Battery complementary energy evaluation method and system based on shallow charging and discharging

Abstract

The invention belongs to the technical field of battery evaluation, and discloses a battery complementary energy evaluation method and system based on shallow charging and discharging. The method comprises the following steps: collecting parameter information of a plurality of different aged battery cells with the same specification; establishing a regression model of the standard capacity and the characteristic value through a shallow charging and discharging test and based on the parameter information; performing a shallow charge and discharge test on the to-be-evaluated battery, and collecting test data of the to-be-evaluated battery; calculating a corresponding characteristic value based on the test data, and selecting a corresponding target regression model based on the calculated characteristic value; and determining a complementary energy evaluation value of the to-be-evaluated battery according to the characteristic value and the target regression model. Through the above mode, the regression model of the battery charge and discharge characteristics and the standard capacity is established through the short-time shallow charge and discharge test, the complementary energy evaluation value of the battery is determined by using the established regression model, the method can adapt to rapid evaluation of the aged battery complementary energy of different aging paths, the evaluation time is saved, and the method also has relatively high accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of battery evaluation, and particularly 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 tests of room temperature discharge capacity, 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 has a strong dependence on battery types and aging paths; the data-driven method requires a large amount of operation data before retirement.

[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 operation data before retirement in the data-driven method for estimating remaining battery energy, the too 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 too long standard test time, and the model method is more difficult and has poor 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. The method for evaluating remaining battery energy based on shallow charge and discharge includes the following steps: Collect parameter information of multiple different aged battery cells of the same specification; Establish a regression model of standard capacity and characteristic values through shallow charge and discharge tests and 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; Conduct shallow charge and discharge tests on the battery to be evaluated, and collect test data of the battery to be evaluated; Calculate corresponding characteristic values based on the test data, and select a corresponding target regression model based on the calculated characteristic values; Determine the remaining energy evaluation value of the battery to be evaluated according to the characteristic values and the target regression model.

[0007] In some embodiments, calculating corresponding eigenvalue based on the test data and selecting a corresponding target regression model based on the calculated eigenvalue includes: If data within a preset time before the battery under evaluation reaches the charging cut-off voltage is obtained according to the test data, select the charging segment regression model as the target regression model, and obtain the voltage of the battery under evaluation at a preset moment before reaching the charging cut-off voltage from the test data. Determine a first eigenvalue based on 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 during constant current and equal time; Obtain the voltage data of the battery under evaluation within a preset time period before reaching the charging cut-off voltage from the test data, and determine a second eigenvalue based on 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; 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 eigenvalue based on the time; Obtain the capacity charged into the battery under evaluation when the current drops from the reference current to the preset current from the test data, and determine a fourth eigenvalue based on the charged capacity.

[0008] 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.

[0009] In some embodiments, calculating corresponding eigenvalue based on the test data and selecting a corresponding target regression model based on the calculated eigenvalue includes: If data within a preset time before the battery under evaluation reaches the charging cut-off voltage is not obtained according to the test data, select the discharging segment 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. Determine a fifth eigenvalue based on the time; 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 eigenvalue based on 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; Obtain the capacity released 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 the seventh eigenvalue according to the released capacity.

[0010] 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, HF7 are the fifth eigenvalue, the sixth eigenvalue and the seventh eigenvalue respectively.

[0011] In addition, to achieve the above object, the present invention also proposes a battery remaining energy evaluation system based on shallow charge and discharge. The battery remaining energy evaluation system based on shallow charge and discharge includes: An acquisition module, configured to acquire parameter information of a plurality of different aged battery cells of the same specification; A construction module, configured to establish a regression model between the standard capacity and the eigenvalue through shallow charge and discharge tests and based on the parameter information. The regression model includes a regression model corresponding to the charge segment and a regression model corresponding to the discharge segment; The acquisition module is configured to perform shallow charge and discharge tests on the battery to be evaluated and acquire test data of the battery to be evaluated; 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; 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.

[0012] 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 from 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. Determine the first eigenvalue according to the voltage at the preset moment before reaching the charge cut-off voltage. 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, indicating the voltage rise amplitude of constant current and equal time; Acquire the voltage data of the battery to be evaluated within a preset time period before reaching the charge 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; Acquire 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; Obtain the capacity charged into the battery to be evaluated when the current drops from the reference current to a preset current from the test data, and determine a fourth characteristic value according to the charged capacity.

[0013] In some embodiments, the formula corresponding to the charging segment regression model is , where A, B, C, D, and E are preset model parameters respectively, and HF1, HF2, HF3, and HF4 are the first characteristic value, the second characteristic value, the third characteristic value, and the fourth characteristic value respectively.

[0014] 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, 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 characteristic value according to the time taken; 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 characteristic value according to the voltage data, the sixth characteristic value is obtained by performing a discrete first-order derivative on the voltage data, and the sixth characteristic value is the maximum first-order derivative; 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 characteristic value according to the discharged capacity.

[0015] 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 respectively, and HF5, HF6, and HF7 are the fifth characteristic value, the sixth characteristic value, and the seventh characteristic value respectively.

[0016] In the present invention, parameter information of multiple different aged battery cells of the same specification is collected; a regression model of the standard capacity and the 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 aged batteries with different aging paths, save the evaluation time, and at the same time has high accuracy. Description of the Drawings

[0017] Figure 1 Schematic diagram of the process of the first embodiment of the battery remaining energy evaluation method based on shallow charge and discharge according to the present invention; Figure 2 Schematic diagram of the charge and discharge voltage-time curves of the 1# battery cell under different aging degrees in the battery remaining energy evaluation method based on shallow charge and discharge according to the present invention; Figure 3 Schematic diagram of the charge and discharge voltage-time curves of the 2# battery cell under different aging degrees in the battery remaining energy evaluation method based on shallow charge and discharge according to the present invention; Figure 4 Block diagram of the structure of the first embodiment of the battery remaining energy evaluation system based on shallow charge and discharge according to the present invention.

[0018] The realization, functional characteristics and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Specific embodiments

[0019] 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.

[0020] The embodiments of the present invention provide a battery remaining energy evaluation method based on shallow charge and discharge. Refer to Figure 1 , Figure 1 Schematic diagram of the process of the first embodiment of a battery remaining energy evaluation method based on shallow charge and discharge according to the present invention.

[0021] In this embodiment, the battery remaining energy evaluation method based on shallow charge and discharge includes the following steps: Step S10: Collect parameter information of multiple different aging battery cells of the same specification.

[0022] Step S20: Establish a regression model of the standard capacity and characteristic values through shallow charge and discharge tests and based on the parameter information.

[0023] 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 make any restrictions on this.

[0024] It should be noted that there are mainly three methods for evaluating battery capacity by domestic and foreign scholars at present: 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 5 standard tests of room temperature discharge capacity, and the test duration is about 10 hours. The model method includes electrochemical models and equivalent circuit models. 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 is strongly dependent 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 state of the battery, the inconsistent aging degree (aging duration) and aging path, 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 poor working condition adaptability of the model method.

[0025] To solve the technical problems, in this embodiment, the parameter information of multiple different aged battery cells of the same specification is collected; a regression model of the standard capacity and the characteristic value 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 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, through short-term 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 aged batteries with different aging paths, save the evaluation time, and at the same time has high accuracy. Specifically, it can be realized in the following manner.

[0026] In specific implementation, in this embodiment, it is necessary to first collect the parameter information of multiple different aged battery cells of the same specification. The same specification, for example, the same physical size, the same chemical composition, the same rated capacity, the same nominal voltage, etc.

[0027] Taking the lithium iron phosphate battery cells of the same model as an example, referring to Figure 2 and Figure 3 as shown, it can be known that under different aging paths, different aging durations, and different initial state of charge, the differences in voltage changes during the charge and discharge process are mainly concentrated at the end of charge and discharge. For example: 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; constant voltage charging stage: as the aging degree deepens, the constant current charging time becomes longer and the corresponding charged capacity increases; constant current discharge stage: as the aging degree deepens, the voltage-time curve gradually becomes steeper and the time to reach the discharge cut-off voltage decreases. The detailed working conditions are shown in Table 1 below.

[0028] Table 1:

[0029] Furthermore, by using the shallow charge-discharge performance test, the difference information at the end of charge and discharge of cells with different aging degrees is extracted, and the relationship between the characteristic value and the remaining energy of the battery is established to realize the rapid evaluation of the remaining energy of retired power batteries with different initial state of charge and different aging paths.

[0030] 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 the subsequent evaluation, 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 evaluation is realized by obtaining the parameters in the charge and discharge processes of the battery to be evaluated.

[0031] Step S30: Perform a shallow charge-discharge test on the battery to be evaluated, and collect the test data of the battery to be evaluated.

[0032] 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.

[0033] In a specific implementation, in this embodiment, it is necessary to first perform a shallow charge-discharge test on the battery to be evaluated, then obtain the test data, directly calculate the characteristic value according to the obtained test data, calculate whichever characteristic value can be calculated, and finally judge which regression model to use. For example, for a battery with 99% SOC, first perform a shallow charge and discharge directly, and then after the data is exported, according to the definition of the characteristic value, there is no data in the first 1000 s before the charging cut-off voltage U1 is reached in the charging section, that is, it is fully charged without charging for 1000 s. Then, the first to fourth characteristic values of this battery cannot be calculated, and thus the first regression model cannot be used. In this case, calculate the characteristic value of the discharging section and use the discharging section regression model to obtain the remaining energy value.

[0034] Specifically, according to the test data, obtain the data within a preset time before the battery to be evaluated reaches the charging cut-off voltage. Then, select the charging segment 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, representing 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 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. 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.

[0035] It should be noted that the first eigenvalue refers to the constant current and equal time voltage rise. 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, representing 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. 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 and take its maximum first derivative. The third eigenvalue refers to the constant voltage charging time, and the corresponding calculation method is that the time taken for the current to drop from the reference current to the preset current is , HF3 = . The fourth eigenvalue refers to the constant voltage charging capacity. The corresponding calculation method is HF4 = , , is the above-mentioned preset current, and the preset current can be set to 0.5A. The above-mentioned preset time can be set to 1000s, and the preset time period can be set to 1000s. The formula corresponding to the regression model of the charging segment 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.

[0036] Further, if data within a preset time before the battery under evaluation reaches the charging cut-off voltage cannot be obtained based on the test data, a discharge segment regression model is selected as the target regression model, and the time taken for the voltage to drop from a preset voltage to the discharge cut-off voltage is obtained from the test data. A fifth eigenvalue is determined based on the time taken, voltage data of the voltage dropping from the preset voltage to the discharge cut-off voltage is obtained from the test data, and a sixth eigenvalue is determined based on 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. The capacity released 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, and a seventh eigenvalue is determined based on the released capacity.

[0037] It should be noted that the fifth eigenvalue refers to the time for constant current and 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 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 discrete first-order differentiation on and take its maximum first 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 3V. The formula corresponding to the regression model for the discharge segment is , where a, b, c, d, e, f, g, h, i, j are preset model parameters, and HF5, HF6, and HF7 are the fifth eigenvalue, the sixth eigenvalue, and the seventh eigenvalue respectively.

[0038] In this embodiment, a practical application case is further used for illustration. For example, in Instance Taking a batch of commercial lithium iron phosphate power batteries with different aging paths, different aging states, and different initial state of charge as an example for explanation, the information of this batch of batteries is shown in Table 2.

[0039] Table 2:

[0040] 1. Collect basic battery information The rated capacity of this batch of batteries = 52Ah, the charging cut-off voltage U1 = 3.65V, and the discharge cut-off voltage U2 = 2V.

[0041] Shallow charge and discharge performance test. Perform shallow charge and discharge performance tests on all experimental battery cells.

[0042] 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 to HF4 according to Section 3. The results are shown in Table 3, where the battery cells numbered 1 - 32 belong to the cycle aging mode of 25°C, 1C, and 100 DOD, and the battery cells numbered 56 - 91 belong to the cycle aging mode of 10°C, 0.5C, and 30 DOD; 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 to HF7 according to Section 4. The results are shown in Table 4, where the battery cells numbered 33 - 55 belong to the cycle aging mode of 25°C, 1C, and 100 DOD, and the battery cells numbered 96 - 91 belong to the cycle aging mode of 10°C, 0.5C, and 30 DOD.

[0043] Table 3:

[0044] Table 4:

[0045] 4. Standard capacity test Conduct standard capacity tests on all experimental battery cells, and the results are shown in Table 5.

[0046] Table 5:

[0047] 5. Establish a regression model between the standard capacity and the characteristic values According to Section 6 for the regression model, establish the regression models between the charge / discharge characteristic values and the standard capacity respectively. To verify the accuracy and adaptability of the method, establish the regression models in 6 ways as shown in Table 6 and conduct accuracy verification. The corresponding model parameter fitting results and model accuracy are shown in Table 7. It can be seen that: (1) Accuracy: The average absolute error of the 6 regression models does not exceed 1.74 Ah, and the average relative error does not exceed 3.85%. The error can meet the actual requirements.

[0048] (2) Adaptability: When combining the samples with different aging paths (Model 3 and Model 6) for model parameter fitting, the model accuracy is still relatively high, indicating that this method does not depend on the battery aging method.

[0049] (3) Rapidness: In the present invention, the shallow charge / discharge performance test for an average 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 reduces the time consumption 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.

[0050] Table 6:

[0051] Table 7:

[0052] 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 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. 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 the evaluation time, and at the same time have high accuracy.

[0053] Referring to Figure 4 , Figure 4 which 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.

[0054] 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: A collection module 10, configured to collect parameter information of multiple different aged battery cells of the same specification; A construction module 20, configured to establish a regression model of standard capacity and characteristic values 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; The collection module 10 is configured to perform shallow charge and discharge tests on the battery to be evaluated and collect the test data of the battery to be evaluated; A processing module 30, configured to calculate the corresponding characteristic values based on the test data and select the corresponding target regression model based on the calculated characteristic values; An evaluation module 40, configured to determine the remaining energy evaluation value of the battery to be evaluated according to the characteristic values and the target regression model.

[0055] In this embodiment, parameter information of multiple different aged battery cells of the same specification is collected; a regression model of the standard capacity and the 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. In the above manner, by means of short-term 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 aged batteries with different aging paths, save the evaluation time, and at the same time has high accuracy.

[0056] 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 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 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 derivation on the voltage data, and the second characteristic value is the maximum first derivative; Obtain the time taken for the reference current to drop 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 reference current drops to the preset current from the test data, and determine a fourth characteristic value according to the charged capacity.

[0057] 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 characteristic value, the second characteristic value, the third characteristic value, and the fourth characteristic value respectively.

[0058] 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 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 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; 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.

[0059] In some embodiments, the formula corresponding to the discharge section 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.

[0060] 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 used to implement the above-mentioned battery remaining energy evaluation method based on shallow charge and discharge when executing the program stored on the memory.

[0061] The communication bus mentioned in the above battery remaining energy evaluation device based on shallow charge and discharge may be a Peripheral Component Interconnect (PCI) bus or 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.

[0062] The communication interface is used for communication between the battery remaining energy evaluation device based on shallow charge and discharge and other devices.

[0063] The memory may include a Random Access Memory (RAM), and may also include a Non-Volatile Memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located far from the aforementioned processor.

[0064] The above-mentioned processor may be a general-purpose processor, including a central processing unit (abbreviation: CPU in English: Central Processing Unit), a network processor (abbreviation: NP in English: Network Processor), etc.; it may also be a digital signal processor (abbreviation: DSP in English: Digital Signal Processing), an application-specific integrated circuit (abbreviation: ASIC in English: ApplicationSpecific Integrated Circuit), a field-programmable gate array (abbreviation: FPGA in English: Field-Programmable Gate Array), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0065] In the above embodiment, 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 may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more integrated available media. The available medium may be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid-state disk (SSD)).

[0066] It should be noted that in this text, 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 variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes 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 said element.

[0067] Each embodiment in this specification is described in a related manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and reference can be made to the relevant part of the method embodiment for the related content.

[0068] The above embodiments are only used to illustrate the technical solutions of the present invention, not to limit it; 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 on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

[0069] It should be understood that the above is only an example for illustration and does not constitute any limitation to the technical solution of the present invention. In specific applications, those skilled in the art can set it as needed, and the present invention does not limit this.

[0070] 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 no limitation is made here.

[0071] In addition, for the technical details not described in detail in this embodiment, reference can be made to the battery remaining energy assessment method based on shallow charge and discharge provided in any embodiment of the present invention, and details will not be repeated here.

[0072] In addition, it should be noted that in this text, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article or system comprising a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, article or system comprising such element.

[0073] The serial numbers of the above-described embodiments of the present invention are only for description and do not represent the superiority or inferiority of the embodiments.

[0074] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution 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 for causing 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.

[0075] The above are only the preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural 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 equally included in the patent protection scope of the present invention.

[0076] It can be understood that the system provided by the embodiments of the present invention corresponds to the method provided by the embodiments of the present invention. For the explanations, examples and beneficial effects of the relevant content, reference can be made to the corresponding parts in the above method.

Claims

1. A battery remaining energy evaluation method based on shallow charge and discharge, characterized in that: The battery remaining energy evaluation method based on shallow charge and discharge includes: Collect parameter information of multiple aged cells of the same specification; Establishing a regression model of standard capacity and characteristic value based on the parameter information through shallow charge and discharge test, wherein the regression model includes a regression model corresponding to the charging stage and a regression model corresponding to the discharging stage; Performing shallow charge and discharge tests on the battery to be evaluated, and collecting test data of the battery to be evaluated; Calculating corresponding eigenvalues ​​based on the test data, and selecting a corresponding target regression model based on the calculated eigenvalues; The remaining energy evaluation value of the battery to be evaluated is determined according to the characteristic value and the target regression model.

2. The battery remaining energy evaluation method based on shallow charge and discharge as claimed in claim 1, characterized in that: The calculating corresponding eigenvalues ​​based on the test data, and selecting corresponding target regression models based on the calculated eigenvalues, comprises: 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, the charge segment regression model is selected as the target regression model, and the voltage of the battery to be evaluated at a preset time before reaching the charge cut-off voltage is obtained from the test data, and a first characteristic value is determined according to the voltage at the preset time before reaching the charge cut-off voltage, the first characteristic value being the difference between the voltage at the preset time before reaching the charge cut-off voltage and the charge cut-off voltage, indicating the voltage rise amplitude during constant current; Acquire 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 eigenvalue according to the voltage data, wherein the second eigenvalue is obtained by performing a discrete first-order derivative on the voltage data, and the second eigenvalue is a maximum first-order derivative; Obtaining from the test data the time taken for the reference current to drop to the preset current, and determining a third characteristic value according to the time taken; The capacity charged into the battery to be evaluated when the current drops from the reference current to a preset current is obtained from the test data, and a fourth characteristic value is determined according to the charged capacity.

3. The battery remaining energy evaluation method based on shallow charge and discharge as claimed in claim 2, characterized in that: The formula corresponding to the charging stage 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.

4. The battery remaining energy evaluation method based on shallow charge and discharge as claimed in claim 1, characterized in that: The calculating corresponding eigenvalues ​​based on the test data, and selecting corresponding target regression models based on the calculated eigenvalues, comprises: If data within a preset time before the battery to be evaluated reaches the charge cut-off voltage is not obtained according to the test data, the discharge segment 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, and the fifth eigenvalue is determined according to the time taken; Acquire voltage data when the voltage drops from a preset voltage to the discharge cut-off voltage from the test data, and determine a sixth eigenvalue according to the voltage data, wherein the sixth eigenvalue is obtained by performing a discrete first-order derivative on the voltage data, and the sixth eigenvalue is a maximum first-order derivative; The capacity discharged from the battery to be evaluated when the voltage drops from the preset voltage to the discharge cut-off voltage is obtained from the test data, and a seventh characteristic value is determined according to the discharged capacity.

5. The battery remaining energy evaluation method based on shallow charge and discharge as claimed in claim 4, characterized in that: The formula corresponding to the discharge segment regression model is: ， Among them, 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.

6. A battery remaining energy assessment system based on shallow charge and discharge, characterized in that: The battery remaining energy assessment system based on shallow charge and discharge includes: The acquisition module is used to collect parameter information of multiple aged cells of the same specification; A construction module, for establishing a regression model of standard capacity and characteristic value based on the parameter information through shallow charge and discharge test, wherein the regression model includes a regression model corresponding to the charging section and a regression model corresponding to the discharging section; The acquisition module is used to perform a shallow charge and discharge test on the battery to be evaluated, and to collect test data of the battery to be evaluated; A processing module, used for calculating corresponding eigenvalues ​​based on the test data, and selecting a corresponding target regression model based on the calculated eigenvalues; An evaluation module is used to determine a remaining energy evaluation value of the battery to be evaluated according to the characteristic value and the target regression model.

7. The battery remaining energy evaluation system based on shallow charge and discharge as claimed in claim 6, characterized in that: The processing module is configured to select a charging segment regression model as a target regression model if data within a preset time before the battery to be evaluated reaches a charging cut-off voltage is obtained according to the test data, and obtain the voltage of the battery to be evaluated at a preset time before reaching the charging cut-off voltage from the test data, and determine a first characteristic value according to the voltage at the preset time before reaching the charging cut-off voltage, wherein the first characteristic value is the difference between the voltage at the preset time before reaching the charging cut-off voltage and the charging cut-off voltage, and represents the voltage rise amplitude during constant current. Acquire 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 eigenvalue according to the voltage data, wherein the second eigenvalue is obtained by performing a discrete first-order derivative on the voltage data, and the second eigenvalue is a maximum first-order derivative; Obtaining from the test data the time taken for the reference current to drop to the preset current, and determining a third characteristic value according to the time taken; The capacity charged into the battery to be evaluated when the current drops from the reference current to a preset current is obtained from the test data, and a fourth characteristic value is determined according to the charged capacity.

8. The battery remaining energy evaluation system based on shallow charge and discharge as claimed in claim 7, characterized in that: The formula corresponding to the charging stage 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.

9. The battery remaining energy evaluation system based on shallow charge and discharge as claimed in claim 6, characterized in that: The processing module is configured to select a discharge segment regression model as a target regression model if data within a preset time before the battery to be evaluated reaches a charge cut-off voltage is not obtained according to the test data, and obtain a 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; Acquire voltage data when the voltage drops from a preset voltage to the discharge cut-off voltage from the test data, and determine a sixth eigenvalue according to the voltage data, wherein the sixth eigenvalue is obtained by performing a discrete first-order derivative on the voltage data, and the sixth eigenvalue is a maximum first-order derivative; The capacity discharged from the battery to be evaluated when the voltage drops from the preset voltage to the discharge cut-off voltage is obtained from the test data, and a seventh characteristic value is determined according to the discharged capacity.

10. The battery remaining energy evaluation system based on shallow charge and discharge as claimed in claim 9, characterized in that: The formula corresponding to the discharge segment regression model is: ， Among them, 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.

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