SOC calibration method, SOH estimation method, device and storage medium

Abstract

This application discloses a State of Charge (SOC) calibration method, a State of Health (SOH) estimation method, an apparatus, and a storage medium. In this SOC calibration scheme, N target expansion force data points are identified from M first expansion force data points. The SOC during battery charging or discharging is calibrated based on the SOC corresponding to each of the N target expansion force data points. This eliminates the need to detect the absolute value of the expansion force, only the trend of its change. It is unaffected by thermal expansion and contraction, battery aging, and errors between different sensors, thus improving the accuracy of SOC calibration. In this SOH estimation scheme, the battery's health status can be accurately obtained by using a preset initial calibrated state of charge and the integrated state of charge within a first ampere-hour integration interval. At least one of the start and end times of this first ampere-hour integration interval corresponds to any one of the N target expansion force data points.

Description

Technical Field

[0001] This application relates to the field of electronic technology, and in particular to a method for calibrating the state of charge (SOC) of a battery, a method for estimating the state of health (SOH), an apparatus, and a storage medium. Background Technology

[0002] With the development of new energy technologies, lithium battery energy storage technology has been widely used in 3C portable electronic devices, new energy vehicles, and large-scale energy storage equipment. The State of Charge (SOC) and State of Harm (SOH) of a battery are among the most important states of a lithium battery energy storage device. Accurately estimating the battery's state of charge and discharge and its maximum usable capacity can enable more efficient utilization of the energy storage device, avoid unsafe battery use, and improve the user experience.

[0003] Currently, the industry commonly uses the open circuit voltage (OCV)-SOC lookup table method and the ampere-hour integration method to estimate SOC. SOH is mainly calculated by dividing the battery's capacity during full charge / discharge or a fixed voltage range by the initial capacity. These methods have significant estimation errors, and the accuracy of SOC and SOH estimations during use is easily affected by charge / discharge rate, temperature, and charge / discharge range. Furthermore, the estimation error increases further after cell aging, especially for lithium iron phosphate cells, where the accuracy of SOC and SOH estimations is particularly significantly affected by actual operating conditions and environmental factors. Therefore, when using the above methods for SOC estimation, accurate SOC calibration is also necessary.

[0004] There is currently no solution for accurately performing SOC calibration and SOH estimation. Summary of the Invention

[0005] This application provides a SOC calibration method, a SOH estimation method, an apparatus, and a storage medium to achieve accurate SOC calibration and SOH estimation.

[0006] Firstly, a method for calibrating the state of charge (SOC) is provided. The method includes: acquiring M first expansion force data points of a battery within a first time range, where M is an integer greater than 1; identifying N target expansion force data points from the M first expansion force data points, wherein the N target expansion force data points are used to characterize the expansion force variation trend of the M first expansion force data points, and N is an integer less than or equal to M; the N target expansion force data points include one or more of the following: one of the M first expansion force data points showing the smallest change in SOC relative to the battery, and one of the M first expansion force data points showing the largest change in SOC relative to the battery. The data includes: 1) one of the M first expansion force data points, where the change in relative battery state of charge is zero; 2) based on one or more preset target expansion force data points and the relationship between the battery state of charge corresponding to each of the N target expansion force data points, where the value corresponding to each of the N target expansion force data points is included within the preset one or more expansion force target data points; and 3) using the battery state of charge corresponding to each of the N target expansion force data points to calibrate the battery state of charge during the charging or discharging process.

[0007] In this respect, by identifying N target expansion force data out of M first expansion force data, the SOC during the battery charging or discharging process is calibrated according to the SOC corresponding to each of the N target expansion force data. It is not necessary to detect the absolute value of the expansion force, but only to detect the trend of expansion force change. It is not affected by thermal expansion and contraction, battery aging and errors between different sensors, thereby improving the accuracy of SOC calibration.

[0008] In one possible implementation, acquiring M first expansion force data points of the battery within a first time range includes: acquiring the M first expansion force data points of the battery within the first time range using a sensor located inside the battery.

[0009] In this implementation, the battery's expansion force data can be directly acquired through sensors inside the battery, without the need for external sensors, and the acquired expansion force data is more accurate.

[0010] In another possible implementation, the battery cell comprises at least two cores, with the sensor located between two adjacent cores of the at least two cores.

[0011] In another possible implementation, before acquiring M first expansion force data of the battery within a first time range, the method further includes: acquiring multiple second expansion force data of the battery within a second time range, and the battery state of charge corresponding to each of the multiple second expansion force data; identifying one or more preset target expansion force data among the multiple second expansion force data; and generating the relationship between the preset one or more target expansion force data and the battery state of charge corresponding to each of the preset target expansion force data.

[0012] In this implementation, by testing the charging and / or discharging process of a certain type of battery, one or more preset target expansion force data and the relationship between the battery state of charge corresponding to each preset target expansion force data can be obtained. This relationship can be used for subsequent SOC calibration of this type of battery or battery module, thereby improving the accuracy of SOC calibration.

[0013] Secondly, a health status estimation method is provided, the method comprising: collecting M first expansion force data points of a battery within a first time range, where M is an integer greater than 1; identifying N target expansion force data points among the M first expansion force data points, wherein the N target expansion force data points are used to characterize the expansion force change trend of the M first expansion force data points, and N is an integer less than or equal to M; the N target expansion force data points include one or more of the following: one of the M first expansion force data points showing the smallest change relative to the battery's state of charge, one of the M first expansion force data points showing the smallest change relative to the battery's state of charge, and one of the M first expansion force data points showing the smallest change relative to the battery's state of charge. The data includes the data showing the largest change in battery state of charge, and the data where one of the M first expansion force data shows zero change relative to the battery state of charge; the integrated state of charge of a first ampere-hour integration interval during a continuous charging or discharging process is obtained, wherein the first ampere-hour integration interval refers to a period of time between the start time and the end time, and the expansion force data corresponding to at least one of the start time and the end time of the first ampere-hour integration interval is any one of the N target expansion force data; and the battery health status is obtained based on the preset initial state of charge and the integrated state of charge of the first ampere-hour integration interval.

[0014] In this respect, by using a preset initial calibrated state of charge and an integrated state of charge in a first ampere-hour integration interval, at least one of the start time and end time of the first ampere-hour integration interval corresponds to any one of N target expansion force data, thereby accurately obtaining the health status of the battery.

[0015] Since the expansion force data corresponding to at least one of the start time and end time of the first ampere-hour integration interval is any one of the N target expansion force data, and these N target expansion force data are not affected by thermal expansion and contraction, battery aging and errors between different sensors, the SOC accuracy of the obtained first ampere-hour integration interval is relatively high.

[0016] In one possible implementation, the method further includes: acquiring a plurality of second expansion force data of the battery within a second time range, and a battery state of charge corresponding to each of the plurality of second expansion force data; identifying one or more preset target expansion force data among the plurality of second expansion force data; generating a relationship between the preset one or more target expansion force data and the battery state of charge corresponding to each of the preset target expansion force data; acquiring a plurality of voltage values ​​within the second time range, and a battery state of charge corresponding to each of the plurality of voltage values; and determining the preset initial state of charge; wherein the preset initial state of charge is the difference between a first state of charge and a second state of charge, and at least one of the first state of charge and the second state of charge is the battery state of charge corresponding to any one of the preset one or more target expansion force data.

[0017] In this implementation, since the aforementioned multiple second expansion force data are not affected by thermal expansion and contraction, battery aging, and errors between different sensors, the battery state of charge (SOC) corresponding to any preset target expansion force data can be determined. F The predetermined initial state of charge is highly accurate.

[0018] In another possible implementation, the battery health state is the ratio of the integral state of charge in the first ampere-hour integral interval to the preset initial state of charge.

[0019] In another possible implementation, the acquisition of M first expansion force data of the battery within a first time range includes: acquiring the M first expansion force data of the battery within the first time range through a sensor located inside the battery.

[0020] In this implementation, the battery's expansion force data can be directly acquired through sensors inside the battery, without the need for external sensors, and the acquired expansion force data is more accurate.

[0021] In another possible implementation, the battery cell comprises at least two windings, with the sensor located between two adjacent windings of the at least two windings.

[0022] Thirdly, a state of charge calibration device is provided. This state of charge calibration device is used to implement the method described in the first aspect or any of the implementations of the first aspect. The above method can be implemented by software, hardware, or by hardware executing corresponding software.

[0023] In one possible implementation, the state of charge calibration device includes: a data acquisition unit for acquiring M first expansion force data points of the battery within a first time range, where M is an integer greater than 1; and a first identification unit for identifying N target expansion force data points among the M first expansion force data points, wherein the N target expansion force data points are used to characterize the expansion force change trend of the M first expansion force data points, where N is an integer less than or equal to M; the N target expansion force data points include one or more of the following: the data point with the smallest change in the first expansion force data point relative to the battery state of charge, and the data point with the largest change in the first expansion force data point relative to the battery state of charge. The data in which one of the M first expansion force data has zero change relative to the battery state of charge; a first acquisition unit, configured to acquire the battery state of charge corresponding to each of the N target expansion force data according to one or more preset target expansion force data and the relationship between the battery state of charge corresponding to each of the preset target expansion force data, wherein the value corresponding to each of the N target expansion force data is included in the one or more preset expansion force target data; and a calibration unit, configured to calibrate the battery state of charge during the charging or discharging process of the battery using the battery state of charge corresponding to each of the N target expansion force data.

[0024] Optionally, the acquisition unit is used to acquire the M first expansion force data of the battery within the first time range through a sensor located inside the battery.

[0025] Optionally, the battery cell includes at least two winding cores, and the sensor is located between two adjacent winding cores of the at least two winding cores.

[0026] Optionally, the device further includes: a second acquisition unit, configured to acquire a plurality of second expansion force data of the battery within a second time range, and a battery state of charge corresponding to each of the plurality of second expansion force data; a second identification unit, configured to identify one or more preset target expansion force data among the plurality of second expansion force data; and a generation unit, configured to generate the preset one or more target expansion force data and the relationship between the preset target expansion force data and the battery state of charge corresponding to each of the preset target expansion force data.

[0027] In another possible implementation, the state of charge calibration device includes a processor, a memory, an input device, and an output device. The memory stores instructions that, when executed by the processor, cause the state of charge calibration device to perform the following steps:

[0028] M first expansion force data points of the battery are collected within a first time range, where M is an integer greater than 1; N target expansion force data points are identified from the M first expansion force data points, wherein the N target expansion force data points are used to characterize the expansion force change trend of the M first expansion force data points, where N is an integer less than or equal to M; the N target expansion force data points include one or more of the following: one of the M first expansion force data points with the smallest change relative to the battery state of charge, one of the M first expansion force data points with the largest change relative to the battery state of charge, and the M first expansion force data points... One of the first expansion force data is data where the change relative to the battery state of charge is zero; based on one or more preset target expansion force data and the relationship between the battery state of charge corresponding to each of the preset target expansion force data, the battery state of charge corresponding to each of the N target expansion force data is obtained, and the value corresponding to each of the N target expansion force data is included in the preset one or more expansion force target data; and the battery state of charge during the charging or discharging process is calibrated using the battery state of charge corresponding to each of the N target expansion force data.

[0029] Optionally, the processor executes the acquisition of M first expansion force data of the battery within a first time range, including: acquiring the M first expansion force data of the battery within the first time range through a sensor located inside the battery.

[0030] Optionally, the battery cell includes at least two winding cores, and the sensor is located between two adjacent winding cores of the at least two winding cores.

[0031] Optionally, before the processor executes the step of acquiring M first expansion force data of the battery within a first time range, it is further configured to: acquire multiple second expansion force data of the battery within a second time range, and the battery state of charge corresponding to each of the multiple second expansion force data; identify one or more preset target expansion force data among the multiple second expansion force data; and generate the relationship between the preset one or more target expansion force data and the battery state of charge corresponding to each of the preset target expansion force data.

[0032] Fourthly, a health status estimation device is provided. This health status estimation device is used to implement the method described in the first aspect or any of the implementations of the first aspect. The above method can be implemented by software, hardware, or by hardware executing corresponding software.

[0033] In one possible implementation, the health status estimation device includes: a data acquisition unit for acquiring M first expansion force data points of the battery within a first time range, where M is an integer greater than 1; and a first identification unit for identifying N target expansion force data points among the M first expansion force data points, wherein the N target expansion force data points are used to characterize the expansion force change trend of the M first expansion force data points, and N is an integer less than or equal to M; the N target expansion force data points include one or more of the following: one of the M first expansion force data points showing the smallest change in relative to the battery state of charge, and one of the M first expansion force data points showing the smallest change in relative to the battery state of charge. The data showing the largest change in electrical state, and the data where one of the M first expansion force data shows zero change relative to the battery's state of charge; a first acquisition unit, used to acquire the integrated state of charge of a first ampere-hour integration interval during a continuous charging or discharging process, wherein the first ampere-hour integration interval refers to a period of time between the start time and the end time, and the expansion force data corresponding to at least one of the start time and the end time of the first ampere-hour integration interval is any one of the N target expansion force data; and a second acquisition unit, used to acquire the battery's health status based on a preset initial state of charge and the integrated state of charge of the first ampere-hour integration interval.

[0034] Optionally, the device further includes: a third acquisition unit, configured to acquire multiple second expansion force data of the battery within a second time range, and the battery state of charge corresponding to each of the multiple second expansion force data; a second identification unit, configured to identify one or more preset target expansion force data among the multiple second expansion force data; a generation unit, configured to generate the relationship between the preset one or more target expansion force data and the battery state of charge corresponding to each of the preset target expansion force data; a fourth acquisition unit, configured to acquire multiple voltage values ​​within the second time range, and the battery state of charge corresponding to each of the multiple voltage values; and a determination unit, configured to determine the preset initial state of charge; wherein the preset initial state of charge is the difference between a first state of charge and a second state of charge, wherein at least one of the first state of charge and the second state of charge is the battery state of charge corresponding to any one of the preset one or more target expansion force data.

[0035] Optionally, the health state of the battery is the ratio of the integrated state of charge in the first ampere-hour integration interval to the preset initial state of charge.

[0036] Optionally, the acquisition unit is used to acquire the M first expansion force data of the battery within the first time range through a sensor located inside the battery.

[0037] Optionally, the battery cell includes at least two winding cores, and the sensor is located between two adjacent winding cores of the at least two winding cores.

[0038] In another possible implementation, the health state estimation device includes a processor, a memory, an input device, and an output device. The memory stores instructions that, when executed by the processor, cause the state of charge calibration device to perform the following steps: acquiring M first expansion force data points of the battery within a first time range, where M is an integer greater than 1; identifying N target expansion force data points among the M first expansion force data points, wherein the N target expansion force data points are used to characterize the expansion force change trend of the M first expansion force data points, and N is an integer less than or equal to M; the N target expansion force data points include one or more of the following: one of the M first expansion force data points has the smallest change relative to the battery's state of charge. The data includes: the data of the first expansion force data among the M first expansion force data that shows the largest change relative to the battery state of charge; and the data of the first expansion force data among the M first expansion force data that shows zero change relative to the battery state of charge. The integrated state of charge is obtained for a first ampere-hour integration interval during a continuous charging or discharging process, wherein the first ampere-hour integration interval refers to a period of time between the start time and the end time, and the expansion force data corresponding to at least one of the start time and end time of the first ampere-hour integration interval is any one of the N target expansion force data. The battery health status is obtained based on the preset initial state of charge and the integrated state of charge of the first ampere-hour integration interval.

[0039] Optionally, the processor is further configured to perform: acquiring a plurality of second expansion force data of the battery within a second time range, and a battery state of charge corresponding to each of the plurality of second expansion force data; identifying one or more preset target expansion force data among the plurality of second expansion force data; generating a relationship between the preset one or more target expansion force data and the battery state of charge corresponding to each of the preset target expansion force data; acquiring a plurality of voltage values ​​within the second time range, and a battery state of charge corresponding to each of the plurality of voltage values; and determining the preset initial state of charge; wherein the preset initial state of charge is the difference between a first state of charge and a second state of charge, wherein at least one of the first state of charge and the second state of charge is a battery state of charge corresponding to any one of the preset one or more target expansion force data.

[0040] Optionally, the health state of the battery is the ratio of the integrated state of charge in the first ampere-hour integration interval to the preset initial state of charge.

[0041] Optionally, the processor executes the acquisition of M first expansion force data of the battery within a first time range, including: acquiring the M first expansion force data of the battery within the first time range through a sensor located inside the battery.

[0042] Optionally, the battery cell includes at least two winding cores, and the sensor is located between two adjacent winding cores of the at least two winding cores.

[0043] Fifthly, a battery module is provided, including one or more batteries and a state of charge calibration device as described in the third aspect or the third aspect, the state of charge calibration device being used to calibrate the state of charge of the one or more batteries in the battery module during charging or discharging.

[0044] In a sixth aspect, a battery module is provided, including one or more batteries and a health status estimation device as described in the fourth aspect or the fourth aspect, the health status estimation device being used to acquire the health status of one or more batteries in the battery module respectively.

[0045] In a seventh aspect, an energy storage system is provided, including an energy storage converter and a battery module as described in the fifth or sixth aspect, wherein the energy storage converter is used to process current input into the battery module and input it into the battery module.

[0046] Eighthly, a computer-readable storage medium is provided, wherein a computer program or instructions are stored therein, which, when executed by a state of charge calibration device, implement the method as described in the first aspect or any implementation thereof.

[0047] A ninth aspect provides a computer-readable storage medium storing a computer program or instructions that, when executed by a health status estimation device, implement the method as described in the second aspect or any implementation thereof.

[0048] In a tenth aspect, a computer program product is provided that, when executed on a computing device, causes the method described in the first aspect, the second aspect, or any implementation of the first aspect or the second aspect to be performed. Attached Figure Description

[0049] Figure 1 A schematic flowchart of a SOC calibration method provided in an embodiment of this application;

[0050] Figure 2 A schematic diagram of a battery-embedded expansion force sensor provided in an embodiment of this application;

[0051] Figure 3 A schematic diagram showing the relationship between the expansion force and SOC of the battery cell during the charging and discharging process, as well as the expansion force data, provided for embodiments of this application.

[0052] Figure 4 This is a schematic diagram illustrating the SOC calibration effect of a battery throughout its entire lifespan, as provided in an embodiment of this application.

[0053] Figure 5 A flowchart illustrating a SOH estimation method provided in an embodiment of this application;

[0054] Figure 6 This is a schematic diagram illustrating SOH estimation as an example of an embodiment of this application;

[0055] Figure 7 This application provides a schematic diagram illustrating the SOH estimation effect of a battery throughout its entire lifespan.

[0056] Figure 8 This is a schematic diagram of the structure of a SOC calibration device provided in an embodiment of this application;

[0057] Figure 9 This is a schematic diagram of the structure of a SOH estimation device provided in an embodiment of this application;

[0058] Figure 10 This is a schematic diagram of another SOC calibration device provided in the embodiments of this application;

[0059] Figure 11 This is a schematic diagram of another SOH estimation device provided in an embodiment of this application. Detailed Implementation

[0060] The embodiments of this application are described below with reference to the accompanying drawings.

[0061] With the promotion and popularization of new energy vehicles, electric vehicles are receiving increasing attention. Commonly used batteries in hybrid electric vehicles include lead-acid batteries, nickel-cadmium batteries, nickel-metal hydride batteries, and lithium batteries. Lithium batteries, due to their high energy density, light weight, and long lifespan, are superior to other batteries and have become the preferred choice for an increasing number of hybrid electric vehicles.

[0062] Hybrid vehicles, especially severe hybrid vehicles, achieve energy savings by coordinating energy output between the engine and battery during operation. This requires the battery management system (BMS) to constantly display and report accurate State of Charge (SOC) so that the vehicle control system can determine the appropriate energy output from the battery. Inaccurate SOC detection can lead to incorrect SOC reports, causing overcharging or over-discharging of the lithium battery. Frequent and prolonged overcharging or over-discharging can damage lithium batteries, ranging from reducing battery lifespan to causing safety accidents.

[0063] Therefore, how to use lithium batteries safely and reasonably, make full use of their power, and extend their lifespan are problems that must be solved for the promotion of lithium batteries in electric vehicles and hybrid vehicles.

[0064] Currently, the industry commonly uses the OCV-SOC lookup table method and the ampere-hour integration method to estimate SOC. However, lithium batteries rely on electrochemical reactions to store energy, making them highly sensitive to temperature. Excessively low or high temperatures can affect battery capacity and performance; for example, excessively high temperatures can cause thermal runaway within the battery. Furthermore, the charge / discharge rate also impacts battery performance. High-rate charge / discharge not only increases the risk of lithium plating but also reduces battery lifespan. These methods have relatively large estimation errors, and the accuracy of SOC and SOH estimations is easily affected by charge / discharge rate, temperature, and charge / discharge range during use. Moreover, the estimation error increases further as the cell ages, especially for lithium iron phosphate cells, where the accuracy of SOC and SOH estimations is particularly significantly affected by actual operating conditions and environmental factors. Therefore, it is necessary to calibrate the battery's SOC periodically or irregularly; otherwise, the SOC error will gradually increase over time, leading to incorrect SOC estimations.

[0065] One SOC calibration scheme involves performing SOC calibration under full charge and discharge conditions, but the triggering conditions are stringent. Current lithium battery applications typically involve shallow charge and discharge, making full charge and discharge difficult to achieve, which hinders the execution of the SOC calibration process. Furthermore, prolonged periods without SOC calibration will gradually increase the calculation error of SOC.

[0066] Another SOC calibration scheme involves acquiring expansion force data through sensors on the battery module and determining the module's current SOC based on the correlation between the expansion force data and SOC. However, the slope of the expansion force data change is significantly affected by operating temperature, charge / discharge rate, and cell aging status, leading to substantial variations in the correlation between the actual expansion force data and SOC, thus causing significant SOC calibration errors.

[0067] To address this, this application provides a SOC calibration scheme that identifies N target expansion force data out of M first expansion force data. The SOC during battery charging or discharging is calibrated based on the SOC corresponding to each of the N target expansion force data. This eliminates the need to detect the absolute value of the expansion force, but only the trend of expansion force change. It is unaffected by thermal expansion and contraction, battery aging, and errors between different sensors, thereby improving the accuracy of SOC calibration.

[0068] like Figure 1 The diagram shown is a flowchart illustrating a SOC calibration method provided in an embodiment of this application. Exemplarily, the method may include the following steps:

[0069] S101. Obtain multiple second expansion force data of the battery within a second time range, and the battery state of charge corresponding to each of the multiple second expansion force data.

[0070] This SOC calibration method can be executed by a BMS, or by other processors, microcontrollers, devices, equipment, or systems. Of course, the processor or microcontroller can be located inside or outside the battery. This embodiment describes the SOC calibration method executed by a BMS as an example.

[0071] Lithium-ion batteries, as an effective energy storage device, have advantages such as high energy density, high power, high output voltage, low self-discharge, and long service life, and are currently widely used in electric vehicles, electronic products, and other fields. However, during electrochemical cycling, the lithium-ion insertion and extraction process causes the electrode materials to expand and contract in volume, and gas and heat generation occur inside the battery, all of which lead to overall battery deformation, especially in the thickness direction.

[0072] Battery Management Systems (BMS) typically manage batteries by measuring parameters such as battery parameters and temperature, including estimating the battery's state of charge (SOC). SOC refers to the ratio of a battery's remaining capacity after a period of use or long-term storage to its capacity at its fully charged state, usually expressed as a percentage. Its value ranges from 0 to 1; SOC = 0 indicates the battery is fully discharged, and SOC = 1 indicates the battery is fully charged. SOC can also be replaced by the state of charge / discharge, which refers to the battery's or battery module's state of charge and capacity during charging, or its depth of discharge and capacity during discharging.

[0073] However, the accuracy of SOC estimation during use is easily affected by charge / discharge rate, temperature, and charge / discharge range, and the estimation error will further increase after the cell ages. Therefore, it is necessary to calibrate the battery's SOC regularly or irregularly; otherwise, the SOC error will gradually increase over time, causing incorrect SOC estimation.

[0074] Before performing SOC calibration using the method of this embodiment, a certain type of battery can be tested to obtain multiple second expansion force data of that type of battery within a second time range (e.g., in a complete charge and / or discharge process test), and the battery state of charge corresponding to each of the multiple second expansion force data.

[0075] The second expansion force data can be obtained through the following methods:

[0076] In one implementation, multiple second expansion force data points of the battery within a second time range can be collected using an expansion force sensor inside the battery. For example... Figure 2 The diagram illustrates a battery-embedded expansion force sensor according to an embodiment of this application. The battery 201 includes a core 202, an expansion force sensor 203, and a data acquisition harness 204. The battery cell 201 comprises two or more cores 202. The expansion force sensor 203 is placed between two adjacent cores 202 and stacked as a single unit. Multiple second expansion force data collected by the expansion force sensor 203 can be transmitted to the BMS via the data acquisition harness 204 extending to the outside of the battery 201. Therefore, multiple second expansion force data of the battery can be directly acquired through the internal expansion force sensor 203, resulting in high data accuracy. For example, the expansion force sensor 203 can be replaced with a stress sensor, where there is a correlation between expansion force and stress.

[0077] In another implementation, multiple second expansion force data can be collected by a sensor positioned between the end plate of the battery module and the battery cell. The battery module includes one or more battery cells. The sensor is in contact with one or more battery cells. The sensor can be a pressure sensor, such as a piezoresistive film.

[0078] Furthermore, the BMS also simultaneously records the SOC corresponding to each of the multiple second expansion force data.

[0079] S102. Identify one or more preset target expansion force data from a plurality of second expansion force data.

[0080] After the BMS acquires multiple second expansion force data points of the battery within a second time range, and the SOC corresponding to each of the multiple second expansion force data points, it can represent the relationship between the expansion force (F) and SOC of this type of battery or battery module, such as... Figure 3 As shown. Figure 3 The diagram illustrates the relationship between the expansion force of the battery cell and its state of charge (SOC) during charging, as well as the relationship between the expansion force of the battery cell and its SOC during discharging.

[0081] Furthermore, based on this relationship curve, one or more preset target expansion force data points can be identified from among multiple second expansion force data points. These one or more target expansion force data points are used to characterize the changing trends of the multiple second expansion force data points and are unaffected by thermal expansion and contraction, battery aging, and errors between different sensors. For example, as... Figure 3 As shown, the preset target expansion force data includes one or more of the following: the data where the change in the second expansion force relative to the SOC is the smallest among multiple second expansion force data of the battery or battery module during a single charge. 1MIN The data point F representing the largest change in second expansion force relative to the state of charge (SOC) among multiple second expansion force data points for a battery or battery module during a single charge process. 1MAX The data point showing that, during a single charge, one of the multiple second expansion force data points for the battery or battery module exhibits zero change in relative state of charge (SOC). The data point F representing the smallest change in second expansion force relative to the state of charge (SOC) among multiple second expansion force data points during a single discharge of a battery or battery module. 2MIN The data point F representing the largest change in second expansion force relative to the state of charge (SOC) among multiple second expansion force data points during a single discharge of the battery or battery module. 2MAX The data point showing that one of the multiple second expansion force data points during a single discharge of the battery or battery module has zero change relative to the state of charge (SOC).

[0082] For example, the BMS can perform filtering and calculation processing on multiple second expansion force data of the acquired battery within a second time range to obtain one or more preset target expansion force data from the multiple second expansion force data.

[0083] S103. Generate one or more preset target expansion force data and the relationship between the battery state of charge corresponding to each preset target expansion force data.

[0084] The BMS further determines the SOC corresponding to one or more preset target expansion force data, thereby generating or establishing the relationship between the one or more preset target expansion force data and the battery state of charge corresponding to each preset target expansion force data. This relationship can serve as the calibration relationship for this type of battery or battery module.

[0085] The BMS can also pre-store one or more preset target expansion force data and the relationship between the battery state of charge corresponding to each preset target expansion force data, for example, by storing them in the BMS through factory flashing. Therefore, steps S101-S103 are optional steps, and are represented by dashed lines in the figure.

[0086] After obtaining one or more preset target expansion force data and the relationship between the battery state of charge corresponding to each preset target expansion force data, the SOC of this type of battery or battery module can be calibrated during actual operation based on this relationship:

[0087] S104. Collect M first expansion force data of the battery within the first time range.

[0088] During actual operation, the battery or battery module can acquire M first expansion force data points within a first time range using the same implementation method as in step S101 above, where M is an integer greater than 1. The acquired M first expansion force data points are then transmitted to the BMS.

[0089] For example, the first time range may be the same as the second time range described above, such as corresponding to a complete charging or discharging process. The first time range may also be different from the second time range described above, such as corresponding to a segment of a charging or discharging process.

[0090] S105. Identify N target expansion force data points from M first expansion force data points.

[0091] The BMS can filter and calculate the collected M first expansion force data points to obtain N first expansion force data points from the M data points, where N is an integer less than or equal to M. These N target expansion force data points are used to characterize the expansion force change trend of the M first expansion force data points and are unaffected by thermal expansion and contraction, battery aging, and errors between different sensors. For example, the N target expansion force data points may include one or more of the following: the data point with the smallest change in the first expansion force relative to the battery's state of charge during a single charge; the data point with the largest change in the first expansion force relative to the battery's state of charge during a single charge; or the data point with zero change in the first expansion force relative to the battery's state of charge during a single charge. The data point representing the smallest change in the first expansion force relative to the battery's state of charge (SOC) among M first expansion force data points during a single discharge cycle; the data point representing the largest change in the first expansion force relative to the battery's SOC among M first expansion force data points during a single discharge cycle; and the data point representing zero change in the first expansion force relative to the battery's SOC among M first expansion force data points during a single discharge cycle.

[0092] S106. Based on the relationship between one or more preset target expansion force data and the battery state of charge corresponding to each preset target expansion force data, obtain the battery state of charge corresponding to each of the N target expansion force data. The value corresponding to each of the N target expansion force data is included in the one or more preset expansion force target data.

[0093] Since one or more preset target expansion force data and the relationship between them and the corresponding battery state of charge (SOC) have already been obtained, the SOC corresponding to each of the N target expansion force data can be obtained based on this relationship. The value corresponding to each of the N target expansion force data is included within the one or more preset expansion force target data sets.

[0094] S107. Using the battery state of charge corresponding to each of the N target expansion force data, calibrate the battery state of charge during the charging or discharging process.

[0095] The SOC can be calibrated and updated within the BMS using the SOC corresponding to each of the N target expansion force data, serving as the SOC calculation benchmark for the battery or battery module before the next SOC calibration.

[0096] For example, such as Figure 4 The diagram shown illustrates the SOC calibration effect of the battery provided in this application embodiment throughout its entire life cycle. It can be seen that the SOC calibration effect of each cell in the battery module (5 cells are shown in the figure) is relatively stable throughout its entire life cycle (i.e., SOH from high to low).

[0097] According to an embodiment of this application, a SOC calibration method is provided. By identifying N target expansion force data among M first expansion force data, the SOC of the battery during charging or discharging is calibrated based on the SOC corresponding to each of the N target expansion force data. It does not require detecting the absolute value of the expansion force, but only needs to detect the trend of expansion force change. It is not affected by thermal expansion and contraction, battery aging, and errors between different sensors, thereby improving the accuracy of SOC calibration.

[0098] Lithium batteries age continuously during cycling, experiencing increased internal resistance, capacity decay, and expansion. State of Harm (SOH) represents the degree of battery degradation and is a crucial parameter in Battery Management Systems (BMS). The ability to quickly and effectively assess and monitor the SOH of batteries or battery modules is of great significance for electrical devices such as electric vehicles.

[0099] Existing SOH estimation methods primarily assess the SOH of batteries or battery modules based on driving mileage or actual battery capacity. For example, a specific voltage range is selected, and as the battery voltage passes through this range during continuous charging and discharging, the ampere-hour integral is performed to obtain the charge / discharge capacity for that range. This capacity is then divided by the initial rated capacity of that voltage range and multiplied by relevant temperature and rate coefficients to obtain the battery's SOH. The calculation formula is as follows:

[0100]

[0101]

[0102] However, the voltage of a battery in actual operation is easily affected by the charge / discharge rate and ambient temperature, leading to inaccurate capacity accumulation within the voltage range. This effect is particularly pronounced for lithium iron phosphate (LFP) batteries. As the battery ages, its temperature coefficient and rate coefficient will change, resulting in increased error in the SOH calculation method.

[0103] Furthermore, as batteries or battery modules gradually age, their expansion force gradually increases. When batteries or battery modules age to a certain extent, their expansion force can lead to some safety issues. Current SOH estimation methods are not very reliable and pose safety risks.

[0104] Another method for estimating SOH is to measure the SOH of the battery module by detecting its expansion force value. However, the actual operating conditions of a battery differ significantly from its calibrated conditions. The battery expansion force is greatly affected by the charge / discharge rate and ambient temperature. Batteries in the same healthy state may have significantly different absolute expansion force values ​​under different environments, leading to large errors in the SOH estimation of the battery or battery module.

[0105] To this end, this application provides a State of Health (SOH) estimation scheme, which can accurately obtain the battery's health status by using a preset initial calibrated state of charge and the integrated state of charge of the first ampere-hour integration interval, where at least one of the start time and end time of the first ampere-hour integration interval corresponds to any one of N target expansion force data.

[0106] like Figure 5 The diagram shown is a flowchart illustrating a SOH estimation method provided in an embodiment of this application. Exemplarily, the method may include the following steps:

[0107] S501. Acquire multiple second expansion force data of the battery within a second time range, and the battery state of charge corresponding to each of the multiple second expansion force data.

[0108] This SOH estimation method can be executed by a BMS, or by other processors, microcontrollers, devices, equipment, or systems. Of course, the processor or microcontroller can be located inside or outside the battery. This embodiment describes the SOH estimation method executed by a BMS as an example.

[0109] The specific implementation of this step can be referred to step S101 in the above embodiment, and will not be repeated here.

[0110] For example, multiple second expansion force data, and the relationship between each of the multiple second expansion force data, are as follows: Figure 6 As shown in curve 1.

[0111] S502. Identify one or more preset target expansion force data from a plurality of second expansion force data.

[0112] The specific implementation of this step can be referred to step S102 in the above embodiment, and will not be repeated here.

[0113] S503. Generate one or more preset target expansion force data and the relationship between the battery state of charge corresponding to each preset target expansion force data.

[0114] The specific implementation of this step can be referred to step S103 in the above embodiment, and will not be repeated here.

[0115] S504. Obtain multiple voltage values ​​within a second time range, and the battery state of charge corresponding to each of the multiple voltage values.

[0116] In this embodiment, the BMS can also record multiple voltage values ​​within the second time range, and the State of Charge (SOC) corresponding to each of the multiple voltage values. For example, the SOC corresponding to each of the multiple voltage values ​​can be as follows: Figure 6 As shown in curve 2, it can be seen that when the SOC is low, the SOC and the voltage value have an approximately linear relationship; when the SOC rises to a certain level, the trend of SOC changing with the voltage value is not obvious; but when the SOC is close to full capacity, the SOC and the voltage value show another approximately linear relationship.

[0117] S505. Determine the preset initial charging state.

[0118] BMS can select any one of the preset target expansion force data from the one or more preset target expansion force data identified above, such as Figure 6 Point ① shown is one of the multiple second expansion force data points during the battery charging test, where the change in the second expansion force relative to the state of charge (SOC) is zero. The BMS can also select any of the following preset target expansion force data: the data with the largest change in second expansion force relative to SOC among multiple second expansion force data during the battery charging test; the data with the smallest change in second expansion force relative to SOC among multiple second expansion force data during the battery charging test; or the data with zero change in second expansion force relative to SOC among multiple second expansion force data during the battery discharging test. The data includes one of the multiple second expansion force data points during the battery discharge test, which shows the largest change in second expansion force relative to the State of Charge (SOC), and one of the multiple second expansion force data points during the battery discharge test, which shows the smallest change in second expansion force relative to the State of Charge (SOC). This application does not impose any limitations on this.

[0119] In addition, the BMS can select any one voltage value from multiple voltage values ​​obtained in the second time range, such as... Figure 6 Point ② is shown in the diagram.

[0120] A preset initial state of charge (SOC) of the battery is determined, wherein the preset SOC is the difference between a first SOC and a second SOC. At least one of the first SOC and the second SOC corresponds to the battery SOC corresponding to any one of one or more preset target expansion force data.

[0121] In one example, the first state of charge is the battery state of charge (SOC) corresponding to any given voltage value. V The second state of charge is the battery state of charge (SOC) corresponding to any one of the preset target expansion force data. F Then the preset initial state of charge is the difference between the first state of charge and the second state of charge, ΔSOC0 = SOC. V -SOC F .

[0122] In another example, the first state of charge is the battery state of charge (SOC) corresponding to the third expansion force data. Fa The second state of charge is the battery state of charge (SOC) corresponding to the fourth expansion force data. Fb Then the preset initial state of charge is the difference between the first state of charge and the second state of charge, ΔSOC0 = SOC. Fa -SOC Fb The third and fourth expansion force data are any one of the preset target expansion force data.

[0123] Since the aforementioned preset target expansion force data is unaffected by thermal expansion and contraction, battery aging, and errors between different sensors, the battery state of charge (SOC) corresponding to any one of the preset target expansion force data can be determined. F The accuracy of the determined initial state of charge is relatively high.

[0124] S506. Collect M first expansion force data of the battery within the first time range.

[0125] During actual operation, the battery or battery module can acquire M first expansion force data points within a first time range using the same implementation method as in step S501 above, where M is an integer greater than 1. The acquired M first expansion force data points are then transmitted to the BMS.

[0126] S507. Identify N target expansion force data points from M first expansion force data points, wherein the N target expansion force data points are used to characterize the expansion force change trend of the M first expansion force data points, and N is an integer less than or equal to M; the N target expansion force data points include one or more of the following: one of the M first expansion force data points with the smallest change relative to the battery state of charge, one of the M first expansion force data points with the largest change relative to the battery state of charge, and one of the M first expansion force data points with zero change relative to the battery state of charge.

[0127] The specific implementation of this step can be found in step S105 of the above embodiment, and will not be repeated here.

[0128] S508. Obtain the integrated state of charge of the first ampere-hour integration interval during a continuous charging or discharging process, wherein the first ampere-hour integration interval refers to a period of time between the start time and the end time, and the expansion force data corresponding to at least one of the start time and the end time of the first ampere-hour integration interval is any one of the N target expansion force data.

[0129] The method for obtaining the integrated state of charge (SPO) of the first ampere-hour integration interval during a continuous charging or discharging process includes the following implementation methods:

[0130] One implementation involves processing the aforementioned N target expansion force data. During a continuous charging or discharging process, the time corresponding to any one of the N target expansion force data points is used as the starting time of the first ampere-hour integration interval (corresponding time t1). Ampere-hour integration is then performed until a certain voltage value is reached (corresponding time t2), thus obtaining the SOC of this first ampere-hour integration interval. Where i is the current value collected in the first ampere-hour integration interval.

[0131] Another implementation method is that, when processing the above N target expansion force data, during a continuous charging or discharging process, the time corresponding to any one of the N target expansion force data is taken as the starting time of the first ampere-hour integration interval (the corresponding time is t1), and ampere-hour integration begins until the time corresponding to the next first expansion force data is t2), thus obtaining the SOC of the first ampere-hour integration interval. Where i is the current value collected in the first ampere-hour integration interval.

[0132] Another implementation involves, when any of the aforementioned voltage values ​​is obtained, during a continuous charging or discharging process, using the time corresponding to that voltage value as the starting time of the first ampere-hour integration interval (corresponding time t1), and starting ampere-hour integration until the time corresponding to the next target expansion force data (any one of the N target expansion force data points) (corresponding time t2), thus obtaining the SOC of this first ampere-hour integration interval. Where i is the current value collected in the first ampere-hour integration interval.

[0133] Since the expansion force data corresponding to at least one of the start time and end time of the first ampere-hour integration interval is any one of the N target expansion force data, and these N target expansion force data are not affected by thermal expansion and contraction, battery aging and errors between different sensors, the SOC accuracy of the obtained first ampere-hour integration interval is relatively high.

[0134] S509. Obtain the battery health status based on the preset initial state of charge and the integrated state of charge in the first ampere-hour integration interval.

[0135] For example, the health state of the battery is the ratio of the integrated state of charge in the first ampere-hour integration interval to a preset initial state of charge.

[0136] After obtaining ΔSOC0 and ΔSOC, the SOH of the battery or module can be calculated as SOH = ΔSOC / ΔSOC0. Simultaneously, the SOH is updated and used as the displayed SOH value for the battery or battery module until the next SOH update.

[0137] like Figure 7 The diagram shown illustrates the SOH estimation effect of a battery throughout its entire lifespan, as provided in this embodiment of the application. The SOH estimated using the SOH estimation method of this embodiment is basically consistent with the SOH obtained from actual measurement.

[0138] According to an embodiment of this application, a State of Charge (SOH) estimation method can accurately obtain the battery's health status by using a preset initial calibration state of charge and an integrated state of charge in a first ampere-hour integration interval. At least one of the start time and end time of the first ampere-hour integration interval corresponds to any one of N target expansion force data.

[0139] Based on the same concept as the above-described state of charge calibration method, this application also provides a state of charge calibration device. Some or all of the above methods can be implemented by software or firmware. For example... Figure 8The diagram shown is a structural schematic of a state of charge calibration device 8000 provided in an embodiment of this application. This device is used to perform the aforementioned state of charge calibration method. Specifically, the device 8000 includes:

[0140] The acquisition unit 81 is used to acquire M first expansion force data points of the battery within a first time range, where M is an integer greater than 1; the first identification unit 82 is used to identify N target expansion force data points among the M first expansion force data points, wherein the N target expansion force data points are used to characterize the expansion force change trend of the M first expansion force data points, where N is an integer less than or equal to M; the N target expansion force data points include one or more of the following: one of the M first expansion force data points showing the smallest change in relative to the battery's state of charge, one of the M first expansion force data points showing the largest change in relative to the battery's state of charge, and the M first expansion force data points... The data includes: a first expansion force data whose change relative to the battery state of charge is zero; a first acquisition unit 83, configured to acquire the battery state of charge corresponding to each of the N target expansion force data based on one or more preset target expansion force data and the relationship between the battery state of charge corresponding to each of the preset target expansion force data, wherein the value corresponding to each of the N target expansion force data is included in the preset one or more expansion force target data; and a calibration unit 84, configured to calibrate the battery state of charge during the charging or discharging process of the battery using the battery state of charge corresponding to each of the N target expansion force data.

[0141] Optionally, the acquisition unit 81 is used to acquire the M first expansion force data of the battery within the first time range through a sensor located inside the battery.

[0142] Optionally, the battery cell includes at least two winding cores, and the sensor is located between two adjacent winding cores of the at least two winding cores.

[0143] Optionally, the device further includes (shown as dashed lines in the figure): a second acquisition unit 85, configured to acquire multiple second expansion force data of the battery within a second time range, and the battery state of charge corresponding to each of the multiple second expansion force data; a second identification unit 86, configured to identify one or more preset target expansion force data among the multiple second expansion force data; and a generation unit 87, configured to generate the relationship between the preset one or more target expansion force data and the battery state of charge corresponding to each of the preset target expansion force data.

[0144] According to an embodiment of this application, a state of charge calibration device is provided. By identifying N target expansion force data among M first expansion force data, the SOC of the battery during charging or discharging is calibrated based on the SOC corresponding to each of the N target expansion force data. It does not require detecting the absolute value of the expansion force, but only needs to detect the trend of expansion force change. It is not affected by thermal expansion and contraction, battery aging, and errors between different sensors, thereby improving the accuracy of SOC calibration.

[0145] Based on the same concept as the above-described health status estimation method, this application also provides a health status estimation device. Some or all of the above methods can be implemented by software or firmware. For example... Figure 9 The diagram shown is a structural schematic of a health status estimation device 9000 provided in an embodiment of this application. This device is used to execute the aforementioned health status estimation method. Specifically, the device 9000 includes:

[0146] The acquisition unit 91 is used to acquire M first expansion force data points of the battery within a first time range, where M is an integer greater than 1; the first identification unit 92 is used to identify N target expansion force data points among the M first expansion force data points, wherein the N target expansion force data points are used to characterize the expansion force change trend of the M first expansion force data points, and N is an integer less than or equal to M; the N target expansion force data points include one or more of the following: the data point with the smallest change in the first expansion force data point relative to the battery state of charge among the M first expansion force data points, and the data point with the largest change in the first expansion force data point relative to the battery state of charge among the M first expansion force data points. The data includes: one of the M first expansion force data points, where the change in the relative state of charge of the battery is zero; a first acquisition unit 93, used to acquire the integrated state of charge of a first ampere-hour integration interval during a continuous charging or discharging process, wherein the first ampere-hour integration interval refers to a period of time between the start time and the end time, and the expansion force data corresponding to at least one of the start time and the end time of the first ampere-hour integration interval is any one of the N target expansion force data points; and a second acquisition unit 94, used to acquire the health status of the battery based on the preset initial state of charge and the integrated state of charge of the first ampere-hour integration interval.

[0147] Optionally, the device further includes (shown as dashed lines in the figure): a third acquisition unit 95, configured to acquire multiple second expansion force data of the battery within a second time range, and the battery state of charge corresponding to each of the multiple second expansion force data; a second identification unit 96, configured to identify one or more preset target expansion force data among the multiple second expansion force data; a generation unit 97, configured to generate the relationship between the preset one or more target expansion force data and the battery state of charge corresponding to each of the preset target expansion force data; a fourth acquisition unit 98, configured to acquire multiple voltage values ​​within the second time range, and the battery state of charge corresponding to each of the multiple voltage values; and a determination unit 99, configured to determine the preset initial state of charge; wherein the preset initial state of charge is the difference between a first state of charge and a second state of charge, wherein at least one of the first state of charge and the second state of charge is the battery state of charge corresponding to any one of the preset one or more target expansion force data.

[0148] Optionally, the health state of the battery is the ratio of the integrated state of charge in the first ampere-hour integration interval to the preset initial state of charge.

[0149] Optionally, the acquisition unit 91 is used to acquire the M first expansion force data of the battery within the first time range through a sensor located inside the battery.

[0150] Optionally, the battery cell includes at least two winding cores, and the sensor is located between two adjacent winding cores of the at least two winding cores.

[0151] According to an embodiment of this application, a health status estimation device can accurately obtain the battery's health status by using a preset initial calibrated state of charge and an integrated state of charge in a first ampere-hour integration interval, where at least one of the start time and end time of the first ampere-hour integration interval corresponds to any one of N target expansion force data.

[0152] like Figure 10 As shown, this application embodiment also provides a state of charge calibration device, which may include:

[0153] Memory 1003 and processor 1004 (the processor 1004 in this device may be one or more, Figure 10Taking a processor as an example, it may also include an input device 1001 and an output device 1002. In this embodiment, the input device 1001, output device 1002, memory 1003, and processor 1004 can be connected via a bus or other means. Figure 10 Taking the example of a connection between China and Israel via a bus.

[0154] Among them, processor 1004 is used to execute Figure 1 The methods and steps executed within.

[0155] Specifically, the processor 1004 is used to invoke the program instructions to perform the following operations:

[0156] M first expansion force data points of the battery are collected within a first time range, where M is an integer greater than 1; N target expansion force data points are identified from the M first expansion force data points, wherein the N target expansion force data points are used to characterize the expansion force change trend of the M first expansion force data points, where N is an integer less than or equal to M; the N target expansion force data points include one or more of the following: one of the M first expansion force data points with the smallest change relative to the battery state of charge, one of the M first expansion force data points with the largest change relative to the battery state of charge, and the M first expansion force data points... One of the first expansion force data is data where the change relative to the battery state of charge is zero; based on one or more preset target expansion force data and the relationship between the battery state of charge corresponding to each of the preset target expansion force data, the battery state of charge corresponding to each of the N target expansion force data is obtained, and the value corresponding to each of the N target expansion force data is included in the preset one or more expansion force target data; and the battery state of charge during the charging or discharging process is calibrated using the battery state of charge corresponding to each of the N target expansion force data.

[0157] Optionally, the processor 1004 executes the acquisition of M first expansion force data of the battery within a first time range, including: acquiring the M first expansion force data of the battery within the first time range through a sensor located inside the battery.

[0158] Optionally, the battery cell includes at least two winding cores, and the sensor is located between two adjacent winding cores of the at least two winding cores.

[0159] Optionally, before the processor 1004 executes the step of acquiring M first expansion force data of the battery within a first time range, it is further configured to: acquire multiple second expansion force data of the battery within a second time range, and the battery state of charge corresponding to each of the multiple second expansion force data; identify one or more preset target expansion force data among the multiple second expansion force data; and generate the relationship between the preset one or more target expansion force data and the battery state of charge corresponding to each of the preset target expansion force data.

[0160] Optionally, when some or all of the state of charge calibration methods in the above embodiments are implemented by software, the state of charge calibration device may also include only a processor. A memory for storing the program is located outside the state of charge calibration device, and the processor is connected to the memory via circuitry or wires to read and execute the program stored in the memory.

[0161] The processor can be a central processing unit (CPU), a network processor (NP), or a WLAN device.

[0162] The processor may further include hardware chips. These hardware chips may be application-specific integrated circuits (ASICs), programmable logic devices (PLDs), or combinations thereof. The PLDs may be complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), generic array logic (GALs), or any combination thereof.

[0163] Memory may include volatile memory, such as random-access memory (RAM); memory may also include non-volatile memory, such as flash memory, hard disk drive (HDD), or solid-state drive (SSD); memory may also include combinations of the above types of memory.

[0164] The input device 1001 / output device 1002 may include a display screen and a keyboard, and optionally, may also include a standard wired interface and a wireless interface.

[0165] like Figure 11 As shown in the illustration, this application also provides a health status estimation device, which 1100 may include:

[0166] Memory 1103 and processor 1104 (the processor 1104 in this device may be one or more, Figure 11 Taking a processor as an example, it may also include an input device 1101 and an output device 1102. In this embodiment, the input device 1101, output device 1102, memory 1103, and processor 1104 can be connected via a bus or other means. Figure 11 Taking the example of a connection between China and Israel via a bus.

[0167] The processor 1104 is used to execute Figure 5 The methods and steps executed within.

[0168] Specifically, the processor 1104 is used to invoke the program instructions to perform the following operations:

[0169] The system collects M first expansion force data points from the battery within a first time range, where M is an integer greater than 1; identifies N target expansion force data points from the M first expansion force data points, wherein the N target expansion force data points are used to characterize the expansion force change trend of the M first expansion force data points, and N is an integer less than or equal to M; the N target expansion force data points include one or more of the following: one of the M first expansion force data points with the smallest change relative to the battery state of charge, one of the M first expansion force data points with the largest change relative to the battery state of charge, and one of the M first expansion force data points with zero change relative to the battery state of charge; obtains the integrated state of charge of a first ampere-hour integration interval during a continuous charging or discharging process, wherein the first ampere-hour integration interval refers to a period of time between the start time and the end time, and the expansion force data point corresponding to at least one of the start time and the end time of the first ampere-hour integration interval is any one of the N target expansion force data points; and obtains the battery health status based on a preset initial state of charge and the integrated state of charge of the first ampere-hour integration interval.

[0170] Optionally, the processor 1104 is further configured to perform: acquiring a plurality of second expansion force data of the battery within a second time range, and the battery state of charge corresponding to each of the plurality of second expansion force data; identifying one or more preset target expansion force data among the plurality of second expansion force data; generating a relationship between the preset one or more target expansion force data and the battery state of charge corresponding to each of the preset target expansion force data; acquiring a plurality of voltage values ​​within the second time range, and the battery state of charge corresponding to each of the plurality of voltage values; and determining the preset initial state of charge; wherein the preset initial state of charge is the difference between a first state of charge and a second state of charge, wherein at least one of the first state of charge and the second state of charge is the battery state of charge corresponding to any one of the preset one or more target expansion force data.

[0171] Optionally, the health state of the battery is the ratio of the integrated state of charge in the first ampere-hour integration interval to the preset initial state of charge.

[0172] Optionally, the processor 1104 executes the acquisition of M first expansion force data of the battery within a first time range, including: acquiring the M first expansion force data of the battery within the first time range through a sensor located inside the battery.

[0173] Optionally, the battery cell includes at least two winding cores, and the sensor is located between two adjacent winding cores of the at least two winding cores.

[0174] Optionally, when some or all of the health status estimation methods in the above embodiments are implemented by software, the health status estimation device may also include only a processor. A memory for storing the program is located outside the health status estimation device, and the processor is connected to the memory via circuitry or wiring to read and execute the program stored in the memory.

[0175] The processor can be a CPU, an NP, or a WLAN device.

[0176] The processor may further include hardware chips. These hardware chips can be ASICs, PLDs, or combinations thereof. The PLDs can be CPLDs, FPGAs, GALs, or any combination thereof.

[0177] The memory may include volatile memory, such as RAM; the memory may also include non-volatile memory, such as flash memory, HDD or SSD; the memory may also include a combination of the above types of memory.

[0178] The input device 1001 / output device 1002 may include a display screen and a keyboard, and optionally, may also include a standard wired interface and a wireless interface.

[0179] This application embodiment also provides a battery module, including one or more batteries and the above-mentioned state of charge calibration device, wherein the state of charge calibration device is used to calibrate the state of charge of one or more batteries in the battery module during the charging or discharging process.

[0180] This application embodiment also provides a battery module, including one or more batteries and the above-mentioned health status estimation device, wherein the health status estimation device is used to obtain the health status of one or more batteries in the battery module respectively.

[0181] This application embodiment also provides an energy storage system, including an energy storage converter and the aforementioned battery module, wherein the energy storage converter is used to process the current input into the battery module and input it into the battery module.

[0182] This application embodiment also provides a computer-readable storage medium storing a computer program or instructions, which, when executed by a state of charge calibration device, achieve the following: Figure 1 The method described in the illustrated embodiment.

[0183] This application embodiment also provides a computer-readable storage medium storing a computer program or instructions, which, when executed by a health status estimation device, achieve the following: Figure 5 The method described in the illustrated embodiment.

[0184] This application also provides a computer program product that, when executed on a computing device, causes such... Figure 1 The method described in the illustrated embodiment is performed.

[0185] This application also provides a computer program product that, when executed on a computing device, causes such... Figure 5 The method described in the illustrated embodiment is performed.

[0186] It should be noted that the term "multiple" in the embodiments of this application refers to two or more. Therefore, "multiple" can also be understood as "at least two" in the embodiments of this application. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / ", unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0187] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, units, and processes described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0188] In the embodiments provided in this application, it should be understood that the disclosed systems, methods, and approaches can be implemented in other ways. For example, the division of units is merely a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling, direct coupling, or communication connection between the shown or discussed units may be through some interfaces, and the indirect coupling or communication connection between the system or units may be electrical, mechanical, or other forms.

[0189] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0190] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be read-only memory (ROM), random access memory (RAM), or magnetic media, such as floppy disks, hard disks, magnetic tapes, magnetic disks, or optical media, such as digital versatile discs (DVDs), or semiconductor media, such as solid-state disks (SSDs).

Claims

1. A method for calibrating the state of charge, characterized in that, The method includes: Acquire multiple second expansion force data of the battery within a second time range, and the battery state of charge corresponding to each of the multiple second expansion force data; Identify one or more preset target expansion force data among the plurality of second expansion force data; Generate one or more preset target expansion force data and the relationship between the battery state of charge corresponding to each preset target expansion force data; The battery acquires M first expansion force data points within a first time range using a sensor located inside the battery, where M is an integer greater than 1. The battery cell includes at least two cores, and the sensor is located between two adjacent cores of the at least two cores. Identify N target expansion force data points from the M first expansion force data points, wherein the N target expansion force data points are used to characterize the expansion force change trend of the M first expansion force data points, and N is an integer less than or equal to M; the N target expansion force data points include the following: the first expansion force data point with the smallest expansion force among the M first expansion force data points, the first expansion force data point with the largest expansion force among the M first expansion force data points, and the data point where one of the first expansion force data points has zero change relative to the battery state of charge; Based on the relationship between one or more preset target expansion force data and the battery state of charge corresponding to each preset target expansion force data, the battery state of charge corresponding to each of the N target expansion force data is obtained, and the value corresponding to each of the N target expansion force data is included in the preset one or more expansion force target data. The battery state of charge during the charging or discharging process is calibrated using the battery state of charge corresponding to each of the N target expansion force data.

2. A method for estimating health status, characterized in that, The method includes: Acquire multiple second expansion force data of the battery within a second time range, and the battery state of charge corresponding to each of the multiple second expansion force data; Identify one or more preset target expansion force data among the plurality of second expansion force data; Generate one or more preset target expansion force data and the relationship between the battery state of charge corresponding to each preset target expansion force data; The battery acquires M first expansion force data points within a first time range using a sensor located inside the battery, where M is an integer greater than 1. The battery cell includes at least two cores, and the sensor is located between two adjacent cores of the at least two cores. Identify N target expansion force data points from the M first expansion force data points, wherein the N target expansion force data points are used to characterize the expansion force change trend of the M first expansion force data points, and N is an integer less than or equal to M; the N target expansion force data points include the following: the first expansion force data point with the smallest expansion force among the M first expansion force data points, the first expansion force data point with the largest expansion force among the M first expansion force data points, and the data point where one of the first expansion force data points has zero change relative to the battery state of charge; The integral state of charge of the first ampere-hour integral interval during a continuous charging or discharging process is obtained, wherein the first ampere-hour integral interval refers to a period of time between the start time and the end time, and the expansion force data corresponding to at least one of the start time and the end time of the first ampere-hour integral interval is any one of the N target expansion force data. The battery health status is obtained based on the preset initial state of charge and the integrated state of charge in the first ampere-hour integration interval. The preset initial state of charge is the difference between the first state of charge and the second state of charge. At least one of the first state of charge and the second state of charge is the battery state of charge corresponding to any one of the preset target expansion force data.

3. The method according to claim 2, characterized in that, The method further includes: Acquire multiple voltage values ​​within the second time range, and the battery state of charge corresponding to each of the multiple voltage values; Determine the preset initial state of charge.

4. The method according to claim 2 or 3, characterized in that, The health state of the battery is the ratio of the integral state of charge in the first ampere-hour integral interval to the preset initial state of charge.

5. A state of charge calibration device, characterized in that, The device includes: The second acquisition unit is used to acquire multiple second expansion force data of the battery within a second time range, and the battery state of charge corresponding to each of the multiple second expansion force data. The second identification unit is used to identify one or more preset target expansion force data among the plurality of second expansion force data; The generation unit is used to generate one or more preset target expansion force data and the relationship between the battery state of charge corresponding to each preset target expansion force data. The acquisition unit is used to acquire M first expansion force data of the battery within a first time range through a sensor located inside the battery, where M is an integer greater than 1. The battery cell includes at least two cores, and the sensor is located between two adjacent cores of the at least two cores. A first identification unit is configured to identify N target expansion force data points among the M first expansion force data points, wherein the N target expansion force data points are used to characterize the expansion force change trend of the M first expansion force data points, and N is an integer less than or equal to M; the N target expansion force data points include the following: the first expansion force data point with the smallest expansion force among the M first expansion force data points, the first expansion force data point with the largest expansion force among the M first expansion force data points, and the data point where one of the first expansion force data points has zero change relative to the battery state of charge; The first acquisition unit is configured to acquire the battery state of charge corresponding to each of the N target expansion force data based on one or more preset target expansion force data and the relationship between the battery state of charge corresponding to each preset target expansion force data, wherein the value corresponding to each of the N target expansion force data is included in the preset one or more expansion force target data. The calibration unit is used to calibrate the battery state of charge during the charging or discharging process using the battery state of charge corresponding to each of the N target expansion force data.

6. A health status estimation device, characterized in that, The device includes: The third acquisition unit is used to acquire multiple second expansion force data of the battery within a second time range, and the battery state of charge corresponding to each of the multiple second expansion force data. The second identification unit is used to identify one or more preset target expansion force data among the plurality of second expansion force data; The generation unit is used to generate one or more preset target expansion force data and the relationship between the battery state of charge corresponding to each preset target expansion force data. The acquisition unit is used to acquire M first expansion force data of the battery within a first time range through a sensor located inside the battery, where M is an integer greater than 1. The battery cell includes at least two cores, and the sensor is located between two adjacent cores of the at least two cores. The first identification unit is used to identify N target expansion force data among the M first expansion force data, wherein the N target expansion force data are used to characterize the expansion force change trend of the M first expansion force data, and N is an integer less than or equal to M; the N target expansion force data include the following: the first expansion force data with the smallest expansion force among the M first expansion force data, the first expansion force data with the largest expansion force among the M first expansion force data, and the data in which one of the M first expansion force data has a zero change relative to the battery state of charge; The first acquisition unit is used to acquire the integrated state of charge of a first ampere-hour integral interval during a continuous charging or discharging process. The first ampere-hour integral interval refers to a period of time between the start time and the end time. The expansion force data corresponding to at least one of the start time and the end time of the first ampere-hour integral interval is any one of the N target expansion force data. The second acquisition unit is used to acquire the health status of the battery based on a preset initial state of charge and the integrated state of charge in the first ampere-hour integration interval. The preset initial state of charge is the difference between the first state of charge and the second state of charge. At least one of the first state of charge and the second state of charge is the battery state of charge corresponding to any one of the preset target expansion force data.

7. The apparatus according to claim 6, characterized in that, The device further includes: The fourth acquisition unit is used to acquire multiple voltage values ​​within the second time range, and the battery state of charge corresponding to each of the multiple voltage values; A determining unit is used to determine the preset initial charging state.

8. The apparatus according to claim 6 or 7, characterized in that, The health state of the battery is the ratio of the integral state of charge in the first ampere-hour integral interval to the preset initial state of charge.

9. A battery module, comprising one or more batteries and a state of charge calibration device as described in claim 5, the state of charge calibration device being used to calibrate the state of charge of one or more batteries in the battery module during charging or discharging.

10. A battery module, comprising one or more batteries and a health status estimation device as described in any one of claims 6-8, the health status estimation device being configured to acquire the health status of one or more batteries in the battery module respectively.

11. An energy storage system comprising an energy storage converter and a battery module as claimed in claim 9 or 10, wherein the energy storage converter is configured to process current input into the battery module and input it back into the battery module.

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