Battery remaining energy evaluation method and system based on shallow charge and discharge

Abstract

The present invention belongs to the technical field of battery evaluation, and discloses a method and system for evaluating remaining battery energy based on shallow charge and discharge. The method includes: collecting parameter information of multiple different aged battery cells of the same specification; establishing a regression model of standard capacity and characteristic values through shallow charge and discharge tests and based on the parameter information; performing shallow charge and discharge tests on the battery to be evaluated, and collecting test data of the battery to be evaluated; calculating corresponding characteristic values based on the test data, and selecting a corresponding target regression model based on the calculated characteristic values; determining the remaining energy evaluation value of the battery to be evaluated according to the characteristic values and the target regression model. By the above method, through short-term shallow charge and discharge tests, a regression model of battery charge and discharge characteristics and standard capacity is established, and the established regression model is used to determine the remaining energy evaluation value of the battery, which can adapt to the rapid evaluation of the remaining energy of aged batteries with different aging paths, save evaluation time, and at the same time has high accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of battery evaluation, and in particular to a method and system for evaluating remaining battery energy based on shallow charge and discharge. Background Art

[0002] At present, there are mainly three methods for evaluating battery capacity by domestic and foreign scholars: 1) Experimental method; 2) Model method; 3) Data-driven method. For aged batteries, the current mainstream method is to conduct capacity tests based on GB / T34015-2017. The test process includes the first charge and five standard room-temperature discharge capacity tests, and the test duration is about 10 hours. The model method includes an electrochemical model and an equivalent circuit model. The former depends on a large number of structural parameters and material property parameters, and the parameter identification of the latter takes a long time, and the model accuracy is strongly dependent on battery types and aging paths; the data-driven method requires a large amount of pre-retirement operation data.

[0003] However, due to the inconsistent initial charge states of batteries, the inconsistent aging degrees (aging durations) and aging paths, the existing methods have problems such as the lack of pre-retirement operation data in the data-driven method for estimating remaining battery energy, the overly long standard test time, and the greater difficulty and poorer working condition adaptability of the model method.

[0004] The above content is only used to assist in understanding the technical solution of the present invention, and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of the present invention is to provide a method and system for evaluating remaining battery energy based on shallow charge and discharge, aiming to solve the technical problems that the current methods for evaluating remaining energy have an overly long standard test time, and the model method has greater difficulty and poorer working condition adaptability.

[0006] To achieve the above purpose, the present invention provides a method for evaluating remaining battery energy based on shallow charge and discharge, and the method for evaluating remaining battery energy based on shallow charge and discharge includes the following steps:

[0007] Collect parameter information of multiple different aged battery cells of the same specification;

[0008] Conduct shallow charge and discharge tests and establish a regression model of standard capacity and characteristic values based on the parameter information. The regression model includes a regression model corresponding to the charging section and a regression model corresponding to the discharging section;

[0009] Conduct shallow charge and discharge tests on the battery to be evaluated, and collect test data of the battery to be evaluated;

[0010] Calculate corresponding characteristic values based on the test data, and select a corresponding target regression model based on the calculated characteristic values;

[0011] Determine the remaining energy evaluation value of the battery to be evaluated according to the eigenvalue and the target regression model.

[0012] In some embodiments, calculating corresponding eigenvalues based on the test data and selecting a corresponding target regression model based on the calculated eigenvalues includes:

[0013] If data within a preset time before the battery to be evaluated reaches the charging cut-off voltage is obtained according to the test data, select the charging section regression model as the target regression model, and obtain the voltage of the battery to be evaluated at a preset moment before reaching the charging cut-off voltage from the test data. Determine the first eigenvalue according to the voltage at the preset moment before reaching the charging cut-off voltage. The first eigenvalue is the difference between the voltage at the preset moment before reaching the charging cut-off voltage and the charging cut-off voltage, representing the voltage rise amplitude of constant current and equal time.

[0014] Obtain the voltage data of the battery to be evaluated within a preset time period before reaching the charging cut-off voltage from the test data, and determine the second eigenvalue according to the voltage data. The second eigenvalue is obtained by performing discrete first-order differentiation on the voltage data, and the second eigenvalue is the maximum first derivative.

[0015] Obtain the time taken for the current to drop from the reference current to the preset current from the test data, and determine the third eigenvalue according to the time taken.

[0016] Obtain the capacity charged into the battery to be evaluated when the current drops from the reference current to the preset current from the test data, and determine the fourth eigenvalue according to the charged capacity.

[0017] In some embodiments, the formula corresponding to the charging section regression model is , where A, B, C, D, and E are preset model parameters, and HF1, HF2, HF3, and HF4 are the first eigenvalue, the second eigenvalue, the third eigenvalue, and the fourth eigenvalue respectively.

[0018] In some embodiments, calculating corresponding eigenvalues based on the test data and selecting a corresponding target regression model based on the calculated eigenvalues includes:

[0019] If data within a preset time before the battery to be evaluated reaches the charging cut-off voltage is not obtained according to the test data, select the discharging section regression model as the target regression model, and obtain the time taken for the voltage to drop from the preset voltage to the discharging cut-off voltage from the test data, and determine the fifth eigenvalue according to the time taken.

[0020] Obtain voltage data of the voltage dropping from a preset voltage to the discharge cut-off voltage from the test data, determine a sixth eigenvalue according to the voltage data, where the sixth eigenvalue is obtained by performing a discrete first-order derivative on the voltage data, and the sixth eigenvalue is the maximum first-order derivative;

[0021] Obtain the capacity discharged from the battery to be evaluated when the voltage drops from the preset voltage to the discharge cut-off voltage from the test data, and determine a seventh eigenvalue according to the discharged capacity.

[0022] In some embodiments, the formula corresponding to the discharge segment regression model is , where a, b, c, d, e, f, g, h, i, j are preset model parameters respectively, and HF5, HF6, and HF7 are the fifth eigenvalue, the sixth eigenvalue, and the seventh eigenvalue respectively.

[0023] In addition, to achieve the above object, the present invention also proposes a battery remaining energy evaluation system based on shallow charge and discharge, and the battery remaining energy evaluation system based on shallow charge and discharge includes:

[0024] An acquisition module, configured to acquire parameter information of multiple different aged battery cells of the same specification;

[0025] A construction module, configured to establish a regression model of the standard capacity and the eigenvalue through shallow charge and discharge tests and based on the parameter information, where the regression model includes a regression model corresponding to the charge segment and a regression model corresponding to the discharge segment;

[0026] The acquisition module is configured to perform shallow charge and discharge tests on the battery to be evaluated and acquire the test data of the battery to be evaluated;

[0027] A processing module, configured to calculate corresponding eigenvalues based on the test data and select a corresponding target regression model based on the calculated eigenvalues;

[0028] An evaluation module, configured to determine the remaining energy evaluation value of the battery to be evaluated according to the eigenvalue and the target regression model.

[0029] In some embodiments, the processing module is configured to, if data within a preset time before the battery to be evaluated reaches the charge cut-off voltage is obtained according to the test data, select the charge segment regression model as the target regression model, and acquire the voltage of the battery to be evaluated at a preset moment before reaching the charge cut-off voltage from the test data, and determine a first eigenvalue according to the voltage at the preset moment before reaching the charge cut-off voltage, where the first eigenvalue is the difference between the voltage at the preset moment before reaching the charge cut-off voltage and the charge cut-off voltage, representing the voltage rise amplitude of constant current and equal time;

[0030] Obtain the voltage data of the battery to be evaluated within a preset time period before reaching the charging cut-off voltage from the test data, determine a second eigenvalue according to the voltage data, the second eigenvalue is obtained by performing a discrete first-order derivative on the voltage data, and the second eigenvalue is the maximum first-order derivative;

[0031] Obtain the time taken for the reference current to drop to a preset current from the test data, and determine a third eigenvalue according to the time taken;

[0032] Obtain the capacity charged into the battery to be evaluated when the reference current drops to a preset current from the test data, and determine a fourth eigenvalue according to the charged capacity.

[0033] In some embodiments, the formula corresponding to the charging segment regression model is , where A, B, C, D, and E are preset model parameters, and HF1, HF2, HF3, and HF4 are the first eigenvalue, the second eigenvalue, the third eigenvalue, and the fourth eigenvalue respectively.

[0034] In some embodiments, the processing module is configured to, if data within a preset time before the battery to be evaluated reaches the charging cut-off voltage cannot be obtained according to the test data, select the discharge segment regression model as the target regression model, and obtain the time taken for the voltage to drop from a preset voltage to the discharge cut-off voltage from the test data, and determine a fifth eigenvalue according to the time taken;

[0035] Obtain the voltage data of the voltage dropping from a preset voltage to the discharge cut-off voltage from the test data, determine a sixth eigenvalue according to the voltage data, the sixth eigenvalue is obtained by performing a discrete first-order derivative on the voltage data, and the sixth eigenvalue is the maximum first-order derivative;

[0036] Obtain the capacity discharged from the battery to be evaluated when the voltage drops from a preset voltage to the discharge cut-off voltage from the test data, and determine a seventh eigenvalue according to the discharged capacity.

[0037] In some embodiments, the formula corresponding to the discharge segment regression model is , where a, b, c, d, e, f, g, h, i, and j are preset model parameters, and HF5, HF6, and HF7 are the fifth eigenvalue, the sixth eigenvalue, and the seventh eigenvalue respectively.

[0038] In the present invention, parameter information of multiple different aged battery cells of the same specification is collected; a regression model of standard capacity and characteristic values is established through shallow charge and discharge tests and based on the parameter information; a battery to be evaluated is subjected to shallow charge and discharge tests, and test data of the battery to be evaluated is collected; corresponding characteristic values are calculated based on the test data, and a corresponding target regression model is selected based on the calculated characteristic values; the remaining energy evaluation value of the battery to be evaluated is determined according to the characteristic values and the target regression model. In the above manner, by means of short-time shallow charge and discharge tests, a regression model of battery charge and discharge characteristics and standard capacity is established, and the remaining energy evaluation value of the battery is determined by using the established regression model, which can adapt to the rapid evaluation of the remaining energy of aged batteries with different aging paths, save evaluation time, and at the same time have high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic flowchart of the first embodiment of the method for evaluating the remaining energy of a battery based on shallow charge and discharge of the present invention;

[0040] Figure 2 It is a schematic diagram of the charge and discharge voltage-time curve of the 1# battery cell under different aging degrees in the method for evaluating the remaining energy of a battery based on shallow charge and discharge of the present invention;

[0041] Figure 3 It is a schematic diagram of the charge and discharge voltage-time curve of the 2# battery cell under different aging degrees in the method for evaluating the remaining energy of a battery based on shallow charge and discharge of the present invention;

[0042] Figure 4 It is a structural block diagram of the first embodiment of the system for evaluating the remaining energy of a battery based on shallow charge and discharge of the present invention.

[0043] The realization, functional features, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0045] An embodiment of the present invention provides a method for evaluating the remaining energy of a battery based on shallow charge and discharge. Refer to Figure 1 , Figure 1 It is a schematic flowchart of the first embodiment of a method for evaluating the remaining energy of a battery based on shallow charge and discharge of the present invention.

[0046] In this embodiment, the method for evaluating the remaining energy of a battery based on shallow charge and discharge includes the following steps:

[0047] Step S10: Collect parameter information of multiple different aged battery cells of the same specification.

[0048] Step S20: Establish a regression model of standard capacity and characteristic values through shallow charge and discharge tests and based on the parameter information.

[0049] In this embodiment, the execution subject of this embodiment is a battery remaining energy evaluation device based on shallow charge and discharge. Among them, the battery remaining energy evaluation device based on shallow charge and discharge has functions such as data processing, data communication, and program operation. The battery remaining energy evaluation device based on shallow charge and discharge can be a computer terminal device or other network devices. Of course, it can also be other devices with similar functions. This embodiment does not limit this.

[0050] It should be noted that there are mainly three methods for evaluating the battery capacity by domestic and foreign scholars at present: 1) Experimental method 2) Model method 3) Data-driven method. For aging batteries, the current mainstream method is to conduct capacity tests based on GB / T34015-2017. The test process includes the first charge and 5 times of room temperature discharge capacity standard tests, and the test duration is about 10 hours. The model method includes an electrochemical model and an equivalent circuit model. The former relies on a large number of structural parameters and material characteristic parameters, and the parameter identification of the latter takes a long time, and the model accuracy depends strongly on the battery type and aging path; the data-driven method requires a large amount of operation data before retirement. However, due to the inconsistent initial charge states of the batteries, the inconsistent aging degrees (aging durations) and aging paths, the existing methods have problems such as the lack of operation data before retirement in the data-driven method for estimating the remaining energy of the battery, the too long standard test time, and the greater difficulty and poorer working condition adaptability of the model method.

[0051] To solve the technical problems, in this embodiment, the parameter information of multiple different aging battery cores of the same specification is collected; a regression model of the standard capacity and characteristic values is established through shallow charge and discharge tests and based on the parameter information; the battery to be evaluated is subjected to shallow charge and discharge tests, and the test data of the battery to be evaluated is collected; the corresponding characteristic values are calculated based on the test data, and the corresponding target regression model is selected based on the calculated characteristic values; the remaining energy evaluation value of the battery to be evaluated is determined according to the characteristic values and the target regression model. Through the above method, by performing short-time shallow charge and discharge tests, a regression model of the battery charge and discharge characteristics and the standard capacity is established, and the remaining energy evaluation value of the battery is determined by using the established regression model, which can adapt to the rapid evaluation of the remaining energy of aging batteries with different aging paths, save the evaluation time, and at the same time have high accuracy. Specifically, it can be implemented in the following way.

[0052] In the specific implementation, in this embodiment, it is necessary to first collect the parameter information of multiple different aging battery cores of the same specification. The same specification is, for example, the same physical size, the same chemical composition, the same rated capacity, the same nominal voltage, etc.

[0053] Taking the lithium iron phosphate battery core of the same model as an example, referring to Figure 2 and Figure 3As shown, it can be known that under different aging paths, different aging durations, and different initial state of charge (SOC), the differences in voltage changes during the charge and discharge processes mainly concentrate at the end of charge and discharge. For example, in the constant current charging stage: as the aging degree deepens, the voltage-time curve gradually becomes steeper, and the time to reach the charging cut-off voltage decreases; in the constant voltage charging stage: as the aging degree deepens, the constant current charging time becomes longer, and the corresponding charged capacity increases; in the constant current discharging stage: as the aging degree deepens, the voltage-time curve gradually becomes steeper, and the time to reach the discharging cut-off voltage decreases. The detailed working conditions are shown in Table 1 below.

[0054] Table 1:

[0055]

[0056] Furthermore, by using the shallow charge and discharge performance test, the difference information at the end of charge and discharge of cells with different aging degrees is extracted, and a relationship between the characteristic value and the remaining energy of the battery is established to realize the rapid assessment of the remaining energy of retired power batteries with different initial SOC and different aging paths.

[0057] The specific model for the charging process is and the model for the discharging process is The above model parameters are fitted with multiple groups of data. It should be emphasized that the charging section characteristic value and the standard capacity are described by a four-degree polynomial of one variable (a total of 5 unknown parameters). Considering the model accuracy, the number of samples should not be less than 10 groups; the discharging section characteristic value and the standard capacity are described by a three-degree polynomial of three variables (a total of 10 unknown parameters). Considering the model accuracy, the number of samples should not be less than 15 groups. Finally, in subsequent evaluations, the above model parameters A, B, C, D, E and a, b, c, d, e, f, g, h, i, j are known quantities, and the rapid assessment is realized by obtaining the parameters during the charge and discharge processes of the battery to be evaluated.

[0058] Step S30: Perform a shallow charge and discharge test on the battery to be evaluated, and collect the test data of the battery to be evaluated.

[0059] Step S40: Calculate the corresponding characteristic value based on the test data, and select the corresponding target regression model based on the calculated characteristic value.

[0060] In a specific implementation, in this embodiment, it is necessary to first perform a shallow charge and discharge test on the battery to be evaluated, and then obtain the test data. The eigenvalue calculation is directly performed based on the obtained test data. Calculate whichever eigenvalue can be calculated, and finally determine which regression model to use. For example, for a battery with 99% SOC, first perform a shallow charge and discharge directly. Then, after the data is exported, according to the definition of the eigenvalue, the data within 1000 s before the charging cut-off voltage U1 is not reached during the charging section. That is to say, it is fully charged before 1000 s of charging. Then, this battery naturally cannot calculate the first to fourth eigenvalues, and thus cannot use the first regression model. In this case, calculate the discharge section eigenvalue and use the discharge section regression model to obtain the remaining energy value.

[0061] Specifically, according to the test data, obtain the data of the battery to be evaluated within a preset time before reaching the charging cut-off voltage, then select the charging section regression model as the target regression model, and obtain the voltage of the battery to be evaluated at a preset moment before reaching the charging cut-off voltage from the test data. Determine the first eigenvalue according to the voltage at the preset moment before reaching the charging cut-off voltage. Obtain the voltage data of the battery to be evaluated within a preset time period before reaching the charging cut-off voltage from the test data. The first eigenvalue is the difference between the voltage at the preset moment before reaching the charging cut-off voltage and the charging cut-off voltage, indicating the voltage rise amplitude of constant current and equal time. Determine the second eigenvalue according to the voltage data. The second eigenvalue refers to the maximum first derivative of the constant current charging voltage. Obtain the time taken for the reference current to drop to the preset current from the test data, and determine the third eigenvalue according to the time taken; obtain the capacity charged into the battery to be evaluated when the reference current drops to the preset current from the test data, and determine the fourth eigenvalue according to the charged capacity.

[0062] It should be noted that the first eigenvalue refers to the constant current and equal time voltage rise, and the corresponding calculation method is HF1 = U1 - , where U1 is the charging cut-off voltage, is the voltage at the preset moment before reaching the charging cut-off voltage. The first eigenvalue is the difference between the voltage at the preset moment before reaching the charging cut-off voltage and the charging cut-off voltage, indicating the voltage rise amplitude of constant current and equal time. The second eigenvalue refers to the maximum first derivative of the constant current charging voltage, and the corresponding calculation method is HF2 = , is the voltage data of the battery to be evaluated within a preset time period before reaching the charging cut-off voltage. Perform discrete first-order differentiation on it

[0063] and take its maximum first derivative. The third eigenvalue refers to the constant voltage charging time,

[0064] The corresponding calculation method is that the time taken for the reference current to drop to the preset current is , HF3 = . The fourth eigenvalue refers to the constant-voltage charging capacity, and the corresponding calculation method is HF4 = , , is the above-mentioned preset current, the preset current can be set to 0.5 A, the above-mentioned preset time can be set to 1000 s, and the preset time period can be set to 1000 s. The formula corresponding to the regression model for the charging section is , where A, B, C, D, and E are respectively preset model parameters, and HF1, HF2, HF3, and HF4 are respectively the first eigenvalue, the second eigenvalue, the third eigenvalue, and the fourth eigenvalue.

[0065] Furthermore, if the data within the preset time before the battery under evaluation reaches the charging cut-off voltage cannot be obtained from the test data, then the discharge-section regression model is selected as the target regression model, and the time taken for the voltage to drop from the preset voltage to the discharge cut-off voltage is obtained from the test data. The fifth eigenvalue is determined according to the time taken, the voltage data of the voltage dropping from the preset voltage to the discharge cut-off voltage is obtained from the test data, the sixth eigenvalue is determined according to the voltage data, the sixth eigenvalue is obtained by performing a discrete first-order derivative on the voltage data, the sixth eigenvalue is the maximum first-order derivative, and the capacity discharged from the battery under evaluation when the voltage drops from the preset voltage to the discharge cut-off voltage is obtained from the test data. The seventh eigenvalue is determined according to the discharged capacity.

[0066] It should be noted that the fifth eigenvalue refers to the time taken for constant-current equal-voltage drop, and the corresponding calculation method is HF5 = , is the time taken for the voltage to drop from the preset voltage to the discharge cut-off voltage U2. The sixth eigenvalue refers to the maximum first-order derivative of the constant-current discharge voltage, and the corresponding calculation method is HF6 = , where is the voltage data of the voltage dropping from the preset voltage to the discharge cut-off voltage. Perform a discrete first-order derivative on and take its maximum first-order derivative. The seventh eigenvalue refers to the constant-current discharge capacity, HF7 = , is the above-mentioned reference current, and the preset voltage can be set to 3 V. The formula corresponding to the regression model for the discharge section is , where a, b, c, d, e, f, g, h, i, and j are respectively preset model parameters, and HF5, HF6, and HF7 are respectively the fifth eigenvalue, the sixth eigenvalue, and the seventh eigenvalue.

[0067] In this embodiment, a practical application case is further combined for illustration. For example, the example

[0068] Taking a batch of commercial lithium iron phosphate power batteries with different aging paths, different aging states, and different initial state of charge as the object for example explanation, the information of this batch of batteries is shown in Table 2.

[0069] Table 2:

[0070]

[0071] 1. Collect basic battery information

[0072] The rated capacity of this batch of batteries = 52 Ah, the charging cut-off voltage U1 = 3.65 V, and the discharging cut-off voltage U2 = 2 V.

[0073] Perform shallow charge and discharge performance tests on all experimental battery cells.

[0074] Calculate the characteristic values of the charge / discharge section: For the battery cells for which the voltage and current information within 1000 s before the charging cut-off voltage U1 is collected, calculate their charging section characteristic values HF1~HF4 according to Section 3. The results are shown in Table 3, where the battery cells numbered 1-32 belong to the 25°C, 1C, 100DOD cycle aging mode, and the battery cells numbered 56-91 belong to the 10°C, 0.5C, 30DOD cycle aging mode;

[0075] For the battery cells for which the voltage and current information within 1000 s before the charging cut-off voltage U1 cannot be collected, calculate the discharging section characteristic values HF5~HF7 according to Section 4. The results are shown in Table 4, where the battery cells numbered 33-55 belong to the 25°C, 1C, 100DOD cycle aging mode, and the battery cells numbered 96-91 belong to the 10°C, 0.5C, 30DOD cycle aging mode.

[0076] Table 3:

[0077]

[0078] Table 4:

[0079]

[0080] 4. Standard capacity test

[0081] Perform standard capacity tests on all experimental battery cells, and the results are shown in Table 5.

[0082] Table 5:

[0083]

[0084] 5. Establish a regression model between the standard capacity and the characteristic values

[0085] According to Section 6, regression models for charge / discharge characteristic values and standard capacity are established respectively for the regression model. To verify the accuracy and adaptability of the method, regression models are established in the six ways shown in Table 6, and accuracy verification is carried out. The corresponding model parameter fitting results and model accuracy are shown in Table 7. It can be seen that:

[0086] (1) Accuracy: The mean absolute error of the six regression models does not exceed 1.74 Ah, and the mean relative error does not exceed 3.85%. The error can meet the actual requirements.

[0087] (2) Adaptability: Samples with different aging paths are combined (Model 3 and Model 6) for model parameter fitting, and the model accuracy is still relatively high, indicating that this method does not depend on the battery aging method.

[0088] (3) Rapidity: In the present invention, the shallow charge / discharge performance test for an average single battery cell is less than 3.5 h, and the standard capacity test is about 10 h. Therefore, for the verification samples, the remaining energy evaluation time of the present invention is reduced by 65% compared with the standard capacity test. For the situation where the remaining energy of a large number of batteries of the same specification needs to be estimated, the evaluation method of the present invention saves more time.

[0089] Table 6:

[0090]

[0091] Table 7:

[0092]

[0093] In this embodiment, parameter information of multiple different aged battery cells of the same specification is collected; a regression model of standard capacity and characteristic values is established through shallow charge / discharge tests and based on the parameter information; a shallow charge / discharge test is carried out on the battery to be evaluated, and the test data of the battery to be evaluated is collected; the corresponding characteristic values are calculated based on the test data, and the corresponding target regression model is selected based on the calculated characteristic values; the remaining energy evaluation value of the battery to be evaluated is determined according to the characteristic values and the target regression model. In the above manner, through short-time shallow charge / discharge tests, a regression model of battery charge / discharge characteristics and standard capacity is established, and the remaining energy evaluation value of the battery is determined by using the established regression model, which can adapt to the rapid evaluation of the remaining energy of aged batteries with different aging paths, save evaluation time, and at the same time have high accuracy.

[0094] Referring to Figure 4 , Figure 4 is the structural block diagram of the first embodiment of the battery remaining energy evaluation system based on shallow charge and discharge of the present invention.

[0095] As Figure 4 shown, the battery remaining energy evaluation system based on shallow charge and discharge proposed in the embodiment of the present invention includes:

[0096] The acquisition module 10 is used to acquire parameter information of multiple different aged battery cells of the same specification;

[0097] The construction module 20 is used to establish a regression model of the standard capacity and the characteristic value through a shallow charge-discharge test and based on the parameter information, and the regression model includes a regression model corresponding to the charging section and a regression model corresponding to the discharging section;

[0098] The acquisition module 10 is used to perform a shallow charge-discharge test on the battery to be evaluated and acquire the test data of the battery to be evaluated;

[0099] The processing module 30 is used to calculate the corresponding characteristic value based on the test data and select the corresponding target regression model based on the calculated characteristic value;

[0100] The evaluation module 40 is used to determine the remaining energy evaluation value of the battery to be evaluated according to the characteristic value and the target regression model.

[0101] In this embodiment, parameter information of multiple different aged battery cells of the same specification is acquired; a regression model of the standard capacity and the characteristic value is established through a shallow charge-discharge test and based on the parameter information; a shallow charge-discharge test is performed on the battery to be evaluated, and the test data of the battery to be evaluated is acquired; the corresponding characteristic value is calculated based on the test data, and the corresponding target regression model is selected based on the calculated characteristic value; the remaining energy evaluation value of the battery to be evaluated is determined according to the characteristic value and the target regression model. In the above manner, by performing a short-term shallow charge-discharge test, a regression model of the battery charge-discharge characteristics and the standard capacity is established, and the remaining energy evaluation value of the battery is determined by using the established regression model, which can adapt to the rapid evaluation of the remaining energy of aged batteries with different aging paths, save the evaluation time, and at the same time has high accuracy.

[0102] In some embodiments, the processing module 30 is used to, if the data within a preset time before the battery to be evaluated reaches the charging cut-off voltage is obtained according to the test data, select the charging section regression model as the target regression model, and obtain the voltage of the battery to be evaluated at a preset moment before reaching the charging cut-off voltage from the test data, and determine the first characteristic value according to the voltage at the preset moment before reaching the charging cut-off voltage, where the first characteristic value is the difference between the voltage at the preset moment before reaching the charging cut-off voltage and the charging cut-off voltage, representing the voltage rise amplitude of constant current and equal time;

[0103] Obtain the voltage data of the battery to be evaluated within a preset time period before reaching the charging cut-off voltage from the test data, and determine the second characteristic value according to the voltage data, where the second characteristic value is obtained by performing discrete first-order differentiation on the voltage data, and the second characteristic value is the maximum first-order derivative;

[0104] Obtain the time taken for the benchmark current to drop to the preset current from the test data, and determine the third eigenvalue according to the time.

[0105] Obtain the capacity charged into the battery to be evaluated when the benchmark current drops to the preset current from the test data, and determine the fourth eigenvalue according to the charged capacity.

[0106] In some embodiments, the formula corresponding to the charging segment regression model is , where A, B, C, D, and E are preset model parameters, and HF1, HF2, HF3, and HF4 are the first eigenvalue, the second eigenvalue, the third eigenvalue, and the fourth eigenvalue respectively.

[0107] In some embodiments, the processing module 30 is configured to, if data within a preset time before the battery to be evaluated reaches the charging cut-off voltage cannot be obtained from the test data, select the discharge segment regression model as the target regression model, obtain the time taken for the voltage to drop from the preset voltage to the discharge cut-off voltage from the test data, and determine the fifth eigenvalue according to the time;

[0108] Obtain the voltage data when the voltage drops from the preset voltage to the discharge cut-off voltage from the test data, determine the sixth eigenvalue according to the voltage data, the sixth eigenvalue is obtained by performing discrete first-order differentiation on the voltage data, and the sixth eigenvalue is the maximum first derivative;

[0109] Obtain the capacity discharged from the battery to be evaluated when the voltage drops from the preset voltage to the discharge cut-off voltage from the test data, and determine the seventh eigenvalue according to the discharged capacity.

[0110] In some embodiments, the formula corresponding to the discharge segment regression model is , where a, b, c, d, e, f, g, h, i, and j are preset model parameters, and HF5, HF6, and HF7 are the fifth eigenvalue, the sixth eigenvalue, and the seventh eigenvalue respectively.

[0111] An embodiment of the present application further provides a battery remaining energy evaluation device based on shallow charge and discharge, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus. The memory is used to store a battery remaining energy evaluation program based on shallow charge and discharge; the processor is configured to, when executing the program stored on the memory, implement the above-mentioned battery remaining energy evaluation method based on shallow charge and discharge.

[0112] The communication bus mentioned in the above-mentioned battery remaining energy evaluation device based on shallow charge and discharge can be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0113] The communication interface is used for communication between the above-mentioned battery remaining energy evaluation device based on shallow charge and discharge and other devices.

[0114] The memory can include a Random Access Memory (RAM), or can also include a Non-Volatile Memory (NVM), such as at least one disk memory. Optionally, the memory can also be at least one storage device located far from the aforementioned processor.

[0115] The above-mentioned processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0116] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center via wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)).

[0117] It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0118] Each embodiment in this specification is described in a related manner. The same or similar parts between the embodiments can be referred to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiment.

[0119] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

[0120] It should be understood that the above is only for illustration and does not constitute any limitation to the technical solutions of the present invention. In specific applications, those skilled in the art can set according to needs, and the present invention does not limit this.

[0121] It should be noted that the above-described work process is only illustrative and does not limit the protection scope of the present invention. In actual applications, those skilled in the art can select some or all of them according to actual needs to achieve the purpose of the solution of this embodiment, and there is no limitation here.

[0122] In addition, for the technical details not described in detail in this embodiment, reference can be made to the battery remaining energy evaluation method based on shallow charge and discharge provided in any embodiment of the present invention, which will not be elaborated here.

[0123] In addition, it should be noted that in this article, the terms "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such a process, method, article or system. Without more limitations, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or system including that element.

[0124] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0125] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course, they can also be implemented through hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solutions of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as Read Only Memory (ROM) / RAM, magnetic disk, optical disk), and includes several instructions to enable a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0126] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

[0127] It can be understood that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention. The explanations, examples and beneficial effects of related contents can refer to the corresponding parts in the above method.

Claims

1. A method for evaluating the remaining energy of a battery based on shallow charge and discharge, characterized in that, The battery remaining energy evaluation method based on shallow charge and discharge includes: Collecting parameter information of multiple different aged battery cells of the same specification; Establishing a regression model between the standard capacity and the characteristic value through shallow charge and discharge tests and based on the parameter information, where the regression model includes a regression model corresponding to the charging section and a regression model corresponding to the discharging section; Performing shallow charge and discharge tests on the battery to be evaluated and collecting test data of the battery to be evaluated; Calculating corresponding characteristic values based on the test data and selecting a corresponding target regression model based on the calculated characteristic values; Among them, calculating the corresponding characteristic values based on the test data includes: If data within a preset time before the battery to be evaluated reaches the charging cut-off voltage is obtained according to the test data, select the charging section regression model as the target regression model, and obtain the voltage of the battery to be evaluated at a preset moment before reaching the charging cut-off voltage from the test data. Determine the first characteristic value according to the voltage at the preset moment before reaching the charging cut-off voltage. The first characteristic value is the difference between the voltage at the preset moment before reaching the charging cut-off voltage and the charging cut-off voltage, indicating the voltage rise amplitude of constant current and equal time; Obtain the voltage data of the battery to be evaluated within a preset time period before reaching the charging cut-off voltage from the test data, and determine the second characteristic value according to the voltage data. The second characteristic value is obtained by performing discrete first-order differentiation on the voltage data, and the second characteristic value is the maximum first derivative; Obtain the time taken for the current to drop from the reference current to the preset current from the test data, and determine the third characteristic value according to the time taken; Obtain the capacity charged into the battery to be evaluated when the current drops from the reference current to the preset current from the test data, and determine the fourth characteristic value according to the charged capacity; Determine the remaining energy evaluation value of the battery to be evaluated according to the characteristic values and the target regression model.

2. The method for evaluating remaining battery energy based on shallow charge and discharge according to claim 1, wherein The formula corresponding to the charging segment regression model is , where A, B, C, D, and E are preset model parameters, and HF1, HF2, HF3, and HF4 are the first eigenvalue, the second eigenvalue, the third eigenvalue, and the fourth eigenvalue respectively, is the standard capacity.

3. The method for evaluating remaining battery energy based on shallow charge and discharge according to claim 1, characterized in that Calculating the corresponding characteristic values based on the test data and selecting a corresponding target regression model based on the calculated characteristic values includes: If data within a preset time before the battery to be evaluated reaches the charging cut-off voltage is not obtained according to the test data, select the discharging section regression model as the target regression model, and obtain the time taken for the voltage to drop from the preset voltage to the discharging cut-off voltage from the test data, and determine the fifth characteristic value according to the time taken; Obtain the voltage data of the voltage dropping from the preset voltage to the discharging cut-off voltage from the test data, and determine the sixth characteristic value according to the voltage data. The sixth characteristic value is obtained by performing discrete first-order differentiation on the voltage data, and the sixth characteristic value is the maximum first derivative; Obtain the capacity discharged from the battery to be evaluated when the voltage drops from the preset voltage to the discharging cut-off voltage from the test data, and determine the seventh characteristic value according to the discharged capacity.

4. The method for evaluating remaining battery energy based on shallow charge and discharge according to claim 3, characterized in that, The formula corresponding to the discharging section regression model is: ， Wherein, a, b, c, d, e, f, g, h, i, and j are respectively preset model parameters, and HF5, HF6, and HF7 are respectively the fifth eigenvalue, the sixth eigenvalue, and the seventh eigenvalue. is the standard capacity.

5. A battery remaining energy evaluation system based on shallow charge and discharge, characterized in that, The battery remaining energy evaluation system based on shallow charge and discharge includes: A collection module for collecting parameter information of multiple different aged battery cells of the same specification; A building module for establishing a regression model of standard capacity and characteristic values through shallow charge-discharge tests and based on the parameter information, where the regression model includes a regression model corresponding to the charging section and a regression model corresponding to the discharging section; The acquisition module for performing shallow charge-discharge tests on the battery to be evaluated and acquiring the test data of the battery to be evaluated; A processing module for calculating corresponding characteristic values based on the test data and selecting a corresponding target regression model based on the calculated characteristic values; Wherein, the processing module is further configured to, if data within a preset time before the battery to be evaluated reaches the charging cut-off voltage is obtained according to the test data, select the charging section regression model as the target regression model, obtain the voltage of the battery to be evaluated at a preset moment before reaching the charging cut-off voltage from the test data, and determine a first characteristic value according to the voltage at the preset moment before reaching the charging cut-off voltage, where the first characteristic value is the difference between the voltage at the preset moment before reaching the charging cut-off voltage and the charging cut-off voltage, representing the voltage rise amplitude of constant current and equal time; Obtain the voltage data of the battery to be evaluated within a preset time period before reaching the charging cut-off voltage from the test data, and determine a second characteristic value according to the voltage data, where the second characteristic value is obtained by performing discrete first-order differentiation on the voltage data, and the second characteristic value is the maximum first derivative; Obtain the time taken for the current to drop from the reference current to the preset current from the test data, and determine a third characteristic value according to the time taken; Obtain the capacity charged into the battery to be evaluated when the current drops from the reference current to the preset current from the test data, and determine a fourth characteristic value according to the charged capacity; An evaluation module for determining the remaining energy evaluation value of the battery to be evaluated according to the characteristic values and the target regression model.

6. The battery remaining energy evaluation system based on shallow charge and discharge according to claim 5, characterized in that, The formula corresponding to the charging section regression model is ， Wherein, A, B, C, D, and E are respectively preset model parameters, and HF1, HF2, HF3, and HF4 are respectively the first eigenvalue, the second eigenvalue, the third eigenvalue, and the fourth eigenvalue. is the standard capacity.

7. The battery remaining energy evaluation system based on shallow charge and discharge according to claim 5, characterized in that The processing module is configured to, if data within a preset time before the battery to be evaluated reaches the charging cut-off voltage is not obtained according to the test data, select the discharging section regression model as the target regression model, and obtain the time taken for the voltage to drop from the preset voltage to the discharging cut-off voltage from the test data, and determine a fifth characteristic value according to the time taken; Obtain the voltage data of the voltage dropping from the preset voltage to the discharging cut-off voltage from the test data, and determine a sixth characteristic value according to the voltage data, where the sixth characteristic value is obtained by performing discrete first-order differentiation on the voltage data, and the sixth characteristic value is the maximum first derivative; Obtain the capacity discharged from the battery to be evaluated when the voltage drops from the preset voltage to the discharging cut-off voltage from the test data, and determine a seventh characteristic value according to the discharged capacity.

8. The battery remaining energy evaluation system based on shallow charge and discharge according to claim 7, characterized in that The formula corresponding to the discharge segment regression model is , where a, b, c, d, e, f, g, h, i, and j are preset model parameters, and HF5, HF6, and HF7 are the fifth eigenvalue, the sixth eigenvalue, and the seventh eigenvalue respectively, is the standard capacity.

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