Consistency evaluation method, device and equipment of battery pack, medium and product

Abstract

The invention provides a consistency evaluation method, device and equipment of a battery pack, a medium and a product, and relates to the technical field of batteries, and the method comprises the steps: obtaining corresponding voltage data, temperature data, capacity increment data and differential temperature data of the battery pack in a charging process; respectively determining a voltage root-mean-square error corresponding to the voltage data, a temperature root-mean-square error corresponding to the temperature data, a capacity increment root-mean-square error corresponding to the capacity increment data and a differential temperature root-mean-square error corresponding to the differential temperature data; and determining a consistency evaluation result corresponding to the battery pack based on the voltage root-mean-square error, the temperature root-mean-square error, the capacity increment root-mean-square error and the differential temperature root-mean-square error. According to the technical scheme, the inconsistency corresponding to the battery pack is evaluated based on fusion of four different characteristics of voltage, temperature, capacity increment and differential temperature, and the accuracy of a consistency evaluation result is improved.

Description

Technical Field

[0001] The present invention relates to the field of battery technology, and in particular to a battery pack consistency evaluation method, device, equipment, medium and product. Background Art

[0002] With the development of industries such as renewable energy and electric vehicles, the application scope of batteries is expanding. However, since the capacity and voltage of single batteries cannot meet the needs of practical applications, a large number of batteries are usually connected in series and parallel to form a battery pack.

[0003] In the production and use of battery packs, inconsistency between battery cells is inevitable, which will directly affect the performance, life and safety of the battery pack. Therefore, the evaluation of the consistency of battery cells in the battery pack is particularly important.

[0004] Existing methods usually use a single feature to evaluate the consistency of a battery pack, which results in inaccurate consistency evaluation and low precision. Summary of the invention

[0005] The present invention provides a battery pack consistency evaluation method, device, equipment, medium and product to solve the defect that a single feature is used to evaluate the consistency of a battery pack in the prior art, resulting in inaccurate consistency evaluation. The technical solution of the present invention evaluates the inconsistency corresponding to the battery pack based on the fusion of four different features: voltage, temperature, capacity increment and differential temperature, thereby improving the accuracy of the consistency evaluation results.

[0006] The present invention provides a battery pack consistency evaluation method, comprising the following steps.

[0007] Obtaining voltage data, temperature data, capacity increment data and differential temperature data corresponding to the battery pack during the charging process; Respectively determining a voltage root mean square error corresponding to the voltage data, a temperature root mean square error corresponding to the temperature data, a capacity increment root mean square error corresponding to the capacity increment data, and a differential temperature root mean square error corresponding to the differential temperature data; A consistency evaluation result corresponding to the battery pack is determined based on the voltage root mean square error, the temperature root mean square error, the capacity increment root mean square error, and the differential temperature root mean square error.

[0008] According to a consistency evaluation method for a battery pack provided by the present invention, the consistency evaluation result corresponding to the battery pack is determined based on the voltage root mean square error, the temperature root mean square error, the capacity increment root mean square error and the differential temperature root mean square error, including: Determine a target voltage weight corresponding to the voltage data, a target temperature weight corresponding to the temperature data, a target capacity increment weight corresponding to the capacity increment data, and a target differential temperature weight corresponding to the differential temperature data; The product of the voltage root mean square error and the target voltage weight, the product of the temperature root mean square error and the target temperature weight, the product of the capacity increment root mean square error and the target capacity increment weight, and the product of the differential temperature root mean square error and the target differential temperature weight are summed to obtain the consistency evaluation result.

[0009] According to a consistency evaluation method for a battery pack provided by the present invention, the step of determining a target voltage weight corresponding to the voltage data, a target temperature weight corresponding to the temperature data, a target capacity increment weight corresponding to the capacity increment data, and a target differential temperature weight corresponding to the differential temperature data includes: The first preset voltage weight corresponding to the voltage data, the second preset voltage weight corresponding to the historical voltage data, the first preset temperature weight corresponding to the temperature data, the second preset temperature weight corresponding to the historical temperature data, the first preset capacity increment weight corresponding to the capacity increment data, the second preset capacity increment weight corresponding to the historical capacity increment data, the first preset differential temperature weight corresponding to the differential temperature data, and the second preset differential temperature weight corresponding to the historical differential temperature data are input into a pre-trained weight optimization model to obtain the target voltage weight corresponding to the voltage data, the target temperature weight corresponding to the temperature data, the target capacity increment weight corresponding to the capacity increment data, and the target differential temperature weight corresponding to the differential temperature data output by the weight optimization model; wherein the weight optimization model is a neural network model pre-trained based on a weight training data set and labels corresponding to the weight training data set.

[0010] According to a method for evaluating the consistency of a battery pack provided by the present invention, the capacity increment data includes: the capacity increment values ​​corresponding to each single battery in the battery pack at all times during the charging process; Determining a capacity increment root mean square error corresponding to the capacity increment data includes: For each moment in the charging process, the maximum capacity increment value at the moment is determined as the maximum capacity increment value corresponding to the moment; the minimum capacity increment value at the moment is determined as the minimum capacity increment value corresponding to the moment; The capacity increment root mean square error is determined based on the maximum capacity increment value and the minimum capacity increment value corresponding to all the time moments.

[0011] According to a battery pack consistency evaluation method provided by the present invention, the differential temperature data includes: differential temperature values ​​corresponding to each of the single cells in the battery pack at all times during the charging process; Determining a differential temperature root mean square error corresponding to the differential temperature data includes: For each of the moments in the charging process, the maximum differential temperature value at the moment is determined as the maximum differential temperature value corresponding to the moment; the minimum differential temperature value at the moment is determined as the minimum differential temperature value corresponding to the moment; The differential temperature root mean square error is determined based on the maximum differential temperature value and the minimum differential temperature value corresponding to all the moments.

[0012] According to a battery pack consistency evaluation method provided by the present invention, the method further includes: charging and discharging the battery pack according to a preset power and a preset rest time; During the charging and discharging process, if at least one of the discharge capacity, full pressure difference, empty pressure difference, temperature difference and maximum temperature of the battery pack does not meet the corresponding preset threshold range, a battery pack fault warning is triggered.

[0013] The present invention also provides a battery pack consistency assessment device, comprising the following modules: An acquisition module is used to acquire voltage data, temperature data, capacity increment data and differential temperature data corresponding to the battery pack during the charging process; a determination module, configured to respectively determine a voltage root mean square error corresponding to the voltage data, a temperature root mean square error corresponding to the temperature data, a capacity increment root mean square error corresponding to the capacity increment data, and a differential temperature root mean square error corresponding to the differential temperature data; An evaluation module is used to determine a consistency evaluation result corresponding to the battery pack based on the voltage root mean square error, the temperature root mean square error, the capacity increment root mean square error and the differential temperature root mean square error.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a consistency evaluation method for a battery pack as described above is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the consistency evaluation method of a battery pack as described in any one of the above is implemented.

[0016] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the consistency evaluation method of a battery pack as described in any one of the above is implemented.

[0017] The consistency evaluation method, device, equipment, medium and product of the battery pack provided by the present invention obtain the voltage data, temperature data, capacity increment data and differential temperature data corresponding to the battery pack during the charging process; respectively determine the voltage root mean square error corresponding to the voltage data, the temperature root mean square error corresponding to the temperature data, the capacity increment root mean square error corresponding to the capacity increment data and the differential temperature root mean square error corresponding to the differential temperature data; based on the voltage root mean square error, the temperature root mean square error, the capacity increment root mean square error and the differential temperature root mean square error, determine the consistency evaluation result corresponding to the battery pack. The technical solution of the present invention evaluates the inconsistency corresponding to the battery pack based on the fusion of four different characteristics of voltage, temperature, capacity increment and differential temperature, thereby improving the accuracy of the consistency evaluation result. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a flow chart of the consistency evaluation method of a battery pack provided by the present invention.

[0020] Figure 2 It is a structural schematic diagram of a battery pack consistency evaluation device provided by the present invention.

[0021] Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] In view of the above problems in the prior art, the present invention provides a battery pack consistency evaluation method. Figure 1 is a flow chart of the consistency evaluation method of a battery pack provided by the present invention, such as Figure 1As shown, the method includes the following steps 110, 120 and 130.

[0024] Step 110: Obtain voltage data, temperature data, capacity increment data and differential temperature data corresponding to the battery pack during the charging process.

[0025] Specifically, the battery pack can be charged and discharged, and during the charging and discharging process, the voltage data, temperature data, capacity increment data and differential temperature data of the battery pack can be obtained in real time, wherein the voltage data can be obtained through a voltage sensor, the temperature data can be obtained through a temperature sensor, the capacity increment data is further calculated based on the data obtained by the voltage and current sensors, and the differential temperature data is further calculated based on the data obtained by the temperature sensor.

[0026] In one embodiment, the method further comprises: charging and discharging the battery pack according to a preset power and a preset rest time; During the charging and discharging process, if at least one of the discharge capacity, full pressure difference, empty pressure difference, temperature difference and maximum temperature of the battery pack does not meet the corresponding preset threshold range, a battery pack fault warning is triggered.

[0027] Specifically, the battery pack may be charged and discharged according to a preset power and a preset rest time.

[0028] Exemplarily, the battery pack can first be discharged at a constant power of 0.5 times (c) until the single cells in the battery pack are cut off at a minimum voltage of 2.5 volts (V), and the battery pack is allowed to stand for 30 minutes. The battery pack can then be charged at a constant power of 0.5c until the single cells in the battery pack are cut off at a maximum voltage of 3.65 volts (V), and the battery pack is allowed to stand for 30 minutes. The battery pack can then be further discharged at a constant power of 0.5c until the single cells in the battery pack are cut off at a minimum voltage of 2.5 volts (V), and the battery pack is allowed to stand for 30 minutes. Finally, the battery pack can be charged at a current of 140 amperes (A) for 60 minutes, and the battery pack is allowed to stand for 30 minutes to complete the charging process.

[0029] Furthermore, the discharge capacity, full pressure difference, empty pressure difference, temperature difference and maximum temperature each correspond to a preset threshold range, and each preset threshold range can be set as needed. The embodiment of the present invention does not make specific limitations here. During the entire charge and discharge process, the discharge capacity, full pressure difference, empty pressure difference, temperature difference and maximum temperature corresponding to the battery pack need to meet the respective corresponding preset threshold ranges. If they all meet the preset threshold ranges, step 110 is executed. Otherwise, a battery pack fault warning is triggered to prompt the user to immediately check and maintain the battery pack.

[0030] Exemplarily, when the discharge capacity of the battery pack is ≥288 ampere hours (Ah), the full pressure difference is ≤250 millivolts (mv), the empty pressure difference is ≤350mV, the temperature difference is ≤10 degrees Celsius (℃), and the maximum temperature is ≤55℃, step 110 is executed, otherwise the battery pack fault warning is triggered.

[0031] In the above embodiment, the battery pack is charged and discharged according to the preset power and the preset standing time. During the charging and discharging process, if at least one of the discharge capacity, full pressure difference, empty pressure difference, temperature difference and maximum temperature of the battery pack does not meet the corresponding preset threshold range, a battery pack fault warning is triggered, which can ensure that in the event of a battery failure, the user is promptly prompted to perform inspection and maintenance, and provides a guarantee for the accuracy of subsequent consistency evaluation.

[0032] Step 120: respectively determine a voltage root mean square error corresponding to the voltage data, a temperature root mean square error corresponding to the temperature data, a capacity increment root mean square error corresponding to the capacity increment data, and a differential temperature root mean square error corresponding to the differential temperature data.

[0033] Specifically, the voltage root mean square error corresponding to the voltage data can be determined according to the voltage data. It can be determined by the following formula:

[0034]

[0035]

[0036] in, Indicates the maximum moment of the charging process, Indicates the charging process time, Indicates the battery pack The single cell is in The voltage value at the moment, Indicates taking the maximum value, Indicates taking the minimum value, Indicated in The maximum voltage value of each single battery at the moment, All the values ​​of constitute the highest voltage curve of the charging process. Indicated in The minimum voltage value of each single cell at the moment, All the values ​​of constitute the lowest voltage curve of the charging process, and the voltage root mean square error It is actually the root mean square error between the highest voltage curve and the lowest voltage curve. Voltage root mean square error The larger it is, the worse the voltage consistency of the battery pack.

[0037] The temperature root mean square error corresponding to the temperature data can be determined based on the temperature data. It can be determined by the following formula:

[0038]

[0039]

[0040] in, Indicates the battery pack The single cell is in The temperature value at the moment, Indicated in The maximum temperature value of each single cell at the moment, All the values ​​of constitute the highest temperature curve of the charging process. Indicated in The minimum temperature value of each single cell at the moment, All the values ​​of constitute the lowest temperature curve of the charging process, and the temperature root mean square error It is actually the root mean square error between the highest temperature curve and the lowest temperature curve. Temperature root mean square error The larger it is, the worse the temperature consistency of the battery pack.

[0041] It is also possible to determine a capacity increment root mean square error corresponding to the capacity increment data based on the capacity increment data, and to determine a differential temperature root mean square error corresponding to the differential temperature data based on the differential temperature data.

[0042] In one embodiment, the capacity increment data includes: the capacity increment value corresponding to each single battery in the battery pack at all times during the charging process; Determining a capacity increment root mean square error corresponding to the capacity increment data includes: For each moment in the charging process, the maximum capacity increment value at the moment is determined as the maximum capacity increment value corresponding to the moment; the minimum capacity increment value at the moment is determined as the minimum capacity increment value corresponding to the moment; The capacity increment root mean square error is determined based on the maximum capacity increment value and the minimum capacity increment value corresponding to all the time moments.

[0043] Specifically, for each moment in the charging process, the maximum capacity increment value at that moment can be determined as the maximum capacity increment value corresponding to that moment; and the minimum capacity increment value at that moment can also be determined as the minimum capacity increment value corresponding to that moment. Maximum capacity increment value and minimum capacity increment value It can be determined by the following formula:

[0044]

[0045] in, Indicates the battery pack The single cell is in The capacity increment value at the moment, it should be noted that the single battery In the The capacity increment value at the moment is the single battery In the The capacity at the moment minus the single battery In the The capacity of the moment, Indicated in The maximum capacity increment value of each single battery at the moment, All the values ​​of constitute the maximum capacity increment curve of the charging process. Indicated in The minimum capacity increment value of each single battery at the moment, All the values ​​of constitute the minimum capacity increment curve of the charging process.

[0046] Furthermore, the capacity increment root mean square error can be determined based on the maximum capacity increment value and the minimum capacity increment value corresponding to all moments. It can be determined by the following formula:

[0047] Among them, the capacity increment root mean square error It is actually the root mean square error between the highest capacity increment curve and the lowest capacity increment curve. The larger it is, the worse the capacity increment consistency of the battery pack.

[0048] In the above embodiment, the capacity increment root mean square error is determined based on the maximum capacity increment value and the minimum capacity increment value corresponding to all moments, which lays a foundation for the subsequent consistency evaluation of the battery pack.

[0049] In one embodiment, the differential temperature data includes: differential temperature values ​​corresponding to each of the single cells in the battery pack at all times during the charging process; Determining a differential temperature root mean square error corresponding to the differential temperature data includes: For each of the moments in the charging process, the maximum differential temperature value at the moment is determined as the maximum differential temperature value corresponding to the moment; the minimum differential temperature value at the moment is determined as the minimum differential temperature value corresponding to the moment; The differential temperature root mean square error is determined based on the maximum differential temperature value and the minimum differential temperature value corresponding to all the moments.

[0050] Specifically, for each moment in the charging process, the maximum differential temperature value at that moment can be determined as the maximum differential temperature value corresponding to that moment; and the minimum differential temperature value at that moment can also be determined as the minimum differential temperature value corresponding to that moment. Maximum differential temperature value and minimum differential temperature It can be determined by the following formula:

[0051]

[0052] in, Indicates the battery pack The single cell is in The differential temperature value at the moment, it should be noted that the single battery In the The differential temperature value at the moment is the single cell In the The temperature at the moment minus the temperature of the single cell In the The temperature of the moment, Indicated in The maximum differential temperature value in each single cell at the moment, All values ​​of constitute the highest differential temperature curve of the charging process, Indicated in The minimum differential temperature value of each single cell at the moment, All values ​​of constitute the minimum differential temperature curve of the charging process.

[0053] Furthermore, the differential temperature root mean square error can be determined based on the maximum differential temperature value and the minimum differential temperature value corresponding to all moments. It can be determined by the following formula:

[0054] Where, the differential temperature root mean square error It is actually the RMS error between the highest differential temperature curve and the lowest differential temperature curve. Differential Temperature RMS Error The larger it is, the worse the differential temperature consistency of the battery pack.

[0055] In the above embodiment, the differential temperature root mean square error is determined based on the maximum differential temperature value and the minimum differential temperature value corresponding to all moments, which lays a foundation for the subsequent consistency evaluation of the battery pack.

[0056] Step 130: Determine a consistency evaluation result corresponding to the battery pack based on the voltage root mean square error, the temperature root mean square error, the capacity increment root mean square error and the differential temperature root mean square error.

[0057] In one embodiment, determining the consistency evaluation result corresponding to the battery pack based on the voltage root mean square error, the temperature root mean square error, the capacity increment root mean square error and the differential temperature root mean square error includes: Determine a target voltage weight corresponding to the voltage data, a target temperature weight corresponding to the temperature data, a target capacity increment weight corresponding to the capacity increment data, and a target differential temperature weight corresponding to the differential temperature data; The product of the voltage root mean square error and the target voltage weight, the product of the temperature root mean square error and the target temperature weight, the product of the capacity increment root mean square error and the target capacity increment weight, and the product of the differential temperature root mean square error and the target differential temperature weight are summed to obtain the consistency evaluation result.

[0058] Specifically, a target voltage weight corresponding to the voltage data, a target temperature weight corresponding to the temperature data, a target capacity increment weight corresponding to the capacity increment data, and a target differential temperature weight corresponding to the differential temperature data may be determined.

[0059] Furthermore, the product of the voltage root mean square error and the target voltage weight, the product of the temperature root mean square error and the target temperature weight, the product of the capacity increment root mean square error and the target capacity increment weight, and the product of the differential temperature root mean square error and the target differential temperature weight can be summed to obtain the consistency evaluation result. It can be determined by the following formula:

[0060] in, represents the target voltage weight, represents the target temperature weight, represents the target capacity increment weight, Represents the target differential temperature weight.

[0061] In the above embodiment, the product of the voltage root mean square error and the target voltage weight, the product of the temperature root mean square error and the target temperature weight, the product of the capacity increment root mean square error and the target capacity increment weight, and the product of the differential temperature root mean square error and the target differential temperature weight are summed to obtain a consistency evaluation result, making the consistency evaluation result more reasonable.

[0062] In one embodiment, determining the target voltage weight corresponding to the voltage data, the target temperature weight corresponding to the temperature data, the target capacity increment weight corresponding to the capacity increment data, and the target differential temperature weight corresponding to the differential temperature data includes: The first preset voltage weight corresponding to the voltage data, the second preset voltage weight corresponding to the historical voltage data, the first preset temperature weight corresponding to the temperature data, the second preset temperature weight corresponding to the historical temperature data, the first preset capacity increment weight corresponding to the capacity increment data, the second preset capacity increment weight corresponding to the historical capacity increment data, the first preset differential temperature weight corresponding to the differential temperature data, and the second preset differential temperature weight corresponding to the historical differential temperature data are input into a pre-trained weight optimization model to obtain the target voltage weight corresponding to the voltage data, the target temperature weight corresponding to the temperature data, the target capacity increment weight corresponding to the capacity increment data, and the target differential temperature weight corresponding to the differential temperature data output by the weight optimization model; wherein the weight optimization model is a neural network model pre-trained based on a weight training data set and labels corresponding to the weight training data set.

[0063] Specifically, the historical voltage data is the voltage data stored at the historical charging time, the historical temperature data is the temperature data stored at the historical charging time, the historical capacity increment data is the capacity increment data stored at the historical charging time, and the historical differential temperature data is the differential temperature data stored at the historical charging time.

[0064] Optionally, each target weight can also be expressed by the following formula:

[0065] in, Indicates indicators, Indicates The target weight corresponding to each indicator is No. The weight corresponding to the historical data of each indicator, Indicates The weight of the real-time data corresponding to each indicator, represents the historical data impact factor, Indicates the real-time data impact factor, for example In the case of represents the target voltage weight, represents a second preset voltage weight corresponding to the historical voltage data, Indicates a first preset voltage weight corresponding to the voltage data.

[0066] Optionally, the target voltage weight, target temperature weight, target capacity increment weight and target differential temperature weight can be determined by a recursive least squares algorithm based on a first preset voltage weight corresponding to the voltage data, a second preset voltage weight corresponding to the historical voltage data, a first preset temperature weight corresponding to the temperature data, a second preset temperature weight corresponding to the historical temperature data, a first preset capacity increment weight corresponding to the capacity increment data, a second preset capacity increment weight corresponding to the historical capacity increment data, a first preset differential temperature weight corresponding to the differential temperature data, and a second preset differential temperature weight corresponding to the historical differential temperature data.

[0067] In the above embodiment, the weights are optimized according to the real-time data through the weight optimization model. Compared with the static fixed weights commonly used in the prior art, the target weights obtained by the present invention based on the real-time data optimization can adapt to the changes in the battery pack characteristics and the external environment in which the battery pack is located, making the consistency evaluation results more accurate, thereby improving the performance, life and safety of the battery pack.

[0068] The consistency evaluation method of the battery pack provided by the present invention obtains the voltage data, temperature data, capacity increment data and differential temperature data corresponding to the battery pack during the charging process; respectively determines the voltage root mean square error corresponding to the voltage data, the temperature root mean square error corresponding to the temperature data, the capacity increment root mean square error corresponding to the capacity increment data and the differential temperature root mean square error corresponding to the differential temperature data; based on the voltage root mean square error, the temperature root mean square error, the capacity increment root mean square error and the differential temperature root mean square error, the consistency evaluation result corresponding to the battery pack is determined. The technical solution of the present invention evaluates the inconsistency corresponding to the battery pack based on the fusion of four different characteristics of voltage, temperature, capacity increment and differential temperature, thereby improving the accuracy of the consistency evaluation result.

[0069] The consistency evaluation device for a battery pack provided by the present invention is described below. The consistency evaluation device for a battery pack described below and the consistency evaluation method for a battery pack described above can be referred to each other.

[0070] Figure 2 is a schematic diagram of the structure of the battery pack consistency evaluation device provided by the present invention, such as Figure 2 As shown, the battery pack consistency evaluation device 200 includes the following modules: An acquisition module 210 is used to acquire voltage data, temperature data, capacity increment data and differential temperature data corresponding to the battery pack during the charging process; A determination module 220, configured to respectively determine a voltage root mean square error corresponding to the voltage data, a temperature root mean square error corresponding to the temperature data, a capacity increment root mean square error corresponding to the capacity increment data, and a differential temperature root mean square error corresponding to the differential temperature data; The evaluation module 230 is used to determine a consistency evaluation result corresponding to the battery pack based on the voltage root mean square error, the temperature root mean square error, the capacity increment root mean square error and the differential temperature root mean square error.

[0071] In one embodiment, the evaluation module 230 is specifically configured to: Determine a target voltage weight corresponding to the voltage data, a target temperature weight corresponding to the temperature data, a target capacity increment weight corresponding to the capacity increment data, and a target differential temperature weight corresponding to the differential temperature data; The product of the voltage root mean square error and the target voltage weight, the product of the temperature root mean square error and the target temperature weight, the product of the capacity increment root mean square error and the target capacity increment weight, and the product of the differential temperature root mean square error and the target differential temperature weight are summed to obtain the consistency evaluation result.

[0072] In one embodiment, the evaluation module 230 is further configured to: The first preset voltage weight corresponding to the voltage data, the second preset voltage weight corresponding to the historical voltage data, the first preset temperature weight corresponding to the temperature data, the second preset temperature weight corresponding to the historical temperature data, the first preset capacity increment weight corresponding to the capacity increment data, the second preset capacity increment weight corresponding to the historical capacity increment data, the first preset differential temperature weight corresponding to the differential temperature data, and the second preset differential temperature weight corresponding to the historical differential temperature data are input into a pre-trained weight optimization model to obtain the target voltage weight corresponding to the voltage data, the target temperature weight corresponding to the temperature data, the target capacity increment weight corresponding to the capacity increment data, and the target differential temperature weight corresponding to the differential temperature data output by the weight optimization model; wherein the weight optimization model is a neural network model pre-trained based on a weight training data set and labels corresponding to the weight training data set.

[0073] In one embodiment, the capacity increment data includes: the capacity increment values ​​corresponding to each single battery in the battery pack at all times during the charging process; the determination module 220 is specifically used to: For each moment in the charging process, the maximum capacity increment value at the moment is determined as the maximum capacity increment value corresponding to the moment; the minimum capacity increment value at the moment is determined as the minimum capacity increment value corresponding to the moment; The capacity increment root mean square error is determined based on the maximum capacity increment value and the minimum capacity increment value corresponding to all the time moments.

[0074] In one embodiment, the differential temperature data includes: differential temperature values ​​corresponding to each of the single cells in the battery pack at all times during the charging process; the determination module 220 is further configured to: For each of the moments in the charging process, the maximum differential temperature value at the moment is determined as the maximum differential temperature value corresponding to the moment; the minimum differential temperature value at the moment is determined as the minimum differential temperature value corresponding to the moment; The differential temperature root mean square error is determined based on the maximum differential temperature value and the minimum differential temperature value corresponding to all the moments.

[0075] In one embodiment, the battery pack consistency assessment device 200 further includes an early warning module, which is specifically used to: charging and discharging the battery pack according to a preset power and a preset rest time; During the charging and discharging process, if at least one of the discharge capacity, full pressure difference, empty pressure difference, temperature difference and maximum temperature of the battery pack does not meet the corresponding preset threshold range, a battery pack fault warning is triggered.

[0076] The consistency evaluation device of the battery pack provided by the present invention obtains the voltage data, temperature data, capacity increment data and differential temperature data corresponding to the battery pack during the charging process; respectively determines the voltage root mean square error corresponding to the voltage data, the temperature root mean square error corresponding to the temperature data, the capacity increment root mean square error corresponding to the capacity increment data and the differential temperature root mean square error corresponding to the differential temperature data; and determines the consistency evaluation result corresponding to the battery pack based on the voltage root mean square error, the temperature root mean square error, the capacity increment root mean square error and the differential temperature root mean square error. The technical solution of the present invention evaluates the inconsistency corresponding to the battery pack based on the fusion of four different characteristics of voltage, temperature, capacity increment and differential temperature, thereby improving the accuracy of the consistency evaluation result.

[0077] Figure 3 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 3 As shown, the electronic device may include: a processor 310, a communications interface 320, a memory 330 and a communication bus 340, wherein the processor 310, the communications interface 320 and the memory 330 communicate with each other through the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the consistency evaluation method of the battery pack, and the method includes: Obtaining voltage data, temperature data, capacity increment data and differential temperature data corresponding to the battery pack during the charging process; Respectively determining a voltage root mean square error corresponding to the voltage data, a temperature root mean square error corresponding to the temperature data, a capacity increment root mean square error corresponding to the capacity increment data, and a differential temperature root mean square error corresponding to the differential temperature data; A consistency evaluation result corresponding to the battery pack is determined based on the voltage root mean square error, the temperature root mean square error, the capacity increment root mean square error, and the differential temperature root mean square error.

[0078] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0079] On the other hand, the present invention further provides a computer program product, the computer program product includes a computer program, the computer program can be stored in a non-transitory computer-readable storage medium, when the computer program is executed by a processor, the computer can execute the battery pack consistency evaluation method provided by the above methods, the method includes: Obtaining voltage data, temperature data, capacity increment data and differential temperature data corresponding to the battery pack during the charging process; Respectively determining a voltage root mean square error corresponding to the voltage data, a temperature root mean square error corresponding to the temperature data, a capacity increment root mean square error corresponding to the capacity increment data, and a differential temperature root mean square error corresponding to the differential temperature data; A consistency evaluation result corresponding to the battery pack is determined based on the voltage root mean square error, the temperature root mean square error, the capacity increment root mean square error, and the differential temperature root mean square error.

[0080] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the consistency evaluation method of the battery pack provided by the above methods is implemented, and the method includes: Obtaining voltage data, temperature data, capacity increment data and differential temperature data corresponding to the battery pack during the charging process; Respectively determining a voltage root mean square error corresponding to the voltage data, a temperature root mean square error corresponding to the temperature data, a capacity increment root mean square error corresponding to the capacity increment data, and a differential temperature root mean square error corresponding to the differential temperature data; A consistency evaluation result corresponding to the battery pack is determined based on the voltage root mean square error, the temperature root mean square error, the capacity increment root mean square error, and the differential temperature root mean square error.

[0081] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0082] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A battery pack consistency evaluation method, characterized in that: include: Obtaining voltage data, temperature data, capacity increment data and differential temperature data corresponding to the battery pack during the charging process; Respectively determining a voltage root mean square error corresponding to the voltage data, a temperature root mean square error corresponding to the temperature data, a capacity increment root mean square error corresponding to the capacity increment data, and a differential temperature root mean square error corresponding to the differential temperature data; Determining a consistency evaluation result corresponding to the battery pack based on the voltage root mean square error, the temperature root mean square error, the capacity increment root mean square error, and the differential temperature root mean square error; The determining a consistency evaluation result corresponding to the battery pack based on the voltage root mean square error, the temperature root mean square error, the capacity increment root mean square error and the differential temperature root mean square error includes: Determine a target voltage weight corresponding to the voltage data, a target temperature weight corresponding to the temperature data, a target capacity increment weight corresponding to the capacity increment data, and a target differential temperature weight corresponding to the differential temperature data; The product of the voltage root mean square error and the target voltage weight, the product of the temperature root mean square error and the target temperature weight, the product of the capacity increment root mean square error and the target capacity increment weight, and the product of the differential temperature root mean square error and the target differential temperature weight are summed to obtain the consistency evaluation result.

2. The consistency evaluation method of a battery pack according to claim 1, characterized in that: The determining of the target voltage weight corresponding to the voltage data, the target temperature weight corresponding to the temperature data, the target capacity increment weight corresponding to the capacity increment data, and the target differential temperature weight corresponding to the differential temperature data includes: The first preset voltage weight corresponding to the voltage data, the second preset voltage weight corresponding to the historical voltage data, the first preset temperature weight corresponding to the temperature data, the second preset temperature weight corresponding to the historical temperature data, the first preset capacity increment weight corresponding to the capacity increment data, the second preset capacity increment weight corresponding to the historical capacity increment data, the first preset differential temperature weight corresponding to the differential temperature data, and the second preset differential temperature weight corresponding to the historical differential temperature data are input into a pre-trained weight optimization model to obtain the target voltage weight corresponding to the voltage data, the target temperature weight corresponding to the temperature data, the target capacity increment weight corresponding to the capacity increment data, and the target differential temperature weight corresponding to the differential temperature data output by the weight optimization model; wherein the weight optimization model is a neural network model pre-trained based on a weight training data set and labels corresponding to the weight training data set.

3. The consistency evaluation method of a battery pack according to claim 1, characterized in that: The capacity increment data includes: the capacity increment values ​​corresponding to each single battery in the battery pack at all times during the charging process; Determining a capacity increment root mean square error corresponding to the capacity increment data includes: For each moment in the charging process, the maximum capacity increment value at the moment is determined as the maximum capacity increment value corresponding to the moment; the minimum capacity increment value at the moment is determined as the minimum capacity increment value corresponding to the moment; The capacity increment root mean square error is determined based on the maximum capacity increment value and the minimum capacity increment value corresponding to all the time moments.

4. The consistency evaluation method of a battery pack according to claim 3, characterized in that: The differential temperature data includes: differential temperature values ​​corresponding to each of the single cells in the battery pack at all times during the charging process; Determining a differential temperature root mean square error corresponding to the differential temperature data includes: For each of the moments in the charging process, the maximum differential temperature value at the moment is determined as the maximum differential temperature value corresponding to the moment; the minimum differential temperature value at the moment is determined as the minimum differential temperature value corresponding to the moment; The differential temperature root mean square error is determined based on the maximum differential temperature value and the minimum differential temperature value corresponding to all the moments.

5. The method for evaluating the consistency of a battery pack according to any one of claims 1 to 4, characterized in that: The method further comprises: charging and discharging the battery pack according to a preset power and a preset rest time; During the charging and discharging process, if at least one of the discharge capacity, full pressure difference, empty pressure difference, temperature difference and maximum temperature of the battery pack does not meet the corresponding preset threshold range, a battery pack fault warning is triggered.

6. A battery pack consistency evaluation device, characterized in that: include: An acquisition module is used to acquire voltage data, temperature data, capacity increment data and differential temperature data corresponding to the battery pack during the charging process; a determination module, configured to respectively determine a voltage root mean square error corresponding to the voltage data, a temperature root mean square error corresponding to the temperature data, a capacity increment root mean square error corresponding to the capacity increment data, and a differential temperature root mean square error corresponding to the differential temperature data; an evaluation module, configured to determine a consistency evaluation result corresponding to the battery pack based on the voltage root mean square error, the temperature root mean square error, the capacity increment root mean square error, and the differential temperature root mean square error; The evaluation module is specifically used for: Determine a target voltage weight corresponding to the voltage data, a target temperature weight corresponding to the temperature data, a target capacity increment weight corresponding to the capacity increment data, and a target differential temperature weight corresponding to the differential temperature data; The product of the voltage root mean square error and the target voltage weight, the product of the temperature root mean square error and the target temperature weight, the product of the capacity increment root mean square error and the target capacity increment weight, and the product of the differential temperature root mean square error and the target differential temperature weight are summed to obtain the consistency evaluation result.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the consistency evaluation method for a battery pack according to any one of claims 1 to 5 is implemented.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the consistency evaluation method of a battery pack as claimed in any one of claims 1 to 5 is implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the consistency evaluation method of a battery pack as claimed in any one of claims 1 to 5 is implemented.

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