Automatic acquisition multi-channel battery detection integrated test system

Abstract

The invention relates to an automatic acquisition multi-channel battery detection comprehensive test system, and belongs to the technical field of battery comprehensive test. The system comprises a data acquisition module, a capacitance analysis module, a feature selection module, a model estimation module and a charge correction module. The data acquisition module is used for the battery pack to obtain full-life-cycle dynamic capacitance data of the battery pack through the battery detection model; the capacitance analysis module analyzes through a dynamic capacitance method according to the full-life-cycle dynamic capacitance data of the battery pack to obtain estimation model characteristics; the feature selection module obtains closely related features of the aging of the battery through a Pearson correlation analysis method according to the features of the estimation model; the model estimation module analyzes an SOH estimation model through principal component regression according to the closely related characteristics of battery aging to obtain a battery pack health state; and the charge correction module obtains the charge state of the battery pack through a charge correction model according to the health state of the battery pack. Accurate estimation of the health state of the battery pack is realized, and the detection efficiency of the battery management system is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of comprehensive battery testing, and in particular relates to an automatic acquisition multi-channel battery detection comprehensive testing system. Background Art

[0002] In order to reduce energy carbon emissions, changing the energy development mode has become an inevitable measure, which has promoted the development of new energy storage technologies. Lithium batteries occupy an important position in energy storage technology due to their long cycle life, high energy storage density and low self-discharge rate. The battery management system ensures the stable operation of lithium batteries through real-time monitoring of lithium batteries, and the battery pack charge state is particularly important as an important parameter detected by the battery management system.

[0003] At present, there are relatively few methods for estimating the state of charge of battery packs, and many methods are only for individual batteries and battery groups. The traditional method strictly screens the battery cells before they are packaged, and packages the battery cells with similar performance for research, but this method cannot guarantee the long-term consistency of the battery pack, resulting in the difficulty in achieving long-term accuracy of the estimation results. Using characteristic cells to characterize the characteristics of the battery pack can characterize the inconsistency between battery cells to a certain extent, but the screening process has limitations. Therefore, the above methods do not take into account the health status of the battery pack, and it is difficult to ensure the long-term accuracy of the estimation results. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides an automatic acquisition multi-channel battery detection comprehensive test system.

[0005] The purpose of the present invention can be achieved by the following technical scheme, including: a data acquisition module, a capacitance analysis module, a feature selection module, a model estimation module, and a charge correction module, characterized in that:

[0006] The data acquisition module is used for obtaining dynamic capacitance data of the battery pack over its entire life cycle through a battery detection model;

[0007] The capacitance analysis module is used to obtain the estimation model characteristics through dynamic capacitance method analysis based on the dynamic capacitance data of the battery pack throughout its life cycle;

[0008] The feature selection module is used to obtain features closely related to battery aging through Pearson correlation analysis method according to the estimation model features;

[0009] The model estimation module is used to obtain the health status of the battery pack through principal component regression analysis of the SOH estimation model according to the characteristics closely related to battery aging;

[0010] The charge correction module is used to obtain the battery pack charge state through a charge correction model according to the battery pack health state.

[0011] Preferably, the battery pack obtains the dynamic capacitance data of the battery pack throughout its life cycle through the battery detection model, including:

[0012] S101: The battery pack is charged in a constant current constant voltage battery charging mode to obtain a fully charged battery pack and charging data;

[0013] S102: discharging the fully charged battery pack in a constant current discharge mode to obtain an empty battery pack and a discharge capacity;

[0014] S103: obtaining a cycle battery pack through charge and discharge cycles according to the empty battery pack, wherein the cycle battery pack carries a cycle number label;

[0015] S104: Preset a cycle number threshold, and obtain the dynamic capacitance data of the battery pack over its entire life cycle by judging the cycle number according to the cycle number label and the cycle number threshold.

[0016] Preferably, the charge and discharge cycle represents charging and discharging the empty battery pack 50 times, and the charge and discharge cycle parameters are current 1C, upper cut-off voltage 16.9V, lower cut-off voltage 9.9V, and charge and discharge interval 10min.

[0017] Preferably, the cycle number judgment includes judging whether the cycle number label is equal to the cycle number threshold. If so, the dynamic capacitance data of the battery pack over its entire life cycle is obtained; if not, steps S101 to S103 are repeated until the cycle number label is equal to the cycle number threshold.

[0018] Preferably, the estimation model features obtained by analyzing the dynamic capacitance data of the battery pack over its entire life cycle through a dynamic capacitance method include:

[0019] The Origin drawing software obtains a dynamic capacitance curve of the battery pack over its entire life cycle by drawing the dynamic capacitance data of the battery pack over its entire life cycle;

[0020] The estimation model characteristic parameters are obtained by analyzing the positions and heights of the peaks and troughs on the dynamic capacitance curve of the battery pack throughout its life cycle.

[0021] Preferably, obtaining the battery aging-related features by Pearson correlation analysis method according to the estimation model feature parameters includes:

[0022] Obtaining the maximum available capacity of the battery pack, and obtaining a Pearson correlation coefficient by performing Pearson correlation calculation according to the estimation model characteristic parameters and the maximum available capacity of the battery;

[0023] The battery aging-related features are obtained by sorting the estimation model features and the Pearson correlation coefficient.

[0024] Preferably, obtaining the battery pack health status by analyzing the SOH estimation model through principal component regression according to the characteristics closely related to battery aging includes:

[0025] Obtaining a principal component closely related to battery aging by principal component analysis according to the characteristics closely related to battery aging;

[0026] Obtaining standardized features closely related to battery aging through standardized calculation according to the main components closely related to battery aging;

[0027] A multiple linear regression model is obtained by multiple linear regression according to the standardized features closely related to battery aging;

[0028] The battery pack health status is obtained by denormalizing the multivariate linear regression model.

[0029] Preferably, obtaining the battery pack charge state through a charge correction model according to the battery pack health state includes:

[0030] Acquire the initial state of charge of the battery pack, and obtain the state of charge of the battery pack by battery pack charge calculation according to the initial state of charge of the battery pack;

[0031] The battery pack state of charge is obtained by battery pack charge correction calculation according to the battery pack health state and the battery pack state of charge.

[0032] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned automatic acquisition multi-channel battery detection comprehensive test system is realized.

[0033] A storage medium containing computer executable instructions, which are used to execute the above-mentioned automatic acquisition multi-channel battery detection comprehensive test system when executed by a computer processor.

[0034] The beneficial effects of the present invention are:

[0035] (1) The Pearson correlation analysis method is used to select features that are closely related to battery aging from the estimation model features, fully considering the impact of battery aging on capacitance, and improving the accuracy of subsequent calculations of the health status of the battery pack.

[0036] (2) Principal component regression analysis is performed on the characteristics closely related to battery aging to estimate the health status of the battery pack, making it closer to the actual state of the battery. The battery pack charge state is then corrected based on the health status of the battery pack to improve the accuracy and efficiency of the battery detection and management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0038] Figure 1 The present invention is a flowchart of an automatic acquisition multi-channel battery detection comprehensive test system. DETAILED DESCRIPTION

[0039] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0040] See also Figure 1 , an automatic acquisition multi-channel battery detection comprehensive test system, including: data acquisition module, capacitance analysis module, feature selection module, model estimation module, charge correction module;

[0041] The data acquisition module is used for obtaining dynamic capacitance data of the battery pack over its entire life cycle through a battery detection model;

[0042] The capacitance analysis module is used to obtain the estimation model characteristics through dynamic capacitance method analysis based on the dynamic capacitance data of the battery pack throughout its life cycle;

[0043] The feature selection module is used to obtain features closely related to battery aging through Pearson correlation analysis method according to the estimation model features;

[0044] The model estimation module is used to obtain the health status of the battery pack through principal component regression analysis of the SOH estimation model according to the characteristics closely related to battery aging;

[0045] The charge correction module is used to obtain the battery pack charge state through a charge correction model according to the battery pack health state.

[0046] Specifically, the battery detection model includes:

[0047] S101: The battery pack is charged in a constant current constant voltage battery charging mode to obtain a fully charged battery pack and charging data;

[0048] S102: discharging the fully charged battery pack in a constant current discharge mode to obtain an empty battery pack and a discharge capacity;

[0049] S103: obtaining a cycle battery pack through charge and discharge cycles according to the empty battery pack, wherein the cycle battery pack carries a cycle number label;

[0050] S104: Preset a cycle number threshold, and obtain the dynamic capacitance data of the battery pack over its entire life cycle by judging the cycle number according to the cycle number label and the cycle number threshold.

[0051] The cycle number judgment includes judging whether the cycle number label is equal to the cycle number threshold. If yes, the dynamic capacitance data of the battery pack over its entire life cycle is obtained; if not, steps S101 to S103 are repeated until the cycle number label is equal to the cycle number threshold.

[0052] It should be noted that in step S103, the charge and discharge cycle represents charging and discharging the empty battery pack 50 times, and the charge and discharge cycle parameters are current 1C, upper cut-off voltage 16.9V, lower cut-off voltage 9.9V, and charge and discharge interval 10min.

[0053] In this embodiment, the cut-off voltage value and the cut-off current value are preset, the battery pack is placed in a constant temperature box at a temperature of 25°C, the new battery is charged and discharged for the first time, and a 0.2C low-rate constant current constant voltage battery charging mode is set, the sampling accuracy is 10mv, and the charging is switched to constant voltage charging when the cut-off voltage value is charged until the charging current is reduced to the cut-off current value, and the charging is immediately terminated and the charging data is exported. A constant current discharge mode with a 1C rate is set, and when the voltage reaches 10V, it is allowed to stand for 10 minutes, and then discharged to 10V again with a current of 0.5C and allowed to stand for 10 minutes, and finally discharged to 10V again with a rate of 0.2C and the discharge capacity is recorded. The charging data and the discharge capacity are recorded every 50 charge and discharge cycles, and the dynamic capacitance data of the battery pack over its entire life cycle is obtained after a total of 350 cycles.

[0054] Specifically, the method for obtaining the estimation model features by analyzing the dynamic capacitance data of the battery pack throughout its life cycle through the dynamic capacitance method includes:

[0055] The Origin drawing software obtains a dynamic capacitance curve of the battery pack over its entire life cycle by drawing the dynamic capacitance data of the battery pack over its entire life cycle;

[0056] The estimation model characteristic parameters are obtained by analyzing the positions and heights of the peaks and troughs on the dynamic capacitance curve of the battery pack throughout its life cycle.

[0057] Specifically, the method of obtaining the battery aging-related features by Pearson correlation analysis method according to the estimation model feature parameters includes:

[0058] Obtaining the maximum available capacity of the battery pack, and obtaining a Pearson correlation coefficient by performing Pearson correlation calculation according to the estimation model characteristic parameters and the maximum available capacity of the battery;

[0059] The Pearson correlation calculation expression is:

[0060]

[0061] Wherein, X is the characteristic parameter of the estimation model, Y is the maximum available capacity of the battery, and ρ X,Y represents the Pearson correlation coefficient, COV(X,Y) represents the covariance between the estimation model characteristic parameters and the maximum available capacity of the battery, σ X is the standard deviation of the characteristic parameters of the estimation model, σ Y is the standard deviation of the maximum available capacity of the battery.

[0062] The battery aging-related features are obtained by sorting the estimation model features and the Pearson correlation coefficient.

[0063] In this embodiment, five features closely related to battery aging are obtained by sorting the estimation model features and the Pearson correlation coefficient.

[0064] Specifically, obtaining the battery pack health status through principal component regression analysis of the SOH estimation model according to the battery aging-related characteristics includes:

[0065] Obtaining a principal component closely related to battery aging by principal component analysis according to the characteristics closely related to battery aging;

[0066] Obtaining standardized features closely related to battery aging through standardized calculation according to the main components closely related to battery aging;

[0067] The standardized calculation expression is:

[0068]

[0069] Among them, x′ i represents the standardized feature closely related to battery aging, xi represents the principal component closely related to battery aging, u represents the mean of the principal component closely related to battery aging, and σ represents the standard deviation of the principal component closely related to battery aging;

[0070] A multiple linear regression model is obtained by multiple linear regression according to the standardized features closely related to battery aging;

[0071] The multivariate linear regression model is expressed as:

[0072] Q′=0.202x′1-0.202x′2+0.198x′3+0.202x′4+0.202x′5,

[0073] Wherein, Q′ represents the remaining maximum available standardized capacity, and x′1, x′2, x′3, x′4, and x′5 represent standardized characteristics closely related to battery aging;

[0074] Obtaining the battery pack health status by denormalizing the multivariate linear regression model;

[0075] The denormalization calculation expression is:

[0076] Q=0.358x1-0.561x2+0.666x3+1.650x4+0.175x5+8.4,

[0077] Among them, x1, x2, x3, x4, and x5 are characteristics closely related to battery aging, and Q represents the health status of the battery pack;

[0078] Specifically, obtaining the battery pack charge state through a charge correction model according to the battery pack health state includes:

[0079] Acquire the initial state of charge of the battery pack, and obtain the state of charge of the battery pack by battery pack charge calculation according to the initial state of charge of the battery pack;

[0080] The battery pack charge calculation expression is:

[0081]

[0082] Wherein, SOC represents the state of charge of the battery pack, SOC init Indicates the initial charge state of the battery pack, Q rated represents the nominal capacity of the battery pack, i is the discharge current, η is the coulomb efficiency, and ∫iηdt represents the calculus calculation;

[0083] Obtaining the battery pack state of charge by battery pack charge correction calculation according to the battery pack health state and the battery pack state of charge;

[0084] The battery pack charge correction calculation expression is:

[0085]

[0086] Among them, SOC M Indicates the battery pack state of charge, SOC i represents the state of charge of the ith battery pack, Q i Indicates the health status of the ith battery pack.

[0087] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device.

[0088] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0089] The program code contained on the computer readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or their combinations, and the programming language includes object-oriented programming languages-such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the 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 it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0090] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. An automatic acquisition multi-channel battery detection comprehensive test system, including a data acquisition module, a capacitance analysis module, a feature selection module, a model estimation module, and a charge correction module, characterized in that: The data acquisition module is used for obtaining dynamic capacitance data of the battery pack over its entire life cycle through a battery detection model; The capacitance analysis module is used to obtain the estimation model characteristics through dynamic capacitance method analysis based on the dynamic capacitance data of the battery pack throughout its life cycle; The feature selection module is used to obtain features closely related to battery aging through Pearson correlation analysis method according to the estimation model features; The model estimation module is used to obtain the health status of the battery pack through principal component regression analysis of the SOH estimation model according to the characteristics closely related to battery aging; The charge correction module is used to obtain the battery pack charge state through a charge correction model according to the battery pack health state.

2. The automatic acquisition multi-channel battery detection comprehensive test system according to claim 1 is characterized in that: The battery pack obtains the dynamic capacitance data of the battery pack throughout its life cycle through the battery detection model, including: S101: The battery pack is charged in a constant current constant voltage battery charging mode to obtain a fully charged battery pack and charging data; S102: discharging the fully charged battery pack in a constant current discharge mode to obtain an empty battery pack and a discharge capacity; S103: obtaining a cycle battery pack through charge and discharge cycles according to the empty battery pack, wherein the cycle battery pack carries a cycle number label; S104: Preset a cycle number threshold, and obtain the dynamic capacitance data of the battery pack over its entire life cycle by judging the cycle number according to the cycle number label and the cycle number threshold.

3. The automatic acquisition multi-channel battery detection comprehensive test system according to claim 2 is characterized in that: The charge and discharge cycle means charging and discharging the empty battery pack 50 times, and the charge and discharge cycle parameters are current 1C, upper cut-off voltage 16.9V, lower cut-off voltage 9.9V, and charge and discharge interval 10min.

4. The automatic acquisition multi-channel battery detection comprehensive test system according to claim 2 is characterized in that: The cycle number judgment includes judging whether the cycle number label is equal to the cycle number threshold. If yes, the dynamic capacitance data of the battery pack over its entire life cycle is obtained; if not, steps S101 to S103 are repeated until the cycle number label is equal to the cycle number threshold.

5. The automatic acquisition multi-channel battery detection comprehensive test system according to claim 1 is characterized in that: The estimation model features obtained by analyzing the dynamic capacitance data of the battery pack over its entire life cycle through the dynamic capacitance method include: The Origin drawing software obtains a dynamic capacitance curve of the battery pack over its entire life cycle by drawing the dynamic capacitance data of the battery pack over its entire life cycle; The estimation model characteristic parameters are obtained by analyzing the positions and heights of the peaks and troughs on the dynamic capacitance curve of the battery pack throughout its life cycle.

6. The automatic acquisition multi-channel battery detection comprehensive test system according to claim 1 is characterized in that: The method of obtaining the battery aging-related features by Pearson correlation analysis method according to the estimation model feature parameters includes: Obtaining the maximum available capacity of the battery pack, and obtaining a Pearson correlation coefficient by performing Pearson correlation calculation according to the estimation model characteristic parameters and the maximum available capacity of the battery; The battery aging-related features are obtained by sorting the estimation model features and the Pearson correlation coefficient.

7. The automatic acquisition multi-channel battery detection comprehensive test system according to claim 1 is characterized in that: The obtaining of the battery pack health status by using the principal component regression analysis SOH estimation model according to the characteristics closely related to battery aging includes: Obtaining a principal component closely related to battery aging by principal component analysis according to the characteristics closely related to battery aging; Obtaining standardized features closely related to battery aging through standardized calculation according to the main components closely related to battery aging; A multiple linear regression model is obtained by multiple linear regression according to the standardized features closely related to battery aging; The battery pack health status is obtained by denormalizing the multivariate linear regression model.

8. The automatic acquisition multi-channel battery detection comprehensive test system according to claim 1 is characterized in that: The obtaining of the battery pack charge state through the charge correction model according to the battery pack health state includes: Acquire the initial state of charge of the battery pack, and obtain the state of charge of the battery pack by battery pack charge calculation according to the initial state of charge of the battery pack; The battery pack state of charge is obtained by battery pack charge correction calculation according to the battery pack health state and the battery pack state of charge.

9. 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 automatic acquisition multi-channel battery detection comprehensive test system as described in any one of claims 1-8 is implemented.

10. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to execute the automatic acquisition multi-channel battery detection comprehensive test system as described in any one of claims 1-8 when executed by a computer processor.

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