Method, device and storage medium for assessing state of health of a battery

Abstract

The application discloses a method, device and storage medium for evaluating battery health state, and relates to the technical field of batteries, and the method comprises the following steps: acquiring operation data of a battery cluster to be evaluated, wherein the operation data comprises data of each battery cell in the battery cluster during operation; determining first capacity loss of each battery cell caused by intrinsic aging and second capacity loss of each battery cell caused by consistency difference of battery cell capacity in the battery cluster at a target moment based on the operation data; determining capacity loss of the battery cluster based on the first capacity loss and the second capacity loss of each battery cell; and evaluating the health state of the battery cluster at the target moment based on the capacity loss of the battery cluster.

Description

Technical Field

[0001] This disclosure relates to the field of battery technology, and more particularly to a method, apparatus, and storage medium for assessing the health status of a battery. Background Technology

[0002] With the increasing global demand for clean energy, energy storage technology, as a key means to address the intermittency and volatility of renewable energy, has received widespread attention and rapid development. Energy storage stations play a vital role in power systems, enabling the storage and release of electrical energy and effectively improving the stability, reliability, and flexibility of the power system.

[0003] Batteries are the core component of energy storage stations, and the State of Health (SOH) of a battery is a key indicator for measuring battery performance and remaining lifespan. Accurately estimating the battery's state of health is crucial for the safe and efficient operation of energy storage stations. It helps maintenance personnel to develop maintenance plans in advance, avoiding downtime or accidents caused by battery failures, and also helps to optimize the charging and discharging strategies of energy storage stations, improving battery efficiency and lifespan, and reducing operating costs.

[0004] In related technologies, battery health status is usually assessed based on physical models. While this method can theoretically describe the internal physical changes of the battery relatively accurately, it ignores the impact of changes in battery usage and environmental conditions on battery capacity. This makes it difficult to accurately obtain and update the model parameters, resulting in low accuracy in battery health status assessment. Summary of the Invention

[0005] This disclosure provides a method, apparatus, and storage medium for assessing the health status of a battery.

[0006] In a first aspect, embodiments of this disclosure provide a method for assessing the health status of a battery, comprising: acquiring operational data of a battery cluster to be assessed, the operational data including operational data of each cell in the battery cluster; based on the operational data, determining a first capacity loss of each cell at a target time due to intrinsic aging and a second capacity loss due to inconsistencies in the capacity of cells within the battery cluster; determining the capacity loss of the battery cluster based on the first and second capacity losses of each cell; and assessing the health status of the battery cluster at the target time based on the capacity loss of the battery cluster.

[0007] Secondly, embodiments of this disclosure provide an electronic device, including a processor and a memory storing a computer program, characterized in that, when the computer program is executed by the processor, it can implement the method for assessing battery health status described in the above embodiments.

[0008] Thirdly, embodiments of this disclosure provide a non-transient computer storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method for evaluating battery health status described in the above embodiments.

[0009] The method for assessing battery health status according to embodiments of this disclosure can determine, based on the operating data of the battery cluster, the first capacity loss caused by intrinsic aging of each cell within the battery cluster at a target time, and the second capacity loss caused by the inconsistency of cell capacity within the battery cluster. Then, based on the first and second capacity losses of each cell, the capacity loss of the battery cluster is determined, and finally, based on the capacity loss of the battery cluster, the health status of the battery cluster at the target time is assessed. By combining the battery's own capacity decay with the impact of the battery's usage environment on capacity, the health status of the battery cluster can be assessed more accurately.

[0010] Other features and advantages of this disclosure will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the disclosure. Other advantages of this disclosure may be realized and obtained by means of the methods described in the description and the accompanying drawings. Attached Figure Description

[0011] The accompanying drawings are used to provide an understanding of the technical solutions disclosed herein and form part of the specification. They are used together with the embodiments of the present disclosure to explain the technical solutions of the present disclosure and do not constitute a limitation on the technical solutions of the present disclosure.

[0012] Figure 1 This is a schematic flowchart of one embodiment of the method for assessing battery health status disclosed herein; Figure 2 This is a schematic flowchart of one embodiment of the method for assessing battery health status disclosed herein; Figure 3 This is a schematic flowchart of one embodiment of the method for assessing battery health status disclosed herein; Figure 4 This disclosure provides a flowchart illustrating one embodiment of a method for assessing battery health status. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this disclosure clearer, the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described in this disclosure can be arbitrarily combined with each other.

[0014] The embodiments disclosed herein are not necessarily limited to the dimensions shown in the drawings, and the shapes and sizes of the components in the drawings do not reflect actual proportions. Furthermore, the drawings schematically illustrate ideal examples, and the embodiments of this disclosure are not limited to the shapes or values ​​shown in the drawings.

[0015] The ordinal numbers such as "first" and "second" in this disclosure are used to avoid confusion among the constituent elements and do not indicate any order, quantity, or importance.

[0016] In this disclosure, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linkage" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; a mechanical connection or an electrical connection; a direct connection, an indirect connection via an intermediate component, or a connection within two components. Those skilled in the art can understand the specific meaning of these terms in this disclosure according to the specific circumstances.

[0017] like Figure 1 As shown, the method for assessing battery health status disclosed herein may include the following steps.

[0018] Step 110: Obtain the operational data of the battery cluster to be evaluated.

[0019] The operational data includes data on the operation of each cell in the battery cluster.

[0020] Typically, an energy storage station can include multiple battery clusters, and each battery cluster can include multiple battery cells. The energy storage station can record the operational data of each battery cell through a Battery Management System (BMS) and upload it to a server. This data can include, for example, time-series data of the cell's voltage (such as voltage curves) and current (such as current curves). As an example, the identifier of the battery cluster to be evaluated can be used as an index to retrieve the operational data of the battery cluster from the BMS or server. This data can include operational data for each cell within the cluster, such as the cell's voltage and current curves.

[0021] Step 120: Based on the operating data, determine the first capacity loss of each cell due to intrinsic aging at the target time and the second capacity loss due to the inconsistency of cell capacity within the battery cluster.

[0022] In this example, the first capacity loss of a battery cell refers to the capacity loss caused by the cell's own properties, such as capacity loss caused by physical changes within the cell. Battery cells are charged and discharged in the form of battery clusters. The charging and discharging time and current of multiple cells in a battery cluster are consistent. When one or more cells with smaller capacity (i.e., larger capacity loss) in a battery cluster complete charging, the battery cluster considers charging complete and stops charging. However, the cells with larger capacity (i.e., smaller capacity loss) do not complete charging, resulting in the capacity of these larger cells not being fully utilized. This lost capacity is the second capacity loss of the battery cell.

[0023] The target time can be in the form of a precise time, such as a historical moment that the battery cluster has already completed, the current moment that it is currently running, or a future moment that it has not yet started running. Alternatively, the target time can also be in the form of the number of charge-discharge cycles (or number of cycles) of the battery cluster, such as any number of cycles of the battery cluster, which can be the number of historical cycles that the battery cluster has already completed, the number of current cycles that the battery cluster is currently running, or the number of future cycles that the battery cluster has not yet started running.

[0024] As an example, when the target time is a historical time or the current time, the first capacity loss and the second capacity loss of the battery cell can be determined from the operating data based on the target time; when the target time is a future time, the first capacity loss and the second capacity loss of the battery cell can be predicted based on the operating data.

[0025] Step 130: Determine the capacity loss of the battery cluster based on the first capacity loss and the second capacity loss of each cell.

[0026] In this embodiment, the capacity loss of the battery cluster may include a first capacity loss of the battery cluster and a second capacity loss of the battery cluster. The first capacity loss of the battery cluster can be determined based on the first capacity loss of each cell, and the second capacity loss of the battery cluster can be determined based on the second capacity loss of each cell.

[0027] Step 130: Based on the capacity loss of the battery cluster, assess the health status of the battery cluster at the target time.

[0028] As an example, the health status of the battery cluster at a target time can be assessed based on the ratio between the capacity loss of the battery cluster and the rated capacity of the battery cluster. Step 130 may further include: determining the ratio of the capacity loss of the battery cluster to the rated capacity; and assessing the health status of the battery cluster at the target time based on the ratio.

[0029] The method for assessing battery health in this embodiment can determine, based on the battery cluster's operational data, the first capacity loss caused by intrinsic aging of each cell within the cluster at a target time, and the second capacity loss caused by inconsistencies in cell capacity within the cluster. Then, based on the first and second capacity losses of each cell, the capacity loss of the battery cluster is determined, and finally, based on the capacity loss of the battery cluster, the health status of the battery cluster at the target time is assessed. Combining the battery's own capacity decay with the impact of the battery's operating environment on capacity assessment helps improve the accuracy of the battery cluster's health status evaluation.

[0030] In some embodiments, step 120 described above can be achieved through... Figure 2 The process shown determines the capacity loss of the battery cluster, such as Figure 2 As shown, the process may include the following steps.

[0031] Step 210: Determine the first capacity loss of the battery cluster based on the first capacity loss of each cell.

[0032] As an example, the average of the first capacity loss of all cells in a battery cluster can be taken as the first capacity loss of the battery cluster.

[0033] Step 220: Determine the second capacity loss of the battery cluster based on the second capacity loss of each cell.

[0034] As an example, the average of the second capacity loss of all cells in a battery cluster can be taken as the second capacity loss of the battery cluster.

[0035] Step 230: Based on predetermined weights, determine the weighted sum of the first capacity loss and the second capacity loss of the battery cluster as the capacity loss of the battery cluster.

[0036] In this embodiment, the weights can characterize the proportions of the first capacity loss and the second capacity loss in the total capacity loss of the battery cluster. As an example, the weights can be determined empirically, or they can be determined by statistical analysis of sample data of the battery cluster to determine the proportions of the first capacity loss and the second capacity loss in the total capacity loss of the battery cluster, and used as the weights of the first capacity loss and the second capacity loss of the battery cluster.

[0037] As an example, step 130 above can be used to evaluate the health status of the battery cluster using the following formula.

[0038] In the formula, SOH Indicates the health status of the battery cluster. This indicates the first capacity loss of the battery cluster. This represents the second capacity loss of the battery cluster. This indicates the rated capacity of the battery cluster.

[0039] In the above formula, the weighting coefficients for the first capacity loss and the second capacity loss of the battery cluster are both 1, indicating that the first capacity loss and the second capacity loss account for the same proportion of the total capacity loss of the battery cluster. Accordingly, the capacity loss of the battery cluster can be expressed as: .

[0040] In other examples of this embodiment, the weighting coefficients for the first capacity loss and the second capacity loss of the battery cluster may also be different, and this application does not limit this.

[0041] exist Figure 2 In the illustrated embodiment, the first capacity loss and the second capacity loss of the battery cluster can be determined based on the first capacity loss and the second capacity loss of the battery cell, respectively. The weighted sum of the first capacity loss and the second capacity loss of the battery cluster is taken as the capacity loss of the battery cluster. The capacity loss of the battery cluster can be calculated by combining the intrinsic aging of the battery itself and the external usage environment, which can more accurately calculate the capacity loss of the battery cluster.

[0042] In some optional embodiments of this example, step 210 may include the following steps: determining the correspondence between the capacity decay of each cell and the number of charge-discharge cycles based on the operating data; and determining the first capacity loss of each cell at the target time based on the number of charge-discharge cycles and the corresponding relationship of each cell at the target time.

[0043] Typically, the capacity decay of a battery cell due to intrinsic aging is positively correlated with the number of charge-discharge cycles. This embodiment can perform statistical analysis on the operating data of the battery cells to determine the correspondence between the capacity decay of each cell and the number of charge-discharge cycles. In this way, the first capacity loss of the battery cell at any given time can be determined based on this correspondence.

[0044] As an example, the correspondence can be represented as follows: In the formula, This indicates the first capacity loss of the battery cell. Indicates the rated capacity of the battery cell. Represents a constant. n This indicates the number of charge / discharge cycles.

[0045] In this embodiment, the first capacity loss of the battery cell at the target time can be determined based on the correspondence between the capacity decay of the battery cell and the number of charge-discharge cycles, which helps to improve the calculation accuracy of the first capacity loss.

[0046] In some embodiments, the relationship between the capacity decay of the battery cell and the number of charge-discharge cycles can be determined by... Figure 3 The process shown is determined, such as Figure 3As shown, the process may include the following steps.

[0047] Step 310: Identify the charge and discharge events of each cell from the operating data, and determine the number of charge and discharge cycles corresponding to each charge and discharge event.

[0048] In the local example, a charge / discharge event includes one charging process and one discharging process of the battery cell, with each charge / discharge event corresponding to one charge / discharge cycle.

[0049] As an example, the voltage curve of the battery cell can be extracted from the operating data. Then, the critical point of charging and discharging of the battery cell can be determined based on the voltage of the battery cell. In this way, the charging process and the discharging process of the battery cell can be determined. A continuous charging process and a continuous discharging process are regarded as a charging and discharging event. The charging and discharging events are sorted according to time, thereby determining the number of charging and discharging cycles corresponding to each charging and discharging event.

[0050] Step 320: Identify complete charging events from the charging and discharging events, and extract the data corresponding to the complete charging events.

[0051] In this embodiment, a complete charging event refers to a charging process in which the battery cell's charge reaches or approaches its maximum capacity. As an example, a complete charging event can be identified from the charging and discharging events determined in step 310 based on preset constraints, and the corresponding data can be extracted from the operating data based on the time information corresponding to the complete charging event. Here, the constraints can characterize whether a charging event is complete by the difference or ratio between the charge amount and the maximum capacity, or by the charging time.

[0052] Step 330: Based on the extracted data, determine the amount of charge for each cell in a complete charging event.

[0053] As an example, the extracted data may include the timing information of the current. Based on the time information corresponding to the complete charging event (such as the start time and end time of charging), an integral algorithm can be used to determine the amount of charge the cell receives in the complete charging event.

[0054] Step 340: Based on the rated capacity of each cell and the amount of charge in a complete charging event, determine the capacity loss of each cell in this charge-discharge cycle.

[0055] Step 350: Based on the capacity loss and number of charge / discharge cycles of each cell during the charge / discharge cycle, determine the correspondence between the capacity decay and the number of charge / discharge cycles of each cell.

[0056] In a specific example, assuming the operating data includes 5 charge-discharge events of the battery cell, including 3 complete charge events, the capacity loss corresponding to each charge event is determined based on these 3 complete charge events. Then, the correspondence between capacity decay and the number of charge-discharge cycles is determined based on the determined capacity loss and the corresponding number of charge-discharge cycles.

[0057] In this embodiment, a complete charging event is determined from the operating data, and the capacity loss of the battery cell in the corresponding charge-discharge cycle is determined accordingly. Then, the correspondence between the capacity decay of the battery cell and the number of charge-discharge cycles is determined. This can avoid interference from incomplete charging events and invalid data under low charge state, and help improve the accuracy of the correspondence between capacity decay and the number of charge-discharge cycles.

[0058] In some optional embodiments of this example, step 310 above can identify the charging and discharging events of each cell from the operating data in the following way: using a dynamic sliding window to identify the switching critical point between the charging state and the discharging state of each cell from the operating data; and determining the start flag and end flag of the charging event and the start flag and end flag of the discharging event based on the timestamp of the critical point, thereby obtaining the charging and discharging events of each cell.

[0059] In this embodiment, the switching critical point refers to the critical point at which the battery cell transitions from a resting state to the start of charging or discharging. As an example, the operating data may include the battery cell's current and voltage curves, which may include current and voltage values ​​from multiple sampling points arranged in chronological order. A dynamic sliding window is used to identify the current and voltage curves. Based on the current and voltage change trends at the sampling points, the switching critical points for charging (e.g., the critical points for starting and ending charging) and discharging (e.g., the critical points for starting and ending discharging) are identified. Then, the timestamps of the identified switching critical points are used as flag bits to determine the time periods corresponding to the battery cell's charging and discharging events, thereby obtaining the battery cell's charging and discharging events.

[0060] In this embodiment, the switching critical point of the battery cell is identified by using a dynamic sliding window, which can more accurately and efficiently determine the charging and discharging events of the battery cell.

[0061] In one example of this embodiment, step 320 above can identify a complete charging event from the charging and discharging events in the following way: based on the start flag and end flag of the charging event, extract the data corresponding to the charging event from the running data; based on the data corresponding to the charging event, determine whether the charging event meets preset conditions, the preset conditions including: the charging time is greater than a first threshold and less than a second threshold, the battery cell's charge level at the end of charging is greater than a third threshold, and the difference between the battery cell's charge level at the end of charging and the charge level at the start of charging is greater than a fourth threshold; if the preset conditions are met, determine that the charging event is a complete charging event.

[0062] In this example, preset conditions can constrain charging events from three dimensions: charging time, final charge level, and charge amount, thereby distinguishing between complete and incomplete charging events and helping to improve the accuracy of data filtering.

[0063] In some embodiments, step 120 described above can be achieved through... Figure 4 The process shown determines the second capacity loss of each cell due to inconsistencies in cell capacity within the battery cluster, and the process may include the following steps.

[0064] Step 410: Align and merge the timing data of the running data to obtain the aligned data.

[0065] In this embodiment, time alignment refers to uniformly calibrating runtime data from different sources, with different sampling frequencies, and different timestamps onto the same standard time axis, thereby eliminating time deviations between multiple devices and systems. As an example, runtime data can be time-aligned by determining the timestamp interval.

[0066] Step 420: Detect the voltage inflection point of each cell in a single charging event in the aligned data, and determine the timestamp of each voltage inflection point.

[0067] In this embodiment, the voltage inflection point can represent the start and end flags of a charging event. As an example, the operating data of each cell in a single charging event of the battery cluster can be extracted from the aligned data. Then, the operating data of each cell is detected separately to determine two voltage inflection points for each cell in that charging event and to determine the timestamp of each voltage inflection point. The voltage inflection point with the smaller timestamp is the start flag indicating the cell begins charging, and the voltage inflection point with the larger timestamp is the end flag indicating the cell ends charging.

[0068] Step 430: Determine the voltage inflection point with the smallest timestamp among all voltage inflection points as the first voltage inflection point, and determine the voltage inflection point with the largest timestamp as the second inflection point.

[0069] In this embodiment, the first voltage inflection point can indicate that the battery cluster has started charging, and the second voltage inflection point can indicate that the battery cluster has stopped charging.

[0070] As an example, assuming the battery cluster includes cell 1, cell 2, and cell 3, after step 420, the timestamps of the voltage inflection points for cell 1 are 1 minute 5 seconds and 3 minutes 1 second, respectively; the timestamps of the voltage inflection points for cell 2 are 2 minutes 40 seconds and 5 minutes 2 seconds, respectively; and the timestamps of the voltage inflection points for cell 3 are 0 minutes 55 seconds and 4 minutes 3 seconds, respectively. By comparing the timestamps of each voltage inflection point, it can be determined that the first voltage inflection point is the start flag indicating that cell 3 has started charging, with a timestamp of 0 minutes 55 seconds; and the second voltage inflection point is the end flag indicating that cell 2 has finished charging, with a timestamp of 5 minutes 2 seconds.

[0071] Step 440: Based on the first voltage inflection point and the second voltage inflection point, the charging current in the battery cluster is integrated to obtain the second capacity loss of each cell.

[0072] As an example, the second capacity loss of the battery cell can be calculated using the following formula.

[0073] In the formula, This indicates the second capacity loss of the battery cell. T max The timestamp representing the first voltage inflection point. T min The timestamp indicating the second voltage inflection point.

[0074] In this embodiment, the first voltage inflection point at the start of charging and the second voltage inflection point at the end of charging are determined based on the operating data of each cell in the battery cluster. This is used to determine the second capacity loss caused by the inconsistency of capacity among the cells in the battery cluster, so that the second capacity loss of the cells can more accurately reflect the impact of the external environment on the capacity loss.

[0075] In some optional implementations of this embodiment, step 420 can detect the voltage inflection point of each cell in a charging event in the following manner: extract the voltage data of each cell corresponding to a charging event from the running data; determine the first and second derivatives of the voltage with respect to time of each cell using the central difference algorithm based on a predetermined number of sampling points, wherein the number of sampling points is not less than 15; and determine the voltage inflection point of each cell based on the first and second derivatives corresponding to the voltage data of each cell.

[0076] In this embodiment, the central difference algorithm can be used to calculate the first and second derivatives of the cell voltage with respect to time at each sampling point. The first derivative can characterize the rate of change of voltage, and the second derivative can characterize the direction of the acceleration of voltage change.

[0077] As an example, when calculating the first derivative of the target sampling point, voltage data from 15 sampling points can be selected for calculation. For instance, the calculation can be performed based on the voltage data of the target sampling point, the 7 sampling points before the target sampling point, and the 7 sampling points after the target sampling point. For example, the first derivative of the target sampling point can be calculated using the following formula.

[0078] In the formula, x i Indicates the target sampling point. f ( x () represents the voltage value at the sampling point. denoted by , where h represents the first derivative of the target sampling point, and h represents the sampling period.

[0079] In this embodiment, combining the first and second derivatives to determine the voltage inflection point can improve the accuracy of voltage inflection point location. Furthermore, calculating the first and second derivatives of the cell voltage using a preset number of sampling points can more accurately capture the true trend of voltage changes, reduce calculation errors caused by data fluctuations, and further improve the accuracy of voltage inflection point location.

[0080] In one example of this embodiment, the voltage data of each cell corresponding to a charging event can be extracted from the operating data in the following manner: extract the original current data and original voltage data of each cell corresponding to a charging event from the operating data; determine the stable charging stage of each cell in a charging event based on the original current data of each cell; and extract the voltage data of each cell from the original voltage data of each cell according to the stable charging stage of each cell in a charging event.

[0081] In this example, the stable charging phase represents the period when the charging current of the battery cell fluctuates relatively little. By extracting the raw voltage data from the stable charging phase of the battery cell as the voltage data for identifying voltage inflection points, the interference of sudden current changes on the voltage curve can be effectively avoided, thereby improving the physical consistency of voltage changes and helping to improve the accuracy of voltage inflection point identification.

[0082] As an example, the stable charging phase of each cell in a single charging event can be determined as follows: sampling the raw current data of each cell, obtaining the current value of each sampling point, and taking the difference between the current values ​​of adjacent sampling points as the current change of adjacent sampling points; if the current change corresponding to a preset number of consecutive sampling points is less than a preset threshold, the time interval corresponding to the preset number of consecutive sampling points is determined as the stable charging phase.

[0083] In this example, the difference in current values ​​between adjacent sampling points can characterize the amount of current change between adjacent sampling points. By monitoring the current changes at the sampling points, the stable charging phase during the battery cell charging process can be accurately and efficiently identified.

[0084] As an example, the voltage data of each cell can be extracted from the raw voltage data of each cell in the following way: extract the data corresponding to the stable charging stage from the raw voltage data of each cell; perform cascade filtering on the extracted data based on a preset sliding window to obtain the voltage data of each cell.

[0085] In this example, a multi-level sliding window can be used to perform cascaded filtering on the extracted data. This not only eliminates abnormal noise in the data but also avoids the additional computational complexity introduced by dynamic adjustment of the sliding window. Compared to conventional single-pass smoothing, a better balance can be achieved between noise suppression and signal fidelity.

[0086] As an example, a sliding window may include: a first-level sliding window with a window width of a first preset length, a second-level sliding window with a window width of a second preset length, and a third-level sliding window with a window width of a third preset length, wherein the first preset length, the second preset length, and the third preset length increase sequentially.

[0087] For example, the first preset length can be 5, the second preset length can be 8, and the third preset length can be 10. A wider sliding window can collect data over a longer period of time, while a narrower sliding window focuses more on local information.

[0088] By employing multi-stage sliding windows for cascaded filtering, high-frequency interference components can be effectively suppressed, and signal delay can be kept stable through structural optimization. This helps to demonstrate strong robustness in scenarios with drastic data fluctuations, thereby improving the quality of the extracted voltage data.

[0089] This disclosure also provides an electronic device, including a processor and a memory storing a computer program. In some embodiments, when the computer program is executed by the processor, it can implement the method for assessing battery health status in any of the above embodiments.

[0090] This disclosure also provides a non-transient computer storage medium storing a computer program, which in some embodiments implements the method for assessing battery health status in any of the above embodiments when executed by a processor.

[0091] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

Claims

1. A method for assessing the health status of a battery, characterized in that, include: Obtain operational data of the battery cluster to be evaluated, including operational data of each cell in the battery cluster during operation; Based on the operational data, determine the first capacity loss of each cell at the target time due to intrinsic aging and the second capacity loss due to the inconsistency of cell capacity within the battery cluster. The capacity loss of the battery cluster is determined based on the first capacity loss and the second capacity loss of each of the battery cells; The health status of the battery cluster at the target time is assessed based on the capacity loss of the battery cluster.

2. The method according to claim 1, characterized in that, The capacity loss of the battery cluster is determined based on the first capacity loss and the second capacity loss of each of the battery cells, including: The first capacity loss of the battery cluster is determined based on the first capacity loss of each of the battery cells; The second capacity loss of the battery cluster is determined based on the second capacity loss of each of the battery cells; Based on predetermined weights, the weighted sum of the first capacity loss and the second capacity loss of the battery cluster is determined as the capacity loss of the battery cluster.

3. The method according to claim 1, characterized in that, Based on the capacity loss of the battery cluster, the health status of the battery cluster at the target time is evaluated, including: Determine the ratio of the capacity loss of the battery cluster to its rated capacity; Based on the ratio, the health status of the battery cluster at the target time is evaluated.

4. The method according to claim 1, characterized in that, Based on the operational data, the first capacity loss due to intrinsic aging in each cell within the battery cluster at a target time is determined, including: Based on the operational data, the relationship between the capacity decay of each cell and the number of charge-discharge cycles is determined; Based on the corresponding relationship between the number of charge-discharge cycles of each cell at the target time and the target time, the first capacity loss of each cell at the target time is determined.

5. The method according to claim 4, characterized in that, Based on the operational data, the relationship between the capacity decay of each battery cell and the number of charge-discharge cycles is determined, including: The charging and discharging events of each battery cell are identified from the operational data, and the number of charging and discharging cycles corresponding to each charging and discharging event is determined. Identify complete charging events from the charging and discharging events, and extract the data corresponding to the complete charging events; Based on the extracted data, the amount of charge for each cell in a complete charging event is determined. Based on the rated capacity of each cell and the amount of charge in a complete charging event, determine the capacity loss of each cell in the charge-discharge cycle. Based on the capacity loss and number of charge / discharge cycles of each cell, the correspondence between the capacity decay and the number of charge / discharge cycles of each cell is determined.

6. The method according to claim 5, characterized in that, Identifying the charge / discharge events of each cell from the operational data includes: The switching critical point between the charging and discharging states of each battery cell is identified from the operating data using a dynamic sliding window. Based on the timestamp of the critical point, the start flag and end flag of the charging event and the start flag and end flag of the discharging event are determined to obtain the charging and discharging events of each cell.

7. The method according to claim 6, characterized in that, Identifying a complete charging event from the charging and discharging events includes: Based on the start and end flags of the charging event, extract the data corresponding to the charging event from the running data; Based on the data corresponding to the charging event, it is determined whether the charging event meets the preset conditions. The preset conditions include: the charging time is greater than a first threshold and less than a second threshold; the battery cell's charge level at the end of charging is greater than a third threshold; and the difference between the battery cell's charge level at the end of charging and the charge level at the start of charging is greater than a fourth threshold. If the preset conditions are met, the charging event is determined to be a complete charging event.

8. The method according to claim 1, characterized in that, Based on the operational data, determine the second capacity loss of each cell due to the inconsistency of cell capacity within the battery cluster, including: The timing sequence of the running data is aligned and then merged to obtain aligned data; The voltage inflection point of each cell in a single charging event is detected in the aligned data, and the timestamp of each voltage inflection point is determined. The voltage inflection point with the smallest timestamp among all voltage inflection points is determined as the first voltage inflection point, and the voltage inflection point with the largest timestamp is determined as the second voltage inflection point. Based on the first voltage inflection point and the second voltage inflection point, the charging current in the battery cluster is integrated to obtain the second capacity loss of each cell.

9. The method according to claim 8, characterized in that, Detecting the voltage inflection point of each cell in a single charging event from the aligned data includes: Extract the voltage data of each cell corresponding to a single charging event from the operational data; Based on a predetermined number of sampling points, the first and second derivatives of the voltage with respect to time for each battery cell are determined using the central difference algorithm, wherein the number of sampling points is not less than 15. The voltage inflection point of each cell is determined based on the first and second derivatives corresponding to the voltage data of each cell.

10. The method according to claim 9, characterized in that, Extract the voltage data of each cell corresponding to a single charging event from the operational data, including: Extract the raw current and raw voltage data of each cell corresponding to a single charging event from the operational data; Based on the raw current data of each cell, the stable charging phase of each cell in a single charging event is determined; Based on the stable charging phase of each cell in a single charging event, the voltage data of each cell is extracted from the raw voltage data of each cell.

11. The method according to claim 10, characterized in that, Based on the raw current data of each cell, determine the stable charging phase of each cell in a single charging event, including: The original current data of each of the battery cells is sampled to obtain the current value of each sampling point, and the difference between the current values ​​of adjacent sampling points is taken as the current change of adjacent sampling points. If the current change corresponding to a preset number of consecutive sampling points is less than a preset threshold, the time interval corresponding to the preset number of consecutive sampling points is determined as a stable charging stage.

12. The method according to claim 10, characterized in that, Based on the stable charging phase of each cell in a single charging event, voltage data for each cell is extracted from the raw voltage data of each cell, including: Extract the data corresponding to the stable charging stage from the raw voltage data of each of the battery cells; The extracted data is cascaded and filtered based on a preset sliding window to obtain the voltage data of each battery cell.

13. The method according to claim 12, characterized in that, The sliding window includes: a first-level sliding window with a window width of a first preset length, a second-level sliding window with a window width of a second preset length, and a third-level sliding window with a window width of a third preset length, wherein the first preset length, the second preset length, and the third preset length increase sequentially.

14. An electronic device comprising a processor and a memory storing a computer program, characterized in that, When the computer program is executed by the processor, it can implement the method for assessing battery health status as described in any one of claims 1 to 13.

15. A non-transient computer storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method for assessing battery health as described in any one of claims 1 to 13.

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