Battery pack charging state determination method and device, storage medium and program product

Abstract

The invention discloses a battery pack charging state determination method and device, a storage medium and a program product, and relates to the technical field of data processing. According to the method, the residual error of the battery module is determined through the estimated charging state of the battery module at the current moment and the average charging state of the battery module in the battery pack, and the residual error is used for representing the difference between the estimated charging state of the battery module and the average charging state of the battery module; determining the weight of the battery module based on the residual error of the battery module; and then the charging state of the battery pack is calculated according to the weight of each battery module and the charging state of each battery module, so that complex physical characteristic modeling is avoided, the calculation amount is reduced, the calculation process of the charging state of the battery pack is simpler, the change of the battery state can be quickly responded, and real-time application is facilitated.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, storage medium and program product for determining a charging state of a battery pack. Background Art

[0002] A battery pack is a combination of multiple battery cells connected in a certain way to meet specific voltage and capacity requirements. The calculation of the state of charge (SOC) in a battery pack is crucial for battery management. At present, the method for calculating the SOC of a battery pack mainly combines the equivalent circuit model and the electrochemical model, starting from the internal reaction mechanism of the battery, and simulating the charging and discharging behavior of the battery for calculation. This requires the analysis of the material properties, electrochemical properties, and thermodynamic properties of the battery, and converting these properties into mathematical models for calculation. At the same time, because the battery pack is composed of multiple battery cells, the state of each battery cell is different, and a model needs to be established and maintained for each battery cell or each group of battery cells. As the battery life, environmental conditions and other factors change, the model parameters also need to be calibrated and updated regularly. Therefore, the model-based battery pack state of charge calculation method has a large amount of calculation, resulting in complex battery pack SOC calculation, which is not suitable for real-time applications.

[0003] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0004] The main purpose of the present application is to provide a method, device, storage medium and program product for determining the charging state of a battery pack, aiming to solve the technical problem of the complex charging state calculation process of the current battery pack.

[0005] To achieve the above objectives, the present application proposes a method for determining a charging state of a battery pack, the method comprising: Determine a residual of the battery module based on an estimated state of charge of the battery module at the current moment and an average state of charge of the battery modules in the battery pack, wherein the residual is used to represent a difference between the estimated state of charge of the battery module and the average state of charge of the battery module; Determining a weight of the battery module based on the residual of the battery module; The state of charge of the battery group is determined based on the weight of each of the battery modules and the estimated state of charge of each of the battery modules.

[0006] In one embodiment, the step of determining the weight of the battery module based on the residual of the battery module includes: Determine the mean and variance of the residual based on the residual of the battery module within a preset time window before the current moment; Determine the conditional density probability function value of the battery module at the current moment based on the mean value and variance of the residual; Based on the conditional density probability function value, a weight of the battery module is determined.

[0007] In one embodiment, the battery module includes at least two battery cells, and the method further includes: Determine a target battery cell in the battery module based on the estimated internal resistance of the battery cell at each moment and the average internal resistance of the battery module, wherein the average internal resistance is the average value of the internal resistances of all battery cells in the battery module; Determining an open circuit voltage-charge state-temperature correspondence relationship of a battery pack based on the open circuit voltage-charge state correspondence relationship of the target battery cell; The charging state of the battery pack is determined according to the corresponding relationship between the open circuit voltage, the charging state and the temperature of the battery pack.

[0008] In one embodiment, the step of determining the target battery cell in the battery module based on the estimated internal resistance of the battery cell at each moment and the average internal resistance of the battery module comprises: Determining a cosine similarity between an estimated internal resistance of each battery cell in a battery module and an average internal resistance of the battery module; The battery cell with the largest cosine similarity is determined as the target battery cell.

[0009] In one embodiment, the step of determining the open circuit voltage-charge state-temperature correspondence relationship of the battery pack according to the open circuit voltage-charge state correspondence relationship of the target battery cell includes: Under different temperature conditions, the battery pack is subjected to charge and discharge tests to obtain open circuit voltage data of the battery pack under different charge states and different temperatures; Based on the open circuit voltage data and the open circuit voltage-charge state correspondence relationship of the target battery cell, the open circuit voltage-charge state-temperature correspondence relationship of the battery pack is determined.

[0010] In one embodiment, the step of determining the charging state of the battery pack according to the open circuit voltage-charging state-temperature correspondence relationship of the battery pack includes: Get the real-time open circuit voltage and temperature of the battery pack; The charging state of the battery pack is determined based on the real-time open circuit voltage, the real-time temperature, and a corresponding relationship between the open circuit voltage, the charging state, and the temperature of the battery pack.

[0011] In one embodiment, before the step of determining the cosine similarity between the estimated internal resistance of each battery cell in the battery module and the average internal resistance of the battery module, the step further includes: Obtain battery operation data within a preset time window; Based on the battery operation data, determining the estimated internal resistance of the battery cell in the battery module by a recursive least squares method; The average value of the estimated internal resistance of each battery cell in the battery module is calculated at each moment to obtain the average internal resistance of the battery module.

[0012] In addition, to achieve the above-mentioned purpose, the present application also proposes a battery pack charging status determination device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the battery pack charging status determination method as described above.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the battery pack charging status determination method as described above are implemented.

[0014] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the method for determining the charging state of a battery pack as described above are implemented.

[0015] The present application provides a method for determining the charging state of a battery pack, which determines the residual of the battery module by the estimated charging state of the battery module at the current moment and the average charging state of the battery modules in the battery pack, wherein the residual is used to represent the difference between the estimated charging state of the battery module and the average charging state of the battery module; and based on the residual of the battery module, the weight of the battery module is determined; and then the charging state of the battery pack is determined according to the weight of each battery module and the charging state of each battery module. The above method avoids complex physical property modeling, and uses the residual and weight of the battery module charging state to calculate the charging state of the battery pack, instead of performing detailed electrochemical modeling and calculation for each battery cell, reducing the reliance on complex chemical reactions and physical processes inside the battery, thereby reducing the complexity of the calculation, reducing the amount of calculation, making the charging state calculation process of the battery pack simpler, able to quickly respond to changes in the battery state, and convenient for real-time application. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 A schematic diagram of a flow chart provided in Embodiment 1 of a method for determining a charging state of a battery pack of the present application; Figure 2 A schematic diagram of a method flow chart provided in Embodiment 1 of the method for determining the charging state of a battery pack of the present application; Figure 3 A schematic diagram of a flow chart provided for Embodiment 2 of the method for determining the charging state of a battery pack of the present application; Figure 4 A schematic diagram of a first method flow chart provided in Embodiment 2 of the method for determining the charging state of a battery pack of the present application; Figure 5 A schematic diagram of a second method flow chart provided in Embodiment 2 of the method for determining the charging state of a battery pack of the present application; Figure 6 A schematic diagram of a third method flow chart provided in Embodiment 2 of the method for determining the charging state of a battery pack of the present application; Figure 7 A schematic diagram of a flow chart provided in Embodiment 3 of the method for determining the charging state of a battery pack of the present application; Figure 8 Schematic diagram of the device structure of the hardware operating environment involved in the method for determining the charging state of a battery pack in an embodiment of the present application.

[0019] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0021] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0022] A battery pack is a combination of multiple battery cells connected in a certain way to meet specific voltage and capacity requirements. The calculation of SOC in a battery pack is crucial for battery management. At present, the method of calculating the SOC of a battery pack mainly combines the equivalent circuit model and the electrochemical model, starting from the internal reaction mechanism of the battery, and simulating the charging and discharging behavior of the battery for calculation. However, the model-based calculation method usually requires detailed modeling of the physical characteristics of the battery pack, which requires a large amount of calculation, resulting in complex calculation of the battery pack SOC and is not suitable for real-time applications.

[0023] In view of the above problems, the present application proposes a method for determining the charging state of a battery pack, which determines the residual of the battery module through the estimated charging state of the battery module at the current moment and the average charging state of the battery modules in the battery pack, wherein the residual is used to represent the difference between the estimated charging state of the battery module and the average charging state of the battery module; and based on the residual of the battery module, the weight of the battery module is determined; and then the charging state of the battery pack is calculated according to the weight of each battery module and the charging state of each battery module, thereby avoiding complex physical property modeling, reducing the amount of calculation, making the charging state calculation process of the battery pack simpler, being able to quickly respond to changes in the battery state, and facilitating real-time application.

[0024] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, etc. The following takes a battery pack charging state detection device as an example to illustrate this embodiment and the following embodiments.

[0025] Based on this, the first embodiment of the present application provides a method for determining the charging state of a battery pack, referring to Figure 1 In this embodiment, the battery pack includes at least two battery modules, and the method for determining the charging state of the battery pack includes steps S10 to S30: Step S10, based on the estimated state of charge of the battery module at the current moment and the average state of charge of the battery modules in the battery pack, determine the residual of the battery module, wherein the residual is used to represent the difference between the estimated state of charge of the battery module and the average state of charge of the battery module.

[0026] Among them, it should be noted that the estimated SOC of the battery module is the percentage of the remaining available power of the battery module to its total capacity, which can reflect the current health status and remaining endurance of the battery module. For example, a battery module with an SOC of 50% means that the battery module has half of its power remaining and can continue to be used for a period of time. The average SOC of the battery modules in the battery pack refers to the arithmetic mean of the estimated SOCs of all battery modules in the battery pack, which is used to reflect the endurance of the entire battery pack. Due to differences in manufacturing processes, material properties and usage conditions between battery modules in the battery pack, there will be differences in capacity, internal resistance, etc. The estimated SOC of each battery module is subtracted from the average SOC of the battery module to obtain the residual of each battery module.

[0027] Optionally, the remaining capacity is calculated by accumulating the charge transferred into or out of the battery module, and the remaining capacity is compared with the total capacity to serve as the estimated SOC of the battery module; or, since the mapping relationship between OCV and SOC is known and the SOC is constant within a shorter time window, the corresponding SOC can be found based on the open circuit voltage (OCV) of the battery module as the estimated SOC of the battery module.

[0028] Step S20: determining the weight of the battery module based on the residual of the battery module.

[0029] It should be noted that the residual of the battery module can reflect the consistency between the battery modules. If the residual is small, it means that the consistency between the battery modules is good; if the residual is large, it means that the consistency between the battery modules is poor. The residual can also be used as part of the Bayesian probability formula to calculate the weight of each battery module, thereby more accurately determining the overall SOC of the battery pack.

[0030] The residual can distinguish the performance differences of different battery modules. If the residual of a battery module is large, it means that the performance difference between the battery module and other battery modules in the battery pack is large, and the error caused by using this battery module to calculate the battery pack SOC is also large. It is necessary to reduce its weight in the battery pack SOC calculation.

[0031] Optionally, in a feasible implementation manner, referring to Figure 2 , step S20 also includes steps S21 to S23: Step S21, based on the residual of the battery module in a preset time window before the current moment, determine the mean value and variance of the residual.

[0032] Specify the specific time point for battery pack SOC calculation as the current moment, calculate a time range from the current moment forward, and calculate the arithmetic mean and variance of the residuals of all battery modules within the time range. The mean and variance of the residuals can reflect the stability of battery pack SOC calculation using battery modules. The smaller the mean, the closer the calculated SOC value of the battery pack is to the true value; the smaller the variance, the smaller the fluctuation of the calculated SOC value of the battery pack is, and the more stable it is.

[0033] Set an appropriate time window length, such as 10 minutes, 30 minutes, etc., and calculate the residual of each battery module based on the estimated SOC value and average SOC of the battery module at the current moment. Add all the residuals in the time window and divide them by the number of residuals to get the average value of the residuals. Add the squares of the difference between each residual in the time window and the residual average value and divide them by the number of residuals to get the variance of the residuals.

[0034] For example, the Bayesian probability algorithm is used to first calculate the residual of the estimated SOC and the average SOC of each battery module at a certain moment, which is set as , represents the residual of the i-th module at time j.

[0035] Then calculate the time window before the current time t, assuming that the time window length is W, and calculate the average value of the residual of the i-th battery module, set as , represents the average value of the residual of the i-th module at the current time t:

[0036] Then calculate the variance of the residual of the i-th module at the current time t, set it to :

[0037] Step S22: determining the conditional density probability function value of the battery module at the current moment based on the mean value and variance of the residual.

[0038] It should be noted that the conditional density probability function refers to the probability density function of a random variable taking a specific value under certain given conditions, that is, at the current moment, given the mean and variance of the residual, the probability distribution of the SOC calculated value of each battery module.

[0039] For example, assuming that a given battery module and the SOC value of the battery module at the previous time step Under the condition of , the SOC value of the battery module observed at the current moment is The conditional probability density function is:

[0040] Optionally, set is the average SOC value of the battery module, set For a given battery module The SOC of the battery pack is determined by calculating the relative importance of the battery module in the overall SOC calculation of the battery pack according to the calculated conditional density probability function value of each battery module at the current moment.

[0041] The SOC of different battery modules is weighted based on the Bayesian probability formula to achieve the SOC calculation of the entire battery pack. Bayesian probability can reasonably allocate weights based on the uncertainty and historical performance in the SOC estimation of different modules, thereby improving the overall accuracy of the battery pack SOC calculation. While ensuring the prediction accuracy, this method does not rely on the physical characteristics of the battery for detailed modeling, reduces the computational complexity, and is suitable for application scenarios with high real-time requirements.

[0042] Step S23: determining the weight of the battery module based on the conditional density probability function value.

[0043] It should be noted that the weight of each battery module reflects its contribution to the SOC calculation of the entire battery pack. A battery module with a higher conditional density probability function value means that the confidence in using the battery module to calculate the battery pack SOC is higher, so it should be given a higher weight in the overall state calculation.

[0044] Since the probability density function value may have a wide range, it needs to be normalized to ensure that the sum of the weights of all battery modules is 1. For example, the conditional density probability function value of each module is divided by the sum of the probability density function values ​​of all modules to achieve normalization of the probability density function value.

[0045] The normalized conditional density probability function value is the weight of the battery module. Weight It can be calculated by the following formula:

[0046] in, is the conditional density probability function value of the ith battery module, and N is the total number of battery modules in the battery pack.

[0047] Optionally, the variation trend of the residual is considered, for example, the first-order derivative or the second-order derivative of the residual is calculated and used as the basis for weight calculation, or the weight is determined directly by using the residual at the current moment.

[0048] Exemplarily, the residual data of each battery module is first collected within a preset time window. That is, the difference between the estimated SOC and the average SOC of the battery module. The first-order derivative of the residual is calculated to represent the rate of change of the residual over time, and then the second-order derivative of the residual is calculated to represent the rate of change of the residual change rate, that is, the acceleration of the residual. The positive or negative sign of the first-order derivative can indicate whether the residual is increasing or decreasing, the size can indicate the speed of the change, and the positive or negative sign of the second-order derivative can indicate whether the rate of change of the residual is accelerating or decelerating. If the absolute value of the first-order derivative of the residual of a battery module is large, it means that the residual of the battery module is increasing, that is, the difference between the estimated SOC and the average SOC of the battery module is increasing, and its weight needs to be reduced. If the second-order derivative of the residual of a battery module is positive, this may indicate that the change of the residual of the battery module is accelerating, and its weight is also considered to be reduced. On the contrary, if both the first-order and second-order derivatives of the residual indicate that the residual change is stable or tends to be stable, its weight can be increased. Finally, the calculated weight is applied to the SOC calculation of the battery pack to obtain a more accurate overall SOC of the battery pack.

[0049] In this embodiment, compared with other complex methods such as neural networks, using residuals to determine the weights of battery modules in battery pack SOC calculation can reduce the computational complexity and improve the real-time performance of battery pack SOC calculation. At the same time, by considering the performance differences between battery modules, using residuals to determine weights can improve the accuracy of battery pack SOC calculation, and using residuals to dynamically adjust weights also allows battery pack SOC calculation to adapt to different working conditions and battery states.

[0050] Step S30 , determining the charging state of the battery pack based on the weight of each battery module and the estimated charging state of each battery module.

[0051] For example, assume that a battery pack consists of three battery modules, and their estimated SOCs are SOC1, SOC2, and SOC3, respectively, and their corresponding weights are , , , then the SOC of the entire battery pack is ×SOC1+ ×SOC2+ ×SOC3.

[0052] In this embodiment, by comparing the estimated SOC of the battery module with the average SOC of the battery pack, the residual of each battery module is determined, and then the weight of each battery module is calculated. These weights reflect the relative importance of the battery module in the overall state estimation of the battery pack. Finally, the SOC of the entire battery pack is calculated using the weighted SOC, which avoids complex physical property modeling, reduces the amount of calculation, and makes the battery pack SOC calculation process simpler. Due to the reduction in the amount of calculation, this method can quickly respond to changes in the battery state and is convenient for real-time application. In addition, by considering the actual performance and relative importance of each battery module, a more accurate SOC calculation result can be guaranteed without relying on a sophisticated model.

[0053] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can refer to the above introduction, and will not be repeated later. Figure 3 The method for determining the charging state of the battery pack further includes steps A10 to A20: Step A10, determining a target battery cell in the battery module based on the estimated internal resistance of the battery cell at each moment and the average internal resistance of the battery module, wherein the average internal resistance is the average value of the estimated internal resistances of all battery cells in the battery module.

[0054] It should be noted that the battery module is composed of battery cells. The estimated internal resistance of a battery cell refers to the resistance generated by the battery cell during the charge and discharge process. The average internal resistance of the battery module refers to the average value of the estimated internal resistance of all battery cells in the battery module, which is used to represent the average resistance level of the entire battery module. Compare the estimated internal resistance of each battery cell with the average internal resistance, find the battery cell in each battery module that is closest to the average internal resistance, and use this battery cell as the target battery cell to represent the performance of the entire battery module.

[0055] For example, it is assumed that a battery module consists of four battery cells, and their estimated internal resistances are , , , Calculate the average value of these four internal resistances to get the average internal resistance = Then, the internal resistance of each battery cell is compared with , find the closest The battery cell is used as the target battery cell.

[0056] Optionally, in a feasible implementation manner, referring to Figure 4 , step A10 also includes steps A11~A12: Step A11, determining the cosine similarity between the estimated internal resistance of each battery cell in the battery module and the average internal resistance of the battery module.

[0057] It should be noted that cosine similarity is used to measure the degree of similarity between two vectors in direction, and its value is between -1 and 1, 1 means they are exactly the same, -1 means they are completely opposite, and 0 means they are orthogonal or independent.

[0058] First, the estimated internal resistance of each battery cell needs to be considered as a vector, and then the average internal resistance of the battery module is calculated, which is also considered as a vector. Next, the cosine similarity between the estimated internal resistance vector of each battery cell and the average internal resistance vector of the battery module is calculated.

[0059] Exemplarily, the cosine similarity between the internal resistance of each battery cell in the battery module and the average internal resistance of the battery module is calculated using the following formula:

[0060] in, represents the cosine similarity of the kth battery cell in the i-th battery module, represents the average internal resistance of the ith battery module at time t, It represents the estimated internal resistance of the kth battery cell in the i-th battery module at time t, and L is the total number of time steps. The difference between the internal resistance of each battery cell and the average internal resistance of the battery module in which it is located is compared, and the consistency between the battery cell and other battery cells in the battery module is quantified by calculating their cosine similarity.

[0061] Step A12: determine the battery cell with the largest cosine similarity as the target battery cell.

[0062] It should be noted that the battery cell that is most similar to the average internal resistance vector of the battery module is found as the target battery cell. In this way, when estimating the SOC of the battery module, the target battery cell can represent the entire battery module, simplifying the complexity of the estimation.

[0063] Optionally, the least square method is used to calculate the difference between the estimated internal resistance and the average internal resistance of the battery cells, and the battery cell with the smallest difference is selected as the target battery cell.

[0064] For example, first collect the internal resistance measurement values ​​of each battery cell at different time points. Assume there are n battery cells and each battery cell has m measurement values. Calculate the average resistance value of all battery cells at each time point. Define a loss function Used to evaluate the difference between the average internal resistance of each battery cell and the overall average internal resistance:

[0065] in, represents the internal resistance of the ith battery cell at time j, Represents the average internal resistance of all battery cells in the battery module at time j.

[0066] For each battery cell, find the loss function The smallest constant , which is the difference between the estimated resistance of each battery cell and the overall average resistance.

[0067] Compare the differences of all battery cells , select the battery cell with the smallest difference as the target battery cell.

[0068] Optionally, the Pearson correlation coefficient between the estimated internal resistance and the average internal resistance of each battery cell is calculated to select the cell with the highest correlation coefficient, or the battery cells are grouped using a cluster analysis method, and then the cell closest to the cluster center is selected as the target battery cell.

[0069] In this embodiment, the cosine similarity is used to quantify the inconsistency of each battery cell in the battery module, which can more accurately reflect the differences between the battery cells, so as to select representative battery cells to represent the characteristics of the battery module and reduce the accumulation of battery pack state errors that may occur when simply taking an average or arbitrarily selecting representative cells.

[0070] Step A20, determining the open circuit voltage-charge state-temperature correspondence relationship of the battery pack based on the open circuit voltage-charge state correspondence relationship of the target battery cell.

[0071] It should be noted that OCV refers to the terminal voltage of the battery when it is not connected to an external circuit. The correspondence between the open circuit voltage and the state of charge of the target battery cell indicates the change rule of the OCV of the battery module where the target battery cell is located under different SOCs.

[0072] In addition, it should be noted that OCV is the voltage of the battery when no current flows. It is directly related to the chemical composition and SOC of the battery, and the relationship between OCV and SOC is usually nonlinear. Different types of batteries such as sodium-ion batteries, lithium-ion batteries, lead-acid batteries, etc. have different OCV-SOC correspondences. Among them, temperature will affect the rate of chemical reactions inside the battery, thereby affecting OCV. Temperature can also affect the charge and discharge efficiency of the battery, which may cause the battery pack to be overcharged or over-discharged, making the SOC calculation of the battery pack inaccurate. Therefore, it is necessary to consider the operating state of the battery pack at different temperatures, compensate and correct the open circuit voltage-charge state correspondence of the target battery cell, and obtain the open circuit voltage-charge state-temperature correspondence of the battery pack.

[0073] Optionally, in a feasible implementation manner, referring to Figure 5 , step A20 includes steps A21~A22: Step A21, performing charge and discharge tests on the battery pack under different temperature conditions to obtain open circuit voltage data of the battery pack under different charging states and different temperatures.

[0074] Step A22, determining the open circuit voltage-charge state-temperature correspondence of the battery pack based on the open circuit voltage data and the open circuit voltage-charge state correspondence of the target battery cell.

[0075] Exemplarily, the battery pack is subjected to a charge and discharge cycle under different temperature conditions, wherein a temperature control chamber or a hot plate can be used to control the temperature of the battery pack. At each preset SOC interval point, such as 10%, 20%, ..., 90%, 100%, stop charging and discharging, and measure the OCV of the battery pack. Repeat the above steps until OCV data under a preset temperature range and a preset SOC combination is collected. Using the experimentally obtained OCV data, combined with the open circuit voltage-state of charge relationship of the target battery cell, such as the OCV-SOC curve diagram of the target battery cell, analyze the data to determine the open circuit voltage-state of charge-temperature relationship of the entire battery pack.

[0076] Optionally, based on the open circuit voltage-state of charge curve of the target battery cell, the OCV-SOC data of the battery pack at different temperatures are compared, the effect of temperature on OCV is analyzed, and the OCV-SOC curve of the target battery cell is corrected, including the offset and slope change of the correction curve.

[0077] Optionally, based on the open circuit voltage-state of charge correspondence of the target battery cell, a lookup table containing OCV correction values ​​at different temperatures and SOCs is created, and the corresponding correction value is queried according to the real-time temperature and SOC.

[0078] Step A30, determining the charging state of the battery pack according to the corresponding relationship between the open circuit voltage-charging state-temperature of the battery pack.

[0079] Optionally, in a feasible implementation manner, referring to Figure 6 , step A30 includes steps A31~A32: Step A31, obtaining the real-time open circuit voltage and real-time temperature of the battery pack.

[0080] Step A32, determining the charging state of the battery pack based on the real-time open circuit voltage, the real-time temperature and the corresponding relationship between the open circuit voltage-charging state-temperature of the battery pack.

[0081] Exemplarily, by monitoring the OCV and temperature of the battery pack in real time and transmitting the data to the battery pack charging status detection device, the battery pack charging status detection device uses a pre-established open circuit voltage-charging status-temperature correspondence model or lookup table, combined with real-time OCV and temperature data, to calculate the current SOC of the battery pack.

[0082] Optionally, the SOC of the battery in the storage state or in the low-load working state usually changes slowly, and the open circuit voltage and temperature of the battery pack can be obtained regularly. For example, the battery pack in the home energy storage system is mainly used to store the electricity generated by the solar panels during the day and supply power at night or when the power grid is insufficient. In this case, the SOC changes of the battery pack are usually regular, and it will go through a complete charge and discharge cycle every day. Therefore, the battery pack charging state detection device can be set to update every night after the battery is charged to reflect the performance changes of the battery during the day. This method can ensure the accuracy of the calculation and reduce the consumption of computing resources.

[0083] In this embodiment, by real-time monitoring of the OCV and temperature of the battery pack, and combining a pre-established open circuit voltage-charge state-temperature correspondence model or lookup table, the battery pack charge state detection device can adapt to instant changes in battery performance, such as the impact of temperature fluctuations on the internal resistance of the battery, thereby improving the reliability of SOC calculation.

[0084] In this embodiment, the target battery cell is selected by comparing the internal resistance of each battery cell with the average internal resistance of the battery module, and the open circuit voltage-charge state curve of the target battery cell is used to construct an open circuit voltage-charge state-temperature relationship applicable to the entire battery pack. The SOC of the battery pack can be calculated under different temperature conditions, thereby enhancing the robustness and accuracy of the battery pack SOC calculation.

[0085] Based on the above embodiments of the present application, in the third embodiment of the present application, the same or similar contents as those in the above embodiments can be referred to the above introduction, and will not be described in detail later. Figure 7 , before step A11, it also includes steps A13 to A15: Step A13, obtaining battery operation data within a preset time window.

[0086] Step A14, based on the battery operation data, determining the estimated internal resistance of the battery cell in the battery module by recursive least squares method.

[0087] Step A15, calculating the average value of the estimated internal resistance of each battery cell in the battery module at each moment to obtain the average internal resistance of the battery module.

[0088] During the battery operation process, a specific time period, i.e., a time window, is selected to collect data. Within this time window, data related to battery performance is collected, including but not limited to the voltage and current of the battery cells. The collected battery operation data is processed using the recursive least squares method to dynamically estimate the internal resistance of each battery cell. Each time new data is received, the estimated value of the internal resistance is updated based on the collected data. After obtaining the estimated internal resistance of all battery cells, the average value of these internal resistances is calculated, which represents the overall internal resistance level of the battery module at the current moment.

[0089] Specifically, an initial internal resistance estimate and covariance matrix are set first. The covariance matrix is ​​used to represent the uncertainty of the internal resistance estimate. In each preset time window, the voltage and current data of the battery cell are collected, and the actually measured internal resistance is calculated using the collected voltage and current data. Then, the prediction error between the internal resistance estimate and the actually measured internal resistance is calculated. Based on the prediction error and the covariance matrix, a gain value is calculated. The gain value determines the degree of influence of the new data on the update of the internal resistance estimate. In other words, if the gain value is large, it means that the current prediction error is large or the uncertainty of the internal resistance estimate is high, so the newly introduced data will have a greater impact on the internal resistance estimate. At the same time, as the internal resistance estimate is updated, the covariance matrix can also be updated by a preset recursive formula to reflect the impact of the new data on the uncertainty of the internal resistance estimate. The above process is recursively performed within the time window. As the data is continuously collected and processed, the estimated value of the internal resistance will become closer and closer to the actual value.

[0090] In this embodiment, when the battery performance changes over time, the internal resistance of the battery cell can be estimated more accurately through the recursive least squares method, thereby helping to find a battery cell that is more representative of the battery module and calculate the SOC of the battery pack more accurately.

[0091] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the method for determining the state of charge of the battery pack of the present application. Simple transformations in more forms based on this technical concept are all within the scope of protection of the present application. In combination with all the above embodiments, the method for determining the state of charge of the battery pack proposed in the present application has significant advantages over the existing model-based methods in dealing with inconsistencies between battery cells, coping with temperature changes, reducing computational complexity, and improving generalization capabilities. Specifically, the present application quantifies the inconsistency of battery cells by using cosine similarity, and selects representative target battery cells based on this, thereby constructing an open circuit voltage-state of charge-temperature relationship applicable to the entire battery pack. Combined with the multi-branch fusion SOC estimation method of Bayesian probability estimation, the present invention not only improves the accuracy of the battery pack SOC calculation, but also enhances the applicability and stability of the battery pack SOC calculation.

[0092] The present application provides a device for determining the charging status of a battery pack, the device comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for determining the charging status of the battery pack in the first embodiment.

[0093] Reference below Figure 8 , which shows a schematic diagram of the structure of a battery pack charging state determination device suitable for implementing the embodiment of the present application. The battery pack charging state determination device in the embodiment of the present application may include but is not limited to mobile terminals such as laptop computers, PADs (Portable Application Description: tablet computers), PMPs (Portable Media Player: portable multimedia players), etc., and fixed terminals such as desktop computers, etc. Figure 8 The battery pack charging state determination device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0094] like Figure 8As shown, the battery pack charge state determination device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the battery pack charge state determination device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the battery pack charge state determination device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a battery pack charge state determination device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0095] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0096] The battery pack charging state determination device provided by the present application adopts the battery pack charging state determination method in the above embodiment, which can solve the technical problem of the complicated charging state calculation process of the current battery pack. Compared with the prior art, the beneficial effects of the battery pack charging state determination device provided by the present application are the same as the beneficial effects of the battery pack charging state determination method provided by the above embodiment, and the other technical features in the battery pack charging state determination device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0097] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0098] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0099] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the method for determining the charging state of a battery pack in the above-mentioned embodiment.

[0100] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0101] The computer-readable storage medium may be included in the device for determining the state of charge of the battery pack; or may exist independently without being assembled into the device for determining the state of charge of the battery pack.

[0102] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the battery pack charge state determination device, the battery pack charge state determination device can write a computer program code for performing the operation of the present application in one or more programming languages ​​or a combination thereof. The programming language includes an object-oriented programming language, such as Java, Smalltalk, C++, and a conventional procedural programming language, such as "C" language or a similar programming language. The program code can be executed entirely on the user's computer, partially on the user's computer, or as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0103] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0104] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0105] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method for determining the charging state of the battery pack, and can solve the technical problem of the complex charging state calculation process of the current battery pack. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the method for determining the charging state of the battery pack provided in the above-mentioned embodiment, and will not be repeated here.

[0106] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned method for determining the charging state of a battery pack when executed by a processor.

[0107] The computer program product provided by the present application can solve the technical problem of the complicated charging state calculation process of the current battery pack. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the charging state determination method of the battery pack provided by the above embodiment, which will not be repeated here.

[0108] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for determining a charging state of a battery pack, characterized in that: The battery pack includes at least two battery modules, and the method includes: Determine a residual of the battery module based on an estimated state of charge of the battery module at the current moment and an average state of charge of the battery modules in the battery pack, wherein the residual is used to represent a difference between the estimated state of charge of the battery module and the average state of charge of the battery module; Determining a weight of the battery module based on the residual of the battery module; The state of charge of the battery group is determined based on the weight of each of the battery modules and the estimated state of charge of each of the battery modules.

2. The method for determining the charging state of a battery pack according to claim 1, wherein: The step of determining the weight of the battery module based on the residual of the battery module comprises: Determine the mean and variance of the residual based on the residual of the battery module within a preset time window before the current moment; Determine the conditional density probability function value of the battery module at the current moment based on the mean value and variance of the residual; Based on the conditional density probability function value, a weight of the battery module is determined.

3. The method for determining the charging state of a battery pack according to claim 1, wherein: The battery module includes at least two battery cells, and the method further includes: Determine a target battery cell in the battery module based on the estimated internal resistance of the battery cell at each moment and the average internal resistance of the battery module, wherein the average internal resistance is the average value of the internal resistances of all battery cells in the battery module; Determining an open circuit voltage-charge state-temperature correspondence relationship of a battery pack based on the open circuit voltage-charge state correspondence relationship of the target battery cell; The charging state of the battery pack is determined according to the corresponding relationship between the open circuit voltage, the charging state and the temperature of the battery pack.

4. The method for determining the charging state of a battery pack according to claim 3, wherein: The step of determining the target battery cell in the battery module based on the estimated internal resistance of the battery cell at each moment and the average internal resistance of the battery module comprises: Determining a cosine similarity between an estimated internal resistance of each battery cell in a battery module and an average internal resistance of the battery module; The battery cell with the largest cosine similarity is determined as the target battery cell.

5. The method for determining the charging state of a battery pack as claimed in claim 3, wherein: The step of determining the open circuit voltage-charge state-temperature correspondence relationship of the battery pack according to the open circuit voltage-charge state correspondence relationship of the target battery cell comprises: Under different temperature conditions, the battery pack is subjected to charge and discharge tests to obtain open circuit voltage data of the battery pack under different charge states and different temperatures; Based on the open circuit voltage data and the open circuit voltage-charge state correspondence relationship of the target battery cell, the open circuit voltage-charge state-temperature correspondence relationship of the battery pack is determined.

6. The method for determining the charging state of a battery pack as claimed in claim 3, wherein: The step of determining the charging state of the battery pack according to the open circuit voltage-charging state-temperature correspondence relationship of the battery pack comprises: Get the real-time open circuit voltage and temperature of the battery pack; The charging state of the battery pack is determined based on the real-time open circuit voltage, the real-time temperature, and a corresponding relationship between the open circuit voltage, the charging state, and the temperature of the battery pack.

7. The method for determining the charging state of a battery pack as claimed in claim 3, wherein: Before the step of determining the cosine similarity between the estimated internal resistance of each battery cell in the battery module and the average internal resistance of the battery module, the method further includes: Obtain battery operation data within a preset time window; Based on the battery operation data, determining the estimated internal resistance of the battery cell in the battery module by a recursive least squares method; The average value of the estimated internal resistance of each battery cell in the battery module is calculated at each moment to obtain the average internal resistance of the battery module.

8. A device for determining a state of charge of a battery pack, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for determining the state of charge of a battery pack according to any one of claims 1 to 7.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method for determining the charging state of a battery pack according to any one of claims 1 to 7 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method for determining the state of charge of a battery pack according to any one of claims 1 to 7 are implemented.

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