System and method for assessing health of battery cell groups in a battery pack

Abstract

The present invention relates to evaluating the health of a battery cell group in a battery pack. A system for evaluating the health of a battery cell group within a module of a battery pack includes a controller having a processor and a tangible, non-transitory memory having instructions recorded thereon. One or more sensors are configured to acquire a series of corresponding average battery cell group voltages at different stages. The controller is adapted to acquire the measured voltage of the battery cell group at a calibration event occurring after a third stage ( V M The controller is adapted based in part on the difference factor ( ΔV ) and the third stage battery cell group voltage ( V 3 ) and calculate the predicted voltage of the battery cell group ( V P ). Based at least in part on the measured voltage ( V M ) and predicted voltage ( V P ) between the control battery cell groups.

Description

TECHNICAL FIELD

[0001] The present disclosure relates generally to systems and methods of assessing battery cell health in a battery pack. BACKGROUND

[0002] The use of rechargeable energy sources has greatly increased over the past several years. For example, mobile platforms, such as electric vehicles, can use rechargeable energy sources as both a dedicated energy source and a non-dedicated energy source. Rechargeable energy storage devices with battery packs can store and discharge electrochemical energy as needed during a given mode of operation. This electrochemical energy can be used for propulsion, heating or cooling of a vehicle cabin, powering vehicle accessories, and other uses.

[0003] During formation of individual battery cells of a battery pack, active materials can be mixed with a polymeric binder, a conductive additive, and a solvent to form a mixture. The mixture can be coated onto a current collector foil and dried to remove the solvent and form a porous electrode coating. Uniformity and structure of the electrode material is tested during the battery cell manufacturing process and prior to assembly of the battery pack. After individual battery cells are assembled into modules and multiple modules are assembled into a battery pack, testing for various characteristics of the battery cells becomes more constrained and challenging. SUMMARY

[0004] Disclosed herein are systems and methods for assessing health of a group of battery cells within a module of a battery pack. One or more sensors are configured to acquire a series of respective average battery cell group voltages, including a first stage battery cell group voltage (VI) at a first stage, a second stage battery cell group voltage (V2) at a second stage, and a third stage battery cell group voltage (V3) at a third stage. The first stage occurs before the second stage, and the second stage occurs before the third stage.

[0005] The system includes a controller having a processor and a tangible non-transitory memory having instructions recorded thereon. Execution of the instructions by the processor causes the controller to acquire a measured voltage (V M ) of the group of battery cells at a calibration event occurring after the third stage. The controller is adapted to calculate a predicted voltage (V P ) of the group of battery cells based in part on a sum of the difference factor (AV) and the third stage battery cell group voltage (V3). The group of battery cells is controlled based at least in part on a difference between the measured voltage (V M ) and the predicted voltage (V P ). Controlling the group of battery cells includes flagging the group of battery cells as acceptable for use if the predicted voltage (V P ) is less than or equal to the measured voltage (V M ). Controlling the group of battery cells includes flagging the group of battery cells as unacceptable for use if the predicted voltage (V P ) is greater than the measured voltage (VM ), then the battery cell group is assigned or designated for further evaluation.

[0006] The second phase corresponds to the time of assembly of the module. The third phase corresponds to the end-of-line measurements after assembly of the battery pack. The first phase, the second phase, the third phase, and the calibration event correspond to approximately 15, 45, 46, and 60 days, respectively, after manufacturing the battery cell group. In one example, the battery cell group includes at least three individual battery cells.

[0007] The difference factor (AV) depends in part on the first duration (T1), the first set of constants (a, b, t), and an exponent of the time reset factor (F). The first duration (T1) is the time between the calibration event and the third phase. The first duration (T1) is in days, and the difference factor (AV) in millivolts can be calculated as:

[0008] In some embodiments, the first duration (T1) is between approximately 10 days and 16 days after assembly of the battery cell group. The time reset factor (F) depends on the first phase battery cell group voltage (V1), the second phase battery cell group voltage (V2), the third phase battery cell group voltage (V3), a second duration (T2), and a reset constant (g). The second duration (T2) is the time between the first phase and the third phase. If the second duration (T2) is in days, the first phase battery cell group voltage (V1), the second phase battery cell group voltage (V2), and the third phase battery cell group voltage (V3) are in millivolts, then the time reset factor (F) can be calculated as:

[0009]

[0010] The second duration (T2) can be between approximately 28 days and 32 days after assembly of the battery cell group. In some embodiments, the predicted voltage (V P ) of the battery cell group is the sum of the difference factor (AV), the third phase battery cell group voltage (V3), and a rest time adjustment factor. The rest time adjustment factor is based on the first duration (T1), which is the time between the calibration event and the third phase, and a second set of constants (A, B, C, D). The rest time adjustment factor is determined as: [A*ln(T1) + B*(T1) 2 +C*(T1) + D].

[0011] Disclosed herein is a method for assessing health of a battery cell group within a module of a battery pack in a system having a controller with a processor and tangible non-transitory memory. The method includes, via one or more sensors, obtaining a data series of respective average battery cell group voltages, the battery cell group voltages including a first stage battery cell group voltage (VI) at a first stage, a second stage battery cell group voltage (V2) at a second stage, and a third stage battery cell group voltage (V3) at a third stage, the first stage occurring before the second stage, and the second stage occurring before the third stage. Calculating a predicted voltage (V P ) for the battery cell group based on the data series, the predicted voltage (V P ) being a sum of at least a difference factor (AV) and the third stage battery cell group voltage (V3). The method includes obtaining a measured voltage (V M ) for the battery cell group at a calibration event occurring after the third stage. Controlling the battery cell group based at least in part on a difference between the measured voltage (V M ) and the predicted voltage (V P ).

[0012] The above features and advantages and other features and advantages of the present disclosure are readily apparent through consideration of the following detailed description of the best modes for carrying out the present disclosure when taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 is a schematic illustration of a system for assessing battery cell health in a battery cell group, the system having a controller;

[0014] Figure 2 is a schematic flowchart of a method implemented by the system of Figure 1 ;

[0015] Figure 3 is a schematic plot of self-discharge curves for example battery cell groups, showing instantaneous self-discharge rates on a vertical axis and time on a horizontal axis; and

[0016] Figure 4 is a schematic visualization of data obtained for a plurality of battery cell groups, with a vertical axis showing a difference between measured and predicted voltages for each battery cell group and a horizontal axis showing an order of the battery cell groups. DETAILED DESCRIPTION

[0017] Reference is made to the drawings, wherein like reference numerals are used to refer to like components throughout Figure 1A system 10 for assessing the health of a portion of a battery pack 12 is schematically illustrated. The battery pack 12 can be used as one of many battery packs in a rechargeable energy storage device 14. In some embodiments, the rechargeable energy storage device 14 is located in an electric vehicle 16. The electric vehicle 16 can be a mobile platform such as, but not limited to, a passenger car, a sport utility vehicle, a light truck, a heavy duty vehicle, an ATV, a van, a bus, a transit vehicle, a bicycle, a mobile robot, an agricultural implement (e.g., a tractor), a sports-related equipment (e.g., a golf cart), a boat, an airplane, and a train. It should be understood that the electric vehicle 16 can take many different forms and have additional components.

[0018] Referring to Figure 1 The battery pack 12 includes a plurality of modules 18, such as a first module 20 and a second module 22. Each of the plurality of modules 18 has at least one battery cell group 24 (hereafter “one or more” is omitted). Referring to Figure 1 The battery cell group 24 is composed of individual battery cells 26. Although the battery cell group 24 includes three respective battery cells in the example, the number of battery cells per battery cell group, the number of battery cell groups per module, and the number of modules per battery pack can vary based on the current application.

[0019] The individual battery cells 26 can include battery cells having different chemical compositions including, but not limited to, lithium-ion, lithium-iron, nickel-metal hydride, and lead-acid batteries. The battery pack 12 is first manufactured as an electrode group and then assembled into individual battery cells 26, which are then assembled into the plurality of modules 18 to form the battery pack 12. After the battery cell groups 24 are assembled into the first module 20 and the battery pack 12, testing for various characteristics of the battery cell groups 24 becomes more constrained and challenging.

[0020] Referring to Figure 1 The system 10 includes a controller C having at least one processor P and at least one memory M (or non-transitory, tangible computer-readable storage medium) having instructions recorded thereon for performing a method 100 for assessing the health of the battery cell groups 24 after the battery cell groups 24 are assembled into the first module 20 and the battery pack 12. The method 100 is described below with reference to Figure 2 The memory M is capable of storing a set of executable instructions and the processor P is capable of executing the set of instructions stored in the memory M.

[0021] System 10 takes into account peripheral or environmental noise factors to predict an acceptable discharge rate for example battery cell groups. Noise factors can include post-formation aging, electrical neutrality at the battery pack level, charge levels at end-of-line measurements, changes in charge from module assembly (battery cell groups 24 assembled into first modules 20) to battery pack assembly (multiple modules 18 assembled into battery pack 12), changes in charge within battery cell groups 24, formation temperature, storage temperature, electrical neutrality at the module level, respective measurement accuracy at various stages, and calibration events. An accurate assessment of battery cell health is made by comparing the predicted discharge rate to the observed discharge rate.

[0022] System 10 provides robust monitoring of various battery cell groups in battery pack 12 without the need to disassemble components of battery pack 12, and as a result, improved detection of defective battery cell groups. System 10 can be implemented prior to use of battery pack 12 in an electrically powered device, such as electric vehicle 16. Figure 1

[0023] Referring to Figure 1 , each of multiple modules 18 can include a module management unit 30 embedded within it accordingly. Module management unit 30 is configured to store one or more parameters about the module as a whole or individual battery cells 26 in the module, such as voltage from each of battery cell groups 24, module current, and module temperature. Similarly, battery pack 12 can include a pack management unit 32 embedded within it and adapted to measure / store various parameters. Module management unit 30 and pack management unit 32 can be adapted to interface with controller C.

[0024] Controller C is specifically configured to perform the blocks of method 100, and can receive input from one or more measurement devices or sensors 34. Referring to Figure 1 , sensors 34 can include pack sensors 36, module sensors 38, and battery cell group sensors 40. Sensors 34 can be configured to acquire data about temperature, voltage, current, state of charge, capacity, state of health, and other factors of different components of battery pack 12. Sensor technology available to those skilled in the art can be employed.

[0025] Referring to Figure 1 , sensors 34 can communicate with controller C via a wireless network 42, which can be a short-range network or a long-range network. Wireless network 42 can be a communication bus, which can be in the form of a serial controller area network (CAN-BUS). Wireless network 42 can be a wireless local area network (LAN) that links multiple devices using a wireless distribution method, a wireless metropolitan area network (MAN) that connects several wireless LANs, or a wireless wide area network (WAN) that covers a large area such as a neighborhood town and city. Other types of connections can be employed.​

[0026] Referring now to Figure 2 , a flowchart of a method 100 for assessing the health of the battery cell group 24 is shown. The method 100 can be stored on the controller C of the battery pack 12 and can be executed by the controller C. The method 100 need not be applied in the specific order recited herein, and can be executed dynamically. Further, it should be understood that some steps can be omitted. As used herein, the terms “dynamic” and “dynamically” describe a step or process that is executed in real-time, and is characterized by monitoring or otherwise determining the state of a parameter during execution of the routine or between iterations of execution of the routine and regularly or periodically updating the state of the parameter. Figure 1

[0027] According to block 102 of Figure 2 , the controller C acquires, via the sensors 34, a data series of respective average battery cell group voltages. The data series is acquired throughout several stages of a test / diagnostic that unfolds over time. The data series includes a first stage battery cell group voltage (VI) at a first stage, a second stage battery cell group voltage (V2) at a second stage, and a third stage battery cell group voltage (V3) at a third stage. The first stage occurs before the second stage, and the second stage occurs before the third stage. Table 1 below shows a non-limiting example of a timeline for the first stage, the second stage, the third stage, and a calibration event, respectively, after assembly of the battery cell group 24.

[0028] Table 1

[0029] First stage Second stage Third stage Calibration event Time (days) 15 45 46 60 .

[0030] In one example, the first stage corresponds to an open circuit voltage of the battery cell group 24 prior to the battery cell group 24 being assembled into the first module 20. The second stage can correspond to the time of assembly of the first module 20, i.e., when the battery cell group 24 (and other battery cell groups) are assembled to form the first module 20. The third stage can correspond to an end-of-line (EOL) measurement after the battery pack 12 is assembled, i.e., when the plurality of modules 18 are assembled to form the battery pack 12. The end-of-line (EOL) measurement refers to a measurement that is completed prior to reaching the end of the production line once the battery pack 12 is assembled.

[0031] Further, according to block 102 of Figure 2 , the controller C computes a predicted voltage (V P ) of the battery cell group 24 based in part on the data series. As described below, the predicted voltage (V P ) is used to distinguish between respective data sets characterizing unacceptable devices and acceptable devices according to predefined criteria. The predicted voltage (V​P ) is at least the sum of the difference factor (AV) and the third stage battery cell group voltage (V3). In some embodiments, the predicted voltage (V P ) is the sum of the difference factor (AV) and the respective voltage at the third stage (V3), and such that:

[0032] [V P = AV + V3].

[0033] The difference factor (AV) depends in part on the first duration (T1), the first constant set (a, b, t), and an exponent of the time reset factor (F). Here, the first duration (T1) is the time between the calibration event and the third stage. In one example, the first duration (T1) is between about 10 days and 16 days after manufacturing of the battery cell group 24. In one example, the first constant set (a, b, t) has the following values, respectively: [0.5, -0.10, 15]. In the case where the first duration (T1) is in days, the difference factor (AV) is calculated as:

[0034]

[0035] The time reset factor (F) depends on the first stage battery cell group voltage (V1), the second stage battery cell group voltage (V2), the third stage battery cell group voltage (V3), the second duration (T2), and a reset constant (g). The second duration (T2) is the time between the first stage and the third stage. In one example, the second duration (T2) is between 28 days and 32 days after manufacturing of the battery cell group. In one example, the reset constant (g) is 0.9. If the second duration (T2) is in days, the first stage battery cell group voltage (V1), the second stage battery cell group voltage (V2), and the third stage battery cell group voltage (V3) are in millivolts, then the time reset factor (F) can be calculated as:

[0036]

[0037] In some embodiments, the predicted voltage (V P ) is the sum of the difference factor (AV), the respective voltage at the third stage (V3), and a rest time adjustment factor, such that: P = AV + V3 + Rest Time Adjustment Factor.

[0038] The rest time adjustment factor is based on the first duration (T1) and a second set of constants (A, B, C, D). In one example, the second set of constants (A, B, C, D) has the following values: [0.01, 2.0e-06, -0.0005, -0.02]. The rest time adjustment factor is determined as:

[0039] [A * ln(T1) + B * (T1 2 + C * (T1) + D].

[0040] The first set of constants (a, b, t), the second set of constants (A, B, C, D), and the reset constant (g) can be obtained using various methods available to those skilled in the art, including but not limited to, empirical-based regression models, numerical simulations, experimental designs, and machine learning models. Referring to Figure 1 , the controller C can access the machine learning model 50 from a cloud unit or a remote server 44 via the network 42. Alternatively, the machine learning model 50 can be embedded in the controller C. The machine learning model 50 can be configured to find parameters, weights, or structures that minimize a corresponding cost function. The machine learning model 50 can include a neural network, such as a feedforward artificial neural network having an input layer, at least one hidden layer, and an output layer. The machine learning model 50 can be trained with a training set of battery cell groups, where the training set has paired values of a data series and an obtainable predicted voltage, which are input into the input layer, such that the output layer produces the first set of constants (a, b, t), the second set of constants (A, B, C, D), and the reset constant (g). The machine learning model 50 can contain multiple regression models.

[0041] The first set of constants (a, b, t) can be obtained by regression modeling of the self-discharge rate of the battery cell group 24. Figure 3 A schematic diagram showing an example self-discharge rate curve is shown. The vertical axis 202 shows the instantaneous self-discharge rate (dV / dt), while the horizontal axis 204 shows time (t). The first set of constants includes an early cycle constant (a), a late cycle constant (b), and a time constant (t). The self-discharge rate can be modeled as follows:

[0042]

[0043] Referring to Figure 3 , at position 206 (corresponding to time = 0), the self-discharge rate corresponds to (dV / dt = a + b). At position 210 (corresponding to time approaching infinity), the self-discharge rate corresponds to (dV / dt = b). The time constant (t) connects the early cycle constant (a) and the late cycle constant (b). Referring to Figure 3At position 208 (corresponding to time t = T), the contribution of the early cycle constant (a) to the self-discharge rate drops by 63.2%. In one example, the first phase is selected to be before or at time t = T. For example, line 212 (occurring before time t = T) can correspond to the first phase.

[0044] Referring now to Figure 2 , according to block 104, the controller C obtains a measured voltage (V M ) of the battery cell group 24 at a calibration event occurring after the third phase. In some embodiments, the calibration event is set to be between 7 and 21 days after the third phase. In some embodiments, the calibration event is set to be exactly 14 days after the third phase. The method 100 then proceeds to block 104.

[0045] According to block 106, the controller C is configured to determine whether the predicted voltage (V P ) is less than or equal to the measured voltage (V M ). The operation of the battery cell group 24 is controlled based at least in part on the difference between the measured voltage (V M ) and the predicted voltage (V P ). If the predicted voltage (V P ) is less than or equal to the measured voltage (V M ), the method 100 proceeds to block 108, at which controlling the battery cell group 24 includes flagging the battery cell group 24 as acceptable for use, and the method 100 terminates. If the predicted voltage (V P ) is greater than the measured voltage (V M ), the method 100 proceeds to block 110, at which controlling the battery cell group 24 includes assigning the battery cell group 24 for further evaluation, and the method 100 terminates. In other words, the controller C can be programmed to run further diagnostic tests and / or determine whether remedial measures are necessary.

[0046] Referring now to Figure 4 , a schematic visualization of data obtained for a plurality of battery cell groups in a data set is shown. The vertical axis 302 shows the difference between the predicted voltage (V P ) and the measured voltage (V M ). The horizontal axis 304 shows the order of the battery cell groups. The horizontal axis is plotted in time (in days). The line 306 indicates the position at which the predicted voltage (V P ) is equal to the measured voltage (V M ). Referring to Figure 4 , test battery cell groups falling into a first data set 308 (defined as at or above the line 306) are considered to be acceptable, and test battery cell groups falling into a second data set 310 (defined as below the line 306) are considered to be unacceptable.

[0047] In summary, the system 10 (via execution of the method 100) accounts for environmental noise factors, including factors affecting the battery cells during both the assembly process and the upstream battery cell production process. The system 10 reduces the evaluation time of the battery cell group 24, which correspondingly reduces the manufacturing cost of the battery pack 12. The system 10 improves the detection of defective battery cell groups in the battery pack 12. Accordingly, the system 10 improves the functionality of the battery pack 12.

[0048] Figure 2 The flow diagrams depicted herein are examples of architectures, functions, and operations that can implement possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. To this end, each block in the flow diagrams or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions implementing the specific logical functions required. It should also be noted that each block in the flow diagrams and / or block diagrams and combinations of blocks in the flow diagrams and / or block diagrams can be implemented by dedicated hardware-based energy systems that perform the specified functions or acts, or combinations of dedicated hardware and computer instructions.

[0049] The controller C includes computer-readable media (also referred to as processor-readable media), including non-transitory (e.g., tangible) media that participate in providing data (e.g., instructions) that can be read by a computer (e.g., by a processor of a computer). Such media can take many forms, including but not limited to, non-volatile media and volatile media. Non-volatile media can include, for example, optical or magnetic disks and other persistent memory. Volatile media can include, for example, dynamic random access memory (DRAM), which can constitute a main memory. Such instructions can be transmitted by one or more transmission media including coaxial cables; copper wire and fiber optics, including the wires that comprise a system bus coupled to a processor of a computer. Some forms of computer-readable media include the following: a floppy disk; a flexible disk; a hard disk; magnetic tape; other magnetic media; a CD-ROM; CDS; DVDs; other optical media; punch cards; paper tape; other physical media with patterns of holes; a RAM; a PROM; an EPROM; a FLASH-EPROM; any other memory chip or cartridge; or any other medium from which a computer can read.

[0050] The lookup tables, databases, data repositories, or other data stores described herein can include various kinds of mechanisms for storing, accessing, and retrieving various kinds of data, including a hierarchical database, a set of files in a file system, an application database in a proprietary format, a relational database management system (RDBMS), etc. Each such data store can be included within a computing device employing a computer operating system (such as one of those mentioned above) and can be accessed via a network in one or more manners. A file system can be employed to access files in which the data is stored and can include files stored in various formats. An RDBMS can employ the Structured Query Language (SQL) in addition to a language for creating, storing, editing, and executing stored procedures. The SQL can be the PL / SQL language mentioned above.

[0051] The detailed description and drawings are supportive and descriptive of the disclosure, but the scope of the disclosure is defined solely by the claims. Although there has been described in detail a best mode for carrying out the disclosure claimed, and some other embodiments, various alternatives, modifications, and equivalents can be used. Furthermore, the features of the various embodiments described in this specification and illustrated in the drawings can not necessarily be dependent on each other for implementation. Also, it is possible for each of the features described in one example of an embodiment to be combined with one or more of the other desirable features from other embodiments, resulting in other embodiments that are not described or illustrated in the text or drawings. Therefore, such other embodiments fall within the scope of the claims.

Claims

1. A system for evaluating the health of a battery cell group within a module of a battery pack, the system comprising: One or more sensors configured to acquire a data series of corresponding average battery cell group voltages, including a first-stage battery cell group voltage V1 at a first stage, a second-stage battery cell group voltage V2 at a second stage, and a third-stage battery cell group voltage V3 at a third stage, the first stage occurring before the second stage, and the second stage occurring before the third stage; A controller having a processor and tangible, non-transitory memory having instructions recorded thereon, execution of the instructions by the processor causing the controller to: Obtain the data series and calculate the predicted voltage V of the battery cell group based on the data series P , the predicted voltage V P is the sum of at least one difference factor ΔV and the battery cell group voltage V3 in the third stage; Obtaining the measured voltage V of the battery cell group at a calibration event occurring after the third stage M ;and Based at least in part on the measured voltage V M and the predicted voltage V P The difference between the two controls the battery cell group; wherein the difference factor ΔV depends in part on the first duration T1, the first set of constants (α, β, τ) and an exponent of a time reset factor F; and The first duration T1 is the time between the calibration event and the third stage, and the time reset factor F depends on the first stage battery cell group voltage V1, the second stage battery cell group voltage V2, the third stage battery cell group voltage V3, the second duration T2, and a reset constant γ; and The second duration T2 is the time between the first stage and the third stage.

2. The system of claim 1, wherein: Controlling the battery cell group includes, if the predicted voltage V P is less than or equal to the measured voltage V M , the battery cell pack is marked as acceptable for use.

3. The system of claim 2, wherein: Controlling the battery cell group includes, if the predicted voltage V P is greater than the measured voltage V M , the battery cell group is assigned for further evaluation.

4. The system of claim 1 , wherein: The second phase corresponds to the time of assembly of the modules, and the third phase corresponds to end-of-line measurements after the battery packs are assembled.

5. The system according to claim 4, wherein: The first phase, the second phase, the third phase, and the calibration event correspond to 15, 45, 46, and 60 days, respectively, after manufacture of the battery cell pack.

6. The system according to claim 1, wherein: The battery cell group includes at least three individual battery cells.

7. The system of claim 1 , wherein: The first duration T1 is in days, and the difference factor ΔV in millivolts is calculated as:

8. The system according to claim 1, wherein: The first duration T1 is between 10 days and 16 days after assembly of the battery cell group.

9. The system of claim 1 , wherein: The second duration T2 is in days, the first-stage battery cell group voltage V1, the second-stage battery cell group voltage V2, and the third-stage battery cell group voltage V3 are in millivolts, and the time reset factor F is calculated as:

10. The system according to claim 1, wherein: The second duration T2 is between 28 days and 32 days after assembly of the battery cell group.

11. The system of claim 1 , wherein: The predicted voltage V of the battery cell group P is the sum of the difference factor ΔV, the third stage battery cell group voltage V3, and the rest time adjustment factor; and The rest time adjustment factor is based on a first duration T1 , which is the time between the calibration event and the third phase, and a second set of constants (A, B, C, D).

12. The system according to claim 11, wherein The rest period adjustment factor is determined as: [A*ln(T1)+B*(T1) 2 +C*(T1)+D].

13. A method for evaluating the health of a battery cell group within a module of a battery pack in a system having a controller with a processor and tangible, non-transitory memory, the method comprising: acquiring, via one or more sensors, a data series of corresponding average battery cell group voltages, including a first-stage battery cell group voltage V1 at a first stage, a second-stage battery cell group voltage V2 at a second stage, and a third-stage battery cell group voltage V3 at a third stage, wherein the first stage occurs before the second stage, and the second stage occurs before the third stage; Calculate the predicted voltage V of the battery cell group based on the data series P , the predicted voltage V P is the sum of at least the difference factor ΔV and the battery cell group voltage V3 in the third stage; Obtaining the measured voltage V of the battery cell group at a calibration event occurring after the third stage M ;and Based at least in part on the measured voltage V M and the predicted voltage V P The difference between the two controls the battery cell group; determining the difference factor ΔV based at least in part on a first time duration T1 between the calibration event and the third phase, a first set of constants (α, β, τ), and an exponent of a time reset factor F; The time reset factor F is determined based on the first stage battery cell group voltage V1, the second stage battery cell group voltage V2, the third stage battery cell group voltage V3, a second duration T2 and a reset constant γ; the second duration T2 is the time between the first stage and the third stage.

14. The method according to claim 13, wherein Controlling the battery cell group includes: If the predicted voltage V P is less than or equal to the measured voltage V M , then marking the battery cell pack as acceptable for use; and If the predicted voltage V P is greater than the measured voltage V M , the battery cell group is assigned for further evaluation.

15. The method according to claim 13, further comprising: The predicted voltage V of the battery cell group P is determined as the sum of the difference factor ΔV, the third phase battery cell group voltage V3, and a rest time adjustment factor based on a second constant set (A, B, C, D), and a first duration T1 between the calibration event and the third phase.

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