CN115932631A - Method and device for predicting battery life, electronic equipment and readable storage medium - Google Patents
Method and device for predicting battery life, electronic equipment and readable storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a method and a device for predicting the service life of a battery, electronic equipment and a readable storage medium. The method comprises the following steps: acquiring a first curve of the current of the first battery changing along with time and a second curve of the temperature changing along with time; generating real-time temperature, charge state, calendar time, discharge depth, multiplying power and cycle number of the first battery at each stage according to the first curve and the second curve; respectively generating a first capacity attenuation rate and a second capacity attenuation rate in a calendar aging model and a cyclic aging model under a constant working condition according to the real-time temperature, the charge state, the calendar time, the discharge depth, the multiplying power and the cycle number in each stage; and generating a total decay rate according to the first capacity decay rate and the second capacity decay rate to determine a life prediction curve of the first battery. The invention can realize accurate prediction of the service life of the battery, and the service life prediction curve of the battery can reflect the service life of the battery in the actual application process more truly.
Description
Technical Field
The present invention relates to the field of battery life assessment technologies, and in particular, to a method and an apparatus for predicting battery life, an electronic device, and a readable storage medium.
Background
In the use process of the lithium ion battery, a series of physical and chemical changes occur inside the battery, so that the performance and the capacity of the battery show a decline trend, the change of the external environment also influences the health state of the battery, and finally the service life of the battery is prolonged. Lithium ion batteries are an important part of electric vehicle systems, and the damage of the batteries can cause the whole system to be in failure. Therefore, the service life of the battery is predicted in time, corresponding measures are taken, and the safe and reliable operation of the battery and an application system thereof can be ensured.
At present, when the battery life is predicted under the driving working condition of an electric automobile, the battery life is generally predicted by adopting a fixed-value discharge depth under the current of the known driving working condition. However, the fixed-value depth of discharge predicts the battery life, neglects the influence of different depths of discharge on the battery life, and further causes a larger deviation in the prediction of the battery life under the driving condition. Meanwhile, the current change is complex in the driving process of the electric automobile, the charge-discharge rate change is large, and the fixed value discharge depth cannot truly reflect the battery service life attenuation under the real-time working condition in the driving process.
Disclosure of Invention
The embodiment of the invention provides a method and a device for predicting the service life of a battery, electronic equipment and a readable storage medium, which can realize accurate prediction of the service life of the battery without adopting a fixed value discharge depth to predict the service life of the battery, and further can reflect the service life of the battery in the practical application process more truly.
In a first aspect, an embodiment of the present invention provides a method for predicting a battery life, including:
acquiring a first curve of the current of the first battery changing along with time and a second curve of the temperature changing along with time;
generating real-time temperature, charge state, calendar time, discharge depth, multiplying power and cycle number of the first battery at each stage according to the first curve and the second curve;
generating a first capacity decay rate of the first battery in each stage according to the real-time temperature, the charge state and the calendar time in each stage in a calendar aging model under a constant working condition;
generating a second capacity decay rate of the first battery in each stage according to the real-time temperature, the discharge depth, the multiplying power and the cycle number in each stage in a cyclic aging model under a constant working condition;
generating a total attenuation rate of the first battery in each stage according to the first capacity attenuation rate and the second capacity attenuation rate;
determining a life prediction curve of the first battery according to the total decay rate at each stage.
In a second aspect, an embodiment of the present invention provides an apparatus for predicting battery life, including:
the first acquisition unit is used for acquiring a first curve of the current of the first battery along with time change and a second curve of the temperature along with time change;
the first generation unit is used for generating the real-time temperature, the state of charge, the calendar time, the discharge depth, the multiplying power and the cycle number of the first battery in each stage according to the first curve and the second curve;
the second generation unit is used for generating a first capacity attenuation rate of the first battery in each stage according to the real-time temperature, the charge state and the calendar time in each stage in a calendar aging model under a constant working condition;
the third generation unit is used for generating a second capacity attenuation rate of the first battery in each stage according to the real-time temperature, the discharge depth, the multiplying power and the cycle frequency in each stage in a cyclic aging model under a constant working condition;
a fourth generating unit, configured to generate a total decay rate of the first battery at each stage according to the first capacity decay rate and the second capacity decay rate;
a first determining unit, configured to determine a life prediction curve of the first battery according to the total decay rate at each stage.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the method for predicting battery life according to the first aspect.
In a fourth aspect, the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor is caused to execute the method for predicting battery life according to the first aspect.
The embodiment of the invention provides a method, a device, electronic equipment and a readable storage medium for predicting the service life of a battery, wherein the method comprises the steps of obtaining a first curve of the current changing along with time and a second curve of the temperature changing along with time in the actual application process of the battery, generating the real-time temperature, the state of charge, the calendar time, the discharge depth, the multiplying power and the cycle number of the battery in each stage according to the first curve and the second curve, respectively calculating a first capacity attenuation rate and a second capacity attenuation rate of the battery in each stage in the actual application process of the battery in a calendar aging model and a cycle aging model under a constant working condition, and finally generating the total attenuation rate of the first battery in each stage according to the first capacity attenuation rate and the second capacity attenuation rate, so that the service life prediction curve in the actual application process of the battery can be determined, the service life of the battery can be accurately predicted without adopting a fixed-value discharge depth to predict the service life of the battery, and the generated service life prediction curve can reflect the service life of the battery in the actual application process more truly.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for predicting battery life according to an embodiment of the present invention;
FIG. 2 is a sub-flow diagram of a method for predicting battery life according to an embodiment of the present invention;
FIG. 3 is another schematic flow chart of a method for predicting battery life according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a distribution of the state of charge of the battery under varying operating conditions according to an embodiment of the present invention;
FIG. 5 is a schematic flow chart illustrating a method for predicting battery life according to an embodiment of the present invention;
FIG. 6 is another schematic flow chart of a method for predicting battery life according to an embodiment of the present invention;
FIG. 7 is a schematic block diagram of a device for predicting battery life provided by an embodiment of the present invention;
fig. 8 is a schematic block diagram of an electronic device provided in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a method for predicting battery life according to an embodiment of the invention. The method for predicting the battery life is applied to the terminal equipment and is executed through application software installed in the terminal equipment. The terminal equipment can be a notebook computer, a tablet personal computer or a mobile phone, an electric automobile and the like.
The method for predicting the battery life will be described in detail below.
As shown in fig. 1, the method includes the following steps S110 to S160.
S110, acquiring a first curve of the current of the first battery changing along with time and a second curve of the temperature changing along with time.
The first curve is a distribution curve of real-time current of the first battery in an actual application process, the second curve is a distribution curve of real-time temperature of the first battery in the actual application process, and an actual application scene of the first battery can be an application scene in which the first battery is configured in terminal devices such as vehicles for use. When the first battery is actually used, a first curve of the current of the first battery changing along with time and a second curve of the temperature changing along with time can be obtained through a battery management system of a terminal device end where the first battery is located.
And S120, generating the real-time temperature, the charge state, the calendar time, the discharge depth, the multiplying power and the cycle number of the first battery in each stage according to the first curve and the second curve.
In this embodiment, by dividing the first curve and the second curve into a plurality of stages, each stage includes a plurality of time instants, and each stage can be understood as that the first battery is in a constant operating condition state. Wherein, the shorter the time of each stage, the closer it can be to the working condition state of the first battery in practical application. After the first curve and the second curve are obtained, the state of charge, calendar time, depth of discharge, multiplying power and cycle number of the first battery in each stage can be calculated through the real-time current and the real-time temperature in each stage.
In another embodiment, as shown in fig. 2, step S120 includes steps S121, S122, S123, and S124.
S121, determining real-time current, real-time temperature and calendar time of the first battery at each stage according to the first curve and the second curve;
s122, generating the charge state of the first battery in each stage according to the charge state of the first battery at the initial moment and the real-time current in each stage;
s123, determining the multiplying power of the first battery in each stage according to the state of charge and the real-time current in each stage;
and S124, determining the discharge depth and the cycle number of the first battery in each stage according to the charge state in each stage.
In this embodiment, the calendar time at each stage may be determined according to the time at the corresponding stage and the factory time of the first battery. When the time in the corresponding stage is determined, if the time in the corresponding stage is short, any time in the corresponding stage can be used as an end point of the calendar time; if the time of the corresponding phase is longer, the middle time of the corresponding phase can be used as an end point of the calendar time.
The real-time current and the real-time temperature of each stage can be directly obtained through the first curve and the second curve respectively. When the state of charge of the first battery in each stage is generated, if the previous stage of the current stage is the initial moment, the real-time current of the current stage can be directly subjected to practical integration and the state of charge of the initial moment is added, so that the state of charge of the first battery in the current stage can be obtained; similarly, if the previous stage of the current stage is not the initial time, the real-time current at the current time can be directly subjected to practical integration and added to the charge state of the previous stage, so that the charge state of the first battery at the current stage can be obtained, and the like, so that the charge state of the first battery at each stage can be obtained. The state of charge of the first battery at each stage is calculated as:
therein, SOC 0 The state of charge at the initial moment of the first battery is n, the micro time of each stage is n, and the corresponding real-time current of each stage is i.
After the state of charge of the first battery at each stage is determined, the remaining capacity of the first battery at each stage can be determined, and then the multiplying power of the first battery at each stage can be obtained through the real-time current of the first battery at each stage, and the specific calculation formula can be as follows:
wherein i is the real-time current of the current stage, and Qi is the remaining capacity of the first battery at the current stage.
In addition, after determining the state of charge of the first battery at each stage, the distribution of the state of charge of the first battery at each stage as shown in fig. 3 can be obtained, and the depth of discharge and the number of cycles of the first battery at each stage can be determined by the distribution of the state of charge of the first battery.
In another embodiment, as shown in fig. 3, step S123 includes steps S1231 and S1232.
S1231, generating the charge state distribution of the first batteries according to the charge state at each stage, and determining the cycle number of each first battery at each stage according to the charge state distribution;
and S1232, generating the discharge depth of the first battery at the corresponding stage according to the difference value between the maximum charge state and the minimum charge state of the first battery at each stage.
In one embodiment, as shown in fig. 4, the first battery has three stages a to B, B to C, and C to D, which can be used as one stage of the first battery in the actual use process. As can be seen from FIG. 4, stages A-B are the discharge stage of the first cell, stages B-C are the charge stage of the first cell, and stages C-D are the discharge stage of the first cell. In order to predict the service life of the first battery more accurately, a to B, B to C, and C to D may be cycles of the first battery, that is, each charging stage or each discharging stage of the first battery in the actual application process is used as a cycle of the first battery, and after the state of charge of the first battery in each stage is determined, the cycle number of the first battery in each stage may be determined directly according to the distribution of the state of charge of the first battery in each stage.
It can be understood that, in the present application, the phases a to C may also be directly used as a cycle of the first battery, that is, a charge and discharge cycle, and the phases a to D may also be used as a cycle of the first battery, that is, two charge and discharge cycles are used as a cycle of the first battery, and the selection may be selected according to practical applications, and this embodiment is not limited in particular.
In this embodiment, each stage is taken as a constant working condition time of the first battery, so that after the state of charge of the first battery in each stage is determined, the discharge depth of the first battery in each stage can be directly calculated through the difference between the maximum state of charge and the minimum state of charge of the stage. The specific calculation formula can be as follows:
therein, SOC max For maximum state of charge, SOC, of the first battery in each stage min To the minimum state of charge, t, of the first cell in the corresponding phase 1 Is the initial time of the corresponding phase, t n Is the end time of the corresponding phase.
In addition, after the depth of discharge of the first cell at each stage is generated, the following program can also be programmed in Matlab to solve for the DOD distribution over time.
S130, generating a first capacity decay rate of the first battery in each stage according to the real-time temperature, the charge state and the calendar time in each stage in a calendar aging model under the constant working condition.
And S140, generating a second capacity attenuation rate of the first battery in each stage according to the real-time temperature, the discharge depth, the multiplying power and the cycle number in each stage in a cyclic aging model under the constant working condition.
Specifically, in the actual use process of the battery, two states exist, one of which is a storage state in which the battery is not subjected to current exchange, and the other is a charge-discharge state. The two states influencing the service life of the battery are equivalent to a calendar life attenuation factor and a cycle life attenuation factor respectively, so that after the real-time temperature, the charge state, the calendar time, the discharge depth, the multiplying power and the cycle frequency of the first battery in each stage are obtained, relevant parameters are input into a calendar aging model and a cycle aging model which are corresponding to constant working conditions respectively, a first capacity attenuation rate and a second capacity attenuation rate of the first battery in each stage can be generated, and further the total capacity attenuation rate of the first battery in each stage can be obtained.
In another embodiment, as shown in fig. 5, the steps of constructing the calendar aging model and the cycle aging model of the first battery under the constant condition include S210 and S220.
S210, acquiring calendar aging data of a second battery tested under a first parameter and cyclic aging data tested under a second parameter respectively; the first parameters comprise temperature, state of charge and calendar time, the second parameters comprise temperature, depth of discharge, multiplying power and cycle number, and the second battery and the first battery are the same type of battery;
and S220, respectively constructing the calendar aging model and the cyclic aging model according to the calendar aging data and the cyclic aging data.
In this embodiment, in order to predict the service life of the first battery more accurately, the second battery and the first battery used for constructing the calendar aging model and the cyclic aging model are of the same type, the calendar aging data is capacity fading data of the second battery tested at different temperatures, different states of charge and different calendar times, and the cyclic aging data is capacity fading data of the second battery tested at different temperatures, different depths of discharge, different multiplying powers and different cycle times. After calendar aging data tested by the second battery under the first parameter and cyclic aging data tested by the second battery under the second parameter are obtained, the calendar aging data and the cyclic aging data are fitted, and then a calendar aging model and a cyclic aging model can be constructed.
In another embodiment, as shown in fig. 6, step S220 includes steps S221, S222, and S223.
S221, fitting the calendar aging data and the cyclic aging data by adopting an exponential function to obtain a first fitting factor of the calendar aging data and a second fitting factor of the cyclic aging data;
s222, constructing the calendar aging model according to the first fitting factor and the first parameter;
and S223, constructing the cyclic aging model according to the second fitting factor and the second parameter.
In this embodiment, the first fitting factor is a plurality of numerical values obtained when the calendar aging data is fitted by using an exponential function, the second fitting factor is a plurality of numerical values obtained when the cyclic aging data is fitted by using an exponential function, and when the calendar aging data and the cyclic aging data are fitted by using an exponential function, software fitting such as Matlab and Origin can be adopted, and the fitting can be specifically selected according to actual application. Wherein the exponential function may be y = a x B When the exponential function is adopted to fit calendar aging data, the capacity attenuation amount is used as a dependent variable y, the calendar time is used as an independent variable x, and the temperature and the charge state can be used for fitting A and B in the exponential function, so that a first fitting factor of the calendar aging data can be obtained; when the exponential function is adopted to fit the cyclic aging data, the capacity attenuation is used as a dependent variable y, the cycle number is used as an independent variable x, and the temperature, the discharge depth and the multiplying power can be used for fitting A and B in the exponential function, so that a second fitting factor of the cyclic aging data can be obtained.
After the calendar aging data and the cyclic aging data are respectively fitted by adopting an exponential function to obtain a first fitting factor and a second fitting factor, a calendar aging equation can be constructed by the first fitting factor and the first parameter, and a cyclic aging equation can be constructed by the second fitting factor and the second parameter.
The equation for the calendar aging model may be:
wherein, Q1 loss A, b, c, d, e, f, g, h, i, j are first fitting factors, T is temperature, T is ref For reference temperature, SOC is state of charge and t is calendar time.
The equation for the cyclic aging model is:
wherein, Q2 loss K, l, m, n, o, p, q, r, s, u, v, w, x, y, z are second fitting factors, T is temperature, T is ref Is a reference temperature, C rate For magnification, DOD is depth of discharge and N is cycle number.
And S150, generating a total attenuation rate of the first battery in each stage according to the first capacity attenuation rate and the second capacity attenuation rate.
And S160, determining a life prediction curve of the first battery according to the total decay rate in each stage.
Specifically, after a first capacity decay rate and a second capacity decay rate of the first battery at each stage in the actual application process are generated, the first capacity decay rate and the second capacity decay rate are added to obtain a total decay rate of the first battery at each stage in the actual application process, and finally, a graph of the total decay rate of the first battery can be obtained through the total decay rate at each stage, so that the service life of the first battery is predicted.
In the method for predicting the service life of the battery provided by the embodiment of the invention, a first curve of the current of a first battery changing along with time and a second curve of the temperature changing along with time are obtained; generating real-time temperature, charge state, calendar time, discharge depth, multiplying power and cycle number of the first battery at each stage according to the first curve and the second curve; generating a first capacity decay rate of the first battery in each stage according to the real-time temperature, the charge state and the calendar time in each stage in a calendar aging model under a constant working condition; generating a second capacity decay rate of the first battery in each stage according to the real-time temperature, the discharge depth, the multiplying power and the cycle number in each stage in a cyclic aging model under a constant working condition; generating a total attenuation rate of the first battery in each stage according to the first capacity attenuation rate and the second capacity attenuation rate; the service life prediction curve of the first battery is determined according to the total attenuation rate at each stage, the service life of the battery can be accurately predicted under the variable working condition without adopting a fixed value discharge depth to predict the service life of the battery, and the discharge depth of the battery under the variable working condition is redefined and calculated at the same time, so that the service life of the battery in the actual application process can be reflected more truly, and the accuracy of the service life prediction of the battery is improved.
The embodiment of the invention also provides a device 100 for predicting the service life of the battery, which is used for executing any embodiment of the method for predicting the service life of the battery.
Specifically, referring to fig. 7, fig. 7 is a schematic block diagram of a battery life predicting apparatus 100 according to an embodiment of the present invention.
As shown in fig. 7, the battery life prediction apparatus 100 includes: a first acquisition unit 110, a first generation unit 120, a second generation unit 130, a third generation unit 140, a fourth generation unit 150, and a first determination unit 160.
The first obtaining unit 110 is configured to obtain a first curve of a current of the first battery over time and a second curve of a temperature of the first battery over time.
The first generating unit 120 is configured to generate a real-time temperature, a state of charge, a calendar time, a discharge depth, a magnification, and a cycle number of the first battery at each stage according to the first curve and the second curve.
In other inventive embodiments, the first generating unit 120 includes: the device comprises a second determining unit, a fifth generating unit, a third determining unit and a fourth determining unit.
The second determining unit is used for determining the real-time current, the real-time temperature and the calendar time of the first battery at each stage according to the first curve and the second curve; the fifth generating unit is used for generating the charge state of the first battery in each stage according to the charge state of the first battery at the initial moment and the real-time current in each stage; the third determining unit is used for determining the multiplying power of the first battery in each stage according to the charge state and the real-time current in each stage; and the fourth determining unit is used for determining the discharge depth and the cycle number of the first battery in each stage according to the charge state in each stage.
In other inventive embodiments, the fourth determination unit includes: a fifth determining unit and a sixth generating unit.
A fifth determining unit, configured to generate a state of charge distribution of the first battery according to the state of charge at each stage, and determine, according to the state of charge distribution, a cycle number of each first battery at each stage; and the sixth generating unit is used for generating the discharge depth of the first battery in the corresponding stage according to the difference value between the maximum charge state and the minimum charge state of the first battery in each stage.
And the second generating unit 130 is used for generating a first capacity fading rate of the first battery in each stage according to the real-time temperature, the state of charge and the calendar time in each stage in the calendar aging model under the constant working condition.
And a third generating unit 140, configured to generate a second capacity fading rate of the first battery at each stage in a cyclic aging model under a constant working condition according to the real-time temperature, the depth of discharge, the rate of magnification, and the number of cycles at each stage.
In other embodiments of the present invention, the apparatus 100 for predicting battery life further comprises: the device comprises a second acquisition unit and a first construction unit.
The second acquisition unit is used for acquiring calendar aging data tested by the second battery under the first parameter and cyclic aging data tested by the second parameter; the first parameters comprise temperature, state of charge and calendar time, the second parameters comprise temperature, depth of discharge, multiplying power and cycle times, and the second battery and the first battery are the same type of battery; and the first construction unit is used for respectively constructing the calendar aging model and the cyclic aging model according to the calendar aging data and the cyclic aging data.
In other inventive embodiments, the first building element comprises: the device comprises a fitting unit, a second construction unit and a third construction unit.
The fitting unit is used for respectively fitting the calendar aging data and the cyclic aging data by adopting an exponential function to obtain a first fitting factor of the calendar aging data and a second fitting factor of the cyclic aging data; the second construction unit is used for constructing the calendar aging model according to the first fitting factor and the first parameter; and the third constructing unit is used for constructing the cyclic aging model according to the second fitting factor and the second parameter.
A fourth generating unit 150, configured to generate a total decay rate of the first battery at each stage according to the first capacity decay rate and the second capacity decay rate.
A first determining unit 160, configured to determine a life prediction curve of the first battery according to the total decay rate at each stage.
The battery life prediction apparatus 100 according to the embodiment of the present invention is configured to obtain a first curve of a current of a first battery changing with time and a second curve of a temperature changing with time; generating real-time temperature, charge state, calendar time, discharge depth, multiplying power and cycle number of the first battery at each stage according to the first curve and the second curve; generating a first capacity decay rate of the first battery in each stage according to the real-time temperature, the charge state and the calendar time in each stage in a calendar aging model under a constant working condition; generating a second capacity decay rate of the first battery in each stage according to the real-time temperature, the discharge depth, the multiplying power and the cycle number in each stage in a cyclic aging model under a constant working condition; generating a total attenuation rate of the first battery in each stage according to the first capacity attenuation rate and the second capacity attenuation rate; determining a life prediction curve of the first battery according to the total decay rate at each stage.
Referring to fig. 8, fig. 8 is a schematic block diagram of an electronic device according to an embodiment of the invention.
Referring to fig. 8, the device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a storage medium 503 and an internal memory 504.
The storage medium 503 may store an operating system 5031 and computer programs 5032. The computer program 5032, when executed, may cause the processor 502 to perform a method of predicting battery life.
The processor 502 is used to provide computing and control capabilities that support the operation of the overall device 500.
The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 may be caused to perform a method for predicting battery life.
The network interface 505 is used for network communication, such as providing transmission of data information. Those skilled in the art will appreciate that the configuration shown in fig. 8 is a block diagram of only a portion of the configuration associated with aspects of the present invention and does not constitute a limitation of the apparatus 500 to which aspects of the present invention may be applied, and that a particular apparatus 500 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
Wherein the processor 502 is configured to execute the computer program 5032 stored in the memory to perform the following functions: acquiring a first curve of the current of the first battery changing along with time and a second curve of the temperature changing along with time; generating real-time temperature, state of charge, calendar time, depth of discharge, multiplying power and cycle number of the first battery in each stage according to the first curve and the second curve; generating a first capacity decay rate of the first battery in each stage according to the real-time temperature, the charge state and the calendar time in each stage in a calendar aging model under a constant working condition; generating a second capacity decay rate of the first battery in each stage according to the real-time temperature, the discharge depth, the multiplying power and the cycle number in each stage in a cyclic aging model under a constant working condition; generating a total attenuation rate of the first battery in each stage according to the first capacity attenuation rate and the second capacity attenuation rate; determining a life prediction curve of the first battery according to the total decay rate at each stage.
Those skilled in the art will appreciate that the embodiment of the apparatus 500 illustrated in fig. 8 does not constitute a limitation on the specific construction of the apparatus 500, and in other embodiments, the apparatus 500 may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components. For example, in some embodiments, the apparatus 500 may only include the memory and the processor 502, and in such embodiments, the structure and function of the memory and the processor 502 are the same as those of the embodiment shown in fig. 8, and are not repeated herein.
It should be understood that, in the embodiment of the present invention, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 may also be other general-purpose processors 502, a Digital Signal Processor (DSP) 502, an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The general-purpose processor 502 may be a microprocessor 502 or the processor 502 may be any conventional processor 502 or the like.
In another embodiment of the present invention, a computer storage medium is provided. The storage medium may be a nonvolatile computer-readable storage medium or a volatile storage medium. The storage medium stores a computer program 5032, wherein the computer program 5032 when executed by the processor 502 performs the steps of: acquiring a first curve of the current of the first battery changing along with time and a second curve of the temperature changing along with time; generating real-time temperature, charge state, calendar time, discharge depth, multiplying power and cycle number of the first battery at each stage according to the first curve and the second curve; generating a first capacity decay rate of the first battery in each stage according to a calendar aging model of the real-time temperature, the charge state and the calendar time in each stage under a constant working condition; generating a second capacity decay rate of the first battery in each stage according to the real-time temperature, the discharge depth, the multiplying power and the cycle number in each stage in a cyclic aging model under a constant working condition; generating a total attenuation rate of the first battery in each stage according to the first capacity attenuation rate and the second capacity attenuation rate; determining a life prediction curve of the first battery according to the total decay rate at each stage.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses, devices and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly 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 technical 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 invention.
In the embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only a logical division, and there may be other divisions when the actual implementation is performed, or units having the same function may be grouped into one unit, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a device 500 (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for predicting battery life, comprising:
acquiring a first curve of the current of a first battery along with the change of time and a second curve of the temperature along with the change of time;
generating real-time temperature, state of charge, calendar time, depth of discharge, multiplying power and cycle number of the first battery in each stage according to the first curve and the second curve;
generating a first capacity decay rate of the first battery in each stage according to a calendar aging model of the real-time temperature, the charge state and the calendar time in each stage under a constant working condition;
generating a second capacity decay rate of the first battery in each stage according to the real-time temperature, the discharge depth, the multiplying power and the cycle number in each stage in a cyclic aging model under a constant working condition;
generating a total attenuation rate of the first battery in each stage according to the first capacity attenuation rate and the second capacity attenuation rate;
determining a life prediction curve of the first battery according to the total decay rate at each stage.
2. The method for predicting the life of a battery according to claim 1, wherein the method for constructing the calendar aging model and the cyclic aging model comprises:
acquiring calendar aging data of a second battery respectively tested under a first parameter and cyclic aging data tested under a second parameter; the first parameters comprise temperature, state of charge and calendar time, the second parameters comprise temperature, depth of discharge, multiplying power and cycle times, and the second battery and the first battery are the same type of battery;
and respectively constructing the calendar aging model and the cyclic aging model according to the calendar aging data and the cyclic aging data.
3. The method for predicting battery life according to claim 2, wherein the building the calendar aging model and the cycle aging model according to the calendar aging data and the cycle aging data respectively comprises:
fitting the calendar aging data and the cyclic aging data by adopting an exponential function to obtain a first fitting factor of the calendar aging data and a second fitting factor of the cyclic aging data;
constructing the calendar aging model according to the first fitting factor and the first parameter;
and constructing the cyclic aging model according to the second fitting factor and the second parameter.
4. The method of predicting battery life according to claim 3, wherein the equation of the calendar aging model is:
wherein, Q1 loss A, b, c, d, e, f, g, h, i, j are first fitting factors, T is temperature, T is ref For reference temperature, SOC is state of charge and t is calendar time.
5. The method of predicting battery life according to claim 3, wherein the equation of the cyclic aging model is:
wherein, Q2 loss K, l, m, n, o, p, q, r, s, u, v, w, x, y, z are second fitting factors, T is temperature, T is ref Is a reference temperature, C rate For magnification, DOD is depth of discharge and N is cycle number.
6. The method for predicting the life of the battery according to any one of claims 1 to 5, wherein the step of generating the real-time temperature, the state of charge, the calendar time, the depth of discharge, the rate and the cycle number of the first battery at each stage according to the first curve and the second curve comprises the following steps:
determining the real-time current, the real-time temperature and the calendar time of the first battery at each stage according to the first curve and the second curve;
generating the charge state of the first battery at each stage according to the charge state of the first battery at the initial moment and the real-time current at each stage;
determining the multiplying power of the first battery in each stage according to the state of charge and the real-time current in each stage;
and determining the discharge depth and the cycle number of the first battery in each stage according to the charge state in each stage.
7. The method of claim 6, wherein said determining depth of discharge and number of cycles of said first battery at each stage from said state of charge at each stage comprises:
generating a state of charge distribution of the first battery according to the state of charge at each stage, and determining the cycle number of each first battery at each stage according to the state of charge distribution;
and generating the discharge depth of the first battery at the corresponding stage according to the difference value between the maximum charge state and the minimum charge state of the first battery at each stage.
8. An apparatus for predicting battery life, comprising:
the first acquisition unit is used for acquiring a first curve of the current of the first battery along with time change and a second curve of the temperature along with time change;
the first generation unit is used for generating the real-time temperature, the state of charge, the calendar time, the discharge depth, the multiplying power and the cycle number of the first battery in each stage according to the first curve and the second curve;
the second generation unit is used for generating a first capacity decay rate of the first battery in each stage according to the real-time temperature, the charge state and the calendar time in each stage in a calendar aging model under a constant working condition;
the third generation unit is used for generating a second capacity decay rate of the first battery in each stage in a cyclic aging model under a constant working condition according to the real-time temperature, the discharge depth, the multiplying power and the cycle number in each stage;
a fourth generating unit, configured to generate a total decay rate of the first battery at each stage according to the first capacity decay rate and the second capacity decay rate;
a first determining unit, configured to determine a life prediction curve of the first battery according to the total decay rate at each stage.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of predicting battery life of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, causes the processor to carry out the method of predicting battery life according to any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116908705A (en) * | 2023-09-12 | 2023-10-20 | 宁德时代新能源科技股份有限公司 | Capacity decay model building method, battery cycle life testing method and device |
| CN117169733A (en) * | 2023-11-01 | 2023-12-05 | 车城智能装备(武汉)有限公司 | Power battery monitoring method, system, equipment and storage medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116908705A (en) * | 2023-09-12 | 2023-10-20 | 宁德时代新能源科技股份有限公司 | Capacity decay model building method, battery cycle life testing method and device |
| CN116908705B (en) * | 2023-09-12 | 2024-02-23 | 宁德时代新能源科技股份有限公司 | Capacity decay model building method, battery cycle life testing method and device |
| CN117169733A (en) * | 2023-11-01 | 2023-12-05 | 车城智能装备(武汉)有限公司 | Power battery monitoring method, system, equipment and storage medium |
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