Method for estimating state of charge and state of health of battery and electronic equipment

Abstract

The invention discloses a method for estimating the state of charge and the state of health of a battery and electronic equipment. The method for estimating the state of charge and the state of health of the battery comprises the following steps: acquiring the current state of health of the battery; obtaining the state of charge of the estimated platform inflection point of the battery; based on the current state of health, obtaining the state of charge of the actual platform inflection point of the battery; and calibrating the charge state of the estimated platform inflection point according to the charge state of the actual platform inflection point. According to the method and the device, the charge state of the battery platform inflection point estimated by the system algorithm is calibrated based on the current real-time acquired battery health state, so that the current charge state of the battery is displayed more accurately, and the battery health state reflected by the charge state is more accurate; inconvenience and potential safety hazards caused by inaccurate estimation of the state of charge and the state of health of the battery are reduced, and the use experience is improved; meanwhile, the method is higher in estimation accuracy, and the estimation method is simpler and more convenient.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a method for estimating the state of charge and health of a battery and an electronic device. Background Art

[0002] In the related art, during the use of the battery, for example, during the driving of an electric vehicle, the SOC (State of Charge) is quickly corrected downward based on the OCV (Open circuit voltage) calibration when the vehicle reaches the end; after the battery is aged, the OCV-SOC curve and the SOC change at the inflection point near 60% SOC lead to the SOC miscalibration problem, making the battery's SOC and SOH (State of Health) estimation inaccurate. Summary of the invention

[0003] In view of this, the present application provides a method for estimating the state of charge and state of health of a battery, an electronic device, and a computer-readable storage medium to solve the problem of inaccurate estimation of the SOC and SOH of the battery in the related art.

[0004] In order to solve the above technical problems, the first technical solution provided by the present application is: to provide a method for estimating the state of charge and health state of a battery, comprising:

[0005] Get the current health status of the battery;

[0006] Obtaining a state of charge of an estimated platform inflection point of the battery;

[0007] Based on the current health state, obtaining a state of charge of an actual platform inflection point of the battery;

[0008] The state of charge of the estimated platform inflection point is calibrated according to the state of charge of the actual platform inflection point.

[0009] In one embodiment, obtaining the current health status of the battery includes:

[0010] Obtaining the current charging state of the battery;

[0011] In response to the current charging state of the battery being a first charging state and the duration of the first charging state reaching a first preset time, obtaining a first state of charge of the battery within a first preset range; wherein the first preset time is greater than or equal to 3 hours;

[0012] In response to the current charging state of the battery being a second charging state and the temperature of the battery reaching a first preset temperature, obtaining a second state of charge of the battery within a second preset range; wherein the first preset temperature is greater than or equal to 15° C.;

[0013] The current health state of the battery is obtained according to the first state of charge and the second state of charge.

[0014] In one embodiment, obtaining the state of charge of the estimated platform inflection point of the battery includes:

[0015] Obtaining the current charging state of the battery;

[0016] In response to the current charging state of the battery being a third charging state, the charging state of the estimated platform inflection point is acquired; wherein the charging state of the estimated platform inflection point is defined as the third charging state.

[0017] In one embodiment, obtaining the state of charge of the actual platform inflection point of the battery based on the current health state includes:

[0018] Acquiring the state of charge of the actual platform inflection point of the battery in real time; wherein the state of charge of the actual platform inflection point is defined as a fourth state of charge;

[0019] The calibrating the state of charge of the estimated platform inflection point according to the state of charge of the actual platform inflection point includes:

[0020] The third state of charge is calibrated based on the fourth state of charge.

[0021] In one embodiment, the real-time acquisition of the state of charge of the actual platform inflection point of the battery includes:

[0022] The fourth state of charge of the battery is identified using a regression algorithm.

[0023] In one embodiment, the real-time acquisition of the state of charge of the actual platform inflection point of the battery includes:

[0024] Acquiring state of charge data of a preset segment of the battery;

[0025] Identifying the state of charge data of the preset segment to obtain an open circuit voltage corresponding to the preset segment;

[0026] Determining whether the battery is at a platform inflection point according to the open circuit voltage of the preset segment;

[0027] In response to the battery being at a platform inflection point, a state of charge at the platform inflection point is acquired.

[0028] In one embodiment, the real-time acquisition of the state of charge of the actual platform inflection point of the battery further includes:

[0029] Obtaining an estimated open circuit voltage of the battery;

[0030] The estimated open circuit voltage is updated based on the open circuit voltage of the preset segment.

[0031] In one embodiment, in response to the current charging state of the battery being the third charging state, obtaining the charge state of the estimated platform inflection point includes:

[0032] In response to the battery being in the third charging state, the temperature of the battery reaching a second preset temperature, and the charging current of the third charging state reaching a preset range, a voltage differential curve corresponding to a unit amount of electricity charged into the battery is acquired in real time; wherein the second preset temperature is greater than or equal to 15° C.;

[0033] Obtaining a platform inflection point of the battery in the third charging state based on a voltage difference curve corresponding to a unit amount of electricity charged into the battery;

[0034] The third state of charge is acquired based on a platform inflection point of the third charging state.

[0035] In one embodiment, the obtaining the platform inflection point of the battery in the third charging state based on the voltage difference curve corresponding to the unit power charged into the battery includes:

[0036] In response to the charge amount of the battery in the third charging state being greater than or equal to 20% of the state of charge of the battery, and the voltage corresponding to the unit amount of charge charged in the third charging state being less than or equal to 2mV, the maximum value of the voltage difference curve corresponding to the unit amount of charge charged into the battery is obtained; wherein the maximum value is the platform inflection point of the battery in the third charging state.

[0037] In one embodiment, before calibrating the state of charge of the estimated platform inflection point based on the state of charge of the actual platform inflection point, the method includes:

[0038] Obtaining a difference between the third state of charge and the fourth state of charge;

[0039] In response to the difference exceeding a first preset threshold, the third state of charge is corrected; wherein the first preset threshold is [-5%, +5%].

[0040] In one embodiment, calibrating the state of charge of the estimated platform inflection point based on the state of charge of the actual platform inflection point includes:

[0041] Based on the fourth state of charge, an ampere-hour integral accumulation error corresponding to the fourth state of charge is corrected.

[0042] In one embodiment, the correcting the ampere-hour integral accumulation error corresponding to the fourth state of charge based on the fourth state of charge includes:

[0043] Acquire a voltage-capacity curve of the battery, and acquire an open circuit voltage of the battery in real time based on the voltage-capacity curve;

[0044] In response to the open circuit voltage matching the platform inflection point of the voltage-capacity curve, the ampere-hour integral cumulative error is corrected; wherein a correction threshold of the ampere-hour integral cumulative error is less than or equal to 1%.

[0045] In order to solve the above technical problems, the second technical solution provided in the present application is: to provide an electronic device, including: a processor and a memory, the memory is connected to the processor and is used to store a computer program that can be run on the processor; wherein, when the processor executes the computer program, any of the methods described above is implemented.

[0046] In order to solve the above technical problems, the third technical solution provided in the present application is: providing a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements any of the methods described above.

[0047] Beneficial effects of the present application: Different from the prior art, the method for estimating the state of charge and health of a battery of the present application includes: obtaining the current health state of the battery; obtaining the state of charge of the estimated platform inflection point of the battery; based on the current health state, obtaining the state of charge of the actual platform inflection point of the battery; and calibrating the state of charge of the estimated platform inflection point according to the state of charge of the actual platform inflection point. The present application calibrates the state of charge of the battery platform inflection point estimated by the system algorithm based on the current real-time acquired battery health state, so that the current state of charge of the battery is displayed more accurately, thereby making the battery health state reflected by the state of charge more accurate, reducing the inconvenience and safety hazards caused by inaccurate estimation of the battery's SOC and SOH, and improving the user experience; at the same time, the above method provided by the present application can perform a joint estimation of the battery's SOC and SOH, with higher accuracy and a simpler and more convenient estimation method. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technical workers in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 It is a schematic diagram of the OCV-SOC curve in the SOC and SOH joint estimation method in the related art; wherein A is the platform inflection point;

[0050] Figure 2 It is an overall flow chart of a method for estimating the state of charge and health of a battery provided in an embodiment of the present application;

[0051] Figure 3 yes Figure 2 A flowchart of the sub-steps of step S1 is provided;

[0052] Figure 4 It is a curve diagram of a battery SOH online estimation method provided in an embodiment of the present application;

[0053] Figure 5 yes Figure 2 A flowchart of the sub-steps of step S2 is provided;

[0054] Figure 6 This is a schematic diagram of identifying the platform inflection point of a battery in a slow charging state provided by an embodiment of the present application; wherein point A is the platform inflection point;

[0055] Figure 7 yes Figure 5 A flowchart of the sub-steps of step S22 is provided;

[0056] Figure 8 This is a voltage curve diagram of driving from 100% SOC to 10% SOC provided by an embodiment of the present application; wherein A is the platform inflection point;

[0057] Fig. 9 yes Figure 2 A flowchart of the sub-steps of step S3 is provided;

[0058] Fig.10 is a flowchart of the sub-steps of step S30 provided in an embodiment of the present application;

[0059] Fig.11 It is an OCV-SOC curve diagram at the inflection point of the battery platform provided by an embodiment of the present application; wherein OCV_Seg_Ini is at intervals of 0.5% SOC;

[0060] Fig.12 yes Figure 2A flowchart of the steps before step S4 is provided;

[0061] Fig.13 is a flow chart of the SOC calibration strategy for platform inflection point identification provided by an embodiment of the present application;

[0062] Fig.14 yes Figure 2 A flowchart of the sub-steps of step S4 is provided;

[0063] Fig.15 is a structural schematic diagram of an electronic device provided by an embodiment of the present application;

[0064] Fig.16 It is a schematic block diagram of the structure of a computer-readable storage medium provided in one embodiment of the present application. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technical workers in the field without making creative work are within the scope of protection of this application.

[0066] The terms "first", "second" and "first" in this application are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first" and "second" can expressly or implicitly include at least one of the features. All directional indications (such as up, down, left, right, front, back ...) in the embodiments of the present application are only used to explain the relative position relationship, movement conditions, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the steps or units listed, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0067] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0068] If the technical solution of this application involves personal information, the product using the technical solution of this application has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using the technical solution of this application has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to the collection of his or her personal information; or on the device for processing personal information, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

[0069] During the research process of this application, it was found that during the use of the battery, for example, when the SOH of the lithium iron phosphate battery is high, the SOC jumps to 100% when fully charged, and when the vehicle reaches the end, the SOC state of charge is quickly corrected downward based on the OCV (Open circuit voltage) calibration; at the same time, after the lithium iron phosphate battery is aged, the OCV-SOC curve and the SOC change at the inflection point near 60% SOC, which leads to the SOC miscalibration problem; the lithium iron phosphate battery is used in the platform area for a long time and is not fully charged, which causes the SOC error to continue to increase.

[0070] Most of the SOH estimation technologies for related lithium iron phosphate batteries use the battery aging empirical model and the feature-based SOH estimation method. Both methods have their inherent defects: the battery aging empirical model is based on the fitting of specific aging life test data and cannot adapt to the full working conditions during vehicle use; the feature-based SOH estimation method is limited by the mapping between battery aging characteristics and SOH, and the aging of different battery failure modes, and the feature-based SOH estimation method also has large errors.

[0071] In addition, most of the SOC estimation schemes for lithium iron phosphate batteries in related technologies are based on specific operating conditions such as full charging, slow charging platform inflection point, and end of driving discharge to calibrate the SOC error. They cannot cover the problem of long-term use in the battery platform area and the lack of SOC calibration opportunities, which leads to continuous growth of SOC.

[0072] The SOC and SOH joint estimation algorithm in the related art is mainly based on the ΔQ / ΔSOC between two points to estimate SOH, such as Figure 1As shown, ΔQ represents the change in charge between two points (calculated by Ah-integration), ΔSOC represents the difference in SOC between two points (SOC is obtained by looking up the OCV-SOC curve); arrow A represents the state of charge SOC in the battery platform area. The main problem with this method is that battery aging will affect the OCV-SOC curve, thereby introducing some uncertain errors; at the same time, battery aging will affect the inflection point SOC value in the lithium iron phosphate battery platform area.

[0073] The above problems result in inaccurate display of the battery's SOC and SOH during battery use, such as when an electric vehicle is driving, causing vehicle users to make inaccurate estimates of the battery's usage time, thereby causing inconvenience to the user or posing a safety hazard.

[0074] In order to solve the above problems, the present application provides a method for estimating the state of charge and health state of a battery, an electronic device, and a computer-readable storage medium.

[0075] See also Figure 2 , Figure 2 It is an overall flow chart of a method for estimating the state of charge and health of a battery provided in an embodiment of the present application.

[0076] In one embodiment, the method for estimating the state of charge and health of a battery provided by the present application may include the following steps:

[0077] S1: Get the current health status of the battery.

[0078] Specifically, the current health state of the battery can be understood as the health state of the battery at the instant. The health state can be understood as the degree of aging of the battery and the degree of performance degradation in terms of usage time, discharge speed, etc. compared with the initial rated capacity of a new battery. Obtaining the current health state of the battery here can be understood as real-time online acquisition to understand the current and latest battery health status.

[0079] In one embodiment, if Figure 3 As shown, step S1 of obtaining the current health status of the battery may include:

[0080] S11: Obtain the current charging status of the battery.

[0081] Specifically, the current charging state may include multiple states, such as starting to charge, charging to a certain percentage, full charging, slow charging, etc. By obtaining the current charging state of the battery, an accurate estimation of the battery health state can be achieved under the current circumstances.

[0082] S12: In response to the current charging state of the battery being the first charging state and the duration of the first charging state reaching a first preset time, obtaining a first state of charge of the battery within a first preset range.

[0083] Specifically, the first charging state may be the current power-on state. Power-on generally refers to the process of connecting an electrical appliance or electronic device to a power source to start working. Before power-on, the electrical appliance is in a power-off state and cannot work normally. During the power-on process, current flows through the circuit, causing the various components of the device to start operating. There are two ways to power on: one is to turn on the power by plugging in a power cord or pressing a switch, and the other is to connect a battery or storage battery to the device. The power-on state of the battery may mean that the battery has been powered on and is ready to work. In this embodiment, when the battery is in the first charging state and the duration reaches a first preset time, for example, when the first preset time is greater than or equal to 3 hours, the charge state of the battery within a first preset range, that is, the first charge state, can be obtained.

[0084] like Figure 4 As shown in the figure, the horizontal axis is the vehicle driving time; the vertical axis is the battery SOC, expressed in %. Since the vehicle has parking time, there is a disconnection in the SOC curve, that is, when the SOC curve is disconnected, time continues to increase, but the battery SOC remains unchanged. Figure 4 In the example shown, the first preset range may be from P2 to P1.

[0085] For example, Figure 4 In this case, when the battery is powered on, if the parking time is long enough (generally ≥3 hours) and the voltage-based SOC state of charge value is in the [X1, X2] range (X1, X2 are generally [3%, 25%]), then point P1 is valid and the information of point P1 is recorded, mainly including: SOC_P1 corresponding to each battery at point P1, the cumulative charge and discharge amount corresponding to point P1, the information update time of point P1, and the ampere-hour integral cumulative error from point P1 to point P2 is cleared (calculated based on the cumulative error of the current sensor).

[0086] If point P1 is valid and meets the conditions for calculating SOH from point P2 to point P1, then SOH (P2 to P1) is calculated as net discharge from point P2 to point P1 / (100%-SOC_P1[full battery number at point P2]) / battery pack rated capacity*100%, and the P2 point information is cleared to the default value.

[0087] Among them, the conditions for calculating SOH from P2 to P1 may include: the cumulative error of the ampere-hour integration from P2 to P1 is ≤2% (recommended value) and the interval from P2 to P1 is ≤7 days (recommended value). The above two conditions can be met at the same time. These two conditions control the error of ΔQ (the change in power between two points in the OCV-SOC curve) within a certain range. The ampere-hour integration is related to the current accuracy and ratio accuracy (accuracy is reflected in the cumulative error), as well as the offset of the current sensor (the offset is reflected in time).

[0088] S13: In response to the current charging state of the battery being the second charging state and the temperature of the battery reaching the first preset temperature, obtaining a second state of charge of the battery within a second preset range.

[0089] Specifically, the second charging state may be a fully charged state, which means that the battery is fully charged, that is, the battery is charged to 100%, that is, it has sufficient power. The first preset temperature may be greater than or equal to 15°C. Figure 4 It can be seen that the first preset range may be from P1 to P2.

[0090] like Figure 4 As shown, for example, when the battery is fully charged, if the battery temperature is ≥15°C (recommended value), the information of point P2 is recorded, mainly including: the fully charged battery number at point P2, the cumulative charge and discharge amount corresponding to point P2, the information update time at point P2, and the accumulated error of the ampere-hour integral from point P2 to point P1 is cleared; at the same time, if the SOH calculation conditions from point P1 to point P2 are met, then the SOH (P1 to P2) is calculated = the net charge amount from point P1 to point P2 / (100%-SOC_P1[fully charged battery number at point P2]) / battery pack rated capacity*100%.

[0091] Among them, the SOH calculation conditions from point P1 to point P2 can be: when the battery is fully charged, the battery temperature is ≥15°C (recommended value), the ampere-hour integral cumulative error from point P1 to point P2 is ≤2% (recommended value) and the interval time from point P1 to point P2 is ≤7 days (recommended value).

[0092] The calculation method of the ampere-hour integral cumulative error from point P1 to point P2 and from point P2 to point P1 can be: first, the current error of the corresponding interval is obtained by looking up the table based on the current current; then, the cumulative error amount is obtained based on the current error and time integral; finally, the cumulative error amount is divided by the rated capacity of the battery to obtain the ampere-hour integral cumulative error percentage.

[0093] S14: Obtaining a current health state of the battery according to the first state of charge and the second state of charge.

[0094] Specifically, after obtaining the specific data of the first state of charge and the second state of charge, the current health state of the battery can be calculated based on the above two states of charge. That is, the operating range of SOC will affect the SOH of the battery. If the battery operates at a high SOC (such as 80%-100%) or a low SOC (such as 0%-20%) for a long time, the aging of the battery will be accelerated, resulting in a faster decrease in SOH. For example, when the battery is often at a high SOC, the positive electrode material inside the battery may undergo an irreversible phase change, which reduces the active material of the battery and increases the internal resistance, thereby reducing the SOH. The SOC level of the battery will also affect the self-discharge rate of the battery, and the change in the self-discharge rate indirectly reflects the health of the battery. Generally speaking, the self-discharge rate at a high SOC may be slightly higher than that at a low SOC. As the battery ages (SOH decreases), the self-discharge rate may increase further, and this change manifests differently at different SOC levels. For example, in a new battery state, the self-discharge rate at an SOC of 80% may be 1%-2% per month; after the SOH drops to 80%, the self-discharge rate at the same SOC may increase to 3%-4% per month.

[0095] S2: Obtain the state of charge of the battery at the estimated platform inflection point.

[0096] Specifically, the estimated state of charge at the platform inflection point can be the state of charge estimation of the battery platform inflection point through the algorithm preset by the system. The battery platform inflection point is used to calibrate the battery power. Generally speaking, the OCV-SOC curve at the front and rear ends of the battery platform inflection point (for example, the platform inflection point is 65% to 70%) is almost flat (for example, 30% to 55%, 65% to 100%). Therefore, it is impossible to estimate the change of SOC based on the preset algorithm, it is impossible to calibrate SOC, and it is impossible to map the SOC value. Therefore, it is necessary to use the platform inflection point information of the 65% to 70% interval to estimate SOC. When it is recognized that the platform inflection point is actually in the 65-70% interval, if the estimated value of the estimated platform inflection point is not in this interval, the SOC should be calibrated to this interval to make the SOC estimation more accurate.

[0097] In one embodiment, if Figure 5 As shown, step S2 of obtaining the state of charge of the estimated platform inflection point of the battery may include:

[0098] S21: Obtain the current charging status of the battery.

[0099] Specifically, the current charging state may include multiple states, such as starting to charge, charging to a certain percentage, full charging, slow charging, etc. By obtaining the current charging state of the battery, the charge state and health state of the battery can be accurately estimated under the current circumstances.

[0100] S22: In response to the current charging state of the battery being the third charging state, obtaining the state of charge of the estimated platform inflection point; wherein the state of charge of the estimated platform inflection point is defined as the third state of charge.

[0101] Specifically, the third charging state here can be understood as the slow charging state of the battery. During the slow charging process, when the battery reaches the platform inflection point, the change in the amount of electricity to be fully charged is relatively small. Therefore, the remaining capacity and total capacity of the battery can be more accurately estimated by accurately obtaining the platform inflection point. For example, it is known that the proportion of the state of charge corresponding to the platform inflection point when the battery is slowly charged in a new state. During subsequent use, by comparing the changes in the platform inflection point under the same charging conditions, the current state of charge of the battery can be roughly estimated. At the same time, the slow charging process itself causes less damage to the battery. In the process of obtaining the platform inflection point, the irreversible damage to the internal structure of the battery, such as the formation of lithium dendrites and the peeling of electrode materials, will not be caused by high current charging. If the platform inflection point is obtained in the fast charging state, frequent high current charging and discharging may accelerate the aging and damage of the battery, shorten the battery life, and may even cause safety hazards.

[0102] In addition, the platform inflection point of the battery under slow charging is closely related to the health status of the battery. As the battery ages and the number of cycles increases, the platform inflection point will gradually change, such as the location of the inflection point may be advanced or the voltage range of the platform period may become narrower. By accurately obtaining the platform inflection point under slow charging, changes in the battery's health status can be discovered in a timely manner, providing a basis for battery maintenance and replacement. For example, for lithium iron phosphate batteries, if the capacity corresponding to the platform inflection point is found to be significantly reduced during slow charging, it may mean that the battery's capacity attenuation is large and the SOH is reduced.

[0103] In one embodiment, if Figure 6 and Figure 7 As shown, in response to the current charging state of the battery being the third charging state, the step S22 of obtaining the charging state of the estimated platform inflection point may include:

[0104] S221: In response to the battery being in the third charging state, the battery temperature reaching the second preset temperature, and the charging current of the third charging state reaching the preset range, a voltage difference curve corresponding to a unit amount of electricity charged into the battery is obtained in real time.

[0105] Specifically, in the slow charging state, the temperature of the battery reaches the second preset temperature. Among them, the second preset temperature is greater than or equal to 15°C. And the charging current in the slow charging state reaches the preset range, for example, the charging current ≤0.1C, then the voltage difference and charge difference curve (dV / dQ curve) in the slow charging state is obtained online in real time. The dV / dQ curve can be understood as the rate curve of the voltage change with the charge during the charging and discharging process of the battery. During the charging and discharging process of the battery, the dV / dQ curve can be obtained by differential calculation of the collected voltage and capacity data. The dV / dQ curve reflects the instantaneous rate of change of the voltage with the capacity of the battery during the charging and discharging process. The specific calculation method of the dV / dQ curve can be: during the slow charging process, for each charge (dQ) corresponding to 1% SOC, the voltage difference dV corresponding to 1% SOC is calculated.

[0106] like Figure 6 As shown, the dV / dQ curve is the voltage difference curve corresponding to the unit charge, and dQ takes the charge corresponding to 1% SOC.

[0107] S222: Obtaining a platform inflection point of the battery in a third charging state based on a voltage difference curve corresponding to a unit amount of electricity charged into the battery.

[0108] Specifically, Figure 6 As shown, based on the above dV / dQ curve, the platform inflection point is obtained. After detecting that the battery enters the charging mode, the dV / dQ value can be determined in real time, wherein point A is the platform inflection point.

[0109] S223: Acquire a third state of charge based on the platform inflection point of the third charging state.

[0110] Specifically, after obtaining the platform inflection point (A) of the battery, if the battery continues to be charged to a fully charged state, the data of the charged power Q_inflection point is recorded from the platform inflection point to the full charge. For example, the power Q_inflection point charged from the platform inflection point to the full charge of the battery is recorded through NVM (Non-Volatile Memory). The platform inflection point SOC can be obtained based on the charged power Q_inflection point. The calculation method of the platform inflection point SOC can be: SOC_inflection point = Q_inflection point / battery pack rated capacity / SOH.

[0111] In one embodiment, step S222 of obtaining a platform inflection point of the battery in the third charging state based on a voltage difference curve corresponding to a unit amount of electricity charged into the battery may include:

[0112] In response to the charge amount of the battery in the third charging state being greater than or equal to 20% of the state of charge of the battery, and the voltage corresponding to the unit charge of the third charging state being less than or equal to 2mV, the maximum value of the voltage difference curve corresponding to the unit charge of the battery is obtained. The maximum value is the platform inflection point of the battery in the third charging state.

[0113] Specifically, Figure 6 As shown, for example, in the slow charging state, if the continuous charging reaches 20% of the battery's state of charge, and the corresponding dV / dQ value is ≤2mV, the platform inflection point judgment stage can be entered to find the point corresponding to the maximum value of the dV / dQ curve; after finding the maximum value of dV / dQ, if the battery continues to charge, and the continuous charging reaches a state of charge of more than 20% (recommended value), and dV / dQ≤2mV (recommended value), it is considered that the found dV / dQ maximum value point is the platform inflection point (A).

[0114] S3: Based on the current health status, obtain the charge state of the actual platform inflection point of the battery.

[0115] Specifically, to put it simply, after obtaining the latest health status SOH corresponding to the battery, the SOC of the actual platform inflection point of the battery can be obtained through SOH. As the SOH decreases, the actual available capacity of the battery will decrease. This will cause errors in capacity-based SOC estimation methods (such as the ampere-hour integration method). After the battery ages, when the SOC algorithm identifies the platform inflection point characteristics, it can move the SOC closer to the SOC_inflection point based on this feature; if the SOC_inflection point is not updated online with aging, it will affect the estimation accuracy of the SOC algorithm.

[0116] For example, a new battery with a rated capacity of 100Ah will have an actual available capacity of 80Ah after its SOH drops to 80%. If the SOC is still estimated using the ampere-hour integration method based on 100Ah, a large deviation will occur. Therefore, after obtaining the actual SOH of the battery, the state of charge of the actual platform inflection point of the battery is obtained based on the SOH, which can greatly improve the accuracy of the estimation of the state of charge of the actual platform inflection point. This solves the problem of SOC miscalibration caused by the change of the SOC at the inflection point of the OCV-SOC curve with battery aging after battery aging.

[0117] In one embodiment, step S3 of obtaining the state of charge of the actual platform inflection point of the battery based on the current health state may include:

[0118] S30: acquiring the state of charge of the battery at the actual platform inflection point in real time; wherein the state of charge at the actual platform inflection point is defined as a fourth state of charge.

[0119] Specifically, it can be understood that the charge state of the actual platform inflection point of the battery obtained in real time is more accurate than the charge state of the estimated platform inflection point obtained by the system preset algorithm, so that the user can more accurately know the current charge state of the battery, so as to reasonably plan the battery usage time and improve the user experience.

[0120] In one embodiment, the step S4 of calibrating the state of charge of the estimated platform inflection point according to the state of charge of the actual platform inflection point may include:

[0121] The third state of charge is calibrated based on the fourth state of charge.

[0122] Specifically, after obtaining the state of charge of the actual platform inflection point of the battery, that is, the fourth state of charge, the state of charge of the estimated platform inflection point (third state of charge) can be calibrated according to the fourth state of charge, so that the battery state of charge obtained when the vehicle is driving is more accurate. For example, the current system estimates that the power is 80%, and the actual detection is at the platform inflection point, and the power at the inflection point is only about 60% to 70%, so it is necessary to correct the estimated power of 80% to the data of 60% to 70% of the platform inflection point. The SOC calibration of lithium iron phosphate batteries mainly occurs at the end of driving and the platform inflection point (the SOC at the inflection point will change with battery aging); based on the calibrated SOH, after the full charge SOC is calibrated to 100%, the battery will not be OCV calibrated to the end, thereby solving the problem of rapid correction of SOC downwards due to the high SOH of the lithium iron phosphate battery during vehicle use, causing the SOC to jump to 100% when fully charged, and driving to the end based on OCV calibration.

[0123] In one embodiment, the step S30 of acquiring the state of charge of the actual platform inflection point of the battery in real time may include:

[0124] S300: Identify a fourth state of charge of the battery using a regression algorithm.

[0125] Specifically, a binary regression algorithm is used, for example, the logistic regression algorithm (LogisticRegression) can be selected. The logistic regression algorithm is a simple and effective linear classification algorithm, which can map the input features to probability values ​​by fitting a logical function, thereby achieving binary classification. The prepared feature data is divided into a training set and a test set, generally in a ratio of 7:3 or 8:2. The training set can be used to train the selected binary regression algorithm. During the training process, for the logistic regression algorithm, it is necessary to determine the coefficients of the model and optimize the model by minimizing the loss function (such as the cross entropy loss function). Figure 8 As shown, Figure 8 This is the voltage curve from 100% SOC to 10% SOC, where point A is the inflection point of the platform. Figure 8 It can be seen that there is a relatively obvious dynamic voltage reduction phenomenon at the inflection point of the battery platform.

[0126] In a specific embodiment, if Fig. 9 As shown, the step S30 of obtaining the charge state of the actual platform inflection point of the battery in real time may also include:

[0127] S31: Acquire charge state data of a preset segment of the battery.

[0128] Specifically, for example, SOC segment driving data of a SOC variation within 1% during driving is obtained, preferably SOC segment driving data of 0.5% to 1%.

[0129] S32: Identify the state of charge data of the preset segment to obtain an open circuit voltage corresponding to the preset segment.

[0130] Specifically, the recursive least square method can be used to identify the driving data of the SOC segment within 1% to obtain the corresponding OCV(t), where t is time, that is, to obtain the open circuit voltage based on time t.

[0131] S33: judging whether the battery is at a platform inflection point according to the open circuit voltage of the preset segment.

[0132] Specifically, OCV_Seg minus OCV_Seg_Ini is used as input, and a binary classification algorithm (such as a Logistic regression algorithm) is input to determine whether the battery platform is currently at an inflection point. OCV_Seg refers to a segment or interval of the open circuit voltage. For example, during the battery charging and discharging process, the open circuit voltage will show different change patterns in different SOC intervals, and OCV_Seg is used to divide these different change intervals. By calculating the OCV in segments, we can understand the voltage characteristics of the battery in different states in more detail, and provide a more accurate basis for battery state estimation, charging and discharging control, etc. "Init" in OCV_Seg_Init is the abbreviation of "Initialization", which means initialization. OCV_Seg_Init refers to the operation or parameter setting for initializing OCV_Seg. For example, when the battery management system is started, it is necessary to initialize the variables, registers, etc. used to store and process OCV_Seg data, or initialize the relevant algorithms and models for determining OCV_Seg, so that the segment information of the open circuit voltage can be accurately obtained and analyzed later.

[0133] S34: In response to the battery being at the platform inflection point, obtaining a state of charge at the platform inflection point.

[0134] Specifically, after obtaining that the platform is at the platform inflection point, the charge state of the platform inflection point can be specifically obtained, for example, when the platform inflection point is 65% to 70%, the specific data of the charge state corresponding to the platform inflection point.

[0135] Exemplarily, the specific determination process of determining whether the battery is currently at a platform inflection point may be:

[0136] 1. Data preparation stage

[0137] 1.1 Data collection:

[0138] 1.1.1 First, you need to obtain relevant data of the lithium iron phosphate battery during the charging and discharging process, mainly the voltage-capacity curve data. This data can be collected through professional battery testing equipment, such as a high-precision battery charging and discharging tester. During the test, ensure the stability of the test environment, including the control of factors such as temperature and humidity, because these factors will affect the performance of the battery.

[0139] For example, for lithium iron phosphate batteries used in electric vehicles, it is generally recommended to keep the test temperature at around 25°C in order to obtain more accurate basic data.

[0140] 1.2 Data cleaning and preprocessing:

[0141] 1.2.1 The collected data may contain noise or outliers. Noise may be caused by factors such as the accuracy limitation of the test equipment or external electromagnetic interference. Outliers may be caused by accidental factors during the test, such as short-term poor contact.

[0142] 1.2.2 Filtering techniques (such as moving average filtering, Kalman filtering, etc.) can be used to remove noise. For outliers, they can be identified and removed by setting a reasonable threshold range. For example, if the difference between the voltage value of a data point and the adjacent data point exceeds a set reasonable range, it can be judged as an outlier and processed.

[0143] 1.2.3 In order to facilitate subsequent algorithm processing, the data needs to be normalized and the voltage and capacity data needs to be mapped to a suitable interval, such as the [0,1] interval.

[0144] 2. Feature extraction stage

[0145] 2.1 Determine the characteristic parameters:

[0146] 2.1.1 In the voltage-capacity curve of lithium iron phosphate batteries, the characteristics near the platform inflection point are key. The slope-related features of the curve can be extracted, such as the voltage change rate between adjacent data points.

[0147] 2.1.2 It is also possible to consider extracting the features of local extreme points, such as the local maximum and minimum voltages in the platform area, and the capacity values ​​corresponding to the maximum and minimum values. These features can reflect the shape and change trend of the platform area to a certain extent.

[0148] 2.2 Feature Engineering:

[0149] 2.2.1 The extracted features can be combined and transformed to obtain more representative features. For example, the difference between adjacent slopes can be calculated, or the voltage and capacity values ​​of local extreme points can be weighted combined to construct a more complex feature vector for subsequent classification algorithms.

[0150] 3. Model selection and training phase

[0151] 3.1 Select the binary classification algorithm:

[0152] 3.1.1 You can choose the logistic regression algorithm. The logistic regression algorithm is a simple and effective linear classification algorithm that can map input features to probability values ​​by fitting a logistic function, thereby achieving binary classification to determine whether the battery platform is currently at an inflection point.

[0153] 3.1.2 You can also choose the support vector machine (SVM) algorithm. SVM separates different categories of data by finding an optimal hyperplane. For the problem of determining the inflection point of the lithium iron phosphate battery platform, SVM can effectively handle linear and nonlinear classification situations in the feature space.

[0154] 3.2 Training Model:

[0155] 3.2.1 Divide the prepared feature data into training set and test set, generally in a ratio of 7:3 or 8:2. Use the training set to train the selected binary regression algorithm.

[0156] 3.2.2 During the training process, for the logistic regression algorithm, it is necessary to determine the coefficients of the model and optimize the model by minimizing the loss function (such as the cross entropy loss function). For the SVM algorithm, it is necessary to determine the optimal hyperplane parameters, such as mapping the data to a high-dimensional space through a kernel function (such as a Gaussian kernel function) and finding the optimal hyperplane in the high-dimensional space.

[0157] 4. Model evaluation and application stage

[0158] 4.1 Model Evaluation:

[0159] 4.1.1 Use the test set to evaluate the trained model. You can use indicators such as accuracy, recall, and F1-score (based on the harmonic mean of precision and recall) to measure the performance of the model.

[0160] For example, if the model's accuracy reaches more than 80%, the recall rate reaches more than 70%, and the F1-score reaches more than 0.75, the model can be considered to have good performance.

[0161] 4.2 Application model to determine the platform inflection point:

[0162] 4.2.1 After feature extraction, the voltage-capacity curve data of the new lithium iron phosphate battery is input into the trained binary classification model. The model will output a classification result to determine whether the data point is near the platform inflection point.

[0163] 4.2.2 Based on the results of the model output in the above steps, the battery status can be further analyzed, for example, to determine the battery's health status or whether the charging and discharging stages have reached a critical node.

[0164] In another embodiment, if Fig.10 As shown, the step S30 of obtaining the charge state of the actual platform inflection point of the battery in real time may also include:

[0165] S301: Obtaining an estimated open circuit voltage of a battery.

[0166] Specifically, the estimated open circuit voltage may be an open circuit voltage OCV obtained under a current state of the battery through an algorithm preset by the system.

[0167] S302: Update the estimated open circuit voltage based on the open circuit voltage of the preset segment.

[0168] Specifically, Fig.11 As shown, for example, the recursive least squares method is used to identify the 0.5% SOC segment driving data, and after obtaining the corresponding OCV(t), the sliding window segment OCV_Seg is updated based on the online estimated OCV:

[0169] OCV_Seg=[OCV(t),OCV(t-1),OCV(t-2),OCV(t-3),OCV(t-4),OCV(t-5),OCV(t-6),OCV(t-7),OCV(t-8),OCV(t-9),OCV(t-10)], the priority sliding window length is 11.

[0170] S4: Calibrate the state of charge of the estimated platform inflection point according to the state of charge of the actual platform inflection point.

[0171] Specifically, after obtaining the state of charge of the actual platform inflection point through the above steps, the fourth state of charge of the battery (the state of charge of the actual platform inflection point) can be identified through a regression algorithm, so as to facilitate calibration of the estimated third state of charge (the estimated state of charge of the platform inflection point), for example, correcting the charge value of the third state of charge to the charge value of the fourth state of charge, so that subsequent estimates of the state of charge of the platform inflection point are more accurate.

[0172] In one embodiment, if Fig.12 and Fig.13 As shown, before step S4 of calibrating the state of charge of the estimated platform inflection point based on the state of charge of the actual platform inflection point, the following steps may be included:

[0173] S401: Obtain a difference between a third state of charge and a fourth state of charge.

[0174] Specifically, during driving, the SOC error is calculated in real time based on the current sensor error and the ampere-hour integration.

[0175] S402: In response to the difference exceeding a first preset threshold, correcting the third state of charge.

[0176] Specifically, when an inflection point is identified during driving, if the current estimated SOC-SOC_inflection point exceeds a certain range, the estimated SOC (third state of charge) is corrected based on the actual SOC error. Among them, the first preset threshold is [-5%, +5%]. For example, when the inflection point of the lithium iron phosphate platform is identified, the estimated error of the current SOC is judged. If ABS (SOC-SOC_inflection point) ≥ 5%, the estimated value of the current SOC is corrected. The specific correction method can be: if SOC ≥ SOC_inflection point, then SOC = SOC-min (1%, SOC error); otherwise, SOC = SOC + min (1%, SOC error); wherein the first preset threshold is preferably 1%. Among them, ABS (SOC-SOC_inflection point) represents the absolute value of the difference between the battery state of charge (SOC) and the state of charge at the inflection point of the battery platform (SOC_inflection point). This value can be used to understand the degree of deviation between the current state of charge of the battery and the state of charge when the inflection point occurs. For example, if the value of ABS (SOC-SOC_inflection point) is small, it means that the current state of charge of the battery is close to the state of charge at the inflection point, and the battery may be about to enter the next obvious charging and discharging stage; if the value is large, it means that the current state of the battery is still some distance away from the inflection point state, and the battery charging and discharging process is still in a relatively stable stage.

[0177] In one embodiment, step S4 of calibrating the state of charge of the estimated platform inflection point based on the state of charge of the actual platform inflection point may include:

[0178] S40: Based on the fourth state of charge, correct the ampere-hour integral accumulation error corresponding to the fourth state of charge.

[0179] Specifically, after obtaining the specific fourth state of charge, that is, the state of charge at the actual platform inflection point, the ampere-hour integral cumulative error can be corrected according to the state of charge at the actual platform inflection point, so that the subsequent estimated state of charge at the platform inflection point is more accurate. The ampere-hour integral cumulative error is due to the error of the current sensor and the non-ideal capacitance characteristics of the battery, which causes the estimated value of the state of charge to gradually deviate from the true value as the integration proceeds, so the ampere-hour integral cumulative error needs to be corrected.

[0180] In one embodiment, if Fig.14 As shown, based on the fourth state of charge, step S40 of correcting the ampere-hour integral accumulation error corresponding to the fourth state of charge may include:

[0181] S41: Obtain a voltage-capacity curve of the battery, and obtain the open circuit voltage of the battery in real time based on the voltage-capacity curve.

[0182] Specifically, when the battery is powered on, the platform inflection point of the voltage-capacity curve is obtained, and the open circuit voltage OCV of the battery is obtained online and in real time based on the voltage-capacity curve.

[0183] S42: In response to the open circuit voltage matching the platform inflection point of the voltage-capacity curve, the ampere-hour integral accumulation error is corrected.

[0184] Specifically, the open circuit voltage OCV of the battery obtained online in real time is matched with the voltage-capacity curve of the platform inflection point, for example, using a binary classification algorithm. If the two match, the ampere-hour integral cumulative error is corrected to avoid a continuous increase in the SOC error; wherein the correction threshold of the ampere-hour integral cumulative error is less than or equal to 1%. That is, the ampere-hour integral cumulative error is corrected by a maximum of 1% each time, thereby avoiding erroneous corrections that affect the user experience.

[0185] This application is based on identifying the inflection point of lithium iron phosphate under slow charging state, identifying the inflection point of static OCV when the battery is powered on, and identifying the platform inflection point and calibrating SOC under driving conditions. By adding an algorithm to identify the platform inflection point under driving conditions, the triggering time is more common, which can effectively avoid the problem of continuous deterioration of SOC estimation error caused by long-term use in the platform area.

[0186] The method for estimating the state of charge and health of a battery disclosed in the present application includes: obtaining the current health state of the battery; obtaining the state of charge of the estimated platform inflection point of the battery; based on the current health state, obtaining the state of charge of the actual platform inflection point of the battery; and calibrating the state of charge of the estimated platform inflection point according to the state of charge of the actual platform inflection point. Based on the current real-time acquired battery health state, the present application calibrates the state of charge of the battery platform inflection point estimated by the system algorithm, so that the current state of charge of the battery is displayed more accurately, thereby making the battery health state reflected by the state of charge more accurate, reducing the inconvenience and safety hazards caused by inaccurate SOC and SOH estimation of the battery, and improving the user experience; at the same time, the above method provided by the present application can perform a joint estimation of the SOC and SOH of the battery, with higher accuracy and a simpler and more convenient estimation method.

[0187] See also Fig.15 , Fig.15 It is a schematic diagram of the structure of an electronic device provided in one embodiment of the present application.

[0188] The electronic device 200 may include a processor 210 and a memory 220. The memory 220 is coupled to the processor 210. The processor 210 is used to execute program instructions stored in the memory 220 to implement the steps in the embodiment of the method for estimating the state of charge and health state of any of the above batteries. In a specific implementation scenario, the electronic device 200 may include but is not limited to: a microcomputer, a server, and in addition, the electronic device 200 may also include a mobile device such as a laptop computer and a tablet computer, which is not limited here.

[0189] Specifically, the processor 210 is used to control the operation of the electronic device 200, and the processor 210 can also be called a CPU (Central Processing Unit). The processor 210 can be an integrated circuit chip with signal processing capabilities. The processor 210 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor 210 can also be any conventional processor, etc. In addition, the processor 210 can be implemented by an integrated circuit chip.

[0190] The memory 220 is used to store computer programs, and may be a RAM, a ROM, or other types of storage devices. Specifically, the memory may include one or more computer-readable storage media, which may be non-transitory. The memory may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory is used to store at least one program code.

[0191] The processor 210 is used to execute the computer program stored in the memory 220 to implement the method for estimating the state of charge and health state of the battery described in the above embodiments of the present application.

[0192] In some embodiments, the electronic device 200 may further include: a peripheral device interface 230 and at least one peripheral device. The processor 210, the memory 220 and the peripheral device interface 230 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 230 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 240, a display screen 250, an audio circuit 260 and a power supply 270.

[0193] The peripheral device interface 230 may be used to connect at least one peripheral device related to I / O (Input / output) to the processor 210 and the memory 220. In some embodiments, the processor 210, the memory 220, and the peripheral device interface 230 are integrated on the same chip or circuit board; in some other implementations, any one or two of the processor 210, the memory 220, and the peripheral device interface 230 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0194] The radio frequency circuit 240 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 240 communicates with the communication network and other communication devices through electromagnetic signals, and the radio frequency circuit 240 is the communication circuit of the electronic device 200. The radio frequency circuit 240 converts the electrical signal into an electromagnetic signal for transmission, or converts the received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 240 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, and the like. The radio frequency circuit 240 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to: the World Wide Web, a metropolitan area network, an intranet, various generations of mobile communication networks (2G, 3G, 4G and 5G), a wireless local area network and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 240 may also include circuits related to NFC (Near Field Communication), which is not limited in this application.

[0195] The display screen 250 is used to display the UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 250 is a touch display screen, the display screen 250 also has the ability to collect touch signals on the surface or above the surface of the display screen 250. The touch signal can be input to the processor 210 as a control signal for processing. At this time, the display screen 250 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen 250 can be one, set on the front panel of the electronic device 200; in other embodiments, the display screen 250 can be at least two, respectively set on different surfaces of the electronic device 200 or in a folding design; in other embodiments, the display screen 250 can be a flexible display screen, set on the curved surface or folding surface of the electronic device 200. Even, the display screen 250 can also be set to a non-rectangular irregular shape, that is, a special-shaped screen. The display screen 250 can be made of materials such as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode, organic light-emitting diode).

[0196] The audio circuit 260 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, and convert the sound waves into electrical signals and input them into the processor 210 for processing, or input them into the radio frequency circuit 240 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there can be multiple microphones, which are respectively arranged at different parts of the electronic device 200. The microphone can also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 210 or the radio frequency circuit 240 into sound waves. The speaker can be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into audible sound waves, but also convert the electrical signal into inaudible sound waves for purposes such as ranging. In some embodiments, the audio circuit 260 may also include a headphone jack.

[0197] The power supply 270 is used to power various components in the electronic device 200. The power supply 270 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 270 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged through a wired line, and a wireless rechargeable battery is a battery that is charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0198] For a detailed description of the functions and execution processes of each functional module or component in the embodiment of the electronic device 200 of the present application, reference can be made to the description in the embodiment of the method for estimating the state of charge and state of health of the battery of the present application, which will not be repeated here.

[0199] In the several embodiments provided in the present application, it should be understood that the disclosed electronic device 200 and the method for estimating the state of charge and health state of the battery can be implemented in other ways. For example, the various embodiments of the electronic device 200 described above are merely schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0200] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0201] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0202] See also Fig.16 , Fig.16 It is a schematic block diagram of the structure of a computer-readable storage medium provided in one embodiment of the present application.

[0203] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0204] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0205] See also Fig.16 , if the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium 300. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the relevant technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions / computer programs to enable an electronic device (which can be a computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of each embodiment of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical disks, and electronic devices such as computers, mobile phones, laptops, tablet computers, cameras, etc. having the above-mentioned storage media.

[0206] The above description is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for estimating the state of charge and health of a battery, characterized in that: include: Get the current health status of the battery; Obtaining a state of charge of an estimated platform inflection point of the battery; Based on the current health state, obtaining a state of charge of an actual platform inflection point of the battery; The state of charge of the estimated platform inflection point is calibrated according to the state of charge of the actual platform inflection point.

2. The method according to claim 1, characterized in that The obtaining of the current health status of the battery includes: Obtaining the current charging state of the battery; In response to the current charging state of the battery being a first charging state and the duration of the first charging state reaching a first preset time, obtaining a first state of charge of the battery within a first preset range; wherein the first preset time is greater than or equal to 3 hours; In response to the current charging state of the battery being a second charging state and the temperature of the battery reaching a first preset temperature, obtaining a second state of charge of the battery within a second preset range; wherein the first preset temperature is greater than or equal to 15° C.; The current health state of the battery is obtained according to the first state of charge and the second state of charge.

3. The method according to claim 1, characterized in that The obtaining the state of charge of the estimated platform inflection point of the battery includes: Obtaining the current charging state of the battery; In response to the current charging state of the battery being a third charging state, the charging state of the estimated platform inflection point is acquired; wherein the charging state of the estimated platform inflection point is defined as the third charging state.

4. The method according to claim 3, characterized in that The acquiring the state of charge of the actual platform inflection point of the battery based on the current health state includes: Acquiring the state of charge of the actual platform inflection point of the battery in real time; wherein the state of charge of the actual platform inflection point is defined as a fourth state of charge; The calibrating the state of charge of the estimated platform inflection point according to the state of charge of the actual platform inflection point includes: The third state of charge is calibrated based on the fourth state of charge.

5. The method according to claim 4, characterized in that The real-time acquisition of the state of charge of the actual platform inflection point of the battery includes: The fourth state of charge of the battery is identified using a regression algorithm.

6. The method according to claim 3, characterized in that: The real-time acquisition of the state of charge of the actual platform inflection point of the battery includes: Acquiring state of charge data of a preset segment of the battery; Identifying the state of charge data of the preset segment to obtain an open circuit voltage corresponding to the preset segment; Determining whether the battery is at a platform inflection point according to the open circuit voltage of the preset segment; In response to the battery being at a platform inflection point, a state of charge at the platform inflection point is acquired.

7. The method according to claim 6, characterized in that The real-time acquisition of the state of charge of the actual platform inflection point of the battery also includes: Obtaining an estimated open circuit voltage of the battery; The estimated open circuit voltage is updated based on the open circuit voltage of the preset segment.

8. The method according to claim 3 or 4, characterized in that: In response to the current charging state of the battery being the third charging state, obtaining the charge state of the estimated platform inflection point includes: In response to the battery being in the third charging state, the temperature of the battery reaching a second preset temperature, and the charging current of the third charging state reaching a preset range, a voltage differential curve corresponding to a unit amount of electricity charged into the battery is acquired in real time; wherein the second preset temperature is greater than or equal to 15° C.; Obtaining a platform inflection point of the battery in the third charging state based on a voltage difference curve corresponding to a unit amount of electricity charged into the battery; The third state of charge is acquired based on a platform inflection point of the third charging state.

9. The method according to claim 8, characterized in that The obtaining the platform inflection point of the battery in the third charging state based on the voltage difference curve corresponding to the unit power charged into the battery includes: In response to the charge amount of the battery in the third charging state being greater than or equal to 20% of the state of charge of the battery, and the voltage corresponding to the unit amount of charge charged in the third charging state being less than or equal to 2mV, the maximum value of the voltage difference curve corresponding to the unit amount of charge charged into the battery is obtained; wherein the maximum value is the platform inflection point of the battery in the third charging state.

10. The method according to claim 8, characterized in that Before calibrating the state of charge of the estimated platform inflection point based on the state of charge of the actual platform inflection point, the method includes: Obtaining a difference between the third state of charge and the fourth state of charge; In response to the difference exceeding a first preset threshold, the third state of charge is corrected; wherein the first preset threshold is [-5%, +5%].

11. The method according to claim 4, characterized in that The calibrating the state of charge of the estimated platform inflection point based on the state of charge of the actual platform inflection point includes: Based on the fourth state of charge, the ampere-hour integral accumulation error corresponding to the fourth state of charge is corrected.

12. The method according to claim 11, characterized in that The correcting the ampere-hour integral accumulation error corresponding to the fourth state of charge based on the fourth state of charge includes: Acquire a voltage-capacity curve of the battery, and acquire an open circuit voltage of the battery in real time based on the voltage-capacity curve; In response to the open circuit voltage matching the platform inflection point of the voltage-capacity curve, the ampere-hour integral cumulative error is corrected; wherein a correction threshold of the ampere-hour integral cumulative error is less than or equal to 1%.

13. An electronic device, characterized in that: include: processor; a memory, connected to the processor, and configured to store a computer program executable on the processor; Wherein, when the processor executes the computer program, the method according to any one of claims 1 to 12 is implemented.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 12 is implemented.

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