Battery health detection method, device, and program

Abstract

A method, device, and medium for detecting the health of a battery are provided, which include: collecting data related to a battery; defining a set of reference parameter groups based on the data, the set including at least the following reference parameters: cell voltage difference, average power consumption, cell temperature difference, cumulative mileage, battery calendar life, cumulative cycle count, battery material factor, number of battery combinations in series, and state of charge; classifying and organizing the reference parameters, performing a normalization process, and calculating an environmental correction factor, a calibration factor, and a dynamic weighting factor based on the classified and organized data; calculating a deterioration contribution value and a total deterioration value of each reference parameter based on the normalized data, the environmental correction factor, the calibration factor, and the dynamic weighting factor; and calculating a battery health value based on the total deterioration value. [Effects] There is no need to disassemble the battery pack, and the detection operation is simple and quick. In addition, a multi-parameter coupling mechanism is adopted in the calculation process, resulting in high detection accuracy.

Description

[Technical Field]

[0001] The present application relates to the field of battery detection technology, and more particularly to a method, apparatus and program for detecting battery health. [Background technology]

[0002] With the development of new energy technology, new energy batteries are widely used in fields such as electric vehicles and energy storage equipment. As a core component of new energy, batteries' performance and lifespan are affected by many factors, such as the number of charge / discharge cycles, temperature, and current. To ensure the safe and reliable operation of new energy batteries, it is very important to detect their health status.

[0003] Conventional battery state-of-health (SOH) detection methods have many drawbacks and lack a unified market standard. For example, they require disassembly of the battery pack, require detection times of several tens of hours, and have low detection accuracy. Furthermore, existing detection devices are typically bulky and complex to operate, making on-site detection and maintenance inconvenient. Therefore, there is a strong demand in the market for a fast and easy battery SOH detection method to meet needs such as detecting new energy batteries and estimating the residual value of battery packs. Summary of the Invention [Problem to be solved by the invention]

[0004] The object of the present application is to provide a method, device and program for detecting battery health, which solves the problems of the existing technology that the operation for detecting battery health is complicated, and the detection is slow and inaccurate. [Means for solving the problem]

[0005] In order to solve the above technical problems, an embodiment of the present application provides a battery health detection method, which includes the following steps: collecting data related to a battery; defining a set of reference parameters based on the data, the set including at least the following reference parameters: voltage difference between battery cells, average power consumption, temperature difference between battery cells, cumulative mileage, calendar life of the battery, cumulative cycle count, battery material coefficient, number of battery combinations in series, and state of charge (SOC); classifying and organizing the reference parameters, and calculating an environmental correction factor, a calibration factor, and a dynamic weighting factor based on the classified and organized data; performing a normalization process on the reference parameters; calculating a deterioration contribution value of each reference parameter based on the normalized data, the environmental correction factor, the calibration factor, and the dynamic weighting factor; calculating a total deterioration value based on the deterioration contribution value of each reference parameter; and calculating a battery health value based on the total deterioration value.

[0006] In one embodiment, before defining a reference parameter group set based on the data, the following steps are further included: filtering, fine-tuning and calibration are performed on the collected data to suppress data fluctuations outside a preset fluctuation range and correct data drift.

[0007] In one embodiment, the step of defining the reference parameter group set includes presetting a normal maximum value and a normal minimum value for each reference parameter; the formula for normalizing the reference parameters is as follows: [Number 1] JPEG0007794519000002.jpg2393

[0008] where B(i) is the value of the i-th parameter, B'(i) is the value of the i-th parameter after normalization, and B max (i) and B min (i) are the normal maximum and minimum values ​​of the i-th parameter, respectively. In one embodiment, the calculation formula for the environmental correction factor is as follows: [Number 2] JPEG0007794519000003.jpg23105

[0009] where k is the temperature response coefficient (0.1≦k≦0.5), and T env is the ambient temperature, and T0=25℃ is the reference temperature.

[0010] In one embodiment, the calculation formula for the dynamic weighting factor is as follows: [Number 3] JPEG0007794519000004.jpg23134

[0011] where α, β, γ, and ε are empirical coefficients, and N dc (i) is the cumulative number of cycles, and L cal is the calendar life of the battery, Q is the battery material coefficient, and ΔV is the voltage difference between the battery cells. The voltage difference between the battery cells may be the difference between the maximum and minimum voltages of all the battery cells.

[0012] In one embodiment, the formula for calculating the degradation contribution value of each criterion parameter is as follows: [Number 4] JPEG0007794519000005.jpg10141

[0013] where Y i is the degradation contribution value of each reference parameter, a(i) is the calibration coefficient, and ω i (t) is the dynamic weighting factor and F(i) is the environmental correction factor.

[0014] In one embodiment, when the reference parameter for calculating the deterioration contribution value is the cumulative mileage, a coupling of a mileage intensity factor is added, and the mileage intensity factor f(D) is expressed as follows: [Number 5] JPEG0007794519000006.jpg2292

[0015] where η is the mileage deterioration coefficient, D is the cumulative mileage, and D max is a constant; the formula for the deterioration contribution value of the cumulative mileage is as follows: [Number 6] JPEG0007794519000007.jpg8120

[0016] where a(i) is the calibration coefficient for the cumulative mileage D, and ω i (t) is the dynamic weighting coefficient of the cumulative mileage D, F(i) is the environmental correction coefficient of the cumulative mileage D, and B'(i) is the cumulative mileage D after normalization.

[0017] In one embodiment, the formula for calculating the total degradation value based on the degradation contribution value of each of the criteria parameters is as follows: [Number 7] JPEG0007794519000008.jpg22165

[0018] where ΔV is the voltage difference between the battery cells, ΔT is the temperature difference between the battery cells, SOC is the state of charge, λ(S) is the coupling adjustment factor related to the number of series batteries S, and Y j are the degradation contribution values ​​of the reference parameters ΔV, ΔT, and SOC. The temperature difference of the battery cells may be the difference between the maximum and minimum temperatures of all the battery cells.

[0019] In one embodiment, the formula for calculating the battery state of health value SOH is as follows: [Number 8] JPEG0007794519000009.jpg24137

[0020] where Y threshold is the degradation threshold, which satisfies the following: [Number 9] JPEG0007794519000010.jpg19149

[0021] Here, Q adj is the battery threshold adjustment factor.

[0022] In one embodiment, the battery health detection method further includes the steps of: outputting the battery health value result in a predetermined format to generate a detection report.

[0023] An embodiment of the present application further provides a battery health detection device, which is applicable to the above-mentioned battery health detection method, and the device includes: a data collection module for collecting data related to a battery; a set definition module for defining a set of reference parameters based on the data; the set includes at least the following reference parameters: voltage difference between battery cells, average power consumption, temperature difference between battery cells, cumulative mileage, calendar life of the battery, cumulative cycle count, battery material coefficient, number of battery combinations in series, and state of charge; a classification module for classifying and organizing the reference parameters; a normalization module for performing a normalization process on the reference parameters; and a calculation module for calculating an environmental correction factor, a calibration factor, and a dynamic weighting factor based on the classified and organized data, calculating a deterioration contribution value of each reference parameter based on the normalized data, the environmental correction factor, the calibration factor, and the dynamic weighting factor, calculating a total deterioration value based on the deterioration contribution value of each reference parameter, and calculating a battery health value based on the total deterioration value.

[0024] In one embodiment, the calculation module is further used to generate a detection report based on the calculation result; the detection device further includes an output module; the output module is used to output the battery health value result and the detection report in a predetermined format.

[0025] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, realizes the steps of the above-mentioned method for detecting battery health.

[0026] The embodiment of the present application collects information about the battery and applies related algorithms and custom variables to calculate the final battery health value, which does not require disassembly of the battery pack and is simple and fast to operate. In addition, the calculation process employs a multi-parameter coupling mechanism, which provides high detection accuracy. [Brief explanation of the drawings]

[0027] One or more embodiments are illustratively illustrated by corresponding figures in the drawings. These illustrative illustrations do not constitute limitations on the embodiments. Elements in the figures having the same reference numerals represent similar elements. Unless otherwise specified, the figures in the drawings are not to be construed as limitations on scale.

[0028] [Figure 1] 2 is a flowchart of a method for detecting battery health according to an embodiment of the present application; [Figure 2] 4 is a flowchart of a method for detecting battery health according to another embodiment of the present application; [Figure 3] 1 is a structural schematic diagram of a battery health detection device according to an embodiment of the present application; [Figure 4] 10 is a structural schematic diagram of a battery health detection device according to another embodiment of the present application; [Figure 5] 1 is a product schematic diagram of a battery health detection device according to an embodiment of the present application; DETAILED DESCRIPTION OF THE INVENTION

[0029] Hereinafter, embodiments of the present application will be described through specific examples. Those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed herein. Obviously, the described examples are only some of the examples of the present application, and not all of the examples. The present application can be implemented or applied in other different specific embodiments, and each detail in the present specification can also be modified or changed in various ways based on different perspectives and applications without departing from the spirit of the present application. Furthermore, unless inconsistent, the following examples and features in the examples can be combined with each other. All other examples obtained based on the examples in the present application without the need for creative work by those skilled in the art are within the scope of protection of the present application. The following description relates to various aspects of embodiments within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on this application, one skilled in the art should understand that any aspect described herein can be implemented independently of any other aspect, and that two or more of these aspects can be combined in various ways. For example, an apparatus can be implemented and / or a method can be practiced using any number and features described herein. Furthermore, the apparatus can be implemented and / or a method can be practiced using other structure and / or functions than one or more of the features described herein.

[0030] It should be further explained that the illustrations provided in the following examples are merely for explaining the basic concept of the present application, and only the components relevant to the present application are shown in the illustrations, and are not drawn according to the number, shape, and dimensions of the components in actual implementation. In actual implementation, the type, number, and proportion of each component may be arbitrarily changed, and the arrangement of the components may also be more complicated. Also, in the following description, specific details are provided for the purpose of providing a thorough understanding of the examples, but it will be understood by those skilled in the art that the practice may be carried out without these specific details.

[0031] Currently, existing technologies have the problem that the battery health detection operation is complicated, the detection time is long, and the accuracy is low. In order to meet the needs of detecting new energy batteries and estimating the residual value of battery packs, there is a strong demand for a fast, simple, and highly accurate method of detecting battery health.

[0032] Based on this, the embodiment of the present application provides a method for detecting the battery health, the flow of which is shown in FIG. 1, and the specific steps are as follows: In step 101, data related to the battery is collected. Specifically, in the embodiment of the present application, there are various ways to collect data, for example, establishing communication with a battery management system (BMS) through a vehicle's quick charge port or a dedicated interface, and collecting battery parameters (e.g., battery cell voltage difference (ΔV), temperature difference (ΔT), state of charge (SOC), charge / discharge energy, current, cumulative cycle count (N dc ), cumulative mileage D, etc.) can be automatically obtained; important data such as battery specifications and manufacturing date can be supplemented by analyzing the vehicle identification code to obtain manufacturer and battery model information, or by scanning or manually entering nameplate information; real-time data (state of charge SOC, voltage, temperature, etc.) can be read directly from the vehicle's instrument cluster or center console system; real-time measurement can be performed via external sensors; or there are other supplement collection methods. The data collected in the embodiments of the present application includes, but is not limited to, the following parameters: battery manufacturer, battery material coefficient, installation method, series / parallel mode, number of battery combinations in series, battery cell type, ideal lifespan, ideal number of cycles, ideal voltage and voltage difference, battery calendar life, balancing mode, fast / slow charge ratio, cumulative number of cycles, cumulative mileage, average power consumption (kWh / 100km), commonly used region, battery cell specified capacity / voltage / energy, total specified capacity / voltage / energy, individual voltage / temperature extremes, state of charge (SOC), etc.

[0033] In step 102, a set of reference parameters is defined based on the data. The set of reference parameters defined in the embodiment of the present application includes at least the following reference parameters: a voltage difference ΔV between battery cells, an average power consumption E avg , battery cell temperature difference ΔT, cumulative mileage D, battery calendar life L cal , cumulative cycle number N dc , battery material coefficient Q, battery series combination number S, and state of charge SOC index. The parameter group set defined in this embodiment is B={B1, B2, ..., B i}, where B icorrespond to the above-mentioned respective standard parameters, and when defining the standard parameter group set, the standard maximum value and the standard minimum value of each standard parameter can be preset, for example: State of charge SOC:B min (9)=0, B max (9)=100(%); Cumulative mileage D:B min (4)=0, B max (4) = constant (km); Battery combination series number S:B min (8)=1, B max (8)=200(serial); Battery material coefficient Q:B min (7)=0.7, B max (7)=1.0; It should be noted that the above-mentioned nomenclature and upper and lower limit data of the standard maximum and minimum values ​​are merely illustrative examples, and can be specifically determined according to actual needs, and are not limited in the embodiments of the present application.

[0034] In step 103, the reference parameters are sorted, and an environmental correction factor, a calibration factor and a dynamic weighting factor are calculated based on the sorted data. Optionally, the calculation formula for the environmental correction factor is as follows: [Number 10] JPEG0007794519000011.jpg27118 where k is the temperature response coefficient (0.1≦k≦0.5), and T env is the environmental temperature, and T0=25°C is the reference temperature. The environmental correction factor F(i) is used to quantify the influence of environmental conditions (e.g., temperature) on the degradation rate of battery parameters, and dynamically adjusts the parameter degradation contribution value under different environments to make the battery health detection result closer to the actual aging situation.

[0035] Optionally, the calculation formula of the dynamic weighting factor is as follows: [Number 11] JPEG0007794519000012.jpg21147where α, β, γ, and ε are empirical coefficients, and N dc (i) is the cumulative number of cycles, and Lcal is the calendar life of the battery, Q is the battery material coefficient, and ΔV is the voltage difference between the battery cells.

[0036] By adjusting these parameters, it is possible to adapt to different battery types (e.g., power batteries vs. energy storage batteries) and usage scenarios (e.g., taxis vs. household cars), achieving highly accurate health assessment.

[0037] The definitions of each experience coefficient are shown in Table 1.

[0038] Table 1: Definitions of each parameter JPEG0007794519000013.jpg48146

[0039] The battery material factor Q can be found in Table 2.

[0040] Table 2: Battery material factors and their physical properties JPEG0007794519000014.jpg37147

[0041] It should be noted that the specific values ​​in Tables 1 and 2 are merely illustrative and may be determined according to actual conditions, and are not limited to the examples of the present application.

[0042] In step 104, the reference parameters are normalized. Specifically, the reference parameters can be normalized through the following formula: [Number 1] JPEG0007794519000015.jpg2393Here, B(i) is the value of the i-th parameter, B'(i) is the value after normalizing the i-th parameter, and B max (i) and B min (i) are the standard maximum and minimum values ​​of the i-th parameter, respectively.

[0043] Although this embodiment provides a method for normalizing the reference parameters, other methods can be used in actual applications and are not limited to the embodiments of this application. By normalizing the reference parameters, data of different dimensions and quantity levels can be converted to the same scale, the influence of the dimensions between data features can be eliminated, comprehensive comparison evaluation can be easily performed, and the speed and accuracy of data processing can be improved.

[0044] In step 105, the degradation contribution value of each reference parameter is calculated based on the normalized data, the environmental correction coefficient, the calibration coefficient, and the dynamic weighting coefficient. Specifically, in one alternative embodiment, the degradation contribution value of each reference parameter can be calculated using Equation 4. [Number 4] JPEG0007794519000016.jpg10141, where a(i) is the calibration coefficient, which can be obtained by experiment or by training a neural network; ω i (t) is a dynamic weighting coefficient, which can be calculated using Equation (11); F(i) is an environmental correction coefficient, which can be calculated using Equation (10).

[0045] Preferably, in order to make the calculated deterioration contribution value more accurate, when the reference parameter for calculating the deterioration contribution value is the cumulative mileage D, a coupling of a mileage intensity factor can be added, and the mileage intensity factor f(D) can be expressed as follows: [Number 5] JPEG0007794519000017.jpg2292, where η is the mileage degradation coefficient, and the preset value can be set to 0.8, and D max is a constant; max The value can be determined based on the product of the maximum mileage and the battery material coefficient Q.

[0046] The formula for the deterioration contribution value of the cumulative mileage D is as follows: [Number 6] JPEG0007794519000018.jpg8120where a(i) is the calibration coefficient for the cumulative mileage D, and ω i (t) is the dynamic weighting coefficient of the cumulative mileage D, F(i) is the environmental correction coefficient of the cumulative mileage D, and B'(i) is the cumulative mileage D after normalization.

[0047] In step 106, a total degradation value is calculated based on the degradation contribution value of each criterion parameter. Specifically, the total degradation value can be calculated based on the degradation contribution value of each criterion parameter and the battery series number coupling adjustment factor. The formula for calculating the total degradation value is as follows: [Number 7] JPEG0007794519000019.jpg22165Where, ΔV is the voltage difference between the battery cells, ΔT is the temperature difference between the battery cells, SOC is the state of charge, and Y j are the degradation contribution values ​​of the reference parameters ΔV, ΔT, and SOC, and λ(S) is the coupling adjustment factor related to the number S of battery combinations in series, and is expressed by the following equation. [Number 12] JPEG0007794519000020.jpg2198λ0 is the basic coupling factor, which can be obtained by training a support vector machine (SVM) or a neural network, e.g., 0.15.

[0048] The coupling mechanism is as follows: when S≦100, that is, when λ(S) satisfies the following equation, the impact on the total degradation value Y is relatively small. [Number 13] When S>100, λ(S) increases significantly with increasing S. That is, when S>100, the cooperative degradation effect of the battery cell voltage difference (ΔV), battery cell temperature difference (ΔT), and state of charge (SOC) can be magnified, and the degradation characteristics of large series battery packs can be reflected with higher accuracy, making it suitable for detecting the battery state of health (SOH) of large series battery systems in new energy vehicles.

[0049] In step 107, a battery health value is calculated based on the total degradation value. Specifically, the battery health value can be calculated based on the total degradation value and the battery threshold adjustment factor, and the formula for calculating the battery health value is as follows: [Number 8] JPEG0007794519000022.jpg24137where Y threshold is the degradation threshold, which satisfies the following: [Number 9] JPEG0007794519000023.jpg19149

[0050] where B' max (i) is the theoretical maximum value of the normalization parameter, Q adj is the battery threshold adjustment factor, and Q adj = ε + 0.1 × Q, where ε is the compensation coefficient, e.g., ε = 0.9. Battery threshold adjustment factor Q adj By introducing this, it is possible to dynamically correct the differences in the degradation characteristics of different battery material coefficients, ensuring compatibility and accuracy for different battery materials and solving the problem of "fixed thresholds" in conventional battery health state of health models.

[0051] The embodiment of the present application collects information about the battery and applies related algorithms and custom variables to calculate the final battery health value, which does not require disassembly of the battery pack during the detection process, making the operation simple and fast, and adopts a multi-parameter coupling mechanism during the calculation process, resulting in high detection accuracy.

[0052] In one optional embodiment, after collecting data related to the battery and before defining a reference parameter group set based on the data, the collected data may be further filtered, fine-tuned, and calibrated to suppress data fluctuations outside a preset fluctuation range (e.g., suppressing data fluctuations within a fluctuation range of 30% or more) and correct data drift.

[0053] In another alternative embodiment, the battery health detection method further includes the steps of: outputting the battery health value result in a predetermined format and generating a detection report, the flow of which is shown in Figure 2, and is specifically as follows: Steps 201 to 207 in this embodiment are similar to steps 101 to 107 in the embodiment shown in FIG. 1, so a detailed description thereof will be omitted here.

[0054] In step 208, the battery health value result is output in a predetermined format to generate a detection report. In this embodiment, the battery health SOH result can be output in a unified output format (e.g., dd.dd%), which avoids human interpretation errors and facilitates automated system processing. The detection report in this embodiment can be generated based on the calculation result. The generated detection report can include content such as the detection result (e.g., current health: 82.3%), health status rating, classification label (e.g., good (80%-90%), warning (<70%)), deterioration analysis, maintenance advice, risk warning, etc., to help customers quickly understand the battery status and formulate charging / discharging or maintenance strategies.

[0055] Based on the same inventive concept, the present application also provides a battery health detection device, and the device exemplified below is an example of a device corresponding to one of the above-mentioned method embodiments, and in other device embodiments, the settings of the functions of the unit modules and the number of modules can be set accordingly according to the above-mentioned method embodiments. As shown in FIG. 3, the battery health detection device includes: a data collection module 1 for collecting data related to the battery; a set definition module 2 for defining a reference parameter group set based on the data, the set including at least the following reference parameters: voltage difference between battery cells, average power consumption, temperature difference between battery cells, cumulative mileage, calendar life of the battery, cumulative cycle count, battery material coefficient, number of battery combinations in series, and state of charge; a classification module 3 for classifying and organizing the reference parameters; a normalization module 4 for normalizing the reference parameters; a calculation module 5 for calculating an environmental correction factor, a calibration factor, and a dynamic weighting factor based on the classified and organized data; a module for calculating a deterioration contribution value of each reference parameter based on the normalized data, the environmental correction factor, the calibration factor, and the dynamic weighting factor; a module for calculating a total deterioration value based on the deterioration contribution value of each reference parameter; and a battery health value based on the total deterioration value.

[0056] In this embodiment, the data collection module 1 collects information about the battery, and then applies related algorithms and custom variables to calculate the final battery health value through the calculation module 5. This eliminates the need to disassemble the battery pack, making the operation simple and fast. In addition, a multi-parameter coupling mechanism is adopted in the calculation process, resulting in high detection accuracy.

[0057] In another embodiment of the present application, as shown in FIG. 4 , the battery health detection device further includes an output module 6, and the calculation module 5 is further used to generate a detection report based on the calculation result; the output module 6 is used to output the battery health value result and the detection report in a predetermined format, helping customers quickly understand the battery status and formulate charging / discharging or maintenance strategies.

[0058] 4 and 5, in one application scenario, the battery health detection device of the embodiment of the present application is as shown in Fig. 5, in which a detection gun head 51 is attached to the front of the device, and a circuit board 52 is provided inside the device. The circuit board 52 may be an integrated circuit chip (e.g., MCU), and the circuit board 52 integrates a data collection module 1, a set definition module 2, a classification module 3, a normalization module 4, a calculation module 5, and a communication unit; a lithium battery 53 supplies power to the entire device.

[0059] When using this detection device to detect the battery state of health (SOH), the switch 54 of the detection device is first turned on, and the detection gun head 51 establishes a communication connection with the vehicle's battery management system (BMS) via the new energy vehicle's fast charging port. Then, the detection device collects and transmits battery-related data to the circuit board 52. The circuit board 52 processes the data, calculates relevant data using a linear regression model or a neural network model, and outputs and displays the calculation results on a display module via a communication unit (e.g., output module 6) so that the user can intuitively view the battery health value results and detection report. Optionally, the communication unit may be a wireless transmission module, such as a Bluetooth module supporting the Bluetooth Low Energy (BLE) protocol. The calculation results can be output to an external terminal device via the wireless transmission unit, allowing the user to view the battery health value results and detection report through the external terminal device. In this embodiment, the collected battery-related data may include manually entered battery replenishment information. Based on the replenishment information, the battery health value can be calculated and battery health management improvement suggestions can be proposed. This helps customers quickly understand the battery status and formulate charging / discharging or maintenance strategies.

[0060] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored therein, which, when executed by a processor, realizes the steps of the battery health detection method described in any one of the embodiments of the present application.

[0061] It should be noted that the computer storage medium includes, but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above. In a possible embodiment, the present invention can further realize data processing in the form of a program product, which includes program code, and when the program product is executed in a terminal device, the program code is used to cause the terminal device to perform a plurality of steps in the method according to any one of the above embodiments. The above description merely illustrates the specific implementation of the present application, and the scope of protection of the present application is not limited thereto. Any modifications or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application shall be equivalent to the scope of protection defined by the claims.

Claims

1. A method for detecting battery health, comprising: collecting data about the battery; defining a set of reference parameters based on the data, the set including at least the following reference parameters: voltage difference between battery cells, average power consumption, temperature difference between battery cells, cumulative mileage, calendar life of the battery, cumulative cycle count, battery material coefficient, number of battery combinations in series, and state of charge; sorting the reference parameters and calculating environmental correction factors, calibration factors and dynamic weighting factors based on the sorted data; performing a normalization process on the reference parameters; calculating a degradation contribution value of each reference parameter based on the normalized data, the environmental correction coefficient, the calibration coefficient, and the dynamic weighting coefficient; calculating a total degradation value based on the degradation contribution value of each of the reference parameters; calculating a battery state of health value (SOH) based on the total degradation value; Including, A method for detecting the health of a battery.

2. Before defining a set of reference parameters based on the data, The method further includes a step of filtering, fine-tuning, and calibrating the collected data to suppress data fluctuations outside a preset fluctuation range and correct data drift: The method for detecting the state of health of a battery according to claim 1 .

3. The step of defining a set of reference parameters includes presetting a standard maximum value and a standard minimum value for each reference parameter; The formula for normalizing the reference parameters is as follows: [Equation 1] where B(i) is the value of the i-th parameter, B'(i) is the value of the i-th parameter after normalization, and B ｍａｘ (i) and B ｍｉｎ (i) are the standard maximum and minimum values ​​of the i-th parameter, respectively. The method for detecting the state of health of a battery according to claim 1 .

4. The formula for calculating the degradation contribution value of each reference parameter is as follows: [Equation 4] Here, Y ｉ is the degradation contribution value of each reference parameter, a(i) is the calibration coefficient, and ω ｉ (t) is the dynamic weighting factor and F(i) is the environmental correction factor.

4. The method for detecting the state of health of a battery according to claim 3.

5. The calculation formula for the environmental correction factor is as follows: [Equation 10] where k is the temperature response coefficient (0.1≦k≦0.5), and T ｅｎｖ is the ambient temperature, and T ０ = 25°C is the standard reference temperature 5. The method for detecting the state of health of a battery according to claim 4.

6. The calculation formula for the dynamic weighting coefficient is as follows: [Equation 11] where α, β, γ, and ε are empirical coefficients, and N ｄｃ (i) is the cumulative number of cycles, and L ｃａｌ is the calendar life of the battery, Q is the battery material coefficient, and ΔV is the voltage difference between the battery cells.

5. The method for detecting the state of health of a battery according to claim 4.

7. When the reference parameter for calculating the deterioration contribution value is the cumulative mileage, a coupling of a mileage intensity factor is added, and the mileage intensity factor f(D) is expressed as follows: [Equation 5] where η is the mileage deterioration coefficient, D is the cumulative mileage, and D ｍａｘ is a constant, The formula for the deterioration contribution value of the cumulative mileage is as follows: [Equation 6] where a(i) is the calibration coefficient for the cumulative travel distance D, and ω ｉ (t) is the dynamic weighting coefficient of the cumulative mileage D, F(i) is the environmental correction coefficient of the cumulative mileage D, and B'(i) is the cumulative mileage D after normalization.

5. The method for detecting the state of health of a battery according to claim 4.

8. The formula for calculating the total degradation value based on the degradation contribution value of each reference parameter is as follows: [Equation 7] where ΔV is the voltage difference between the battery cells, ΔT is the temperature difference between the battery cells, SOC is the state of charge, λ(S) is a coupling adjustment factor related to the number S of battery combinations in series, and Y ｊ are the degradation contribution values ​​of the reference parameters ΔV, ΔT, and SOC 5. The method for detecting the state of health of a battery according to claim 4.

9. The formula for calculating the battery health value SOH is as follows: [Equation 8] Here, Y ｔｈｒｅｓｈｏｌｄ is the degradation threshold, which satisfies [Equation 9] Here, Q ａｄｊ is the battery threshold adjustment factor 9. The method for detecting the state of health of a battery according to claim 8.

10. and outputting the battery health value results in a predetermined format to generate a detection report. The method for detecting the state of health of a battery according to claim 1 .

11. A battery health detection device, which is applied to the battery health detection method according to any one of claims 1 to 10, a data collection module for collecting data regarding the battery; a set definition module for defining a set of reference parameters based on the data, the set including at least the following reference parameters: a voltage difference between battery cells, an average power consumption, a temperature difference between battery cells, a cumulative mileage, a calendar life of the battery, a cumulative number of cycles, a battery material coefficient, a number of battery combinations in series, and a state of charge; a classification module for classifying the reference parameters; a normalization module for normalizing the reference parameters; a calculation module for calculating an environmental correction factor, a calibration factor, and a dynamic weighting factor based on the classified and organized data, calculating a deterioration contribution value of each reference parameter based on the normalized data, the environmental correction factor, the calibration factor, and the dynamic weighting factor, calculating a total deterioration value based on the deterioration contribution value of each reference parameter, and calculating a battery health value based on the total deterioration value; Equipped with A battery health detection device characterized by:

12. the calculation module is further adapted to generate a detection report based on the calculation result; The detection device further includes an output module for outputting the battery health value result and the detection report in a predetermined format.

12. The device for detecting the state of health of a battery according to claim 11.

13. A program that, when executed by a computer, causes the computer to execute the method for detecting the battery health state according to any one of claims 1 to 10.

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