be increased as much as possible, and all batteries can be charged or discharged at the same time. Q1 and Q2 are actually theoretical values. Q2 is the cumulative sum of the power of all batteries, and Q1 is the maximum charge and discharge of all batteries in the current state. This is generally related to the worst battery. For example, in the current state, about 10 seconds later, a battery will be fully charged. At this time, the charging of all batteries will be stopped, so the remaining batteries cannot continue to charge even if they are not fully charged, and then Q1 can be calculated.

[0123] Step 43: Set the number of honey sources and the iteration threshold;

[0124] Step 44: Use a greedy selection method to replace the old nectar source with a new nectar source; repeat the above steps for m consecutive iterations until the best nectar source is found.

[0125] The particle swarm algorithm is a common technical means in this field. When the feasible solution matrix and the objective function f(x) are known, technicians in this field can construct the required particle swarm algorithm.

[0126] In the particle swarm algorithm provided in this application, balancing circuits may be used at different stages. As mentioned above, balancing circuits have multiple levels. When setting feasible solutions, how to allocate balancing circuits? This application provides the following solutions:

[0127] Although the aging rates of the batteries in the battery pack are not consistent, in general, the aging conditions show a normal distribution, so during the charging process, the difference in power between the batteries remains basically unchanged. In this way, the balancing circuit can be used in proportion each time the balancing circuit is adjusted.

[0128] Embodiment 2: A system for predicting battery working time, comprising:

[0129] The BMS management system described in Example 1;

[0130] The prediction unit calculates the expected working time of the battery pack based on the current active balancing scheme.

[0131] Specifically, the BMS management system described in Example 1 can obtain the current balancing solution of the battery pack, so that the battery that can complete charging or discharging the fastest in the current state can be obtained, and then the estimated working time of the battery pack can be obtained.

[0132] For example, after active balancing is completed, the current SOC value and SOC rate of each battery can be obtained. In this way, we can calculate the fastest time for the SOC value to reach the peak value (maximum value during charging and minimum value during discharging), and this time is the expected working time of the battery pack.

[0133] Embodiment 3: A method for predicting battery operating time, using the battery operating time prediction system described in Embodiment 2 to calculate the estimated operating time of a battery pack.

[0134] The above description is only some preferred embodiments of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present application is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above invention concept. For example, the above features are replaced with (but not limited to) the technical features with similar functions disclosed in the embodiments of the present application to form a technical solution.

Claims

1. A BMS battery management system, characterized in that: include: A battery pack is composed of several batteries connected in series and parallel; The equalization module is provided with a plurality of equalization circuits of different equalization levels; A switch module is provided with a plurality of control switches, and the control switches control the connection between the battery and the equalization circuit; Monitoring module, used to monitor the charging and discharging information of each battery; The control module is respectively connected with the battery pack, the balancing module, the switch module and the monitoring module by signals; The control module obtains the SOC of each battery based on the charge and discharge information of each battery, generates a plurality of balancing groups paired in pairs based on the SOC of each battery, and connects the balancing groups to corresponding balancing circuits using control switches to balance the batteries in the balancing groups.

2. The BMS battery management system according to claim 1, characterized in that: The monitoring modules include: The original information monitoring unit is used to monitor the voltage, current and internal resistance of each battery; The SOC calculation unit calculates the SOC value of each battery in real time based on the Kalman filter method.

3. The BMS battery management system according to claim 1, characterized in that: The control module includes: An information acquisition unit, used to obtain the SOC value of each battery and establish a first SOC sequence of the battery pack; An information updating unit, which obtains the rate information of the SOC of each battery and establishes a second SOC sequence of the battery pack; The balancing recording unit obtains the connection information of each balancing circuit and obtains the number of idle balancing circuits; A model building unit, based on the first SOC sequence, the second SOC sequence and the idle number of the balancing circuit, establishes a particle swarm balancing model of the battery pack to solve the division method of the balancing group and the corresponding relationship between each balancing group and the balancing circuit; Execution unit: According to the corresponding relationship between the balancing group and the balancing circuit, the switch module is controlled to connect the balancing group to the corresponding balancing circuit.

4. The BMS battery management system according to claim 3, characterized in that: The first SOC sequence H1 is: H1 = S1, S2, S3, ... S i …S n ; The second SOC sequence H2 is: H2 = V1, V2, V3, ... V i ,…V n ; Among them, S i represents the SOC value of the ith battery, V i represents the rate of change of the SOC of the i-th battery, i represents the index of the battery, and n represents the total number of batteries; The model building unit preprocesses H1 and H2 to obtain battery distribution information; A plane rectangular coordinate system is established with H1 as the ordinate and H2 as the abscissa to obtain the battery distribution diagram M.

5. The BMS battery management system according to claim 4, characterized in that: The construction process of the particle swarm equilibrium model includes the following steps: Step 1: Extract the center point of the battery from the battery distribution map M; Step 2: Take the center point as the reference point, and the time when the battery at the reference point is fully loaded as the reference time. Draw a dividing line on the battery distribution diagram M according to the reference time. The batteries on the right side of the dividing line need positive load, and the batteries on the left side of the dividing line need negative load. Step 3: Taking the dividing line as the reference, translate w1 positions to the right to generate the first dividing line; translate w2 positions to the left to generate the second dividing line; Step 4: Construct a particle swarm model based on w1, w2 and R.

6. The BMS battery management system according to claim 5, characterized in that: Step 1 includes the following steps: Step 11: Extract all data points from the battery distribution map M, each data point is expressed as (V i , S i ) means, i=1,2,…n; Step 12: For each data point i, use the Gaussian kernel function and bandwidth h to calculate the density estimate of data point i; Wherein, exp is: exponential function, used to calculate weight; (V, S) is the point in the battery distribution diagram M where density needs to be calculated: Step 13: Calculate the density estimation value of each point, obtain the division method of the battery distribution map R to obtain the density grid map, map the density value of each point to the density grid map, and obtain the density map U; Step 14: Obtain the area with the largest density value in the density map U of the battery, take the area as the central area, and extract the central point from the central area.

7. The BMS battery management system according to claim 6, characterized in that: Step 4 includes the following steps: Step 41: Establish a feasible solution matrix L; In each element of the feasible solution matrix L, a is w1, b is w2, and the minimum number of batteries to the right of the first dividing line or to the left of the second dividing line is z; in the feasible solution matrix L, each element represents a partitioning scheme of a battery pack; Step 42: Setting the objective function f(x); Among them, α is the first parameter, β is the second parameter, U is the normalized value of the remaining balancing circuit, T is the normalized value of the remaining working time of the battery, Q1 is the maximum charge and discharge capacity of all batteries under the current charging scheme, and Q2 is the theoretical charge capacity of all batteries when they are fully charged; Step 43: Set the number of honey sources and the iteration threshold; Step 44: Use a greedy selection method to replace the old nectar source with a new nectar source; repeat the above steps for m consecutive iterations until the best nectar source is found.

8. A battery working time prediction system, characterized in that: include: The BMS management system according to any one of claims 1 to 7; The prediction unit calculates the expected working time of the battery pack based on the current active balancing scheme.

9. A method for predicting battery working time, characterized in that: The battery operating time prediction system according to claim 8 is used to calculate the expected operating time of the battery pack.

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