Battery health estimation method and apparatus, electronic device, and readable storage medium

Abstract

The application discloses a battery health degree estimation method and device, electronic equipment and a readable storage medium, and belongs to the technical field of batteries. The device use state matching rule of the first state is set to be higher than the second state, the target use state matched with the target device is selected, the battery health degree is calculated in a more optimal estimation accuracy manner, and the estimation feasibility and accuracy of the battery health degree under different device use states are considered. In the case that the target use state is the first state, the resting charge value is determined according to the charge value mapping curve corresponding to the basic battery health degree of the target device, the mapping curve corresponding to the aging stage is dynamically matched according to the basic battery health degree representing the current battery aging degree, the defect that the fixed curve cannot adapt to the characteristic deviation after the battery is aged is avoided, and the long-term accuracy and stability of the battery health degree estimation in the whole life cycle are improved.

Description

Technical Field

[0001] This application belongs to the field of battery technology, specifically relating to a method, apparatus, electronic device, and readable storage medium for predicting battery health. Background Technology

[0002] As the number of charge-discharge cycles increases, the usable capacity of a battery will decrease. After a certain degree of decrease, battery safety risks such as bulging and lithium plating may occur. Therefore, the accuracy requirements for predicting battery health are constantly increasing.

[0003] In existing technologies, the calculation of battery state of health (SOH) often employs a two-point method, namely the open circuit voltage (OCV) integration method. This method involves recording the open circuit voltage of the battery under two fully rested conditions, using a pre-stored fixed charge value mapping curve (OCV-SOC curve) to obtain the corresponding state of charge (SOC), and then combining this with the cumulative charge and discharge amounts between the two rested conditions to calculate the actual full charge capacity (FCC), thereby deriving the battery health value.

[0004] However, with the increasing popularity of new high-capacity batteries such as silicon-based anodes, the expansion of internal silicon materials and the thickening of the SEI film during long-term cycle aging directly lead to a significant nonlinear shift in the charge value mapping curve. If a calibrated fixed charge value mapping curve is used to look up the state of charge (SOC) in a table, a continuously amplified mapping error will be introduced, ultimately resulting in a significant inaccuracy in the battery health estimation, which cannot meet the high-precision estimation requirements after long-term use. Summary of the Invention

[0005] To overcome the problems existing in related technologies, the present invention provides a method, apparatus, electronic device and readable storage medium for predicting battery health.

[0006] In a first aspect, embodiments of this application provide a method for estimating battery health, the method comprising: Based on the matching priority of at least two device usage states, determine the target usage state that matches the target device from the at least two device usage states; When the target usage state is the first state, the target battery health of the target device is determined based on the resting charge value and nominal battery capacity of the target device in the first state. The resting charge value is determined based on the charge value mapping curve corresponding to the current basic battery health of the target device. The first state indicates that the target device has gone through at least two resting states. The resting state is defined as: the duration for which the current of the target device is lower than a preset current threshold is greater than or equal to a preset resting judgment duration. When the target usage state is the second state, the basic battery health is determined as the target battery health; the second state indicates that the target device has not been continuously discharged within the target duration and has not gone through at least two resting states, and the matching priority of the first state is higher than the matching priority of the second state.

[0007] Secondly, embodiments of this application provide a battery health estimation device, the device comprising: The first determining module is used to determine the target usage state that matches the target device from the at least two device usage states according to the matching priority of the at least two device usage states; The second determining module is used to determine the target battery health of the target device based on the resting charge value and nominal battery capacity of the target device in the first state when the target usage state is the first state; the resting charge value is determined based on the charge value mapping curve corresponding to the current basic battery health of the target device; the first state represents that the target device has gone through at least two resting states; the resting state is defined as: the duration for which the current of the target device is lower than a preset current threshold is greater than or equal to a preset resting determination duration. The third determining module is used to determine the basic battery health as the target battery health when the target usage state is the second state; the second state indicates that the target device has not been continuously discharged within the target duration and has not gone through at least two resting states, and the matching priority of the first state is higher than the matching priority of the second state.

[0008] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0009] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0010] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0011] In this embodiment, by setting a device usage state matching rule with a higher priority for the first state than the second state, the target usage state matching the target device is selected. This prioritizes the calculation of battery health using a more accurate estimation method. This approach fully utilizes the accuracy of determining battery health based on the static charge value and nominal battery capacity determined in the first state, while directly using the basic battery health value in the second state to ensure estimation efficiency. This balances the feasibility and accuracy of battery health estimation under different device usage states. Furthermore, when the target usage state is the first state, the static charge value is determined based on the charge value mapping curve corresponding to the target device's basic battery health. This allows for dynamic matching of the charge value mapping curve corresponding to the aging stage based on the basic battery health value representing the current battery aging degree. This avoids the defect of fixed curves being unable to adapt to the characteristic shifts after battery aging, improving the long-term accuracy and stability of battery health estimation throughout the entire lifecycle, thereby meeting the high-precision estimation requirements for battery health after long-term device use. Attached Figure Description

[0012] Figure 1 This is a flowchart of the steps of a battery health estimation method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a charge value mapping curve corresponding to different battery health levels provided in an embodiment of the present invention; Figure 3 This is a flowchart illustrating the specific steps of a battery health estimation method provided in an embodiment of the present invention. Figure 4 This is a system architecture diagram of a battery health estimation method provided in an embodiment of the present invention; Figure 5 This is a structural diagram of a battery health estimation device provided in an embodiment of the present invention; Figure 6 This is a structural diagram of an electronic device provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0015] The method for predicting battery health provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0016] Figure 1 This is a flowchart illustrating the steps of a battery health estimation method provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the method may include: Step 101: Determine the target usage state that matches the target device from the at least two device usage states according to the matching priority of the at least two device usage states.

[0017] In this embodiment of the invention, at least two device usage states are preset, and matching priorities are set for each of the at least two device usage states. The matching priority of device usage states can be determined based on the reliability and accuracy of the battery health prediction rule corresponding to the device usage state. The higher the reliability and accuracy of the corresponding battery health prediction rule, the higher the matching priority of the device usage state. Based on the current state of the target device, it is matched with at least two device usage states to determine the target usage state that matches the target device from the at least two device usage states. The device usage states can be divided into a first state, a second state, and a third state. The first state can be a dual standby state, the second state can be a normal operating state, and the third state can be a full discharge state. The full discharge state can refer to the complete discharge path of the battery continuously discharging from the full charge cutoff point to the system shutdown voltage. The dual standby state can refer to the device entering a steady-state condition where the current is lower than a preset threshold and the duration is longer than a preset standby judgment duration, i.e., a standby state, and a stable and unfluctuating open-circuit voltage value can be collected in both standby states. The second state can be a state where the target device is neither in the first nor the third state; that is, the normal operating state can be a normal usage condition where the device is not fully discharged or in dual standby mode. Different device usage states correspond to different battery health prediction rules. Based on the target usage state that matches the target device, the corresponding battery health prediction rule can be selected to determine the target battery health of the target device.

[0018] For example, the battery health prediction rule for the dual standby state can be the two-point method, the battery health prediction rule for the full discharge state can be the full discharge capacity method, and the battery health prediction rule for the normal operating state can be the battery degradation model algorithm. Therefore, the matching priority of the full discharge state can be set higher than that of the dual standby state, and the matching priority of the dual standby state can be set higher than that of the normal operating state.

[0019] The system sorts devices according to a preset matching priority, filtering out the target usage state that matches the current actual operating condition of the target device from all predefined device usage states. Specifically, it pre-configures matching priorities from high to low for the first, second, and third states (fully discharged, dual standby, and normal operation states). It prioritizes matching the conditions of high-priority states: when a device usage state matching the current operating condition is detected, that state is directly identified as the target usage state, terminating the matching process for lower-priority states. If the matching conditions for high-priority states are not met, the system proceeds to lower-priority states, ultimately identifying the device usage state that matches the current operating condition as the target usage state. This priority matching mechanism ensures that states with higher battery health prediction accuracy are identified first, avoiding overlooking effective scenarios for accurate battery health calculation.

[0020] Step 102: When the target usage state is the first state, determine the target battery health of the target device based on the resting charge value and nominal battery capacity of the target device in the first state; the resting charge value is determined based on the charge value mapping curve corresponding to the current basic battery health of the target device, the first state indicates that the target device has gone through at least two resting states, and the resting state is defined as: the duration for which the current of the target device is lower than a preset current threshold is greater than or equal to a preset resting judgment duration.

[0021] In this embodiment of the invention, the first state represents the target device having undergone at least two resting states. A resting state can be defined as: the duration for which the target device's current is below a preset current threshold is greater than or equal to a preset resting determination duration. When the matched target usage state is the first state, a two-point method is used to determine the target battery health based on the resting charge value and the battery's nominal capacity corresponding to the target device in the first state. The resting charge value is determined based on the charge value mapping curve corresponding to the target device's basic battery health. The basic battery health can be a battery health determined by a battery degradation algorithm and used as a reference value. The charge value mapping curve can be a reference correlation pre-stored in the battery management system's storage unit, used to characterize the mapping relationship between the battery open-circuit voltage (OCV) and state of charge (SOC). This mapping relationship changes with battery health, rather than being a fixed calibration curve. Different battery health corresponds to different charge value mapping curves. The OCV-SOC mapping curves corresponding to different battery health can be obtained in advance through testing, such as... Figure 2 As shown. Based on the baseline battery health, a charge value mapping curve corresponding to the baseline battery health can be determined, and based on this charge value mapping curve, the resting charge value of the target device in the first state can be determined.

[0022] The nominal capacity of a battery can be the rated usable capacity benchmark value calibrated at the battery manufacturing stage, serving as a fixed reference benchmark for calculating battery health. It can be pre-stored in the non-volatile storage medium of the target device's battery management system.

[0023] Specifically, the process begins by collecting open-circuit voltage data under two resting states. Then, the charge value mapping curve corresponding to the current baseline battery health is retrieved. A lookup table is used to obtain the resting charge value for each of the two resting state points, and the difference between these two values ​​is calculated to determine the change in state of charge. Next, the cumulative change in charge within the interval between the two resting states is retrieved. This cumulative change in charge is divided by the change in state of charge to obtain the battery's current actual usable full-charge capacity. Finally, this actual usable full-charge capacity is compared to the battery's nominal capacity to determine the target battery health for the target device. This process relies on fluctuation-free sampling data from two consecutive resting states, avoiding current noise interference under dynamic operating conditions and achieving accurate prediction of battery health.

[0024] Step 103: When the target usage state is the second state, the basic battery health is determined as the target battery health; the second state indicates that the target device has not been continuously discharged within the target duration and has not gone through at least two resting states, and the matching priority of the first state is higher than the matching priority of the second state.

[0025] In this embodiment of the invention, the second state indicates that the target device has not continuously discharged within the target duration and has not undergone at least two periods of inactivity; that is, the target device is neither in the first state nor the third state (i.e., it has not continuously discharged within the target duration). When the matched target usage state is the second state, the basic battery health calculated using the battery degradation model algorithm can be directly determined as the target battery health. The basic battery health can be calculated by continuously accumulating the battery aging loss based on statistical device usage data using a battery degradation algorithm, combined with the battery shutdown loss value when the device is turned off. The basic battery health can be updated in real time or periodically. The basic battery health can be used as the default value when the target device is in the second state.

[0026] Understandably, after determining the target battery health, the target battery health can be output to the target device's battery management system for adjusting or updating the battery management strategy, and can also be output to the user interface for display.

[0027] In summary, this embodiment of the invention sets a device usage state matching rule with a higher priority for the first state than the second state, selecting the target usage state that matches the target device. This prioritizes the calculation of battery health using a method with better estimation accuracy. It fully utilizes the accuracy of determining battery health based on the static charge value and nominal battery capacity determined in the first state, while directly using the basic battery health value in the second state to ensure estimation efficiency. This balances the feasibility and accuracy of battery health estimation under different device usage states. Furthermore, when the target usage state is the first state, the static charge value is determined based on the charge value mapping curve corresponding to the target device's basic battery health. This allows for dynamic matching of the charge value mapping curve corresponding to the aging stage based on the basic battery health value representing the current battery aging degree. This avoids the defect of fixed curves being unable to adapt to the characteristic shift after battery aging, improving the long-term accuracy and stability of battery health estimation throughout the entire life cycle, thereby meeting the high-precision estimation requirements for battery health after long-term device use.

[0028] Optionally, step 101 may include the following steps: Step 201: If the target device continues to discharge within the target duration, then the third state is determined as the target usage state; the target duration is the time it takes for the voltage of the target device to drop from the charging cutoff voltage to the shutdown voltage; the matching priority of the third state is higher than the matching priority of the first state.

[0029] In this embodiment of the invention, voltage and current data of the target device are collected in real time. When it is detected that the battery voltage continuously decreases from the charging cutoff voltage, and the continuous discharge time window covers the complete interval from the charging cutoff voltage to the system shutdown voltage, that is, the target device continuously discharges within the target duration, i.e., the time interval from the charging cutoff voltage to the shutdown voltage, it indicates that the target device has completed a complete discharge cycle from the full charge node to the depletion node. The third state can be directly determined as the target usage state matched by the target device, and the matching process of subsequent low-priority states is terminated. The third state can be a fully discharged state.

[0030] Step 202: If the target device does not continuously discharge within the target duration, and the target device has been in a static state at least twice, then the first state is determined to be the target usage state.

[0031] In this embodiment of the invention, if the third state matching condition is not triggered, the matching of the second-highest priority first state is performed. Specifically, the real-time current data of the target device is continuously monitored. When the current is detected to be lower than a preset current threshold for the first time, and the duration of this low current state reaches or exceeds a preset resting judgment duration, the first resting state is marked as valid. After the first resting state ends, if the current is detected to be lower than the preset current threshold again and the duration meets the resting judgment duration requirement, the second resting state is marked as valid. That is, the identification of two valid resting states is completed, and the first state can be directly determined as the target usage state for matching the target device, terminating the matching process of the lowest priority state. By identifying the two valid scenarios of steady-state resting, qualified sampling conditions are provided for the subsequent calculation of battery health.

[0032] Step 203: If the target device does not continuously discharge within the target duration and has not undergone at least two resting states, then the second state is determined to be the target usage state.

[0033] In this embodiment of the invention, if the matching conditions for the first and third states are not triggered, the lowest priority matching step for the second state is executed. Specifically, after traversing all matching logic for the first two high-priority states, if it is confirmed that the current operating condition of the device neither meets the full-scale condition for complete continuous discharge nor has it completed at least two valid static state matching identifications, then the second state is directly determined as the target usage state for matching the target device, completing this state matching process. This mechanism can cover all non-special operating conditions throughout the battery's entire lifespan, ensuring that the state matching logic is complete and free of null values, providing corresponding states for subsequent determination of battery health at different accuracy levels.

[0034] In this embodiment of the invention, the target usage state is matched step by step according to the priority of the third state, the first state, and the second state. This prioritizes triggering the operating conditions with higher battery health estimation accuracy to complete the battery health calculation, while also taking into account the actual triggering difficulty of different states. When the high-precision operating conditions cannot meet the matching conditions, the system automatically downgrades to adapt to the current device operating scenario. This avoids the operating condition matching failure problem that is prone to occur with a single matching rule, and can select the highest accuracy estimation method that can be achieved as much as possible in different operating scenarios, thus taking into account both the feasibility and accuracy of battery health estimation.

[0035] Optionally, embodiments of the present invention may further include the following steps: Step 301: When the target usage state is the third state, determine the health of the target battery based on the ratio of the total discharge of the target device to the nominal capacity of the battery.

[0036] In this embodiment of the invention, when the target usage state is the third state, the total discharge amount of the target device during the target duration, i.e., the period during which the voltage of the target device drops from the charging cutoff voltage to the shutdown voltage, is obtained.

[0037] For example, the starting point of integration can be the moment when the battery enters the third state, such as when the voltage reaches the charging cutoff voltage. The real-time discharge current can be accumulated over time using ampere-hour integration calculation until the battery voltage drops to the moment when the third state ends, such as when the voltage reaches the power-off voltage. This will stop the integration and finally obtain the total discharge amount for the entire cycle without data truncation or intermediate loss.

[0038] The target battery health is determined using the full discharge capacity method, which is based on the ratio of the total discharge of the target device to the nominal battery capacity. The cumulative total discharge is compared to the pre-stored nominal battery capacity, and the result is normalized to a percentage value to obtain the target battery health.

[0039] In this embodiment of the invention, the target battery health is calculated directly by the ratio of total discharge to nominal capacity in the third state. This allows for priority acquisition of complete full discharge data and direct determination of the battery's actual usable capacity. Compared to the two-point sampling interval estimation in the first state, there is no cumulative deviation in the intermediate mapping process, thus resulting in the highest accuracy of the target battery health.

[0040] Furthermore, by progressively matching the target usage state, continuous and high-trigger-rate aging tracking can be provided through the battery degradation model during the normal operation period, i.e., the second state, to continuously monitor battery health. When the matching conditions of the first state are met, the two-point method is used to quickly correct the deviation based on the measured capacity. And once the third state is matched first, the error is accumulated by returning the true value to zero. Through this three-layer progressive protection mechanism, the target battery health can be made close to the true value.

[0041] Optionally, step 202 may include the following steps: Step 401: If the target device has been in two static states and both static states meet the preset matching conditions, then the first state is determined to be the target usage state. The two static states include a first static state and a second static state. The preset matching conditions include that the difference between the static charge value corresponding to the first static state and the static charge value corresponding to the second static state is greater than or equal to a preset difference, and the difference between the battery temperature corresponding to the first static state and the battery temperature corresponding to the second static state is less than or equal to a preset temperature threshold.

[0042] In this embodiment of the invention, the current data of the target device is collected in real time. When the current is detected to be continuously lower than the preset current threshold for a period of time that reaches or exceeds the static determination time, the first static state is marked as valid. The open circuit voltage and battery temperature at that moment are collected simultaneously. The static charge value corresponding to the first static state is obtained by looking up the charge value mapping curve corresponding to the current basic battery health.

[0043] After the first static state ends, the operating condition of the target device is continuously monitored. When the current is detected to be continuously lower than the preset current threshold for a period of time that reaches or exceeds the static determination time, the second static state is marked as valid. The open circuit voltage and battery temperature at that moment are collected simultaneously, and the static charge value corresponding to the second static state is obtained by reverse lookup.

[0044] To ensure a sufficient charge capacity span between the two resting points and avoid amplifying integration errors due to an excessively narrow interval, a preset difference can be set to characterize the lower limit of the charge difference between the two resting states. Simultaneously, to prevent temperature drift from interfering with OCV sampling accuracy, a preset temperature threshold can be set to characterize the upper limit of the temperature difference between the two resting states.

[0045] The difference in static charge values ​​corresponding to two static states is calculated and compared with a preset difference. Simultaneously, the difference in battery temperature corresponding to the two static states is calculated and compared with a preset temperature threshold. If the difference in static charge values ​​corresponding to the two static states is greater than or equal to the preset difference, and the difference in battery temperature corresponding to the two static states is less than or equal to the preset temperature threshold, then the first state can be determined as the target usage state matched to the target device.

[0046] For example, a preset current threshold of 20mA, a static determination time of 12min, a preset difference of 35%, and a preset temperature threshold of 5℃ can be set. Then, when the current remains below 20mA for an extended period of ≥12min, a first static state and a second static state are sequentially marked. If the difference in static charge values ​​between the first and second static states is greater than or equal to the preset difference (ΔSOC ≥ 35%), and the temperature difference between the first and second static states is less than or equal to the preset temperature threshold (temperature difference ≤ 5℃), then the first state is determined as the target usage state.

[0047] In this embodiment of the invention, by setting preset matching conditions, including: limiting the charge difference between two resting periods to be greater than a preset threshold, the integration error caused by the narrow interval between the two sampling points is avoided; limiting the temperature difference between two resting periods to not exceed a preset threshold, the interference of temperature drift on OCV accuracy is eliminated, and the stability and accuracy of battery health prediction are ensured.

[0048] Optionally, embodiments of the present invention may further include the following steps: Step 501: Calculate the battery health loss value of the target device based on the first operating condition data of the target device in the charging and discharging state and the second operating condition data of the target device in the self-discharging state.

[0049] In this embodiment of the invention, when the target device is in a charging / discharging state, first operating condition data is collected, and when the target device is in a self-discharging state, such as a dormant state, second operating condition data is collected. The first operating condition data can be referred to as cycle operating condition data, and may include battery temperature T, the number of battery charge / discharge cycles, the highest and lowest battery capacity within the temperature range corresponding to battery temperature T. The second operating condition data can be referred to as storage operating condition data, and may include battery temperature T, battery state of charge (SOC), and the battery duration at battery temperature T and battery SOC.

[0050] Using a battery degradation model, the battery health loss value of a target device can be calculated based on data from a first operating condition and a second operating condition. Based on the battery health loss value, the battery health level to be corrected can be determined. The battery degradation model can be a model established through multiple cycle tests and multiple storage tests to calculate the battery health loss value. The battery degradation model can include a cycle degradation model corresponding to the cycle operating condition and a storage degradation model corresponding to the storage operating condition. The cycle degradation model is used to calculate the capacity decay of the battery under different temperatures, different charge / discharge ranges, and charge / discharge cycles. The storage degradation model is used to calculate the cumulative capacity decay of the battery over time under different temperatures and different charge levels.

[0051] For example, the battery health loss value can be determined by integrating and accumulating the data under the first operating condition using the cyclic decay model and integrating and accumulating the data under the second operating condition using the stored decay model. The sum of the calculated values ​​from the cyclic decay model and the stored decay model can then be used as the battery health loss value. Furthermore, the difference between 1 and the battery health loss value can be used as the battery health level to be corrected.

[0052] The battery health loss value can be calculated using the following formula:

[0053] Loss is used to represent the battery health loss value. This is used to represent the cyclic degradation model, where T represents the battery temperature. Used to indicate the highest battery capacity within the temperature range corresponding to battery temperature T. The term "cycle" is used to indicate the minimum charge level within the temperature range corresponding to battery temperature T, and "cycle" is used to indicate the number of charge-discharge cycles of the battery. Used to represent the storage decay model The value t is used to represent the battery charge level, and t is used to represent the battery duration at battery temperature T and battery charge state of charge (SOC).

[0054] Optionally, step 501 may include the following steps: Step 601: If the absolute value of the current battery current of the target device is greater than the preset current threshold, determine that the target device is in a charging / discharging state, and determine the first operating condition data based on the device operating parameters when the target device is in a charging / discharging state.

[0055] In this embodiment of the invention, the absolute value of the current battery current is compared with a preset current threshold. If the absolute value of the current battery current is greater than the preset current threshold, the target device is determined to be in a charging / discharging state. The preset current threshold can be a current classification value used to distinguish between the dynamic charging / discharging behavior and the static self-discharging behavior of the battery.

[0056] The target device's battery current, battery voltage, battery temperature, charge value, operating timestamp, and number of charge / discharge cycles can be read at a fixed sampling period (e.g., 1 second) to obtain the device's operating parameters. Based on multiple sets of device operating parameters when the target device is in a charge / discharge state, for each battery temperature T in the device operating parameters, the highest and lowest charge values ​​within the temperature range corresponding to that battery temperature T are statistically analyzed. The battery temperature T, the number of battery charge / discharge cycles, and the highest and lowest charge values ​​within the temperature range corresponding to that battery temperature T are recorded as the first operating condition data.

[0057] Step 602: If the absolute value of the current battery current of the target device is less than or equal to the preset current threshold, determine that the target device is in a self-discharge state, and determine the second operating condition data based on the device operating parameters when the target device is in a self-discharge state.

[0058] In this embodiment of the invention, when the absolute value of the current is less than or equal to a preset current threshold, the target device is determined to be in a self-discharge state. The battery current, battery voltage, battery temperature, charge value, operating timestamp, and number of battery charge-discharge cycles of the target device can be read at a fixed sampling period (e.g., 1 second) to obtain the device's operating parameters. Based on multiple sets of device operating parameters when the target device is in a self-discharge state, for each set of battery temperature T and battery SOC, the battery's duration at battery temperature T and battery SOC is statistically analyzed. The battery temperature T, battery SOC, and the battery's duration at battery temperature T and battery SOC are recorded as the second operating condition data.

[0059] For example, the preset current threshold can be 20mA. Then, if |current|>20mA, it belongs to the cyclic operating condition, and if |current|≤20mA, it belongs to the storage operating condition.

[0060] In this embodiment of the invention, two types of battery aging scenarios, namely charge-discharge state and self-discharge state, are divided by a preset current threshold. The operating data of dynamic charge-discharge state and static self-discharge state are classified independently to avoid confusion in the calculation of different aging mechanisms. This provides a clear classification input for subsequent calls to the corresponding attenuation models, thereby improving the accuracy of aging accumulation calculation.

[0061] Step 502: Based on the device shutdown parameters during the period when the target device is in the shutdown state, calculate the battery shutdown loss value during the period when the target device is in the shutdown state.

[0062] In this embodiment of the invention, since the scenario in which the target device is in a powered-off state cannot be classified as a charging / discharging state or a self-discharging state, and there will also be losses due to battery aging during the period when the target device is in a powered-off state, the battery power-off loss value during the period when the target device is in a powered-off state can be further calculated to compensate for the battery health loss value.

[0063] Obtain device shutdown parameters during the target device's shutdown state, including shutdown duration, average charge value, and average temperature. Based on the shutdown duration, average charge value, and average temperature, determine the battery shutdown loss value during the device's shutdown period.

[0064] Optionally, step 502 may include the following steps: Step 701: Obtain the device shutdown parameters corresponding to the period when the target device is in the shutdown state; the device shutdown parameters include shutdown duration, average charge value and average temperature.

[0065] In this embodiment of the invention, device shutdown parameters are recorded during the period when the target device is in a shutdown state. These shutdown parameters include shutdown duration, average charge value, and average temperature.

[0066] Specifically, when the target device detects a power-off signal, its battery management system (BMS) records the power-off timestamp, power-off temperature, and power-off charge value, saving these data to a register. This register retains its data even during power-off. The target device then enters a power-off state. During this period, the device receives no power, the BMS is inactive, and parameter statistics are interrupted. Upon detecting a power-on signal, the target device powers on and starts up. The BMS records the power-on timestamp, power-on temperature, and power-on charge value. Based on the power-off and power-on timestamps, the power-off duration is determined. The average temperature is determined based on the average of the power-on and power-off temperatures. It is understood that the target device can also obtain the average ambient temperature during its power-off state via a network. If the target device cannot obtain data via a network, the average temperature is determined based on the average of the power-on and power-off temperatures. Finally, the average charge value is determined based on the average of the power-on and power-off charge values.

[0067] Step 702: Determine the battery shutdown loss value during the shutdown period of the target device based on the shutdown duration, the average charge value, and the average temperature.

[0068] In this embodiment of the invention, a storage degradation model is invoked to calculate the battery shutdown loss value based on the shutdown duration, average charge value, and average temperature.

[0069] For example, the battery shutdown loss value can be determined based on the following formula:

[0070] in, Used to characterize battery shutdown loss value. Used to characterize average temperature Used to characterize the average charge value Used to characterize shutdown duration.

[0071] In this embodiment of the invention, by recording the device shutdown parameters during the shutdown state, the static working conditions during the shutdown state are restored, and the self-discharge aging loss generated during the shutdown state is calculated in a targeted manner, thus filling the blind spot in the aging statistics during the shutdown state.

[0072] Step 503: Determine the basic battery health based on the battery health loss value and the battery shutdown loss value.

[0073] In this embodiment of the invention, the basic battery health is determined by subtracting the battery health loss value from 1 and then subtracting the battery shutdown loss value.

[0074] For example, the basic battery health can be determined by the following formula:

[0075] in, The base battery health level is represented by `Loss`, which represents the battery health loss value. Used to characterize battery shutdown loss value.

[0076] In one possible implementation, the baseline battery health can be calculated periodically. Based on the first and second operating condition data for the current statistical period, the battery health loss value corresponding to the current statistical period is calculated. Based on the shutdown parameters corresponding to the target device being in a powered-off state during the current statistical period, the battery shutdown loss value corresponding to the current statistical period is determined. Based on the baseline battery health calculated in the previous statistical period, the battery health loss value and the battery shutdown loss value corresponding to the current statistical period are subtracted to obtain the baseline battery health for the current statistical period, which is the current baseline battery health of the target device.

[0077] In this embodiment of the invention, by integrating the quantitative calculation of three types of aging paths—operational cycle aging under charging and discharging conditions, standby self-discharge aging under self-discharge conditions, and storage aging during device shutdown—the aging losses under different operating conditions are independently accumulated and the basic battery health is calculated. This not only covers all operating and non-operating periods throughout the entire life cycle of the device, filling the blind spot in aging statistics during device shutdown, but also avoids the dimensionality loss error caused by estimation under a single operating condition, thus improving the robustness and accuracy of full-cycle battery health prediction.

[0078] Optionally, step 102 may include the following steps: Step 801: Obtain the open-circuit voltage and cumulative power change of the target device in two static states.

[0079] Step 802: Based on the charge value mapping curve corresponding to the basic battery health, determine the resting charge value corresponding to each open circuit voltage.

[0080] In this embodiment of the invention, the open-circuit voltage and the cumulative change in charge are obtained under two resting states. Specifically, the first open-circuit voltage can be obtained when calibrating the first resting state, and the second open-circuit voltage can be obtained when calibrating the second resting state. The cumulative change in charge recorded between the two resting states is then obtained.

[0081] Based on the current baseline battery health, determine the charge value mapping curve corresponding to the baseline battery health. Based on the charge value mapping curve, determine the first static charge value corresponding to the first open circuit voltage and the second static charge value corresponding to the second open circuit voltage.

[0082] Step 803: Determine the actual battery capacity based on the cumulative change in battery power and the resting charge value corresponding to each open circuit voltage.

[0083] In this embodiment of the invention, the actual battery capacity is determined based on the cumulative change in charge and the resting charge value corresponding to each open-circuit voltage. Specifically, the absolute value of the difference between the first resting charge value and the second resting charge value can be calculated to obtain the SOC change. The absolute value of the cumulative change in charge within the two resting intervals is divided by this SOC change to derive the current true usable full capacity of the battery, i.e., the actual battery capacity.

[0084] For example, the actual battery capacity can be calculated based on the following formula:

[0085] in, Q is used to represent the actual battery capacity, and Q is used to represent the cumulative change in battery capacity. Used to indicate the first resting charge value. Used to indicate the second resting charge value.

[0086] Step 804: Determine the health status of the battery to be judged based on the actual battery capacity and the nominal battery capacity.

[0087] In this embodiment of the invention, the actual battery capacity obtained by derivation is compared with the pre-stored nominal battery capacity, and the ratio is normalized to obtain the battery health status to be determined in percentage form.

[0088] For example, the battery health to be determined can be calculated based on the following formula:

[0089] in, Used to indicate the health status of the battery to be assessed. Used to indicate actual battery capacity Used to indicate the nominal capacity of a battery.

[0090] Step 805: Determine the target battery health based on the battery health status to be determined.

[0091] In this embodiment of the invention, the health of the target battery can be determined based on the health of the battery to be determined.

[0092] In one possible implementation, before determining the target battery health, the battery health to be determined can be verified. If the value of the battery health to be determined is within a preset battery health range (e.g., 60%-103%) and is lower than the historical battery health to be determined when the target device was in the first target usage state, the verification of the battery health to be determined is considered successful. Otherwise, the verification fails. If the verification of the battery health to be determined fails, the battery health to be determined can be recalculated.

[0093] In this embodiment of the invention, the static charge value is determined by dynamically adapting the charge value mapping curve of the basic battery health, which avoids the offset error of the charge value mapping curve caused by battery aging and improves the prediction accuracy of battery health in non-fully discharged scenarios.

[0094] Optionally, step 805 may include the following steps: Step 901: Determine the battery degradation rate based on the historical battery health status and the battery health status to be determined; the historical battery health status to be determined is the battery health status when the target usage state was the first state last time.

[0095] In this embodiment of the invention, whenever the target usage state of the target device is the first state, the battery health value to be determined calculated in this instance can be stored in a non-volatile storage unit for subsequent comparison.

[0096] After calculating the battery health status to be determined in the current first state, the historical battery health status calculated in the previous first state, recorded in the non-volatile memory, is retrieved. The historical battery health status is the battery health status calculated in the previous first state and serves as the benchmark reference value for this degradation rate calculation.

[0097] The battery degradation rate is determined based on the historical battery health status and the current battery health status. For example, the percentage of battery health degradation per 100 cycles can be calculated as the battery degradation rate. The battery degradation rate is calculated by dividing the absolute value of the difference between the historical battery health status and the current battery health status by the cumulative number of complete charge-discharge cycles completed between two first states, and then multiplying by 100. For example, the formula for calculating the battery degradation rate can be:

[0098] in, Used to indicate the rate of battery degradation The battery health status is pending historical assessment. Used to indicate the health status of the battery to be assessed. Used to indicate the cumulative number of complete charge-discharge cycles completed by the battery between two first states.

[0099] Step 902: If the battery degradation rate is less than or equal to a preset degradation threshold, determine the target battery health based on the battery health to be determined and the basic battery health.

[0100] In this embodiment of the invention, the calculated battery degradation rate is compared with a preset degradation threshold. If the degradation rate is determined to be less than or equal to the preset degradation threshold, it indicates that the battery is in the normal aging range and there is no risk of abnormal jumps. At this time, the current battery health status to be determined and the real-time maintenance baseline battery health status can be input into a Kalman filter algorithm to complete the weighted fusion of the observed and predicted values. The calculation result is used as the target battery health status, realizing the complementary advantages of the two types of data sources. The preset degradation threshold can be the upper limit of the normal aging slope of the battery calibrated based on laboratory full-life-cycle cyclic aging tests, and can be dynamically configured according to the measured data of different battery systems.

[0101] For example, the process of inputting the current battery health status to be determined and the baseline battery health status maintained in real time into the Kalman filter algorithm to complete the weighted fusion of the observed and predicted values ​​and obtain the calculation result can be as follows: the baseline battery health status is used as the state prediction value of the Kalman filter algorithm, corresponding to the prior estimate obtained by recursion based on the historical aging law of the system; the battery health status to be determined calculated in the current first state is used as the measured value of the Kalman filter algorithm, corresponding to the posterior measurement obtained by sampling through actual operating conditions. Based on the pre-calibrated system process noise covariance and observation noise covariance, the Kalman gain is iteratively calculated to complete the adaptive weighted fusion of the state prediction value and the measured value, and the optimal posterior estimate with both smoothness and accuracy is output as the final determined target battery health status.

[0102] Step 903: If the battery degradation rate is greater than the preset degradation threshold, the basic battery health is determined as the target battery health.

[0103] In this embodiment of the invention, if the battery degradation rate is determined to be greater than the preset degradation threshold, it indicates that the decline in the current battery health status exceeds the reasonable range of normal aging and there is a risk of abnormal jump. At this time, it is possible to temporarily not use the current battery health status and directly use the current basic battery health status obtained by continuous model iteration as the temporary output target battery health status.

[0104] In this embodiment of the invention, the battery degradation rate is calculated by examining the battery health status under two consecutive first states, and a branch determination is performed using a preset degradation threshold. This allows for the identification of abnormal jump risks that exceed the normal aging slope calibrated in the laboratory. Simultaneously, under normal aging scenarios, the measured values ​​from dual static conditions are integrated with the output values ​​from the aging model, achieving complementary advantages between the two data sources and improving the reliability of battery health. Under abnormal scenarios, the baseline battery health status is used as the target battery health status, avoiding the output of unreliable data and maintaining the stability and reliability of the target battery health status to a certain extent.

[0105] Optionally, after step 903, the embodiments of the present invention may further include the following steps: Step 1001: If the battery degradation rate is greater than the preset degradation threshold, and the difference between the battery health status to be determined and the first battery health status to be determined is less than or equal to the preset difference threshold, then the target battery health status when the next target usage state is the first state is determined as the first battery health status; the first battery health status to be determined is the battery health status to be determined when the next target usage state is the first state.

[0106] Step 1002: If the difference between the battery health status to be determined and the first battery health status to be determined is greater than the preset difference threshold, then the target battery health status when the target usage state is the first state will be updated to the current basic battery health status.

[0107] In this embodiment of the invention, while outputting the basic battery health value as the target battery health value, the current state is marked as an abnormal pending confirmation state. A secondary sampling trigger logic is initiated, waiting for the next target usage state to be the first state. After the battery health value to be determined in this state is calculated, it is used as the first battery health value to be determined, used to verify the authenticity of the abnormal transition. The first battery health value to be determined is the battery health value to be determined recalculated after the abnormality marker is triggered and the next first state verification passes.

[0108] The difference between the first battery health status to be determined and the current battery health status marked as abnormal is calculated. If the absolute value of the difference is less than or equal to a preset difference threshold, it indicates that the consistency of the two independent sampling results meets the requirements, ruling out the possibility of a single calculation error. In this case, it is determined that the battery has experienced a real capacity drop, and the first battery health status to be determined can be directly used as the final target battery health status when the next target usage state is the first state. The preset difference threshold can be a pre-defined consistency judgment threshold for the two abnormal calculation results, such as an engineering fault tolerance threshold of 2% difference every 20 cycles.

[0109] If the absolute value of the difference is greater than the preset difference threshold, it means that the deviation between the two calculation results is too large. This abnormal jump is caused by a single calculation error. The abnormal calculation result is not adopted. The basic battery health is used as the final output target battery health when the next target usage state is the first state, thus completing the entire abnormal verification process.

[0110] For example, assuming the preset difference threshold is 2% per 20 cycles, the calculation result can be obtained by dividing the absolute value of the difference between the first battery health status and the current battery health status by the cumulative number of complete charge-discharge cycles completed by the battery between the current first state and the next first state, and then multiplying by 20. The calculation result is then compared with 2%.

[0111] Understandably, to further confirm whether there are algorithmic anomalies, we can wait for the target usage state to be in the first state and obtain the battery health status to be determined in that state as the second battery health status to be determined. If the difference between the second battery health status and the first battery health status is still greater than a preset difference threshold, then the battery health status algorithm corresponding to the first state can be marked as abnormal, and an alarm can be issued. We can continue to use the basic battery health status as the final target battery health status for this time, and in subsequent matches where the target usage state is in the first state, we can use the basic battery health status as the final target battery health status until the anomaly is resolved.

[0112] In this embodiment of the invention, after the degradation rate is first detected to exceed the threshold, the single calculation result is not directly used. Instead, a secondary verification logic is triggered, waiting for the second sampling calculation to be completed in the next first state. This provides an independent secondary verification basis for the authenticity of the abnormal jump. By comparing the difference between the two calculation results with a preset difference threshold, two scenarios are distinguished: a real drop in battery capacity and a single algorithm or parameter calculation error. This ensures that real battery aging drop events are not missed, and that the target battery health is not misjudged due to accidental sampling errors, thereby improving the robustness of the battery health output.

[0113] Optionally, after step 1002, the embodiments of the present invention may further include the following steps: Step 1101: Modify the value of the basic battery health status to the first battery health status to be determined.

[0114] In this embodiment of the invention, after completing the anomaly verification, determining that the battery has experienced a real capacity drop, and setting the first battery health value to be determined as the target battery health value, the original stored basic battery health value can be directly overwritten and replaced with the first battery health value to be determined.

[0115] Understandably, after updating the basic battery health, when calculating the basic battery health based on the battery degradation model, the battery health loss value is calculated based on the first operating condition data and the second operating condition data after the update time, the battery shutdown loss value is calculated based on the shutdown operating condition of the target device after the update time, and the battery health loss value and the battery shutdown loss value after the update time are subtracted from the updated basic battery health value to calculate the basic battery health.

[0116] In this embodiment of the invention, since the battery degradation model established based on laboratory tests does not cover abnormal degradation scenarios such as lithium plating and micro-internal short circuits, it can only continuously accumulate aging losses according to the normal aging slope. If the baseline battery health is not updated synchronously in a timely manner, the benchmark value output by the subsequent model will continuously deviate from the actual battery capacity, resulting in systematic drift. Therefore, after confirming that the battery has experienced a degradation phenomenon, the benchmark is directly updated by using the measured value that has been verified twice. This can quickly correct the accumulated deviation of the aging model, so that the battery health calculated based on the model output or the two-point method can be based on the actual battery state. This avoids the continuous inaccuracy of the model output after abnormal aging and improves the robustness of the battery health prediction method under abnormal scenarios.

[0117] Optionally, after step 301, the embodiments of the present invention may further include the following steps: Step 1201: Modify the basic battery health based on the ratio of the total discharge of the target device to the nominal capacity of the battery within the target duration.

[0118] In this embodiment of the invention, since the battery completes a 100% SOC range traversal from full charge to empty in the third state, there is no need to rely on the charge value mapping curve to look up a table and back-calculate the SOC difference. This completely avoids the mapping error caused by the shift in the charge value mapping curve after aging of high-energy-density batteries such as silicon-based batteries. There is no intermediate estimation step, and the total discharge amount obtained is the battery's current actual usable full-charge capacity. The prediction of the target battery health in the third state can be directly traced back to the measurement method of the battery's actual full capacity. Therefore, the ratio of the total discharge amount of the target device within the target time period to the battery's nominal capacity can be used to overwrite the original base battery health value as the updated base battery health value.

[0119] In this embodiment of the invention, the target battery health calculated based on the third state covers and corrects the basic health value previously estimated by the model. This can eliminate the drift error accumulated by the model during long-term operation from the root, provide an "authoritative" benchmark anchor for the aging model calculation throughout the entire life cycle, and ensure the long-term stability and upper limit of accuracy of the entire battery health prediction system.

[0120] For example, Figure 3A flowchart illustrating the specific steps of a method for predicting battery health is shown, such as... Figure 3 As shown, after the target device powers on and completes initialization, it obtains the current basic battery health. In real-time calculation scenarios, the current basic battery health can be calculated in real-time based on battery health loss and battery shutdown loss values. In periodic calculation scenarios, the current basic battery health can also be calculated by adding the battery health loss and battery shutdown loss values ​​of the current statistical period to the basic battery health of the previous statistical period. It then determines whether the target device's target usage state matches the third state. If so, it is based on the target device's total discharge amount. and the nominal capacity of the battery The target battery health is calculated. If not, it is determined whether the target usage state of the target device matches the first state; if not, the target usage state is determined to be the second state, and the target battery health is determined based on the current basic battery health. If the target usage state of the target device matches the first state, the corresponding charge value mapping curve is determined based on the current basic battery health, and the actual battery capacity and the battery health to be determined are determined based on the cumulative charge change and the resting charge value corresponding to each open circuit voltage. The battery health to be determined is verified. If the battery degradation rate of the battery health to be determined and the historical battery health corresponding to the target usage state of the target device in the first state is less than or equal to a preset degradation threshold, the battery health to be determined and the current basic battery health are updated by Kalman filtering; if it is greater than the preset degradation threshold, the current basic battery health is determined as the target battery health. The target battery health is output to the battery management system for adjustment or update of the battery management strategy, and also output to the user interface for display. In this way, one update of the target battery health is completed.

[0121] For example, Figure 4 A system architecture diagram of a method for predicting battery health is shown, such as... Figure 4As shown, the target device comprises a hardware layer, a data acquisition and preprocessing layer, a fusion algorithm layer, and an output layer. The hardware layer includes the battery and a Battery Management System (BMS). The BMS acquires battery parameters, and the data acquisition and preprocessing layer samples these parameters from the BMS in real time, performing real-time device usage status matching and operational status statistics based on these parameters. Real-time operational status statistics include recording device shutdown parameters corresponding to power-on / off events, and recording first operational status data when the target device is in a charging / discharging state and second operational status data when it is in a self-discharging state during normal operation. The fusion algorithm layer determines the target usage status matching the target device based on the matching results of the device usage status detection, and determines the target battery health based on the battery health prediction rules corresponding to the target usage status, including the two-point method for the first status, the full discharge capacity method for the third status, and the battery degradation model algorithm for the second status. The output layer outputs the target battery health to update the power management strategy and display it on the user interface.

[0122] It should be noted that the battery health estimation method provided in this application embodiment can be executed by a battery health estimation device, or by a control module within that device for executing the battery health estimation method. This application embodiment uses the execution of the battery health estimation method by a battery health estimation device as an example to illustrate the battery health estimation device provided in this application embodiment.

[0123] Figure 5 This is a structural diagram of a battery health estimation device provided in an embodiment of this application, referring to... Figure 5 The device may include: The first determining module 1301 is used to determine the target usage state that matches the target device from the at least two device usage states according to the matching priority of the at least two device usage states; The second determining module 1302 is used to determine the target battery health of the target device based on the resting charge value and nominal battery capacity of the target device in the first state when the target usage state is the first state; the resting charge value is determined based on the charge value mapping curve corresponding to the current basic battery health of the target device; the first state represents that the target device has gone through at least two resting states; the resting state is defined as: the duration for which the current of the target device is lower than a preset current threshold is greater than or equal to a preset resting determination duration. The third determining module 1303 is used to determine the basic battery health as the target battery health when the target usage state is the second state; the second state indicates that the target device has not been continuously discharged within the target duration and has not gone through at least two resting states, and the matching priority of the first state is higher than the matching priority of the second state.

[0124] Optionally, the first determining module 1301 includes: The first determining submodule is used to determine the third state as the target usage state if the target device continues to discharge within the target duration; the target duration is the time it takes for the voltage of the target device to drop from the charging cutoff voltage to the power-off voltage; the matching priority of the third state is higher than the matching priority of the first state. The second determining submodule is used to determine the first state as the target usage state if the target device does not continuously discharge within the target duration and the target device has gone through at least two rest states. The third determining submodule is used to determine the second state as the target usage state if the target device does not continuously discharge within the target duration and has not undergone at least two resting states.

[0125] Optionally, the device further includes: The fourth determining module is used to determine the health of the target battery based on the ratio of the total discharge of the target device to the nominal capacity of the battery when the target usage state is the third state.

[0126] Optionally, the second determining submodule includes: The fourth determining submodule is used to determine the first state as the target usage state if the target device has gone through two static states and both static states meet the preset matching conditions. The two static states include a first static state and a second static state. The preset matching conditions include that the difference between the static charge value corresponding to the first static state and the static charge value corresponding to the second static state is greater than or equal to a preset difference, and the difference between the battery temperature corresponding to the first static state and the battery temperature corresponding to the second static state is less than or equal to a preset temperature threshold.

[0127] Optionally, the device further includes: The first calculation module is used to calculate the battery health loss value of the target device based on the first operating condition data of the target device in the charging and discharging state and the second operating condition data in the self-discharging state; The second calculation module is used to calculate the battery shutdown loss value during the period when the target device is in a shutdown state based on the device shutdown parameters during the period when the target device is in a shutdown state. The fifth determining module is used to determine the basic battery health based on the battery health loss value and the battery shutdown loss value.

[0128] Optionally, the device further includes: The sixth determining module is used to determine that the target device is in a charging / discharging state when the absolute value of the current battery current of the target device is greater than a preset current threshold, and to determine the first operating condition data based on the device operating parameters when the target device is in a charging / discharging state. The seventh determining module is used to determine that the target device is in a self-discharge state when the absolute value of the current battery current of the target device is less than or equal to the preset current threshold, and to determine the second operating condition data based on the device operating parameters when the target device is in a self-discharge state.

[0129] Optionally, the second computing module includes: The first recording module is used to acquire device shutdown parameters corresponding to the period when the target device is in a shutdown state; the device shutdown parameters include shutdown duration, average charge value and average temperature; The fifth determining submodule is used to determine the battery shutdown loss value during the shutdown period of the target device based on the shutdown duration, the average charge value, and the average temperature.

[0130] Optionally, the second determining module 1302 includes: The first acquisition module is used to acquire the open-circuit voltage and cumulative power change of the target device in two static states; The sixth determining submodule is used to determine the resting charge value corresponding to each of the open circuit voltages based on the charge value mapping curve corresponding to the health of the basic battery. The seventh determining submodule is used to determine the actual battery capacity based on the cumulative change in battery power and the resting charge value corresponding to each of the open circuit voltages; The eighth determination submodule is used to determine the health status of the battery to be judged based on the actual battery capacity and the nominal battery capacity. The ninth determining submodule is used to determine the health of the target battery based on the health of the battery to be determined.

[0131] Optionally, the ninth determining submodule includes: The tenth determination submodule is used to determine the battery degradation rate based on the historical battery health status and the battery health status to be determined; the historical battery health status is the battery health status when the target usage state was the first state last time. The eleventh determination submodule is used to determine the target battery health based on the battery health to be determined and the basic battery health when the battery degradation rate is less than or equal to a preset degradation threshold. The twelfth determination submodule is used to determine the basic battery health as the target battery health when the battery degradation rate is greater than the preset degradation threshold.

[0132] Optionally, the device further includes: The thirteenth determining submodule is used to determine the target battery health when the next target usage state is the first state as the first battery health when the battery degradation rate is greater than the preset degradation threshold and the difference between the battery health to be determined and the first battery health to be determined is less than or equal to the preset difference threshold. The fourteenth determination submodule is used to update the target battery health when the target usage state is in the first state to the current basic battery health if the difference between the battery health to be determined and the first battery health to be determined is greater than the preset difference threshold.

[0133] Optionally, the device further includes: The first modification module is used to modify the value of the basic battery health status to the first battery health status to be determined.

[0134] Optionally, the device further includes: The second modification module is used to modify the basic battery health based on the ratio of the total discharge of the target device to the nominal capacity of the battery within the target duration.

[0135] The battery health estimation device in this application embodiment can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can be mobile phones, tablets, laptops, PDAs, in-vehicle electronic devices, wearable devices, ultra-mobile personal computers (UMPCs), netbooks, or personal digital assistants (PDAs), etc., while non-mobile electronic devices can be servers, network attached storage (NAS), personal computers (PCs), televisions (TVs), ATMs, or self-service machines, etc. This application embodiment does not impose specific limitations.

[0136] The battery health estimation device in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit it.

[0137] Optional, such as Figure 6 As shown, this application embodiment also provides an electronic device, including a processor 1410, a memory 1409, and a program or instructions stored in the memory 1409 and executable on the processor 1410. When the program or instructions are executed by the processor 1410, they implement the various processes of the above-described battery health estimation method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0138] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0139] Figure 7 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of this application.

[0140] The electronic device 1400 includes, but is not limited to, components such as: radio frequency unit 1401, network module 1402, audio output unit 1403, input unit 1404, sensor 1405, display unit 1406, user input unit 1407, interface unit 1408, memory 1409, and processor 1410.

[0141] Those skilled in the art will understand that the electronic device 1400 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 1410 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 7 The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0142] It should be understood that, in this embodiment, the input unit 1404 may include a graphics processing unit (GPU) 14041 and a microphone 14042. The GPU 14041 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 1406 may include a display panel 14061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 1407 includes at least one of a touch panel 14071 and other input devices 14072. The touch panel 14071 is also called a touch screen. The touch panel 14071 may include a touch detection device and a touch controller. Other input devices 14072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be described in detail here.

[0143] The memory 1409 can be used to store software programs and various data. The memory 1409 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 1409 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 1409 in this embodiment includes, but is not limited to, these and any other suitable types of memory.

[0144] Processor 1410 may include one or more processing units; optionally, processor 1410 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 1410.

[0145] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described battery health estimation method embodiment and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0146] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0147] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described battery health estimation method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0148] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0149] This application provides a computer program product stored in a storage medium. The program product is executed by at least one processor to implement the various processes of the battery health estimation method embodiment described above, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0150] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0151] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0152] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for predicting battery health, characterized in that, The method includes: Based on the matching priority of at least two device usage states, determine the target usage state that matches the target device from the at least two device usage states; When the target usage state is the first state, the target battery health of the target device is determined based on the resting charge value and nominal battery capacity of the target device in the first state. The resting charge value is determined based on the charge value mapping curve corresponding to the current basic battery health of the target device. The first state indicates that the target device has gone through at least two resting states. The resting state is defined as: the duration for which the current of the target device is lower than a preset current threshold is greater than or equal to a preset resting judgment duration. When the target usage state is the second state, the basic battery health is determined as the target battery health; the second state indicates that the target device has not been continuously discharged within the target duration and has not gone through at least two resting states, and the matching priority of the first state is higher than the matching priority of the second state.

2. The method according to claim 1, characterized in that, The step of determining the target usage state matching the target device from the at least two device usage states according to the matching priority of at least two device usage states includes: If the target device continues to discharge within the target duration, then the third state is determined as the target usage state; the target duration is the time it takes for the voltage of the target device to drop from the charging cutoff voltage to the power-off voltage; the matching priority of the third state is higher than the matching priority of the first state; If the target device does not continuously discharge within the target duration, and the target device has been in a static state at least twice, then the first state is determined to be the target usage state. If the target device does not continuously discharge within the target duration and has not undergone at least two periods of inactivity, then the second state is determined to be the target usage state.

3. The method according to claim 2, characterized in that, The method further includes: When the target usage state is the third state, the health of the target battery is determined based on the ratio of the total discharge of the target device to the nominal capacity of the battery.

4. The method according to claim 2, characterized in that, The step of determining the first state as the target usage state if the target device has undergone at least two periods of inactivity includes: If the target device has been in a static state twice, and both static states meet the preset matching conditions, then the first state is determined to be the target usage state. The two static states include a first static state and a second static state. The preset matching conditions include that the difference between the static charge value corresponding to the first static state and the static charge value corresponding to the second static state is greater than or equal to a preset difference, and the difference between the battery temperature corresponding to the first static state and the battery temperature corresponding to the second static state is less than or equal to a preset temperature threshold.

5. The method according to claim 1, characterized in that, The method further includes: Based on the first operating condition data of the target device in the charging and discharging state and the second operating condition data in the self-discharging state, the battery health loss value of the target device is calculated. Based on the device shutdown parameters during the period when the target device is in a shutdown state, calculate the battery shutdown loss value during the period when the target device is in a shutdown state; The basic battery health is determined based on the battery health loss value and the battery shutdown loss value.

6. The method according to claim 5, characterized in that, The method further includes: If the absolute value of the current battery current of the target device is greater than a preset current threshold, it is determined that the target device is in a charging / discharging state, and the first operating condition data is determined based on the device operating parameters when the target device is in a charging / discharging state. If the absolute value of the current battery current of the target device is less than or equal to the preset current threshold, the target device is determined to be in a self-discharge state, and the second operating condition data is determined based on the device operating parameters when the target device is in a self-discharge state.

7. The method according to claim 5, characterized in that, The calculation of the battery shutdown loss value during the shutdown state of the target device based on the device shutdown parameters during the shutdown state includes: Obtain the device shutdown parameters corresponding to the period when the target device is in a shutdown state; the device shutdown parameters include shutdown duration, average charge value, and average temperature; The battery shutdown loss value during the shutdown period of the target device is determined based on the shutdown duration, the average charge value, and the average temperature.

8. The method according to claim 1, characterized in that, The step of determining the target battery health of the target device based on the static charge value and nominal battery capacity of the target device in the first state includes: Obtain the open-circuit voltage and cumulative power change of the target device in two static states; Based on the charge value mapping curve corresponding to the basic battery health, determine the resting charge value corresponding to each of the open circuit voltages. The actual battery capacity is determined based on the cumulative change in charge and the resting charge value corresponding to each open circuit voltage. Based on the actual battery capacity and the nominal battery capacity, the health of the battery to be assessed is determined. Based on the health status of the battery to be determined, the health status of the target battery is determined.

9. The method according to claim 8, characterized in that, The step of determining the target battery health based on the battery health to be determined includes: The battery degradation rate is determined based on the historical battery health status and the battery health status to be determined; the historical battery health status is the battery health status when the target usage state was the first state last time. If the battery degradation rate is less than or equal to a preset degradation threshold, the target battery health is determined based on the battery health to be determined and the basic battery health. If the battery degradation rate is greater than the preset degradation threshold, the basic battery health is determined as the target battery health.

10. The method according to claim 9, characterized in that, After determining the baseline battery health as the target battery health, the method further includes: If the battery degradation rate is greater than the preset degradation threshold, and the difference between the battery health status to be determined and the first battery health status to be determined is less than or equal to the preset difference threshold, then the target battery health status when the next target usage state is the first state will be determined as the first battery health status to be determined. If the difference between the battery health status to be determined and the first battery health status to be determined is greater than the preset difference threshold, then the target battery health status when the target usage state is the first state will be updated to the current basic battery health status.

11. The method according to claim 10, characterized in that, After determining the target battery health level when the next target usage state is the first state as the first battery health level to be determined, the method further includes: Modify the value of the basic battery health status to the first battery health status to be determined.

12. The method according to claim 3, characterized in that, The method further includes: The baseline battery health is modified based on the ratio of the total discharge of the target device to the nominal capacity of the battery within the target duration.

13. A device for predicting battery health, characterized in that, The device includes: The first determining module is used to determine the target usage state that matches the target device from the at least two device usage states according to the matching priority of the at least two device usage states; The second determining module is used to determine the target battery health of the target device based on the resting charge value and nominal battery capacity of the target device in the first state when the target usage state is the first state; the resting charge value is determined based on the charge value mapping curve corresponding to the current basic battery health of the target device; the first state represents that the target device has gone through at least two resting states; the resting state is defined as: the duration for which the current of the target device is lower than a preset current threshold is greater than or equal to a preset resting determination duration. The third determining module is used to determine the basic battery health as the target battery health when the target usage state is the second state; the second state indicates that the target device has not been continuously discharged within the target duration and has not gone through at least two resting states, and the matching priority of the first state is higher than the matching priority of the second state.

14. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the battery health estimation method as described in any one of claims 1-12.

15. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the battery health estimation method as described in any one of claims 1-12.

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