Battery pack health state online detection method and device based on multi-feature fusion

Abstract

The invention belongs to the technical field of battery pack health state online detection, and provides a battery pack health state online detection method and device based on multi-feature fusion. The method comprises the following steps: calculating an IC curve of lithium battery aging and a delta Q (V) curve of lithium battery aging through an aging charge-discharge cycle experiment, extracting corresponding characteristic values, regressing the relationship between the characteristic values of the IC curve of lithium battery aging and the delta Q (V) curve and the discharge capacity, and generating discharge capacity equal division points and corresponding characteristics thereof; acquiring a charge-discharge capacity-voltage curve of the lithium battery pack during working on line to obtain the discharge capacity of the current lithium battery pack; clustering the discharge capacities calculated according to different characteristics; and according to a correlation coefficient corresponding to a discharge capacity calculation value in the optimal cluster, calculating a weight by using semi-Gaussian distribution, and taking a weighted average value of the discharge capacity calculation value as a final calculation value of the discharge capacity of the lithium battery pack so as to calculate the health state of the current lithium battery pack.

Description

Technical Field

[0001] The present invention belongs to the technical field of online detection of battery pack health status, and in particular relates to a method and device for online detection of battery pack health status based on multi-feature fusion. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] In order to meet energy and power requirements, a large number of lithium batteries are connected in series and parallel to form a battery pack. Lithium-ion batteries will gradually age during use, resulting in a decrease in battery pack capacity. Accurately estimating the health of the battery pack is the key to optimizing battery operation and controlling the battery status in the battery management system.

[0004] The series-parallel connection mode of the battery pack and the parameter inconsistency between lithium batteries jointly affect the capacity aging trend of the battery pack during its entire life cycle. Battery packs with different series-parallel connection modes and inconsistencies between batteries have different aging characteristics. Although battery pack aging experiments help to gain a deeper understanding of the inconsistency between batteries and the impact of the series-parallel connection mode on battery pack aging, and are one of the important ways to achieve battery pack capacity estimation, battery pack aging experiments are costly and time-consuming, and cannot be widely used in practice. At present, most methods for calculating the health status remain at the battery level. Moreover, the existing battery health status calculation methods do not take into account the series-parallel connection mode of the battery pack and the inconsistency between batteries, and cannot be directly applied to the battery pack health status calculation. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method and device for online detection of the health status of a battery pack based on multi-feature fusion, which uses the cycle aging characteristics of a small number of batteries to calculate the discharge capacity of the battery pack under any series-parallel mode, and considers the inconsistency between batteries to detect the health status of the battery pack online, effectively reducing the cost of battery pack capacity calculation and providing support for improving battery pack management and safety.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A first aspect of the present invention provides a method for online detection of battery pack health status based on multi-feature fusion.

[0008] In one or more embodiments, a method for online detection of battery pack health status based on multi-feature fusion is provided, including:

[0009] The charge and discharge capacity-voltage curves of several lithium batteries are tested through an aging charge and discharge cycle experiment, and the IC curve of lithium battery aging is calculated; the capacity of the charge and discharge capacity-voltage curve of the lithium battery is subtracted from the capacity of the charge and discharge capacity-voltage curve of the first cycle to obtain the ΔQ(V) curve of lithium battery aging;

[0010] Extract the characteristic values ​​of the IC curve and ΔQ(V) curve of lithium battery aging, use the least squares support vector regression to reconstruct the relationship between the characteristic values ​​of the IC curve and ΔQ(V) curve of lithium battery aging and the discharge capacity, and generate the discharge capacity equal points and their corresponding features;

[0011] The charge and discharge capacity-voltage curve of the lithium battery pack during operation is obtained online, the corresponding IC curve and ΔQ(V) curve are calculated, the characteristic values ​​of the current IC curve and ΔQ(V) curve are extracted, and the discharge capacity of the current lithium battery pack is obtained by performing linear interpolation calculation based on the discharge capacity equalization points of the lithium battery aging and their corresponding characteristics;

[0012] The discharge capacities calculated by different features are clustered and the optimal cluster is selected; the weights are calculated using semi-Gaussian distribution according to the correlation coefficients corresponding to the discharge capacity calculation values ​​in the optimal cluster, and the weighted average of the discharge capacity calculation values ​​is used as the final calculated value of the discharge capacity of the lithium battery pack;

[0013] Calculate the current health status of the lithium battery pack based on the final calculated value of the discharge capacity and the rated capacity of the lithium battery pack.

[0014] As an implementation method, an aging cycle test is performed on several new lithium batteries until the battery capacity is less than 70% of the rated capacity; the charging condition is similar to the actual condition of the calculated lithium battery pack; the discharging condition is set according to the standard lithium battery capacity test condition; and the charging capacity, voltage and discharge capacity of the lithium battery are recorded in each cycle.

[0015] As an implementation method, the charge and discharge capacity-voltage curves of several lithium batteries tested through an aging charge and discharge cycle experiment are smoothed, and the voltage derivative of the smoothed lithium battery charge and discharge capacity-voltage curves is calculated to obtain an IC curve of lithium battery aging.

[0016] As an implementation method, the correlation coefficient corresponding to the calculated discharge capacity value is:

[0017]

[0018] Among them, F CellCycle_i is the characteristic of the IC or ΔQ(V) curve of the lithium battery in the i-th cycle; C disCycle_i is the discharge capacity of the lithium battery in the ith cycle; μ F_CellCycle , σ F_CellCycl are all cycles FCellCycle_i The mean and standard deviation of Cell_disC , σ Cell_disC are all loops C disCycle_i The mean and standard deviation of .

[0019] As an implementation method, for F Pack_Cell Greater than or equal to F CellCycle_i The minimum value of F CellCycle_i The maximum value, or F Pack_Cell Less than F CellCycle_i The minimum value and greater than F CellCycle_i The maximum value of F CellCycle_i The maximum value is at F CellCycle_i At the beginning and end of the data, the linear interpolation formula is:

[0020]

[0021] where F Pack_Cell Convert a battery pack feature into a battery level feature, C Pack_Cell Because F Pack_Cell The discharge capacity of the lithium battery pack at the corresponding battery level calculated by linear interpolation, F CellCycle_1 、F CellCycle_2 The closest to F Pack_Cell Two lithium battery characteristics, C CellCycle_1 , C CellCycle_2 F CellCycle_1 、F CellCycle_2 Corresponding lithium battery discharge capacity; F CellCycle_i It is the characteristic of the IC or ΔQ(V) curve of the lithium battery in the i-th cycle.

[0022] As an implementation method, if F CellCycle_i The maximum value of is in the middle of its data sequence, then use the following formula to calculate C Pack_Cell :

[0023]

[0024] where F Pack_Cell Convert a battery pack feature into a battery level feature, C Pack_Cell Because F Pack_Cell The discharge capacity of the lithium battery pack at the corresponding battery level calculated by linear interpolation, F CellCycle_1 、F CellCycle_2 The closest to F Pack_Cell Two lithium battery characteristics, C CellCycle_1 , C CellCycle_2 F CellCycle_1 、F CellCycle_2 Corresponding lithium battery discharge capacity; F CellCycle_iis the characteristic of the IC or ΔQ(V) curve of the lithium battery in the i-th cycle; TempC Pack_Cell_1 and TempC Pack_Cell_2 All are intermediate parameters.

[0025] As an implementation method, the weight calculation formula is:

[0026] W i =N(|ρ Cell_F_C ||1,σ G )

[0027] Where N() represents the Gaussian probability density function. Cell_F_C | is the absolute value of the correlation coefficient of the feature corresponding to the calculation result in the optimal clustering. The mean of the Gaussian probability density function is 1, σ G is the standard deviation of the Gaussian probability density function.

[0028] A second aspect of the present invention provides an online detection device for the health status of a battery pack based on multi-feature fusion.

[0029] In one or more embodiments, a battery pack health status online detection device based on multi-feature fusion includes:

[0030] An aging test module is used to test the charge and discharge capacity-voltage curves of several lithium batteries through an aging charge and discharge cycle experiment, and calculate the IC curve of lithium battery aging; the capacity of the charge and discharge capacity-voltage curve of the lithium battery is subtracted from the capacity of the charge and discharge capacity-voltage curve of the first cycle to obtain the ΔQ(V) curve of lithium battery aging;

[0031] Aging test feature extraction module, which is used to extract the characteristic values ​​of the IC curve and ΔQ(V) curve of lithium battery aging, use the least squares support vector regression to reconstruct the relationship between the characteristic values ​​of the IC curve and ΔQ(V) curve of lithium battery aging and the discharge capacity, and generate the discharge capacity equal points and their corresponding features;

[0032] A linear interpolation calculation module is used to obtain the charge and discharge capacity-voltage curve of the lithium battery pack when it is working online, calculate the corresponding IC curve and ΔQ(V) curve, extract the characteristic values ​​of the current IC curve and ΔQ(V) curve, and perform linear interpolation calculation according to the discharge capacity equalization points of the lithium battery aging and their corresponding characteristics to obtain the current discharge capacity of the lithium battery pack;

[0033] A discharge capacity calculation module is used to cluster the discharge capacities calculated by different features and select the optimal cluster; according to the correlation coefficient corresponding to the discharge capacity calculation value in the optimal cluster, the weight is calculated using the semi-Gaussian distribution, and the weighted average of the discharge capacity calculation value is used as the final calculated value of the discharge capacity of the lithium battery pack;

[0034] The health status calculation module is used to calculate the current health status of the lithium battery pack according to the final calculated value of the discharge capacity and the rated capacity of the lithium battery pack.

[0035] A third aspect of the present invention provides a computer-readable storage medium.

[0036] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the above-mentioned method for online detection of battery pack health status based on multi-feature fusion.

[0037] A fourth aspect of the present invention provides an electronic device.

[0038] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the above-mentioned method for online detection of battery pack health status based on multi-feature fusion are implemented.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] (1) The present invention uses the aging data of a small number of tested lithium batteries to calculate the health status of a lithium battery pack composed of a large number of lithium batteries. It performs multi-feature fusion through various algorithms such as least squares support vector regression and density-based noisy spatial clustering. In any series-parallel mode, the health status of the battery pack can be detected online without training, and the inconsistency between batteries is taken into account.

[0041] (2) The present invention does not require the historical operating data of the battery pack, but only the current primary charging curve data of the battery pack to calculate the current health status of the battery pack, thereby speeding up the online detection speed and avoiding the impact of long-term static aging of the battery pack.

[0042] (3) The present invention calculates the health status of lithium battery packs with high accuracy, wide universality and strong stability. There is no need to conduct aging tests on lithium battery packs, but only a small number of lithium batteries need to be tested. This can greatly reduce the cost of calculating the health status of lithium battery packs, and provide strong support for optimizing battery operation and controlling battery status in battery management systems in electric vehicles and large-scale energy storage systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0044] Figure 1 It is a flowchart of a method for online detection of battery pack health status based on multi-feature fusion according to an embodiment of the present invention;

[0045] Figure 2 It is a structural schematic diagram of a battery pack health status online detection device based on multi-feature fusion according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0047] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0048] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0049] Figure 1 is a flow chart of a method for online detection of battery pack health status based on multi-feature fusion in an embodiment of the present invention, such as Figure 1 The online detection method of the battery pack health status based on multi-feature fusion in the present embodiment shown may include:

[0050] S101, testing the charge and discharge capacity-voltage curves of several lithium batteries through an aging charge and discharge cycle experiment, and calculating the IC curve of lithium battery aging; using the capacity of the charge and discharge capacity-voltage curve of the lithium battery minus the capacity of the charge and discharge capacity-voltage curve of the first cycle, to obtain the ΔQ(V) curve of lithium battery aging;

[0051] S102, extracting characteristic values ​​of the IC curve and ΔQ(V) curve of the lithium battery aging, using least squares support vector regression to reconstruct the relationship between the characteristic values ​​of the IC curve and ΔQ(V) curve of the lithium battery aging and the discharge capacity, and generating discharge capacity equal points and their corresponding characteristics;

[0052] S103, online acquisition of the charge and discharge capacity-voltage curve of the lithium battery pack during operation, calculation of the corresponding IC curve and ΔQ(V) curve, extraction of characteristic values ​​of the current IC curve and ΔQ(V) curve, and linear interpolation calculation based on the discharge capacity equalization points of the lithium battery aging and their corresponding characteristics to obtain the current discharge capacity of the lithium battery pack;

[0053] S104, clustering the discharge capacities calculated by different features and selecting the best cluster; calculating weights using semi-Gaussian distribution according to the correlation coefficients corresponding to the discharge capacity calculation values ​​in the best cluster, and taking the weighted average of the discharge capacity calculation values ​​as the final calculated value of the discharge capacity of the lithium battery pack;

[0054] S105, calculating the current health status of the lithium battery pack according to the final calculated value of the discharge capacity and the rated capacity of the lithium battery pack.

[0055] In step S101, an aging cycle test is performed on a number of new lithium batteries until the battery capacity is less than 70% of the rated capacity; the charging condition is similar to the actual condition of the calculated lithium battery pack; the discharging condition is set according to the standard lithium battery capacity test condition; and the charging capacity, voltage and discharge capacity of the lithium battery in each cycle are recorded.

[0056] The charge and discharge capacity-voltage curves of several lithium batteries tested by aging charge and discharge cycle experiments are smoothed, and the voltage derivative of the smoothed charge and discharge capacity-voltage curves of the lithium batteries is calculated to obtain the IC curve of lithium battery aging.

[0057] In step S102, before extracting the characteristic values ​​of the IC curve and the ΔQ(V) curve of the lithium battery aging, the IC curve and the ΔQ(V) curve of the lithium battery aging are smoothed to remove noise.

[0058] The extracted characteristic values ​​of the IC curve and ΔQ(V) curve of the lithium battery aging include but are not limited to the mean, standard deviation, peak value, voltage at the peak value, maximum value, minimum value, etc.

[0059] Among them, the correlation coefficient between the characteristics of the IC curve and the ΔQ(V) curve and the discharge capacity is:

[0060]

[0061] Among them, F CellCycle_i is the characteristic of the IC or ΔQ(V) curve of the lithium battery in the i-th cycle; C disCycle_i is the discharge capacity of the lithium battery in the ith cycle; μ F_CellCycle , σ F_CellCycl are all cycles F CellCycle_i The mean and standard deviation of Cell_disC , σ Cell_disC are all loops C disCycle_i The mean and standard deviation of .

[0062] Least squares support vector regression is used to clarify the relationship between the lithium battery discharge capacity and the corresponding IC and ΔQ(V) curve characteristics with the lithium battery discharge capacity as the horizontal axis. The purpose is to ignore the points with large deviations and perform regression fitting on the main concentrated points. Then, the discharge capacity equal points and corresponding features are generated based on the least squares support vector regression results.

[0063] In step S103, a charge capacity-voltage curve of the lithium battery pack is obtained online, and the capacity-voltage curve is smoothed to remove noise; the voltage derivative of the smoothed charge and discharge capacity-voltage curve of the lithium battery pack is calculated to obtain an IC curve of the lithium battery pack; the capacity of the capacity-voltage curve of the first cycle is subtracted from the capacity of the capacity-voltage curve to obtain a ΔQ(V) curve of the lithium battery pack;

[0064] Smoothing the IC curve and ΔQ(V) curve of the lithium battery pack to remove noise; extracting multiple features of the IC and ΔQ(V) curves of the lithium battery pack, including but not limited to mean, variance, peak value, voltage at the peak value, maximum value, minimum value, etc.;

[0065] The discharge capacity of the corresponding lithium battery pack is obtained by linear interpolation calculation using multiple characteristics of the battery pack IC and ΔQ(V) curves combined with the corresponding characteristics of the tested lithium battery. Before the linear interpolation calculation, the IC and ΔQ(V) curve characteristics of the parallel battery pack need to be converted to the lithium battery level. The conversion formula is:

[0066] IC cell_peakVol =IC p_peakVol ;

[0067]

[0068] Where m is the number of parallel batteries in the parallel battery pack, IC p_mean 、IC cell_mean is the mean value of the IC curve of the parallel battery pack and the mean value of the IC curve at the corresponding battery level, IC p_peak 、IC cell_peak The peak value of the IC curve of the parallel battery pack and the peak value of the IC curve at the corresponding battery level, IC p_peakVol 、IC cell_peakVol is the voltage at the peak of the IC curve of the parallel battery pack and the voltage at the peak of the IC curve at the corresponding battery level, IC p_var 、IC cell_var is the variance of the IC curve of the parallel battery pack and the variance of the IC curve at the corresponding battery level, IC p_max 、IC cell_max The maximum value of the IC curve of the parallel battery pack and the maximum value of the IC curve at the corresponding battery level, ICp_min 、IC cell_min The minimum value of the IC curve of the parallel battery pack and the minimum value of the IC curve at the corresponding battery level, IC p_var 、IC cell_var is the variance of the IC curve of the parallel battery pack and the variance of the IC curve at the corresponding battery level, ΔQ p_mean (V), ΔQ cell_mean (V) is the average value of the ΔQ(V) curve of the parallel battery group and the average value of the ΔQ(V) curve at the corresponding battery level, ΔQ p (V1), ΔQ cell (V1) is the value of the ΔQ(V) curve of the parallel battery pack at V1 and the value of the ΔQ(V) curve at V1 on the corresponding battery level, V1 is an arbitrarily selected battery terminal voltage value, ΔQ p_var (V), ΔQ cell_var (V) is the variance of the ΔQ(V) curve of the parallel battery pack and the variance of the ΔQ(V) curve at the corresponding battery level, ΔQ p_max (V), ΔQ cell_max (V) is the maximum value of the ΔQ(V) curve of the parallel battery group and the maximum value of the ΔQ(V) curve at the corresponding battery level, ΔQ p_min (V), ΔQ cell_min (V) is the minimum value of the ΔQ(V) curve of the parallel battery pack and the minimum value of the ΔQ(V) curve at the corresponding battery level;

[0069] For series-connected battery packs, the battery management system can directly collect cell-level data and directly calculate the characteristics of the cell-level IC and ΔQ(V) curves in the series-connected battery pack without the need for the above conversion.

[0070] Linear interpolation calculations are performed using multiple features of the battery pack IC and ΔQ(V) curves combined with corresponding features of the tested lithium battery.

[0071] For F Pack_Cell Greater than or equal to F CellCycle_i The minimum value of F CellCycle_i The maximum value, or F Pack_Cell Less than F CellCycle_i The minimum value and greater than F CellCycle_i The maximum value of F CellCycle_i The maximum value is at F CellCycle_i At the beginning and end of the data, the linear interpolation formula is:

[0072]

[0073] where F Pack_Cell Convert a battery pack feature into a battery level feature, C Pack_Cell Because FPack_Cell The discharge capacity of the lithium battery pack at the corresponding battery level calculated by linear interpolation, F CellCycle_1 、F CellCycle_2 The closest to F Pack_Cell Two lithium battery characteristics, C CellCycle_1 , C CellCycle_2 F CellCycle_1 、F CellCycle_2 Corresponding lithium battery discharge capacity; F CellCycle_i It is the characteristic of the IC or ΔQ(V) curve of the lithium battery in the i-th cycle.

[0074] If F CellCycle_i The maximum value of is in the middle of its data sequence, then use the following formula to calculate C Pack_Cell :

[0075]

[0076] where F Pack_Cell Convert a battery pack feature into a battery level feature, C Pack_Cell Because F Pack_Cell The discharge capacity of the lithium battery pack at the corresponding battery level calculated by linear interpolation, F CellCycle_1 、F CellCycle_2 The closest to F Pack_Cell Two lithium battery characteristics, C CellCycle_1 , C CellCycle_2 F CellCycle_1 、F CellCycle_2 Corresponding lithium battery discharge capacity; F CellCycle_i is the characteristic of the IC or ΔQ(V) curve of the lithium battery in the i-th cycle; TempC Pack_Cell_1 and TempC Pack_Cell_2 All are intermediate parameters.

[0077] In step S104, the discharge capacities calculated by different features are clustered using density-based noisy spatial clustering; the optimal cluster is selected, and the criterion for selecting the optimal cluster is the discharge capacity calculation value C contained in the cluster. Pack_Cell The corresponding feature F Pack_Cell The most variety.

[0078] The weight calculation formula is:

[0079] W i =N(|ρ Cell_F_C ||1,σ G )

[0080] Where N() represents the Gaussian probability density function. Cell_F_C | is the absolute value of the correlation coefficient of the feature corresponding to the calculation result in the optimal clustering. The mean of the Gaussian probability density function is 1, σG is the standard deviation of the Gaussian probability density function.

[0081] The standard deviation σ of the Gaussian probability density function G The following formula can be used:

[0082]

[0083] Among them, σ Cell_ρ for |ρ Cell_F_C The standard deviation of |, μ Cell_ρ for |ρ Cell_F_C |The mean value of |ρ Cell_F_C | is the absolute value of all correlation coefficients of the extracted features. Then W i Perform normalization:

[0084]

[0085] Then take the weighted average of the calculated values:

[0086]

[0087] Among them, C Pack_Cel_i Only the calculated discharge capacities included in the optimal cluster are considered.

[0088] The above is just the calculation result of the discharge capacity of the lithium battery pack at the battery level obtained by using the aging data of a tested lithium battery. The average value should be taken as the optimal calculation result when using multiple tested lithium batteries:

[0089]

[0090] Then, calculate the final calculated value of the discharge capacity of the lithium battery pack. For parallel battery packs, it is necessary to convert the calculation results of the discharge capacity of the lithium battery pack at the cell level to the battery pack level:

[0091]

[0092] For a series battery pack, the minimum discharge capacity of the batteries in the series battery pack needs to be used as the discharge capacity of the series battery pack:

[0093]

[0094] In step S105, the current health status of the lithium battery pack is calculated using the following formula:

[0095]

[0096] Among them, C rated is the rated capacity of the battery pack.

[0097] Figure 2 is a schematic diagram of the structure of a battery pack health status online detection device based on multi-feature fusion in an embodiment of the present invention. Figure 1 The online detection method of battery pack health status based on multi-feature fusion corresponds to Figure 2 As shown, the battery pack health status online detection device based on multi-feature fusion in this embodiment may include:

[0098] The aging test module 201 is used to test the charge and discharge capacity-voltage curves of several lithium batteries through an aging charge and discharge cycle experiment, and calculate the IC curve of lithium battery aging; the capacity of the charge and discharge capacity-voltage curve of the lithium battery is subtracted from the capacity of the charge and discharge capacity-voltage curve of the first cycle to obtain the ΔQ(V) curve of lithium battery aging;

[0099] Aging test feature extraction module 202, which is used to extract the characteristic values ​​of the IC curve and ΔQ(V) curve of the lithium battery aging, use the least squares support vector regression to reconstruct the relationship between the characteristic values ​​of the IC curve and ΔQ(V) curve of the lithium battery aging and the discharge capacity, and generate the discharge capacity equal points and their corresponding features;

[0100] The linear interpolation calculation module 203 is used to obtain the charge and discharge capacity-voltage curve of the lithium battery pack online, calculate the corresponding IC curve and ΔQ(V) curve, extract the characteristic values ​​of the current IC curve and ΔQ(V) curve, and perform linear interpolation calculation according to the discharge capacity equalization points of the lithium battery aging and their corresponding characteristics to obtain the discharge capacity of the current lithium battery pack;

[0101] The discharge capacity calculation module 204 is used to cluster the discharge capacities calculated by different features and select the best cluster; calculate the weights using the semi-Gaussian distribution according to the correlation coefficients corresponding to the discharge capacity calculation values ​​in the best cluster, and use the weighted average of the discharge capacity calculation values ​​as the final calculated value of the discharge capacity of the lithium battery pack;

[0102] The health status calculation module 205 is used to calculate the current health status of the lithium battery pack according to the final calculated value of the discharge capacity and the rated capacity of the lithium battery pack.

[0103] It should be noted here that Figure 2 The various modules in the online detection device of battery pack health status based on multi-feature fusion are Figure 1 The various steps in the online detection method of battery pack health status based on multi-feature fusion correspond one to one, and the specific implementation process is the same, which will not be repeated here.

[0104] In one or more embodiments, the electronic device includes a central processing unit (CPU), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage portion into a random access memory (RAM). In the RAM, various programs and data required for system operation are also stored. The central processing unit, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0105] The following components are connected to the I / O interface: an input section including a keyboard, a mouse, etc.; an output section including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section including a hard disk, etc.; and a communication section including a network interface card such as a local area network (LAN) card, a modem, etc. The communication section performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface as needed. Removable media, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., are installed on the drive as needed so that a computer program read therefrom is installed into the storage section as needed.

[0106] When the central processing unit in the electronic device of this embodiment executes the program, the following is achieved: Figure 1 The steps in the online detection method of battery pack health status based on multi-feature fusion are shown.

[0107] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer readable medium, the computer program including a computer program for executing Figure 1 In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit, various functions defined in the device of the present application are executed.

[0108] in, Figure 1 The computer program instructions corresponding to the method shown may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0109] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for online detection of battery health status based on multi-feature fusion, characterized in that: include: The charge and discharge capacity-voltage curves of several lithium batteries are tested through aging charge and discharge cycle experiments, and the IC curve of lithium battery aging is calculated; The capacity of the charge-discharge capacity-voltage curve of the lithium battery is subtracted from the capacity of the charge-discharge capacity-voltage curve of the first cycle to obtain the ΔQ(V) curve of lithium battery aging; Extract the characteristic values ​​of the IC curve and ΔQ(V) curve of lithium battery aging, use the least squares support vector regression to reconstruct the relationship between the characteristic values ​​of the IC curve and ΔQ(V) curve of lithium battery aging and the discharge capacity, and generate the discharge capacity equal points and their corresponding features; The charge and discharge capacity-voltage curve of the lithium battery pack during operation is obtained online, the corresponding IC curve and ΔQ(V) curve are calculated, the characteristic values ​​of the current IC curve and ΔQ(V) curve are extracted, and the discharge capacity of the current lithium battery pack is obtained by performing linear interpolation calculation based on the discharge capacity equalization points of the lithium battery aging and their corresponding characteristics; The discharge capacities calculated by different features are clustered and the optimal cluster is selected; the weights are calculated using semi-Gaussian distribution according to the correlation coefficients corresponding to the discharge capacity calculation values ​​in the optimal cluster, and the weighted average of the discharge capacity calculation values ​​is used as the final calculated value of the discharge capacity of the lithium battery pack; Calculate the current health status of the lithium battery pack based on the final calculated value of the discharge capacity and the rated capacity of the lithium battery pack.

2. The method for online detection of battery health status based on multi-feature fusion according to claim 1, characterized in that: Perform aging cycle tests on several new lithium batteries until the battery capacity is less than 70% of the rated capacity; the charging conditions are similar to the actual conditions of the calculated lithium battery pack; the discharging conditions are set according to the standard lithium battery capacity test conditions; Record the charge capacity, voltage and discharge capacity of each cycle of the lithium battery.

3. The method for online detection of battery health status based on multi-feature fusion according to claim 1, characterized in that: The charge and discharge capacity-voltage curves of several lithium batteries tested by aging charge and discharge cycle experiments are smoothed, and the voltage derivative of the smoothed charge and discharge capacity-voltage curves of the lithium batteries is calculated to obtain the IC curve of lithium battery aging.

4. The method for online detection of battery health status based on multi-feature fusion according to claim 1, characterized in that: Correlation coefficient between the characteristics of IC curve and ΔQ(V) curve and discharge capacity: Among them, F CellCycle_i is the characteristic of the IC or ΔQ(V) curve of the lithium battery in the i-th cycle; C disCycle_i is the discharge capacity of the lithium battery in the ith cycle; μ F_CellCycle , σ F_CellCycl are all cycles F CellCycle_i The mean and standard deviation of Cell_disC , σ Cell_disC All loops C disCycle_i The mean and standard deviation of .

5. The method for online detection of battery health status based on multi-feature fusion according to claim 1, characterized in that: For F Pack_Cell Greater than or equal to F CellCycle_i The minimum value of F CellCycle_i The maximum value, or F Pack_Cell Less than F CellCycle_i The minimum value and greater than F CellCycle_i The maximum value of F CellCycle_i The maximum value is at F CellCycle_i At the beginning and end of the data, the linear interpolation formula is: where F Pack_Cell To convert a battery pack feature into a battery level feature, C Pack_Cell Because F Pack_Cell The discharge capacity of the lithium battery pack at the corresponding battery level calculated by linear interpolation, F CellCycle_1 、F CellCycle_2 The closest to F Pack_Cell Two lithium battery characteristics, C CellCycle_1 , C CellCycle_2 F CellCycle_1 、F CellCycle_2 Corresponding lithium battery discharge capacity; F CellCycle_i It is the characteristic of the IC or ΔQ(V) curve of the lithium battery in the i-th cycle.

6. The method for online detection of battery health status based on multi-feature fusion according to claim 1, characterized in that: If F CellCycle_i The maximum value of is in the middle of its data sequence, then use the following formula to calculate C Pack_Cell : Among them, F Pack_Cell Convert a battery pack feature into a battery level feature, C Pack_Cell Because F Pack_Cell The discharge capacity of the lithium battery pack at the corresponding battery level calculated by linear interpolation, F CellCycle_1 、F CellCycle_2 The closest to F Pack_Cell Two lithium battery characteristics, C CellCycle_1 , C CellCycle_2 F CellCycle_1 、F CellCycle_2 Corresponding lithium battery discharge capacity; F CellCycle_i is the characteristic of the IC or ΔQ(V) curve of the lithium battery at the i-th cycle; TempC Pack_Cell_1 and TempC Pack_Cell_2 All are intermediate parameters.

7. The method for online detection of battery health status based on multi-feature fusion according to claim 1, characterized in that: The weight calculation formula is: W i =N(|ρ Cell_F_C ||1,s G ) Where N() represents the Gaussian probability density function. Cell_F_C | is the absolute value of the correlation coefficient of the feature corresponding to the calculation result in the optimal clustering. The mean of the Gaussian probability density function is 1, σ G is the standard deviation of the Gaussian probability density function.

8. An online detection device for battery pack health status based on multi-feature fusion, characterized in that: include: An aging test module is used to test the charge and discharge capacity-voltage curves of several lithium batteries through an aging charge and discharge cycle experiment, and calculate the IC curve of lithium battery aging; the capacity of the charge and discharge capacity-voltage curve of the lithium battery is subtracted from the capacity of the charge and discharge capacity-voltage curve of the first cycle to obtain the ΔQ(V) curve of lithium battery aging; Aging test feature extraction module, which is used to extract the characteristic values ​​of the IC curve and ΔQ(V) curve of lithium battery aging, use the least squares support vector regression to reconstruct the relationship between the characteristic values ​​of the IC curve and ΔQ(V) curve of lithium battery aging and the discharge capacity, and generate the discharge capacity equal points and their corresponding features; A linear interpolation calculation module is used to obtain the charge and discharge capacity-voltage curve of the lithium battery pack when it is working online, calculate the corresponding IC curve and ΔQ(V) curve, extract the characteristic values ​​of the current IC curve and ΔQ(V) curve, and perform linear interpolation calculation according to the discharge capacity equalization points of the lithium battery aging and their corresponding characteristics to obtain the current discharge capacity of the lithium battery pack; A discharge capacity calculation module is used to cluster the discharge capacities calculated by different features and select the optimal cluster; according to the correlation coefficient corresponding to the discharge capacity calculation value in the optimal cluster, the weight is calculated using the semi-Gaussian distribution, and the weighted average of the discharge capacity calculation value is used as the final calculated value of the discharge capacity of the lithium battery pack; The health status calculation module is used to calculate the current health status of the lithium battery pack according to the final calculated value of the discharge capacity and the rated capacity of the lithium battery pack.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the online detection method of battery pack health status based on multi-feature fusion as described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the method for online detection of battery pack health status based on multi-feature fusion as described in any one of claims 1 to 7 are implemented.

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