CN116136573A - Battery health state prediction method, device, medium and electronic equipment - Google Patents
Battery health state prediction method, device, medium and electronic equipment Download PDFInfo
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Abstract
The application provides a battery health state prediction method, a battery health state prediction device, a battery health state prediction medium and electronic equipment. The battery state of health prediction method comprises the following steps: acquiring battery data in different first battery states; acquiring a temperature difference value corresponding to the battery voltage in the first battery state based on the battery data in the first battery state; acquiring a voltage related fitting value in the first battery state based on the battery voltage and the corresponding temperature difference value in the first battery state; acquiring a first fitting slope and a first fitting intercept based on the voltage-related fitting value and the battery state fitting value in the first battery state; and acquiring a predicted value of the battery state of health 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. The battery state of health prediction method can improve the efficiency of obtaining the battery state of health value.
Description
Technical Field
The application belongs to the field of new energy, and relates to a prediction method, in particular to a battery health state prediction method, a device, a medium and electronic equipment.
Background
With the rapid development of new energy fields, lithium batteries have been widely used in mobile digital products, electric vehicles and energy storage power stations. The service life is an important index for measuring the performance of the battery, and the battery health status has important significance for analyzing the service life of the battery. The battery health state is generally obtained through experimental measurement of the battery, and the time period for obtaining the battery health state through experimental measurement is long, so that the requirement for obtaining the battery health state according to battery data in real time cannot be met, and the current method for obtaining the battery health state through experimental measurement has the problem of low efficiency.
Disclosure of Invention
The invention aims to provide a battery health state prediction method, a device, a medium and electronic equipment, which are used for solving the problem of low efficiency of the existing method for acquiring the battery health state through experimental measurement.
In a first aspect, the present application provides a battery state of health prediction method, including: acquiring battery data in different first battery states, the battery data comprising: battery voltage at different sampling times and battery temperature at different sampling times; acquiring a 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 a difference value of battery temperatures at two adjacent sampling moments; acquiring a voltage correlation fitting value in the first battery state based on the battery voltage and the corresponding temperature difference value in the first battery state, wherein the voltage correlation fitting value is a voltage fitting value or a pressure difference fitting value, and the pressure difference fitting value is a difference value of the battery voltages at two different sampling moments; acquiring a first fitting slope and a first fitting intercept based on the voltage-related fitting value and the battery state fitting value in the first battery state; and acquiring a battery health state predicted value in a second battery state based on the first fitting slope, the first fitting intercept and the voltage related fitting value in the second battery state, wherein the second battery state is a future running state of the battery.
By acquiring the battery state of health prediction value in the second battery state based on the first fitting slope, the first fitting intercept and the voltage-dependent fitting value in the second battery state, it is possible to predict the battery state of health value in the second battery state in real time from the battery data in the second battery state without performing experimental measurement in the second battery state to obtain the battery state of health value. The battery state of health prediction method can improve the efficiency of obtaining the battery state of health value.
In an embodiment of the present application, an implementation method for obtaining a voltage-related fitting value in the first battery state includes: acquiring temperature difference extreme values 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 the temperature difference extreme values is at least two; and acquiring the pressure difference fitting value based on the temperature difference extreme value, wherein the pressure difference fitting value is the difference value between the battery voltages corresponding to any two temperature difference extreme values.
In an embodiment of the present application, an implementation method for obtaining a voltage-related fitting value in the first battery state includes: acquiring a temperature difference extreme 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 the temperature difference extreme values is one; and acquiring the voltage fitting value based on the temperature difference extremum, wherein the voltage fitting value is the battery voltage corresponding to the temperature difference extremum.
In an embodiment of the present application, the method for predicting a state of health of a battery further includes: and acquiring a second fitting slope and a second fitting intercept based on the voltage-related fitting value in the first battery state, the battery health state fitting value in the first battery state, the voltage-related fitting value in the second battery state and the battery health state fitting value in the second battery state.
By obtaining the second fit slope and the second fit intercept, which corresponds to updating the first fit slope and the first fit intercept based on the battery state of health fit value in the second battery state and the voltage dependent fit value in the second battery state, the accuracy of the second fit slope and the second fit intercept is higher, compared to the first fit slope and the first fit intercept, since the second fit slope and the second fit intercept use more fit data, the accuracy of the battery state of health value in the future state is predicted by the second fit slope and the second fit intercept is higher.
In an embodiment of the present application, the method for predicting a state of health of a battery further includes: and acquiring a prediction error of the battery state of health predicted value based on the battery state of health fitted value in the second battery state and the battery state of health predicted value in the second battery state, wherein the battery state of health fitted value is a battery state value obtained through experimental measurement.
By introducing the battery state-of-health fitting value in the second battery state, a prediction error of a battery state-of-health predicted value is obtained, so that reliability of a predicted result can be ensured.
In one embodiment of the present application, the prediction error is represented by the following formula:
wherein SOH test Representing the fit value of the state of health of the battery, SOH for Represents the predicted value of the state of health of the battery, err for Representing the prediction error.
In an embodiment of the present application, the method for predicting a state of health of a battery further includes: and acquiring a differential 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 differential of any two adjacent battery voltages in the differential uniform voltage set is the voltage change unit.
In a second aspect, the present application provides a battery state of health prediction apparatus, comprising: the battery data acquisition module is used for acquiring battery data in different first battery states, and the battery data comprises: battery voltage at different sampling times and battery temperature at different sampling times; the temperature difference value acquisition module is used for acquiring a 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 a difference value of battery temperatures at two adjacent sampling moments; the voltage correlation fitting value obtaining module is used for obtaining a voltage correlation fitting value in the first battery state based on the battery voltage and the temperature difference value corresponding to the battery voltage in the first battery state, wherein the voltage correlation fitting value is a voltage fitting value or a pressure difference fitting value, and the pressure difference fitting value is a difference value of the battery voltages at two different sampling moments; the fitting slope obtaining module is used for obtaining a first fitting slope and a first fitting intercept based on the voltage related fitting value and the battery health state fitting value in the first battery state; the battery state of health prediction value obtaining module is configured to obtain a battery state of health prediction value in a second battery state based on the first fitting slope, the first fitting intercept and the voltage-related fitting value in the second battery state, where the second battery state is a future running state of the battery.
In a third aspect, the present application provides a computer readable storage medium, which when executed by a processor, implements a battery state of health prediction method according to any one of the first aspects of the present application.
In a fourth aspect, the present application provides an electronic device, including: a memory storing a computer program; and the processor is in communication connection with the memory and executes the battery health state prediction method according to any one of the first aspect of the application when the computer program is called.
As described above, the battery state of health prediction method, device, medium and electronic equipment have the following beneficial effects:
first, by acquiring the predicted value of the battery state of health 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, it is possible to predict the value of the battery state of health in the second battery state in real time based on the battery data in the second battery state without performing experimental measurement in the second battery state to obtain the value of the battery state of health. The battery state of health prediction method can improve the efficiency of obtaining the battery state of health value.
Second, by obtaining the second fitting slope and the second fitting intercept, the first fitting slope and the first fitting intercept are updated on the basis of the battery state-of-health fitting value in the second battery state and the voltage-dependent fitting value in the second battery state, and as compared with the first fitting slope and the first fitting intercept, the second fitting slope and the second fitting intercept use more fitting data, and therefore the accuracy of the second fitting slope and the second fitting intercept is higher, and the accuracy of the battery state-of-health value in the future state is predicted by the second fitting slope and the second fitting intercept is higher.
Thirdly, by introducing the battery state-of-health fitting value under the second battery state, a prediction error of a battery state-of-health predicted value is obtained, so that reliability of a predicted result can be ensured.
Drawings
Fig. 1 is a schematic structural diagram of an application scenario in an embodiment of the present application.
Fig. 2 is a flowchart illustrating a method for predicting a battery state of health according to an embodiment of the present application.
Fig. 3 is a flowchart of an implementation method for obtaining the voltage-related fitting value in the first battery state according to the embodiment of the present application.
FIG. 4 is a schematic diagram showing the temperature difference change values according to the embodiment of the present application.
Fig. 5 is a flowchart of an implementation method for obtaining the voltage-related fitting value in the first battery state according to the embodiment of the present application.
Fig. 6 is a schematic structural diagram of a battery state of health prediction device according to an embodiment of the present application.
Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Description of element reference numerals
10. Battery cell
20. Load(s)
30. Predictive gauge
600. Battery state of health prediction device
610. Battery data acquisition module
620. Temperature difference value acquisition module
630. Voltage correlation fitting value acquisition module
640. Fitting slope obtaining module
650. Battery state of health prediction value acquisition
Module
700. Electronic equipment
710. Memory device
720. Processor and method for controlling the same
S11-S15 step
S21-S22 step
S31-S32 step
Detailed Description
Other advantages and effects of the present application will become apparent to those skilled in the art from the present disclosure, when the following description of the embodiments is taken in conjunction with the accompanying drawings. The present application may be embodied or carried out in other specific embodiments, and the details of the present application may be modified or changed from various points of view and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that, the illustrations provided in the following embodiments merely illustrate the basic concepts of the application by way of illustration, and only the components related to the application are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complex.
The following describes the technical solutions in the embodiments of the present application in detail with reference to the drawings in the embodiments of the present application.
As shown in fig. 1, this embodiment provides an application scenario schematic diagram, where the application scenario includes: battery 10, load 20, and predictor 30.
Alternatively, the battery 10 may be a battery pack formed by arranging one or more lithium battery cells in any form for powering electrical devices. The battery 10 may be charged or discharged under controlled conditions, and may have a corresponding capacity, size, etc. according to actual conditions to meet actual needs.
Optionally, the load 20 is electrically connected to the battery 10, and the battery 10 may supply power to the load 20 for the load 20 to operate normally. The load 20 may be any device, module or apparatus requiring a supply voltage to support operation.
Alternatively, the predictor 30 may be a battery state of health prediction system or chip, and the predictor 30 predicts a battery state of health value by collecting corresponding battery data. The predictor 30 may run one or more software programs to record data and data calculations.
As shown in fig. 2, the present embodiment provides a battery state of health prediction method, which may be implemented by a processor of a computer device, and includes:
s11, acquiring battery data in different first battery states, wherein the battery data comprises: battery voltage at different sampling times and battery temperature at different sampling times.
Optionally, the battery data in the first battery state may include battery data of the battery in a historical operating state and battery data of the battery in a current operating state, where the historical operating state may be an operating state of the battery in a historical cycle number, and the current operating state may be an operating state of the battery in the current cycle number. For example, if the current cycle number of the battery is 500, the battery data obtained at the 500 th cycle is the battery data under the current cycle number, that is, the battery data under the current running state, and the battery data obtained at 400 cycles, 300 cycles, 200 cycles, and the like may be the battery data under the historical cycle number, that is, the battery data under the historical running state. The battery voltage may be an actual operating voltage of the battery and the battery temperature may be an actual operating temperature of the battery.
Optionally, the first battery state may be a state of the battery under different cycle times, for example, the different first battery state may be a state of the battery under 500 cycle times, a state under 1000 cycle times, a state under 1500 cycle times, and the battery voltage at different sampling moments and the battery temperature at different sampling moments may be, for example: battery voltage 3.1V and battery temperature 31 ℃ at sampling time t=2s, battery voltage 3.2V and battery temperature 32 ℃ at sampling time t=3s, and battery voltage 3.4V and battery temperature 34 ℃ at sampling time t=4s.
S12, based on the battery data in the first battery state, obtaining a temperature difference value corresponding to the battery voltage in the first battery state, wherein the temperature difference value is a difference value of battery temperatures at two adjacent sampling moments, the difference value is a next value minus a previous value, and in the embodiment, the difference value of the battery temperatures at two adjacent sampling moments can be a difference value between the battery temperature at the current sampling moment and the battery temperature at the previous adjacent sampling moment.
Optionally, the temperature difference value corresponding to the battery voltage may be a difference value between a first battery temperature and a second battery temperature corresponding to the battery voltage, the second battery temperature may be a battery temperature at a sampling time before the first battery temperature, and the battery temperature corresponding to the battery voltage may refer to a battery temperature at the same sampling time as the battery voltage. Taking the first battery state as an example of the battery at 500 cycles, when the battery data includes: the battery voltage 3.1V at the sampling time t=2s and the battery temperature 31 ℃, the battery voltage 3.2V at the time t=3s and the battery temperature 32 ℃, the battery voltage 3.4V at the time t=4s and the battery temperature 34 ℃, the temperature difference corresponding to the battery voltage 3.2V at the time t=3s is 1 ℃, the temperature difference corresponding to the battery voltage 3.4V at the time t=42 ℃, and the temperature difference corresponding to the battery voltage 3.1V can be directly set to 0 because the battery voltage 3.1V does not have the previous sampling time.
Optionally, the battery state of health prediction method further includes: and acquiring a differential 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 differential of any two adjacent battery voltages in the differential uniform voltage set is the voltage change unit. For example, the battery data in the first battery state includes: the battery voltage is 3.12V, the battery voltage is 3.14V, the battery voltage is 3.15V, the battery voltage is 3.16V, the voltage change unit is 0.02V, and the differential uniform voltage set comprises: the battery voltage 3.12V, the battery voltage 3.14V and the battery voltage 3.16V, wherein in the differential uniform voltage set, the battery voltage 3.14V may be the battery voltage corresponding to the first change of the voltage change unit, and the battery voltage 3.16V may be the battery voltage corresponding to the second change of the voltage change unit.
Alternatively, the temperature difference value may be represented by the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
represented asA battery temperature corresponding to a first battery voltage, which is obtained by a voltage change unit u * When the voltage change unit is used as a reference, the corresponding battery voltage at the kth change of the voltage change unit is +.>
Expressed as a battery temperature corresponding to a second battery voltage represented by the voltage variation unit u * And when the voltage is used as a reference, the voltage change unit corresponds to the battery voltage at the k-1 th change. δT (k) And representing the difference between the battery temperature corresponding to the first battery voltage and the battery temperature corresponding to the second battery voltage.
And S13, acquiring a voltage related fitting value in the first battery state based on the battery voltage and the temperature difference value corresponding to the battery voltage in the first battery state, wherein the voltage related fitting value is a voltage fitting value or a pressure difference fitting value, and the pressure difference fitting value is a difference value of the battery voltages at two different sampling moments.
Alternatively, the temperature difference value corresponding to the battery voltage in the first battery state may be the temperature difference value corresponding to the battery voltage in the differential uniform voltage set in the first battery state. By acquiring the temperature difference values corresponding to the battery voltages in the differential uniform voltage set, each temperature difference value can be changed on the basis of the uniform voltage change unit, so that the prediction accuracy of the battery health state is improved.
S14, acquiring a first fitting slope and a first fitting intercept based on the voltage related fitting value and the battery health state fitting value in the first battery state.
Optionally, when the voltage-dependent fit value is the voltage-difference fit value, the first fit slope is a fit slope with respect to the voltage-difference fit value and the battery state of health fit value, the first fit intercept is a fit intercept with respect to the voltage-difference fit value and the battery state of health fit value, and when the voltage-dependent fit value is the voltage fit value, the first fit slope is a fit slope with respect to the voltage fit value and the battery state of health fit value, the first fit intercept is a fit intercept with respect to the voltage fit value and the battery state of health fit value.
And S15, acquiring a battery health state predicted value in a second battery state based on the first fitting slope, the first fitting intercept and the voltage related fitting value in the second battery state, wherein the second battery state is a future running state of the battery.
Alternatively, the future operation state of the battery may be an operation state of the battery at a future cycle number, for example, the current cycle number of the battery is 500, and the operation state of the battery at the cycle number of 1000 is the operation state of the battery at the future cycle number.
Alternatively, when the voltage-dependent fit value is the voltage fit value, the battery state of health predicted value may be expressed as:
SOH for ＝m n ×V δT +m 0
wherein SOH for Representing the predicted value of the state of health of the battery, m n For the first fitting slope, m 0 For the first fitting intercept, V δT Representing the voltage fit value. When the voltage-dependent fit value is the pressure difference fit value, the battery state of health prediction value may be expressed as:
SOH for ＝m n ×|▽(V δT )|+m 0
wherein SOH for Representing the predicted value of the state of health of the battery, m n For the first fitting slope, m 0 For the first fit intercept, |v (V δT ) I represents the absolute value of the pressure difference fit value.
For example, the predicted value of the battery state of health in the second battery state is about 91.9% when the first fit intercept is-302.751, the voltage-dependent fit value in the second battery state is 0.04, and the first fit intercept is 104.041.
Optionally, the battery state of health prediction method further includes: and acquiring a second fitting slope and a second fitting intercept based on the voltage-related fitting value in the first battery state, the battery health state fitting value in the first battery state, the voltage-related fitting value in the second battery state and the battery health state fitting value in the second battery state.
Optionally, the battery state of health prediction method further includes: and acquiring a prediction error of the battery state of health predicted value based on the battery state of health fitted value in the second battery state and the battery state of health predicted value in the second battery state, wherein the battery state of health fitted value is a battery state value obtained through experimental measurement.
Optionally, the prediction error is represented by:
wherein SOH test Representing the fit value of the state of health of the battery, SOH for Represents the predicted value of the state of health of the battery, err for Representing the prediction error.
As apparent from the above description, the battery state of health prediction method includes: acquiring battery data in different first battery states, the battery data comprising: battery voltage at different sampling times and battery temperature at different sampling times; acquiring a 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 a difference value of battery temperatures at two adjacent sampling moments; acquiring a voltage correlation fitting value in the first battery state based on the battery voltage and the corresponding temperature difference value in the first battery state, wherein the voltage correlation fitting value is a voltage fitting value or a pressure difference fitting value, and the pressure difference fitting value is a difference value of the battery voltages at two different sampling moments; acquiring a first fitting slope and a first fitting intercept based on the voltage-related fitting value and the battery state fitting value in the first battery state; and acquiring a battery health state predicted value in a second battery state based on the first fitting slope, the first fitting intercept and the voltage related fitting value in the second battery state, wherein the second battery state is a future running state of the battery.
By acquiring the battery state of health prediction value in the second battery state based on the first fitting slope, the first fitting intercept and the voltage-dependent fitting value in the second battery state, it is possible to predict the battery state of health value in the second battery state in real time from the battery data in the second battery state without performing experimental measurement in the second battery state to obtain the battery state of health value. The battery state of health prediction method can improve the efficiency of obtaining the battery state of health value.
As shown in fig. 3, the present embodiment provides a method for obtaining a voltage-related fitting value in the first battery state, including:
s21, obtaining temperature difference extreme values 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 the temperature difference extreme values is at least two.
Optionally, the temperature difference extremum is an extremum of the temperature difference values, and the temperature difference extremum may be a maximum value or a minimum value of the temperature difference values. For example, the temperature difference value corresponding to the battery voltage includes: 0.02 ℃,0.01 ℃,0.03 ℃,0.04 ℃,0.03 ℃, said maximum value being 0.04 ℃, said minimum value being 0.01 ℃,0.04 ℃ and 0.01 ℃ may be two of said temperature extremes.
Optionally, the implementation method for obtaining the temperature difference extremum in the first battery state includes: acquiring a 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 acquiring the temperature difference extreme value based on the temperature difference change value corresponding to the battery voltage in the first battery state. The temperature difference change value is a difference value of temperature difference values corresponding to voltages of two adjacent batteries. The temperature difference change value can be represented by the following formula:
wherein δT is (k) Can be expressed as a differential value of the battery temperature corresponding to the first battery voltage and the battery temperature corresponding to the second battery voltage, δT (k-1) Can be expressed as a differential value between a battery temperature corresponding to a second battery voltage and a battery temperature corresponding to a third battery voltage, the third battery voltage being the battery voltage corresponding to the kth-2 time change of the voltage change unit with reference to the voltage change unit,
the difference value may be represented as a difference value between a first temperature difference value and a second temperature difference value, where the first temperature difference value is a difference value between a battery temperature corresponding to the first battery voltage and a battery temperature corresponding to the second battery voltage, and the second temperature difference value is a difference value between a battery temperature corresponding to the second battery voltage and a battery temperature corresponding to the third battery voltage. In the process that the temperature difference change value gradually increases from about 0 and then gradually decreases to about 0, the temperature difference value used for calculating the temperature difference change value is increased, and the battery voltage corresponding to the maximum value in the temperature difference value is the battery voltage corresponding to the maximum value when the temperature difference change value is 0. In the process that the temperature difference change value gradually decreases from about 0 and then gradually increases to about 0, the temperature difference value used for calculating the temperature difference change value is decreased, and the battery voltage corresponding to the minimum value in the temperature difference value is the battery voltage corresponding to the minimum value in the temperature difference value when the temperature difference change value is 0. And obtaining the corresponding maximum value of the temperature difference value and the corresponding minimum value of the temperature difference value according to the battery voltage corresponding to the maximum value of the temperature difference value and the battery voltage corresponding to the minimum value of the temperature difference value. Referring to fig. 4, the ordinate in fig. 4 is the temperature difference change value, the abscissa is the battery voltage, and it can be seen that the temperature difference change value passes through 0 upwards or downwards a plurality of times, N is selected in this embodiment 1 And X 1 . From the above pair of temperature difference changesDescription of values it follows that X 1 The abscissa of (a) is the battery voltage corresponding to the maximum value of the temperature difference, N 1 And the abscissa of the voltage is the battery voltage corresponding to the temperature difference minimum value.
S22, based on the temperature difference extreme value, acquiring the pressure difference fitting value, wherein the pressure difference fitting value is the difference value between the battery voltages corresponding to any two temperature difference extreme values. For example, when the temperature difference extremum includes 0.01 ℃ and 0.04 ℃, the battery voltage corresponding to 0.01 ℃ is 3.1V, the battery voltage corresponding to 0.04 ℃ is 3.3V, and the pressure difference fitting value is 0.2V.
As shown in fig. 5, the present embodiment provides an implementation method for obtaining a voltage-related fitting value in the first battery state, including:
s31, acquiring a temperature difference extreme 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 the temperature difference extreme values is one. The temperature difference extremum is an extremum in the temperature difference value corresponding to the battery voltage, for example, the temperature difference value corresponding to the battery voltage includes: 0.01 ℃, 0.02 ℃,0.03 ℃ and 0.02 ℃,0.03 ℃ being the maximum values thereof, 0.03 ℃ can be taken as the one temperature difference extreme value.
S32, based on the temperature difference extreme value, acquiring the voltage fitting value, wherein the voltage fitting value is the battery voltage corresponding to the temperature difference extreme value.
Optionally, the temperature difference extreme values determining the voltage fitting values in different battery states are adjacent to each other. For example, the temperature difference extremum of the battery state 1 is 0.03 ℃, the temperature difference extremum of the battery state 2 comprises 0.032 ℃ and 0.08 ℃, the temperature difference extremum of the battery state 3 comprises 0.034 ℃ and 0.05 ℃, the temperature difference extremum corresponding to the voltage fitting value in the battery state 1 can be 0.03 ℃, the temperature difference extremum adjacent to 0.03 ℃ in the battery state 2 is 0.032 ℃, the temperature difference extremum corresponding to the voltage fitting value in the battery state 2 can be 0.032 ℃, the temperature difference extremum adjacent to 0.03 ℃ in the battery state 3 is 0.034 ℃, and the temperature difference extremum corresponding to the voltage fitting value in the battery state 3 can be 0.034 ℃. Similarly, the adjacency between the temperature difference extremum of the voltage difference fitting value is determined under different battery states, and is similar to the principle of the voltage fitting value, and the adjacency is changed from one extremum to two extremum, so that the embodiment is not repeated here.
Optionally, the above voltage-related fitting value in the first battery state is not a voltage-related fitting value of the battery when the battery is fully charged or fully discharged, that is, the voltage-related fitting value can be obtained without fully charging or fully discharging the battery.
In addition, as can be seen from the above description, in the embodiment of the present application, there are multiple sets of correspondence relationships, for example, a "temperature difference value corresponding to a battery voltage", "a battery voltage corresponding to a temperature difference extremum", "a temperature difference change value corresponding to a battery voltage", and the like, and the correspondence relationships in the embodiment of the present application are all one-to-one correspondence relationships, that is, for example, the temperature difference value corresponding to the battery voltage and the battery voltage corresponding to the temperature difference value are the same set of data, and the temperature difference extremum corresponding to the temperature difference extremum and the battery voltage are the same set of data. In addition, the temperature data corresponding to the battery voltage, including the temperature difference value corresponding to the battery voltage and the temperature difference change value corresponding to the battery voltage, are obtained on the basis of the battery temperature at the same sampling time as the battery voltage, and are also necessary conditions for meeting the correspondence in the embodiment of the present application, and on the basis of meeting the conditions, the temperature difference value corresponding to the battery voltage and the temperature difference change value corresponding to the battery voltage may be values not limited in the embodiment, which will not be described in detail in the embodiment.
The protection scope of the battery state of health prediction method according to the embodiment of the present application is not limited to the execution sequence of the steps listed in the embodiment, and all the schemes implemented by adding or removing steps and replacing steps according to the prior art according to the principles of the present application are included in the protection scope of the present application.
As shown in fig. 6, the present embodiment provides a battery state of health prediction apparatus 600, the battery state of health prediction apparatus 600 including:
the battery data obtaining module 610 is configured to obtain battery data in different first battery states, where the battery data includes: battery voltage at different sampling times and battery temperature at different sampling times.
The temperature difference value obtaining module 620 is configured to obtain, based on the battery data in the first battery state, a temperature difference value corresponding to the battery voltage in the first battery state, where the temperature difference value is a difference value of battery temperatures at two adjacent sampling moments.
The voltage-related fitting value obtaining module 630 is configured to obtain a voltage-related fitting value in the first battery state based on the battery voltage in the first battery state and the temperature difference value corresponding to the battery voltage, where the voltage-related fitting value is a voltage fitting value or a pressure difference fitting value, and the pressure difference fitting value is a difference value between the battery voltages at two different sampling moments.
A fitting slope obtaining module 640, configured to obtain a first fitting slope and a first fitting intercept based on the voltage-related fitting value and the battery state of health fitting value in the first battery state.
The battery state of health prediction value obtaining module 650 is configured to obtain a battery state of health prediction value in a second battery state based on the first fitting slope, the first fitting intercept, and the voltage-related fitting value in the second battery state, where the second battery state is a future running state of the battery.
In the battery state of health prediction apparatus 600 provided in this embodiment, the battery data obtaining module 610, the temperature difference value obtaining module 620, the voltage-related fitting value obtaining module 630, the fitting slope obtaining module 640, and the battery state of health prediction value obtaining module 650 are in one-to-one correspondence with the battery state of health prediction methods S11-S15 shown in fig. 2, and will not be described in detail herein.
As can be seen from the above description, the battery state of health prediction device according to the present embodiment obtains the predicted value of the battery state of health 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, so as to predict the value of the battery state of health in the second battery state in real time according to the battery data in the second battery state, without performing experimental measurement in the second battery state to obtain the value of the battery state of health. The battery state of health prediction method can improve the efficiency of obtaining the battery state of health value.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus or method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of modules/units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple modules or units may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules or units, which may be in electrical, mechanical or other forms.
The modules/units illustrated as separate components may or may not be physically separate, and components shown as modules/units may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules/units may be selected according to actual needs to achieve the purposes of the embodiments of the present application. For example, functional modules/units in various embodiments of the present application may be integrated into one processing module, or each module/unit may exist alone physically, or two or more modules/units may be integrated into one module/unit.
Those of ordinary skill would further appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
As shown in fig. 7, the present embodiment provides an electronic device 700, which includes a memory 710 storing a computer program; and a processor 720, communicatively coupled to the memory 710, for executing the battery state of health prediction method of fig. 2 when the computer program is invoked.
Embodiments of the present application also provide a computer-readable storage medium. Those of ordinary skill in the art will appreciate that all or part of the steps in the method implementing the above embodiments may be implemented by a program to instruct a processor, where the program may be stored in a computer readable storage medium, where the storage medium is a non-transitory (non-transitory) medium, such as a random access memory, a read only memory, a flash memory, a hard disk, a solid state disk, a magnetic tape (magnetic tape), a floppy disk (floppy disk), an optical disk (optical disk), and any combination thereof. The storage media may be any available media that can be accessed by a computer or a data storage device such as a server, data center, or the like that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital video disc (digital video disc, DVD)), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
Embodiments of the present application 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, the processes or functions described in accordance with the embodiments of the present application are produced in whole or in part. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center by a wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.).
The computer program product is executed by a computer, which performs the method according to the preceding method embodiment. The computer program product may be a software installation package, which may be downloaded and executed on a computer in case the aforementioned method is required.
The descriptions of the processes or structures corresponding to the drawings have emphasis, and the descriptions of other processes or structures may be referred to for the parts of a certain process or structure that are not described in detail.
The foregoing embodiments are merely illustrative of the principles of the present application and their effectiveness, and are not intended to limit the application. Modifications and variations may be made to the above-described embodiments by those of ordinary skill in the art without departing from the spirit and scope of the present application. Accordingly, it is intended that all equivalent modifications and variations which may be accomplished by persons skilled in the art without departing from the spirit and technical spirit of the disclosure be covered by the claims of this application.
Claims (10)
1. A battery state of health prediction method, characterized in that the battery state of health prediction method comprises:
acquiring battery data in different first battery states, the battery data comprising: battery voltage at different sampling times and battery temperature at different sampling times;
acquiring a 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 a difference value of battery temperatures at two adjacent sampling moments;
acquiring a voltage correlation fitting value in the first battery state based on the battery voltage and the corresponding temperature difference value in the first battery state, wherein the voltage correlation fitting value is a voltage fitting value or a pressure difference fitting value, and the pressure difference fitting value is a difference value of the battery voltages at two different sampling moments;
acquiring a first fitting slope and a first fitting intercept based on the voltage-related fitting value and the battery state fitting value in the first battery state;
and acquiring a battery health state predicted value in a second battery state based on the first fitting slope, the first fitting intercept and the voltage related fitting value in the second battery state, wherein the second battery state is a future running state of the battery.
2. The method of claim 1, wherein one implementation of obtaining a voltage-dependent fit value for the first battery state comprises:
acquiring temperature difference extreme values 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 the temperature difference extreme values is at least two;
and acquiring the pressure difference fitting value based on the temperature difference extreme value, wherein the pressure difference fitting value is the difference value between the battery voltages corresponding to any two temperature difference extreme values.
3. The method of claim 1, wherein one implementation of obtaining a voltage-dependent fit value for the first battery state comprises:
acquiring a temperature difference extreme 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 the temperature difference extreme values is one;
and acquiring the voltage fitting value based on the temperature difference extremum, wherein the voltage fitting value is the battery voltage corresponding to the temperature difference extremum.
4. The battery state of health prediction method of claim 1, further comprising: and acquiring a second fitting slope and a second fitting intercept based on the voltage-related fitting value in the first battery state, the battery health state fitting value in the first battery state, the voltage-related fitting value in the second battery state and the battery health state fitting value in the second battery state.
5. The battery state of health prediction method of claim 1, further comprising: and acquiring a prediction error of the battery state of health predicted value based on the battery state of health fitted value in the second battery state and the battery state of health predicted value in the second battery state, wherein the battery state of health fitted value is a battery state value obtained through experimental measurement.
6. The battery state of health prediction method of claim 5, wherein said prediction error is represented by:
wherein SOH test Representing the fit value of the state of health of the battery, SOH for Represents the predicted value of the state of health of the battery, err for Representing the prediction error.
7. The battery state of health prediction method of claim 1, further comprising: and acquiring a differential 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 differential of any two adjacent battery voltages in the differential uniform voltage set is the voltage change unit.
8. A battery state of health prediction apparatus, characterized in that the battery state of health prediction apparatus comprises:
the battery data acquisition module is used for acquiring battery data in different first battery states, and the battery data comprises: battery voltage at different sampling times and battery temperature at different sampling times;
the temperature difference value acquisition module is used for acquiring a 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 a difference value of battery temperatures at two adjacent sampling moments;
the voltage correlation fitting value obtaining module is used for obtaining a voltage correlation fitting value in the first battery state based on the battery voltage and the temperature difference value corresponding to the battery voltage in the first battery state, wherein the voltage correlation fitting value is a voltage fitting value or a pressure difference fitting value, and the pressure difference fitting value is a difference value of the battery voltages at two different sampling moments;
the fitting slope obtaining module is used for obtaining a first fitting slope and a first fitting intercept based on the voltage related fitting value and the battery health state fitting value in the first battery state;
the battery state of health prediction value obtaining module is configured to obtain a battery state of health prediction value in a second battery state based on the first fitting slope, the first fitting intercept and the voltage-related fitting value in the second battery state, where the second battery state is a future running state of the battery.
9. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the battery state of health prediction method of any of claims 1-7.
10. An electronic device, the electronic device comprising:
a memory storing a computer program;
a processor in communication with the memory, which when invoked, performs the battery state of health prediction method of any one of claims 1-7.
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