Battery state of health prediction method, apparatus, medium, and electronic device

Abstract

The application provides a battery health state prediction method, device, medium and electronic equipment. The battery health state prediction method comprises: obtaining battery data under different first battery states; obtaining a temperature difference value corresponding to a battery voltage under the first battery state based on the battery data under the first battery state; obtaining a voltage-related fitting value under the first battery state based on the battery voltage under the first battery state and the temperature difference value corresponding thereto; obtaining a first fitting slope and a first fitting intercept based on the voltage-related fitting value under the first battery state and a battery health state fitting value; and obtaining a battery health state prediction value under a second battery state based on the first fitting slope, the first fitting intercept and the voltage-related fitting value under the second battery state. The battery health state prediction method can improve the efficiency of obtaining the battery health state value.

Description

Technical Field

[0001] This application belongs to the field of new energy and relates to a prediction method, particularly a battery health status prediction method, device, medium and electronic equipment. Background Technology

[0002] With the rapid development of the new energy field, lithium batteries have been widely used in mobile digital products, electric vehicles, and energy storage power stations. Lifespan is a crucial indicator of battery performance, and battery health status is of great significance in analyzing battery lifespan. However, since battery health status is generally obtained through experimental measurements, the time required for these measurements is too long to meet the need for real-time acquisition of battery health status based on battery data. Therefore, current methods for obtaining battery health status through experimental measurements suffer from low efficiency. Summary of the Invention

[0003] The purpose of this application is to provide a method, apparatus, medium, and electronic device for predicting battery health status, in order to solve the problem of low efficiency in existing methods for obtaining battery health status through experimental measurement.

[0004] In a first aspect, this application provides a method for predicting battery health status. The method includes: acquiring battery data under different first battery states, the battery data including battery voltage and battery temperature at different sampling times; based on the battery data under the first battery states, acquiring a temperature difference value corresponding to the battery voltage under the first battery states, the temperature difference value being the difference between the battery temperatures at two adjacent sampling times; based on the battery voltage under the first battery states and the corresponding temperature difference value, acquiring a voltage-related fitting value under the first battery states, the voltage-related fitting value being a voltage fitting value or a voltage difference fitting value, the voltage difference fitting value being the difference between the battery voltages at two different sampling times; based on the voltage-related fitting value and the battery health status fitting value under the first battery states, acquiring a first fitting slope and a first fitting intercept; based on the first fitting slope, the first fitting intercept, and the voltage-related fitting value under the second battery states, acquiring a predicted battery health status value under the second battery states, the second battery states being the future operating states of the battery.

[0005] By obtaining a predicted battery health status value under the second battery state based on the first fitting slope, the first fitting intercept, and the voltage-related fitting value under the second battery state, this method enables real-time prediction of the battery health status value under the second battery state, without requiring experimental measurements to obtain the battery health status value during the second battery state. This battery health status prediction method improves the efficiency of obtaining battery health status values.

[0006] In one embodiment of this application, a method for obtaining a voltage-related fitting value in the first battery state includes: obtaining extreme temperature differences in the first battery state based on the temperature difference value corresponding to the battery voltage in the first battery state, wherein the number of extreme temperature differences is at least two; and obtaining a pressure difference fitting value based on the extreme temperature differences, wherein the pressure difference fitting value is the difference between the battery voltages corresponding to any two extreme temperature differences.

[0007] In one embodiment of this application, a method for obtaining a voltage-related fitting value in the first battery state includes: obtaining an extreme temperature difference value in the first battery state based on the temperature difference value corresponding to the battery voltage in the first battery state, wherein the number of extreme temperature difference values ​​is one; and obtaining a voltage fitting value based on the extreme temperature difference value, wherein the voltage fitting value is the battery voltage corresponding to the extreme temperature difference value.

[0008] In one embodiment of this application, the battery health state prediction method further includes: obtaining a second fitting slope and a second fitting intercept based on the voltage-related fitting value under the first battery state, the battery health state fitting value under the first battery state, the voltage-related fitting value under the second battery state, and the battery health state fitting value under the second battery state.

[0009] By obtaining the second fitting slope and the second fitting intercept, it is equivalent to updating the first fitting slope and the first fitting intercept based on the battery health state fitting value and the voltage-related fitting value under the second battery state. Compared with the first fitting slope and the first fitting intercept, the second fitting slope and the second fitting intercept use more fitting data, so the accuracy of the second fitting slope and the second fitting intercept is higher, and the accuracy of predicting the battery health state value of the future state through the second fitting slope and the second fitting intercept is higher.

[0010] In one embodiment of this application, the battery health state prediction method further includes: obtaining the prediction error of the battery health state prediction value based on the battery health state fitting value under the second battery state and the battery health state prediction value under the second battery state, wherein the battery health state fitting value is the battery health state value obtained by experimental measurement.

[0011] By introducing the battery health state fitting value under the second battery state, the prediction error of the battery health state prediction value can be obtained, thus ensuring the reliability of the prediction result.

[0012] In one embodiment of this application, the prediction error is represented by the following formula:

[0013]

[0014] Among them, SOH test The SOH value represents the fitted value of the battery's state of health. for The err value represents the predicted battery health status. for This represents the prediction error.

[0015] In one embodiment of this application, the battery health state prediction method further includes: obtaining a differentially uniform voltage set in the first battery state based on the voltage change unit in the first battery state and the battery data in the first battery state, wherein the difference between any two adjacent battery voltages in the differentially uniform voltage set is the voltage change unit.

[0016] Secondly, this application provides a battery health state prediction device, comprising: a battery data acquisition module for acquiring battery data under different first battery states, the battery data including battery voltage at different sampling times and battery temperature at different sampling times; a temperature difference value acquisition module for acquiring a temperature difference value corresponding to the battery voltage under the first battery state based on the battery data under the first battery state, the temperature difference value being the difference between the battery temperatures at two adjacent sampling times; and a voltage correlation fitting value acquisition module for acquiring a voltage correlation fitting value based on the battery voltage under the first battery state and the corresponding temperature difference value. The system acquires a voltage-related fitted value under the first battery state, wherein the voltage-related fitted value is either a voltage fitted value or a voltage difference fitted value, and the voltage difference fitted value is the difference between the battery voltages at two different sampling times; a fitting slope acquisition module is used to acquire a first fitting slope and a first fitting intercept based on the voltage-related fitted value and the battery health state fitted value under the first battery state; a battery health state prediction value acquisition module is used to acquire a battery health state prediction value under the second battery state based on the first fitting slope, the first fitting intercept, and the voltage-related fitted value under the second battery state, wherein the second battery state is the future operating state of the battery.

[0017] Thirdly, this application provides a computer-readable storage medium, wherein when a computer program is executed by a processor, it implements the battery health state prediction method described in any of the first aspects of this application.

[0018] Fourthly, this application provides an electronic device, the electronic device comprising: a memory storing a computer program; and a processor communicatively connected to the memory, which executes the battery health state prediction method according to any one of the first aspects of this application when the computer program is invoked.

[0019] As described above, the battery health status prediction method, apparatus, medium, and electronic device described in this application have the following beneficial effects:

[0020] First, by obtaining a predicted battery health status value under the second battery state based on the first fitting slope, the first fitting intercept, and the voltage-related fitting value under the second battery state, it is possible to predict the battery health status value under the second battery state in real time based on battery data under the second battery state, without the need for experimental measurements under the second battery state to obtain the battery health status value. This battery health status prediction method improves the efficiency of obtaining battery health status values.

[0021] Second, by obtaining the second fitting slope and the second fitting intercept, it is equivalent to updating the first fitting slope and the first fitting intercept based on the battery health state fitting value and the voltage-related fitting value under the second battery state. Compared with the first fitting slope and the first fitting intercept, since the second fitting slope and the second fitting intercept use more fitting data, the accuracy of the second fitting slope and the second fitting intercept is higher, and the accuracy of predicting the battery health state value of the future state through the second fitting slope and the second fitting intercept is higher.

[0022] Third, by introducing the battery health state fitting value under the second battery state, the prediction error of the battery health state prediction value can be obtained, which can ensure the reliability of the prediction result. Attached Figure Description

[0023] Figure 1 The diagram shown is a structural schematic of an application scenario in this application.

[0024] Figure 2 The flowchart shown is a process for predicting the battery health status according to an embodiment of this application.

[0025] Figure 3 The flowchart shown is a method for obtaining voltage-related fitting values ​​in the first battery state according to an embodiment of this application.

[0026] Figure 4 The diagram shown is a schematic representation of the temperature difference change value described in the embodiments of this application.

[0027] Figure 5 The flowchart shown is a method for obtaining voltage-related fitting values ​​in the first battery state according to an embodiment of this application.

[0028] Figure 6 The diagram shown is a structural schematic of the battery health status prediction device described in an embodiment of this application.

[0029] Figure 7 The diagram shown is a structural schematic of the electronic device described in an embodiment of this application.

[0030] Component designation explanation

[0031] 10 batteries

[0032] 20 load

[0033] 30 Forecasting

[0034] 600 Battery Health Prediction Device

[0035] 610 Battery Data Acquisition Module

[0036] 620 Temperature Difference Value Acquisition Module

[0037] 630 Voltage Correlation Fitting Value Acquisition Module

[0038] 640 Fitting Slope Acquisition Module

[0039] 650 Battery Health Status Prediction Value Acquisition Module

[0040] 700 electronic devices

[0041] 710 Memory

[0042] 720 processor

[0043] Steps S11-S15

[0044] Steps S21-S22

[0045] Steps S31-S32 Detailed Implementation

[0046] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0047] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0048] The technical solutions in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0049] like Figure 1 As shown in the figure, this embodiment provides an application scenario diagram, which includes: battery 10, load 20 and predictor 30.

[0050] Optionally, the battery 10 can be a battery pack formed by arranging one or more lithium battery cells in any form to power electrical devices. The battery 10 can be charged or discharged under controlled conditions, and can have corresponding capacity, size, etc. to meet actual needs.

[0051] Optionally, the load 20 is electrically connected to the battery 10, and the battery 10 can supply power to the load 20 to enable the load 20 to operate normally. The load 20 can be any device, module, or equipment that requires power supply voltage to operate.

[0052] Optionally, the predictor 30 can be a battery health status prediction system or chip, which predicts battery health status values ​​by collecting relevant battery data. The predictor 30 may run one or more software programs to record data and perform data calculations.

[0053] like Figure 2 As shown, this embodiment provides a battery health status prediction method, which can be implemented by a computer device's processor. The battery health status prediction method includes:

[0054] S11, acquire battery data under different first battery states, the battery data including: battery voltage at different sampling times and battery temperature at different sampling times.

[0055] Optionally, the battery data in the first battery state may include battery data from historical operating states and battery data from the current operating state. The historical operating states may be the operating states at a historical number of cycles, and the current operating state may be the operating states at the current number of cycles. For example, if the current number of cycles for the battery is 500, then the battery data obtained at the 500th cycle is the battery data at the current number of cycles, i.e., the battery data in the current operating state. The battery data obtained at 400, 300, 200, etc., cycles may be the battery data at historical number of cycles, i.e., the battery data in historical operating states. The battery voltage may be the actual operating voltage of the battery, and the battery temperature may be the actual operating temperature of the battery.

[0056] Optionally, the first battery state can be the state of the battery at different cycle counts. For example, different first battery states can be the state of the battery after 500 cycles, 1000 cycles, and 1500 cycles. The battery voltage and battery temperature at different sampling times can be, for example, the battery voltage of 3.1V and battery temperature of 31°C at sampling time t=2s, the battery voltage of 3.2V and battery temperature of 32°C at sampling time t=3s, and the battery voltage of 3.4V and battery temperature of 34°C at sampling time t=4s.

[0057] S12, based on the battery data in the first battery state, obtain the temperature difference value corresponding to the battery voltage in the first battery state. The temperature difference value is the difference between the battery temperatures at two adjacent sampling times. The difference value is the next value minus the previous value. In this embodiment, the difference between the battery temperatures at two adjacent sampling times can be the difference between the battery temperature at the current sampling time and the battery temperature at the previous adjacent sampling time.

[0058] Optionally, the temperature difference value corresponding to the battery voltage can be the difference between the first battery temperature and the second battery temperature corresponding to the battery voltage. The second battery temperature can be the battery temperature at the previous sampling time of the first battery temperature. The battery temperature corresponding to the battery voltage can refer to the battery temperature at the same sampling time as the battery voltage. Taking the first battery state as the battery in 500 cycles as an example, when the battery data includes: battery voltage of 3.1V and battery temperature of 31℃ at sampling time t=2s, battery voltage of 3.2V and battery temperature of 32℃ at t=3s, and battery voltage of 3.4V and battery temperature of 34℃ at t=4s, the temperature difference value corresponding to the battery voltage of 3.2V at t=3s is 1℃, and the temperature difference value corresponding to the battery voltage of 3.4V at t=4s is 2℃. Since the battery voltage of 3.1V does not have a previous sampling time, the temperature difference value corresponding to the battery voltage of 3.1V can be directly set to 0.

[0059] Optionally, the battery health state prediction method further includes: obtaining a differentially uniform voltage set in the first battery state based on the voltage change unit in the first battery state and the battery data in the first battery state, wherein the difference between any two adjacent battery voltages in the differentially uniform voltage set is the voltage change unit. For example, if the battery data in the first battery state includes: battery voltage 3.12V, battery voltage 3.14V, battery voltage 3.15V, and battery voltage 3.16V, and the voltage change unit is 0.02V, then the differentially uniform voltage set includes: battery voltage 3.12V, battery voltage 3.14V, and battery voltage 3.16V. In the differentially uniform voltage set, the battery voltage of 3.14V can be the battery voltage corresponding to the first change of the voltage change unit, and the battery voltage of 3.16V can be the battery voltage corresponding to the second change of the voltage change unit.

[0060] Optionally, the temperature difference value can be expressed by the following formula:

[0061]

[0062] in, The battery temperature is represented by the first battery voltage, where the first battery voltage is expressed in voltage change unit u. *As a reference, the battery voltage corresponding to the k-th change of the voltage changing unit. This is expressed as the battery temperature corresponding to the second battery voltage, where the second battery voltage is expressed as the voltage change unit u. * Using this as a reference, the battery voltage corresponding to the (k-1)th change of the voltage changing unit. δT (k) This represents the difference between the battery temperature corresponding to the first battery voltage and the battery temperature corresponding to the second battery voltage.

[0063] S13, based on the battery voltage in the first battery state and the corresponding temperature difference value, obtain the voltage correlation fitting value in the first battery state, the voltage correlation fitting value is a voltage fitting value or a pressure difference fitting value, and the pressure difference fitting value is the difference between the battery voltages at two different sampling times.

[0064] Optionally, the temperature difference value corresponding to the battery voltage in the first battery state can be the temperature difference value corresponding to the battery voltage in the differentially uniform voltage set in the first battery state. By obtaining the temperature difference value corresponding to the battery voltage in the differentially uniform voltage set, each temperature difference value can be made to vary based on a unified voltage change unit, thereby improving the prediction accuracy of battery health status.

[0065] S14, based on the voltage-related fitting value and the battery health state fitting value under the first battery state, obtain the first fitting slope and the first fitting intercept.

[0066] Optionally, when the voltage-related fitting value is the voltage difference fitting value, the first fitting slope is the fitting slope of the voltage difference fitting value and the battery health state fitting value, and the first fitting intercept is the fitting intercept of the voltage difference fitting value and the battery health state fitting value; when the voltage-related fitting value is the voltage fitting value, the first fitting slope is the fitting slope of the voltage fitting value and the battery health state fitting value, and the first fitting intercept is the fitting intercept of the voltage fitting value and the battery health state fitting value.

[0067] S15, based on the first fitting slope, the first fitting intercept, and the voltage-related fitting value under the second battery state, obtain the predicted value of the battery health state under the second battery state, where the second battery state is the future operating state of the battery.

[0068] Optionally, the future operating state of the battery can be the operating state of the battery under future cycle counts. For example, if the current cycle count of the battery is 500, then the operating state of the battery when the cycle count is 1000 is the operating state of the battery under future cycle counts.

[0069] Optionally, when the voltage correlation fitting value is the voltage fitting value, the predicted battery health status value can be expressed as:

[0070] SOH for =m n ×V δT +m0

[0071] Among them, SOH for The value m represents the predicted battery health status. n Let m0 be the first fitting slope, m0 be the first fitting intercept, and V be the first fitting slope. δT This represents the voltage fitting value. When the voltage correlation fitting value is the voltage difference fitting value, the predicted battery health status value can be expressed as:

[0072] SOH for =m n ×|▽(V δT )|+m0

[0073] Among them, SOH for The value m represents the predicted battery health status. n Let m be the first fitting slope, m0 be the first fitting intercept, and |▽(V δT | represents the absolute value of the pressure difference fitting value.

[0074] For example, when the first fitting intercept is -302.751, the voltage correlation fitting value in the second battery state is 0.04, and the first fitting intercept is 104.041, the predicted value of the battery health state in the second battery state is approximately 91.9%.

[0075] Optionally, the battery health state prediction method further includes: obtaining a second fitting slope and a second fitting intercept based on the voltage-related fitting value under the first battery state, the battery health state fitting value under the first battery state, the voltage-related fitting value under the second battery state, and the battery health state fitting value under the second battery state.

[0076] Optionally, the battery health status prediction method further includes: obtaining the prediction error of the battery health status prediction value based on the battery health status fitting value under the second battery state and the battery health status prediction value under the second battery state, wherein the battery health status fitting value is the battery health status value obtained by experimental measurement.

[0077] Optionally, the prediction error is represented by the following formula:

[0078]

[0079] Among them, SOH test The SOH value represents the fitted value of the battery's state of health. for The err value represents the predicted battery health status. for This represents the prediction error.

[0080] As described above, the battery health status prediction method includes: acquiring battery data under different first battery states, the battery data including battery voltage and battery temperature at different sampling times; based on the battery data under the first battery states, acquiring the temperature difference value corresponding to the battery voltage under the first battery states, the temperature difference value being the difference between the battery temperatures at two adjacent sampling times; based on the battery voltage under the first battery states and its corresponding temperature difference value, acquiring a voltage-related fitting value under the first battery states, the voltage-related fitting value being a voltage fitting value or a pressure difference fitting value, the pressure difference fitting value being the difference between the battery voltages at two different sampling times; based on the voltage-related fitting value and the battery health status fitting value under the first battery states, acquiring a first fitting slope and a first fitting intercept; based on the first fitting slope, the first fitting intercept, and the voltage-related fitting value under the second battery states, acquiring a predicted battery health status value under the second battery states, the second battery states being the future operating states of the battery.

[0081] By obtaining a predicted battery health status value under the second battery state based on the first fitting slope, the first fitting intercept, and the voltage-related fitting value under the second battery state, this method enables real-time prediction of the battery health status value under the second battery state, without requiring experimental measurements to obtain the battery health status value during the second battery state. This battery health status prediction method improves the efficiency of obtaining battery health status values.

[0082] like Figure 3 As shown, this embodiment provides a method for obtaining voltage-related fitting values ​​in the first battery state, including:

[0083] S21, based on the temperature difference value corresponding to the battery voltage in the first battery state, obtain the extreme temperature difference value in the first battery state, wherein the number of extreme temperature difference values ​​is at least two.

[0084] Optionally, the extreme values ​​of the temperature difference are the extreme values ​​among the temperature difference values. The extreme values ​​of the temperature difference can be either the maximum or minimum value among the temperature difference values. For example, the temperature difference values ​​corresponding to the battery voltage include: 0.02℃, 0.01℃, 0.03℃, 0.04℃, and 0.03℃, where the maximum value is 0.04℃ and the minimum value is 0.01℃. 0.04℃ and 0.01℃ can be two extreme temperature values.

[0085] Optionally, the method for obtaining the extreme value of the temperature difference in the first battery state includes: obtaining the temperature difference change value corresponding to the battery voltage in the first battery state based on the temperature difference value corresponding to the battery voltage in the first battery state; and obtaining the extreme value of the temperature difference based on the temperature difference change value corresponding to the battery voltage in the first battery state. The temperature difference change value is the difference between the temperature difference values ​​corresponding to two adjacent battery voltages. The temperature difference change value can be expressed by the following formula:

[0086]

[0087] Wherein, δT (k) This can be expressed as the difference between the battery temperature corresponding to the first battery voltage and the battery temperature corresponding to the second battery voltage, δT. (k-1) This can be expressed as the difference between the battery temperature corresponding to the second battery voltage and the battery temperature corresponding to the third battery voltage, where the third battery voltage is the battery voltage corresponding to the (k-2)th change of the voltage change unit, with the voltage change unit as the reference. This can be expressed as the difference between a first temperature difference and a second temperature difference. The first temperature difference is the difference between the battery temperature corresponding to the first battery voltage and the battery temperature corresponding to the second battery voltage. The second temperature difference is the difference between the battery temperature corresponding to the second battery voltage and the battery temperature corresponding to the third battery voltage. As the temperature difference gradually increases from around 0 and then gradually decreases back to around 0, the temperature difference used to calculate the temperature difference increases. The battery voltage corresponding to the temperature difference change of 0 is the battery voltage corresponding to the maximum value of the temperature difference. Conversely, as the temperature difference gradually decreases from around 0 and then gradually increases back to around 0, the temperature difference used to calculate the temperature difference decreases. The battery voltage corresponding to the temperature difference change of 0 is the battery voltage corresponding to the minimum value of the temperature difference. Based on the battery voltage corresponding to the maximum and minimum values ​​of the temperature difference, the corresponding maximum and minimum values ​​of the temperature difference can be obtained. Please refer to [link to relevant documentation]. Figure 4 , Figure 4The vertical axis represents the temperature difference change value, and the horizontal axis represents the battery voltage. It can be seen that the temperature difference change value crosses 0 multiple times, either upwards or downwards. In this embodiment, N1 and X1 are selected. From the above description of the temperature difference change value, it can be seen that the horizontal axis of X1 is the battery voltage corresponding to the maximum temperature difference, and the horizontal axis of N1 is the battery voltage corresponding to the minimum temperature difference.

[0088] S22, based on the extreme temperature differences, obtain the pressure difference fitting value, where the pressure difference fitting value is the difference between the battery voltages corresponding to any two extreme temperature differences. For example, when the extreme temperature differences include 0.01℃ and 0.04℃, the battery voltage corresponding to 0.01℃ is 3.1V, and the battery voltage corresponding to 0.04℃ is 3.3V, the pressure difference fitting value is 0.2V.

[0089] like Figure 5 As shown, this embodiment provides a method for obtaining voltage-related fitting values ​​in the first battery state, including:

[0090] S31, based on the temperature difference value corresponding to the battery voltage in the first battery state, obtain the extreme value of the temperature difference in the first battery state, where the number of extreme values ​​of the temperature difference is one. The extreme value of the temperature difference is the extreme value among the temperature difference values ​​corresponding to the battery voltage. For example, the temperature difference values ​​corresponding to the battery voltage include 0.01℃, 0.02℃, 0.03℃, and 0.02℃, with 0.03℃ being the maximum value among them. 0.03℃ can be used as the extreme value of the temperature difference.

[0091] S32, based on the extreme temperature difference, obtain the voltage fitting value, the voltage fitting value being the battery voltage corresponding to the extreme temperature difference.

[0092] Optionally, the extreme temperature differences that determine the voltage fitting value are adjacent to each other in different battery states. For example, the extreme temperature difference for battery state 1 is 0.03℃, the extreme temperature differences for battery state 2 include 0.032℃ and 0.08℃, and the extreme temperature differences for battery state 3 include 0.034℃ and 0.05℃. The extreme temperature difference corresponding to the voltage fitting value in battery state 1 can be 0.03℃. In battery state 2, the extreme temperature difference adjacent to 0.03℃ is 0.032℃, and the extreme temperature difference corresponding to the voltage fitting value in battery state 2 can be 0.032℃. In battery state 3, the extreme temperature difference adjacent to 0.03℃ is 0.034℃, and the extreme temperature difference corresponding to the voltage fitting value in battery state 3 can be 0.034℃. Similarly, the extreme temperature differences that determine the voltage fitting value are adjacent in different battery states. Similar to the principle of the voltage fitting value, it changes from one adjacent extreme value to two adjacent extreme values. This embodiment will not be elaborated further.

[0093] Optionally, the voltage-related fitting value in the first battery state mentioned above is not the voltage-related fitting value of the battery when it is fully charged or fully discharged. That is, it is not necessary to fully charge or discharge the battery to obtain the voltage-related fitting value. Since full charging or full discharging will affect the battery's lifespan during actual use, the battery health state prediction method can be applied to the actual working environment of the battery and will not have any adverse effects on the battery.

[0094] Furthermore, as described above, this application embodiment has multiple sets of correspondences, such as "temperature difference value corresponding to battery voltage," "battery voltage corresponding to extreme temperature difference," and "temperature difference change value corresponding to battery voltage," etc. All correspondences in this application embodiment are one-to-one. That is, for example, the temperature difference value corresponding to battery voltage and the battery voltage corresponding to the temperature difference value are the same set of data; similarly, the battery voltage corresponding to the extreme temperature difference is the same set of data as the extreme temperature difference value corresponding to the battery voltage. Additionally, the temperature data corresponding to battery voltage, including the temperature difference value and the temperature difference change value, are all obtained based on the battery temperature at the same sampling time as the battery voltage. This is also a necessary condition for satisfying the correspondences in this application embodiment. Based on satisfying this condition, the temperature difference value and the temperature difference change value corresponding to battery voltage can be values ​​not limited to those in this embodiment, which will not be elaborated upon further in this embodiment.

[0095] The scope of protection of the battery health status prediction method described in this application is not limited to the execution order of the steps listed in this embodiment. Any solution implemented by adding, deleting, or replacing steps in the prior art based on the principles of this application is included within the scope of protection of this application.

[0096] like Figure 6 As shown, this embodiment provides a battery health status prediction device 600, which includes:

[0097] The battery data acquisition module 610 is used to acquire battery data under different first battery states. The battery data includes battery voltage and battery temperature at different sampling times.

[0098] The temperature difference acquisition module 620 is used to acquire the temperature difference value corresponding to the battery voltage in the first battery state based on the battery data in the first battery state, wherein the temperature difference value is the difference value of the battery temperature at two adjacent sampling times.

[0099] The voltage correlation fitting value acquisition module 630 is used to acquire the voltage correlation fitting value in the first battery state based on the battery voltage in the first battery state and the corresponding temperature difference value. The voltage correlation fitting value is a voltage fitting value or a pressure difference fitting value. The pressure difference fitting value is the difference between the battery voltages at two different sampling times.

[0100] The fitting slope acquisition module 640 is used to acquire a first fitting slope and a first fitting intercept based on the voltage-related fitting value and the battery health state fitting value under the first battery state.

[0101] The battery health status prediction value acquisition module 650 is used to acquire a battery health status prediction value under the second battery state based on the first fitting slope, the first fitting intercept, and the voltage-related fitting value under the second battery state, wherein the second battery state is the future operating state of the battery.

[0102] In the battery health status prediction device 600 provided in this embodiment, the battery data acquisition module 610, the temperature difference value acquisition module 620, the voltage correlation fitting value acquisition module 630, the fitting slope acquisition module 640, and the battery health status prediction value acquisition module 650 are... Figure 2 The battery health status prediction methods S11-S15 shown correspond one-to-one, and will not be elaborated here.

[0103] As described above, the battery health status prediction device in this embodiment obtains the predicted battery health status value in the second battery state based on the first fitting slope, the first fitting intercept, and the voltage-related fitting value in the second battery state. This enables real-time prediction of the battery health status value in the second battery state based on battery data, without requiring experimental measurements in the second battery state to obtain the battery health status value. The battery health status prediction method improves the efficiency of obtaining battery health status values.

[0104] In the several embodiments provided in this application, it should be understood that the disclosed apparatus or method can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0105] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of this application, depending on actual needs. For example, the functional modules / units in the various embodiments of this application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0106] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0107] like Figure 7 As shown, this embodiment provides an electronic device 700, which includes a memory 710 storing a computer program and a processor 720 communicatively connected to the memory 710, which executes the computer program when invoked. Figure 2 The battery health status prediction method shown.

[0108] This application also provides a computer-readable storage medium. Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state drive (SSD)).

[0109] This application embodiment may also provide a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this application embodiment are generated. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0110] When the computer program product is executed by a computer, the computer performs the method described in the foregoing method embodiments. The computer program product can be a software installation package; when the foregoing method is required, the computer program product can be downloaded and executed on the computer.

[0111] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0112] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A battery state of health prediction method, characterized by, The battery health status prediction method includes: Obtain battery data under different first battery states, the battery data including: battery voltage at different sampling times and battery temperature at different sampling times; Based on the battery data in the first battery state, the temperature difference value corresponding to the battery voltage in the first battery state is obtained, and the temperature difference value is the difference between the battery temperatures at two adjacent sampling times. Based on the battery voltage and the corresponding temperature difference value in the first battery state, obtain the voltage-related fitting value in the first battery state; Based on the voltage-related fitting value and the battery health state fitting value under the first battery state, the first fitting slope and the first fitting intercept are obtained. Based on the first fitting slope, the first fitting intercept, and the voltage-related fitting value under the second battery state, a predicted value of the battery health state under the second battery state is obtained. The second battery state is the future operating state of the battery. The voltage-related fitting value is either a voltage fitting value or a voltage difference fitting value. The voltage difference fitting value is the difference between the battery voltages at two different sampling times. The temperature difference can be expressed by the following formula: ； wherein, represents the battery temperature corresponding to the first battery voltage, the first battery voltage being the battery voltage corresponding to the kth change of the voltage change unit as a reference, the battery voltage corresponding to the kth change of the voltage change unit, represents the battery temperature corresponding to the second battery voltage, the second battery voltage being the battery voltage corresponding to the (k-1)th change of the voltage change unit as a reference, the battery voltage corresponding to the (k-1)th change of the voltage change unit, represents the difference between the battery temperature corresponding to the first battery voltage and the battery temperature corresponding to the second battery voltage.

2. The battery health status prediction method according to claim 1, characterized in that, One method for obtaining the voltage correlation fitting value in the first battery state includes: Based on the temperature difference value corresponding to the battery voltage in the first battery state, the extreme values ​​of temperature difference in the first battery state are obtained, and the number of extreme values ​​of temperature difference is at least two. Based on the extreme temperature difference values, the pressure difference fitting value is obtained, wherein the pressure difference fitting value is the difference between the battery voltages corresponding to any two extreme temperature difference values.

3. The battery health status prediction method according to claim 1, characterized in that, One method for obtaining the voltage correlation fitting value in the first battery state includes: Based on the temperature difference value corresponding to the battery voltage in the first battery state, the extreme value of the temperature difference in the first battery state is obtained, and the number of extreme values ​​of the temperature difference is one. Based on the extreme temperature difference, the voltage fitting value is obtained, and the voltage fitting value is the battery voltage corresponding to the extreme temperature difference.

4. The battery health status prediction method according to claim 1, characterized in that, The battery health status prediction method further includes: obtaining a second fitting slope and a second fitting intercept based on the voltage-related fitting value under the first battery state, the battery health status fitting value under the first battery state, the voltage-related fitting value under the second battery state, and the battery health status fitting value under the second battery state.

5. The battery health status prediction method according to claim 1, characterized in that, The battery health status prediction method further includes: obtaining the prediction error of the battery health status prediction value based on the battery health status fitting value under the second battery state and the battery health status prediction value under the second battery state, wherein the battery health status fitting value is the battery health status value obtained by experimental measurement.

6. The battery health status prediction method according to claim 5, characterized in that, The prediction error is expressed by the following formula: ； in, This represents the fitted value of the battery health status. This represents the predicted battery health status value. This indicates the prediction error.

7. The battery health status prediction method according to claim 1, characterized in that, The battery health status prediction method further includes: obtaining a differentially uniform voltage set in the first battery state based on the voltage change unit in the first battery state and the battery data in the first battery state, wherein the difference between any two adjacent battery voltages in the differentially uniform voltage set is the voltage change unit.

8. A battery health status prediction device, characterized in that, The battery health status prediction device includes: A battery data acquisition module is used to acquire battery data under different first battery states. The battery data includes: battery voltage at different sampling times and battery temperature at different sampling times. The temperature difference value acquisition module is used to acquire the temperature difference value corresponding to the battery voltage in the first battery state based on the battery data in the first battery state, wherein the temperature difference value is the difference value of the battery temperature at two adjacent sampling times. The voltage correlation fitting value acquisition module is used to acquire the voltage correlation fitting value of the first battery state based on the battery voltage and the corresponding temperature difference value in the first battery state. The voltage correlation fitting value is a voltage fitting value or a pressure difference fitting value. The pressure difference fitting value is the difference between the battery voltages at two different sampling times. The fitting slope acquisition module is used to acquire a first fitting slope and a first fitting intercept based on the voltage-related fitting value and the battery health state fitting value under the first battery state. The battery health status prediction value acquisition module is used to obtain the battery health status prediction value under the second battery state based on the first fitting slope, the first fitting intercept, and the voltage-related fitting value under the second battery state. The second battery state is the future operating state of the battery. The voltage-related fitting value is a voltage fitting value or a voltage difference fitting value. The voltage difference fitting value is the difference between the battery voltage at two different sampling times. The temperature difference can be expressed by the following formula: ； in, The battery temperature is represented by the first battery voltage, where the first battery voltage is expressed as a voltage change unit. As a reference, the battery voltage corresponding to the k-th change of the voltage changing unit. This is expressed as the battery temperature corresponding to the second battery voltage, where the second battery voltage is the voltage change unit. As a reference, the battery voltage corresponding to the (k-1)th change of the voltage changing unit. This represents the difference between the battery temperature corresponding to the first battery voltage and the battery temperature corresponding to the second battery voltage.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the battery health state prediction method according to any one of claims 1-7.

10. An electronic device, characterized in that, The electronic device includes: A memory that stores a computer program; The processor, which is communicatively connected to the memory, executes the battery health status prediction method according to any one of claims 1-7 when calling the computer program.

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