CN112379297A - Battery system service life prediction method, device, equipment and storage medium - Google Patents
Battery system service life prediction method, device, equipment and storage medium Download PDFInfo
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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Abstract
The invention discloses a method, a device, equipment and a storage medium for predicting the service life of a battery system, and belongs to the field of batteries. The battery system life prediction method comprises the following steps: carrying out cycle life test on a battery pack in a battery system to obtain a plurality of first data sets; testing the storage life of the battery pack to obtain a plurality of second data sets; performing final service life prediction on the battery system according to the plurality of first data sets, the plurality of second data sets and the service life prediction model of the battery system to obtain service life prediction data of the battery system; the battery system service life prediction model comprises a battery electric model, a battery thermal model, a battery pressure model and a battery aging model. The method for predicting the service life of the battery system can eliminate the influence of the use environment on the prediction data, and improves the accuracy of predicting the service life of the battery system.
Description
Technical Field
The present invention relates to the field of batteries, and in particular, to a method, an apparatus, a device, and a storage medium for predicting a battery system life.
Background
With the development of electric tools (such as electric trains of electric automobiles, electric bicycles, and the like), the prediction of the health state and the service life of a battery system becomes important, and at present, the method for predicting the service life of the battery system cannot eliminate the interference of the difference of the use environments on the service life prediction of the battery system, so that the accuracy of the service life prediction of the battery system is influenced.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a method for predicting the service life of a battery system, which can predict the service life of the battery system according to the physical characteristics of a battery, eliminate the influence of the use environment on predicted data and improve the accuracy of service life prediction of the battery system.
The invention also provides a battery system service life prediction device with the battery system service life prediction method.
The invention also provides a battery system service life prediction device with the battery system service life prediction method.
The invention also provides a computer readable storage medium with the battery system service life prediction method.
According to the battery system life prediction method of the embodiment of the first aspect of the invention, the method comprises the following steps:
carrying out cycle life test on a battery pack in a battery system to obtain a plurality of first data sets;
testing the storage life of the battery pack to obtain a plurality of second data sets;
performing final service life prediction on the battery system according to the plurality of first data sets, the plurality of second data sets and a battery system service life prediction model to obtain service life prediction data of the battery system;
the battery system service life prediction model comprises a battery electric model, a battery thermal model, a battery pressure model and a battery aging model.
The method for predicting the service life of the battery system provided by the embodiment of the invention at least has the following beneficial effects: the method for predicting the service life of the battery system performs cycle service life test and storage service life test on the battery pack, establishes a battery system service life prediction model by using the physical characteristics of the battery, performs final service life prediction on the battery system according to the data obtained by the test and the battery system service life prediction model, can eliminate the influence of the service environment on the predicted data, and improves the accuracy of service life prediction of the battery system.
According to some embodiments of the invention, the first data set includes a third data set and a fourth data set, and the cycle life testing of the battery pack in the battery system to obtain the first data sets includes:
performing standard cycle test on a target battery of the battery pack to obtain a plurality of third data sets;
and carrying out composite pulse current test on the target battery to obtain a plurality of fourth data sets.
According to some embodiments of the invention, the performing the storage life test on the battery pack to obtain a plurality of second data sets comprises:
and carrying out standard capacity test on the target battery to obtain a plurality of second data sets.
According to some embodiments of the invention, the battery electrical model is an n-order equivalent circuit model, and the first data set comprises an open-circuit voltage OCV, an internal resistance R, an initial state of charge SOC of the target battery0The second data set comprises the factory rated capacity C of the target battery0The predicting the final service life of the battery system according to the plurality of first data sets, the plurality of second data sets and the battery system service life prediction model to obtain service life prediction data of the battery system includes:
calculating to obtain the terminal voltage Ut, the state of charge SOC, the current I and the equivalent resistance R of the target battery according to the first data sets, the second data sets and the equivalent circuit of the battery electric model1And an equivalent resistance R1Voltage U acrossR1。
According to some embodiments of the present invention, the first data set includes a battery mass m, a battery specific heat capacity Cp, a heat exchange coefficient h, an ambient temperature Tamb, and a battery heat exchange area s of the target battery, and the predicting the final life of the battery system according to the first data sets, the second data sets, and a battery system life prediction model to obtain life prediction data of the battery system includes:
calculating the temperature T of the target battery according to the first data sets and a first formula of the thermal model of the battery;
the first formula is: m Cp dT/dT Qirev + Qrev + Qtran;
wherein Qirev is irreversible heat of the target battery, and Qirev is I2R+R1*(I-dUR1/dt)2；
Qrev is the reversible heat of the target cell, Qrev ═ I × T × dcv/dt (soc);
qtran is the heat exchange of the target cell, Qtran ═ h ═ s (Tamb-T).
According to some embodiments of the present invention, the first data set includes an accumulated discharge power Ah of the target battery, a temperature difference Δ T during a cycle, a battery gasket stiffness cs, a battery gasket elastic coefficient ks, an initial battery metal casing thickness L, and a thickness change β of the target battery during a charge/discharge process, and the final life prediction of the battery system is performed according to the first data sets, the second data sets, and a battery system life prediction model to obtain life prediction data of the battery system, including:
calculating a pressure value Force of the target battery according to the plurality of first data sets and a second formula of the battery pressure model;
the second formula is: force ═ f (Δ T)/(soc)/(Ah) · (1-cs · Δ T) (β × L · Δ T) · Ahn。
According to some embodiments of the present invention, the first data set includes a battery coefficient α, an activation energy Ea, and an accumulated discharge electric quantity Ah of the target battery, the second data set includes a constant n, a constant a, and a natural logarithm exp, and the final life prediction of the battery system according to the first data sets, the second data sets, and a battery system life prediction model to obtain life prediction data of the battery system includes:
calculating the state of health (SOH) of the target battery according to the first data sets, the second data sets and a third formula of the battery aging model;
the third formula is: SOH 1-Qloss, said Qloss being the capacity loss of said target battery;
wherein Qloss is Qcycloss + Qcalendarlos, Qcycloss is the cycle life of the target battery, and Qcalendarlos is the calendar life of the target battery;
Qcycleloss＝α*exp(-Ea/(273+T))*(Ah)z；Qcalendarloss＝A*f(SOC)*(day)*n。
a battery system life prediction apparatus according to an embodiment of the second aspect of the present invention includes:
the first acquisition module is used for carrying out cycle life test on the battery pack in the battery system to obtain a plurality of first data sets;
the second acquisition module is used for carrying out storage life test on the battery pack to obtain a plurality of second data sets;
and the prediction module is used for predicting the final service life of the battery system according to the plurality of first data sets, the plurality of second data sets and the service life prediction model of the battery system to obtain service life prediction data of the battery system.
The device for predicting the service life of the battery system, provided by the embodiment of the invention, has the following beneficial effects: according to the battery system service life prediction device, the first acquisition module and the second acquisition module perform cycle service life test and storage service life test on the battery pack to obtain a plurality of data sets, a battery system service life prediction model is established by using the physical characteristics of the battery, and the prediction module performs final service life prediction on the battery system service life according to the data of the data sets and the battery system service life prediction model, so that service life prediction data are obtained, the influence of the service environment on the prediction result can be eliminated, and the accuracy of service life prediction of the battery system is improved.
According to some embodiments of the invention, the prediction module comprises:
the third acquisition module is used for acquiring a plurality of initial data sets of the battery system;
and the computing module is used for predicting the service life of the battery system according to the plurality of initial data sets, the plurality of first data sets, the plurality of second data sets and the service life prediction model of the battery system to obtain a service life prediction result of the battery system.
A battery system life prediction apparatus according to an embodiment of the third aspect of the present invention includes: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method for battery system life prediction as described above in the embodiments of the first aspect when executing the computer program.
The battery system service life prediction device provided by the embodiment of the invention at least has the following beneficial effects: the battery system service life prediction device performs cycle service life test and storage service life test on the battery pack by implementing the battery system service life prediction method of the first aspect, establishes a battery system service life prediction model by using the physical characteristics of the battery, and performs final service life prediction on the battery system according to the data obtained by the test and the battery system service life prediction model, thereby eliminating the influence of the service environment on the prediction data and improving the accuracy of the battery system service life prediction device.
According to a fourth aspect of the present invention, there is provided a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method for predicting battery system life as described in the first aspect.
The computer-readable storage medium according to the embodiment of the invention has at least the following advantages: the computer-readable storage medium enables a computer to execute the battery system service life prediction method of the first aspect to perform cycle service life test and storage service life test on the battery pack by sending computer-executable instructions, establishes a battery system service life prediction model by using the physical characteristics of the battery, and performs final service life prediction on the battery system according to the data obtained by the test and the battery system service life prediction model, so that the influence of the service environment on the predicted data is eliminated, and the accuracy of the service life prediction of the battery system is improved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a method for predicting battery system life in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart of a method for predicting the life of a battery system according to another embodiment of the present invention;
FIG. 3 is a circuit diagram of an electrical model of a battery for a method of predicting the life of a battery system in accordance with one embodiment of the present invention;
FIG. 4 is a schematic diagram of a life prediction method for a battery system according to another embodiment of the present invention;
fig. 5 is a schematic structural diagram of a battery system life prediction apparatus according to an embodiment of the present invention;
reference numerals: 400. a battery system; 410. a battery electrical model; 420. a battery pressure model; 430. a battery thermal model; 440. a battery aging model; 450. a target battery; 510. a first acquisition module; 520. a second acquisition module; 530. and a prediction module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It should be noted that although functional blocks are partitioned in a schematic diagram of an apparatus and a logical order is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the partitioning of blocks in the apparatus or the order in the flowchart. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
In a first aspect, referring to fig. 1, a method for predicting a life of a battery system according to an embodiment of the present invention includes:
s101, carrying out cycle life test on a battery pack in a battery system to obtain a plurality of first data sets;
s102, testing the storage life of the battery pack to obtain a plurality of second data sets;
s103, performing final service life prediction on the battery system according to the first data sets, the second data sets and the service life prediction model of the battery system to obtain service life prediction data of the battery system;
the battery system service life prediction model comprises a battery electric model, a battery thermal model, a battery pressure model and a battery aging model.
The battery pack in the battery system is generally composed of a plurality of target batteries in a series or parallel connection mode, one or more target batteries are selected, a battery system service life prediction model corresponding to the target batteries is established by using the physical characteristics of the batteries, and the battery pack in the battery system is subjected to cycle service life test, namely the service life of the charge-discharge cycle of the batteries is detected to obtain a plurality of first data sets, wherein the first data sets comprise the pressure, the temperature, the electric quantity, the battery thickness change condition, the battery open-circuit voltage, the internal resistance, the battery quality, the battery specific heat capacity and the like of the target batteries in the test process; and finally predicting the service life of the battery system according to the plurality of first data sets, the plurality of second data sets and the service life prediction model of the battery system obtained by testing to obtain service life prediction data of the battery system. The battery system service life prediction models comprise a battery electric model, a battery thermal model, a battery pressure model and a battery aging model, and the battery system service life prediction models are mutually influenced and can integrally reflect the health state of a target battery, so that the health state of the battery system is integrally reflected.
The method for predicting the service life of the battery system performs cycle service life test and storage service life test on the battery pack, establishes a battery system service life prediction model by using the physical characteristics of the battery, performs final service life prediction on the battery system according to the data obtained by the test and the battery system service life prediction model, can eliminate the influence of the service environment on the predicted data, and improves the accuracy of service life prediction of the battery system.
Referring to fig. 2, in some embodiments, the first data set includes a third data set and a fourth data set, and step S101, a cycle life test is performed on a battery pack in the battery system to obtain a plurality of first data sets, including:
s201, performing standard cycle test on a target battery of the battery pack to obtain a plurality of third data sets;
s202, carrying out composite pulse current test on the target battery to obtain a plurality of fourth data sets.
When a cycle life test is performed on a target battery, pressure sensors are connected to both the front side and the back side of the surface of a battery core of the target battery, thermocouples are connected to the surfaces of a battery core pole and the battery core, and it should be noted that the pressure sensors can be installed in the center of the surface of the battery core so as to more accurately sense pressure changes inside the battery core, and can also be installed at other positions of the surface of the battery core, without being limited thereto; the precision of the pressure sensor and the thermocouple can be selected according to the actual situation, and the flexibility is higher.
In some embodiments, a set of cycle temperatures is preset in a standard cycle test process of a target battery, where the cycle temperatures include at least two different temperature values (the temperature values may be valued according to actual conditions), and then charging and discharging conditions of the target battery at a set temperature are recorded until the target battery life is terminated, that is, the target battery capacity is smaller than a preset threshold, and a plurality of third data sets are recorded, including target battery pressure, temperature, electric quantity, voltage, and battery thickness variation, accumulated discharge electric quantity, battery quality, ambient temperature, and the like in the cycle test process. For example, the cycle temperature may be set at 45 ℃, 25 ℃ and 10 ℃, the target battery capacity threshold is selected to be 80% of the rated capacity of the battery, and then the target battery is subjected to a charge and discharge test to obtain a plurality of first data sets so as to evaluate the cycle life of the target battery, thereby predicting the life of the battery system as a whole.
Further, in the process of testing the cycle life of the target battery, composite pulse battery testing is performed on the target battery at intervals of certain charging and discharging times so as to obtain the power performance of the target battery, and a plurality of fourth data sets are obtained, wherein the fourth data sets comprise data such as open-circuit voltage and direct-current internal resistance of the target battery. For example, the time interval of the composite pulse battery test is set to be one test for every 200 times of charging and discharging of the target battery to acquire relevant data.
In some embodiments, step S102, performing a storage life test on the battery pack to obtain a plurality of second data sets, including:
and carrying out standard capacity test on the target battery to obtain a plurality of second data sets.
In the process of testing the storage life of a target battery, a group of state of charge values (battery residual capacity) is preset, wherein at least one state of charge value is included, a plurality of target batteries in corresponding states of charge are selected, the target batteries are stored at preset cycle temperature (the cycle temperature corresponds to the temperature for testing the cycle life of the batteries), standard capacity testing and state of charge adjustment are carried out on the target batteries at certain intervals, the process of testing the storage life of the batteries lasts for certain time, and in the process, a plurality of second data sets including the pressure change, the thickness change, the rated capacity and the like of the target batteries are recorded and obtained.
The preset SOC values, the interval time, and the duration may be set according to actual conditions, for example, for a target battery with a SOC of 97%, 50%, or 10%, the target batteries with three SOC are stored at a temperature of 45 ℃, 25 ℃, or 10 ℃, a standard capacity test and SOC adjustment are performed every 30 days, the battery storage life test process lasts 360 days, and during this period, data such as pressure change, thickness change, and rated capacity of the target battery are recorded, so as to evaluate the storage life of the target battery, thereby predicting the life of the battery system as a whole.
It should be noted that the first data sets and the second data sets include a series of data obtained from a life test of the target battery, and also include parameters of the target battery itself, environmental parameters, and the like, for example, a coefficient of elasticity of the battery pad, a rigidity of the battery pad, an ambient temperature, and the like.
In some embodiments, the battery electrical model is an n-order equivalent circuit model, and the first data set includes an open-circuit voltage OCV, an internal resistance R, and an initial state of charge SOC of the target battery0The second data set comprises the factory rated capacity C of the target battery0Step S103, performing final life prediction on the battery system according to the plurality of first data sets, the plurality of second data sets, and the battery system life prediction model to obtain life prediction data of the battery system, including:
calculating to obtain the terminal voltage Ut, the state of charge SOC, the current I and the equivalent resistance R of the target battery according to the plurality of first data sets, the plurality of second data sets and the equivalent circuit of the battery electric model1And an equivalent resistance R1Voltage U acrossR1。
The battery electric model is an n-order equivalent circuit model, can meet the calculation of the battery charge state under various conditions, has good applicability, and obtains the terminal voltage Ut, the charge state SOC, the current I and the equivalent resistance R of the target battery through calculation according to the plurality of first data sets, the plurality of second data sets and the equivalent circuit of the battery electric model1And an equivalent resistance R1Voltage U acrossR1And the final service life of the battery system can be well predicted by utilizing the electrical characteristics of the battery.
Referring to fig. 3, taking a battery electrical model as a first-order RC equivalent circuit model as an example, in the process of predicting the service life of the battery system by using the battery electrical model, composite pulse battery test data included in several first data sets, that is, the open-circuit voltage OCV and the internal resistance R of the target battery are obtained, where the open-circuit voltage is a function related to the state of charge SOC, that is, OCV ═ f (SOC). In the circuit structure of the battery electric model, a first resistor R1 and a first capacitor C1 are connected in parallel to form a first loop, an internal resistor R and the first loop are connected in series in the battery loop, an open-circuit voltage OCV, the internal resistor R, a first resistor R1 and a first capacitor C1 are used as input, and a battery terminal voltage Ut and an equivalent resistor R can be obtained through the battery electric model1Voltage U acrossR1。
In the battery electric model, an ampere-hour integration method is adopted to calculate the state of charge SOC of the battery, namely SOC is equal to { SOC }0*C0+∫(η*I)dt}/(SOH*C0) Wherein, SOC0Is the initial state of charge of the target battery, C0The factory rated capacity of the target battery is shown, I is the current of the target battery, eta is the coulomb efficiency of the target battery, and SOH is the state of health of the target battery.
The battery SOC obtained through the formula is the SOC of the target battery, and the method is simple in calculation and saves time.
The state of charge of the battery may be calculated by an open circuit voltage method, an ampere-hour integration method, an internal resistance method, a neural network, a kalman filter method, or the like, but is not limited thereto.
In some embodiments, the first data set includes a battery mass m, a battery specific heat capacity Cp, a heat exchange coefficient h, an ambient temperature Tamb, and a battery heat exchange area S of the target battery, and the step S103 is performed to predict a final life of the battery system according to the plurality of first data sets, the plurality of second data sets, and the battery system life prediction model, so as to obtain life prediction data of the battery system, including:
calculating the temperature T of the target battery according to a plurality of first data sets and a first formula of a thermal model of the battery;
the first formula is: m Cp dT/dT Qirev + Qrev + Qtran;
wherein, Qirev is the irreversible heat of the target battery, and is I2R+R1*(I-dUR1/dt)2；
Qrev is the reversible heat of the target cell, Qrev ═ I × T × dcv/dt (soc);
qtran is the heat exchange of the target cell, and Qtran ═ h ═ s (Tamb-T).
The method can well predict the service life of the battery system by utilizing the thermal characteristics of the battery, eliminates the interference of different temperature environments on the service life prediction of the battery system, and improves the accuracy of the service life prediction of the battery system.
In some embodiments, the first data set includes an accumulated discharge electric quantity Ah of the target battery, a temperature difference Δ T during a cycle, a battery gasket stiffness cs, an elastic coefficient ks of the battery gasket, an initial thickness L of the battery metal casing, and a thickness change β of the target battery during a charge and discharge process, and step S103 is performed to predict a final life of the battery system according to the first data sets, the second data sets, and the battery system life prediction model, so as to obtain life prediction data of the battery system, including:
calculating a pressure value Force of the target battery according to the plurality of first data sets and a second formula of the battery pressure model;
the second formula is: force ═ f (Δ T)/(soc)/(Ah) · (1-cs · Δ T) (β × L · Δ T) · Ahn
Note that β is a thickness change of the target battery during charge and discharge, and can be regarded as a state of charge SOC function (that is, β ═ f (SOC)) during charge and discharge of the target battery. Therefore, the first pressure value Force of the target battery can be obtained by calculating according to the series of data and the second formula, so that the service life of the battery system can be well predicted by utilizing the surface pressure characteristic of the battery, the interference of different pressure environments on the service life prediction of the battery system is eliminated, and the service life prediction accuracy of the battery system is improved.
In some embodiments, the first data set includes a battery coefficient α, an activation energy Ea, and an accumulated discharge electric quantity Ah of the target battery, the second data set includes a constant n, a constant a, and a natural logarithm exp, and step S103 is performed to predict a final life of the battery system according to the first data sets, the second data sets, and the life prediction model of the battery system, so as to obtain life prediction data of the battery system, including:
calculating the state of health (SOH) of the target battery according to the first data sets, the second data sets and a third formula of a battery aging model;
the third formula is: SOH 1-Qloss, Qloss being the capacity loss of the target battery;
wherein Qloss is Qcyclios + Qcalendarlos, Qcyclios is the cycle life of the target battery, and Qcalendarlos is the calendar life of the target battery;
Qcycleloss＝α*exp(-Ea/(273+T))*(Ah)z；Qcalendarloss＝A*f(SOC)*(day)n。
the SOH of the target battery is calculated according to a plurality of first data sets obtained through a battery cycle life test, a plurality of second data sets obtained through a battery storage life test, a plurality of data calculated through a battery electric model and a third formula, the SOH of the target battery is calculated through a battery aging model, the aging degree of the target battery can be reflected visually, therefore, the final life prediction of the battery system is carried out on the whole, and the accuracy of the life prediction is improved.
In some embodiments, the capacity loss of the target battery is the sum of the cycle life of the target battery and the calendar life of the target battery. The capacity loss Qloss of the battery system is regarded as the superposition of the battery cycle life qcycloss and the battery calendar life qcalendarlos, that is, Qloss ═ qcycloss + qcalendarlos, and it should be noted that the cycle life of the target battery refers to the life of the charge-discharge cycle of the target battery, and the calendar life of the battery refers to the life time of the target battery itself.
Further, the cycle life calculation formula of the target battery is as follows:
Qcycleloss＝α*exp(-Ea/(273+T))*(Ah)z(ii) a Wherein T is the temperature of the target battery, Ah is the accumulated discharge electric quantity of the target battery, alpha, Ea and z are coefficients, alpha is the depth of discharge (DOD) and is the charge-discharge timeRate, etc., Ea being the activation energy;
the calendar life calculation formula of the target battery is as follows:
Qcalendarloss＝A*f(SOC)*(day)n；
it should be noted that the constants n, a, and the natural logarithm exp in the state of health calculation of different target batteries are different, and the initial cycle life qcycloss obtained by performing the life cycle test and the storage life test on a plurality of target batteries is used0And initial calendar life Qcalendarlos0And (6) calculating.
The cycle life, the calendar life and the capacity loss of the battery system of the target battery can be calculated through the formulas, the state of health (SOH) of the target battery is further obtained according to a third formula (SOH-1-Qloss), the aging degree of the target battery is further obtained, and the aging degree of the battery system can be predicted and evaluated by integrally evaluating the aging degrees of a plurality of target batteries of the battery pack of the battery system.
Referring to fig. 4, in some embodiments, the initial open circuit voltage OCV of the battery pack in the battery system is obtained from a barcode of a factory battery or the battery system0Initial internal resistance R0Rated capacity C0And the initial data, the record is stored in the first data set so as to be available at any time. Meanwhile, the battery pack in the battery system is subjected to cycle life test and storage life test to obtain a plurality of first data sets and a plurality of second data sets, such as battery mass m, battery specific heat capacity Cp, heat exchange coefficient h, thickness change beta of a target battery in the charging and discharging process, and initial state of charge SOC0Etc. obtaining the initial open circuit voltage OCV0Initial internal resistance R0Rated capacity C0The parameters are used as input condition parameters of the battery electric model, calculation processing is carried out in the battery electric model, dynamic change parameters of the target battery, namely a new battery state of charge (SOC), a new open-circuit voltage (OCV), an internal resistance (R) and the like are updated, and then the battery thermal model, the battery pressure model and the battery aging model are subjected to calculation processing according to the updated dynamic change parameters and the data in the first data set and the second data setAnd calculating the temperature T, the pressure value Force and the state of health SOH of the target battery so as to further obtain the final life prediction data of the battery system.
The battery system is formed by connecting m × n target batteries in series or in parallel, the service life of the battery system can be regarded as the interaction among a plurality of target batteries, and the interaction results, namely the service life SOH of the battery system is integrally embodied by the service life SOH of the target batteries, each target battery is provided with a battery system service life prediction model corresponding to the target battery, the target battery service life prediction model comprises a battery electric model, a battery thermal model, a battery pressure model and a battery aging model, certain differences (such as temperature T, state of charge SOC, internal resistance R and the like) may exist in battery model parameters of different target batteries, the temperature, the pressure, the state of charge and the like of the target battery are dynamically changed in the aging process of the battery system, and the aging process of the battery system and the service life condition of the target battery can be predicted by calculating the changes of the parameters.
For example, fig. 4, the initial open circuit voltage OCV of the battery pack is obtained from the battery system 4000Initial internal resistance R0Rated capacity C0Selecting the target battery 450 to establish a corresponding battery system prediction model including a battery electrical model 410, a battery pressure model 420, a battery thermal model 430 and a battery aging model 440 according to the initial data, wherein the battery electrical model 410 is used for establishing an initial open-circuit voltage OCV0Initial internal resistance R0Rated capacity C0The battery electric model 410 calculates and processes data to obtain a new battery state of charge (SOC), a new open-circuit voltage (OCV), an internal resistance (R) and the like, the battery electric model 410 provides the new open-circuit voltage (OCV), the internal resistance (R) and the current I of the target battery 450 to the battery thermal model 430 as input parameters, the new battery state of charge (SOC) is provided to the battery pressure model 420 as input parameters, pressure values (Force) and temperature (T) are respectively obtained according to the battery pressure model 420 and the battery thermal model 430, the battery pressure model 420 provides the pressure values (Force) to the battery aging model 440 as input parameters, and the battery thermal model 430 provides the temperature (T) to the battery electric model 410, the battery pressure model 420 and the battery aging model 440 as input parametersThe model 440 and the battery aging model 440 calculate the state of health SOH of the target battery, and provide the state of health SOH to the battery electrical model 410, the battery pressure model 420 and the battery thermal model 430, it should be noted that parameters such as the state of charge SOC, the temperature T, the pressure value Force, the state of health SOH and the like are mutually influenced by mutual transmission among the models, and dynamic change of a certain parameter value causes other parameters to change, so that the service life of the target battery can be reflected as a whole, the state of health of the battery system can be reflected as a whole, and the final service life of the battery system can be accurately predicted.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
In a second aspect, referring to fig. 5, a battery system life prediction apparatus according to an embodiment of the present invention includes:
the first obtaining module 510 is configured to perform a cycle life test on a battery pack in a battery system to obtain a plurality of first data sets;
the second obtaining module 520 is configured to perform a storage life test on the battery pack to obtain a plurality of second data sets;
and the predicting module 530 is configured to perform final life prediction on the battery system according to the plurality of first data sets, the plurality of second data sets, and the battery system life prediction model to obtain life prediction data of the battery system.
Selecting one or more target batteries, establishing a battery system service life prediction model corresponding to the target batteries by using the physical characteristics of the batteries, and performing cycle life test on a battery pack in the battery system by using a first acquisition module 510, namely detecting the service life of charge and discharge cycles of the batteries to obtain a plurality of first data sets, wherein the first data sets comprise the pressure, the temperature, the electric quantity, the battery thickness change condition, the battery open-circuit voltage, the internal resistance, the battery quality, the battery specific heat capacity and the like of the target batteries in the test process; the second obtaining module 520 performs storage life test on the battery pack to obtain a plurality of second data sets, wherein the second data sets include pressure change, thickness change, rated capacity and the like of the battery in the storage process, and then the predicting module 530 performs final life prediction on the battery system according to the plurality of first data sets, the plurality of second data sets and the battery system life prediction model obtained through the test to obtain life prediction data of the battery system. The battery system service life prediction models comprise a battery electric model, a battery thermal model, a battery pressure model and a battery aging model, and the battery system service life prediction models are mutually influenced and can integrally reflect the health state of a target battery, so that the health state of the battery system is integrally reflected.
The battery system service life prediction device performs cycle life test and storage life test on a battery pack through the first acquisition module 510 and the second acquisition module 520 to obtain a plurality of data sets, establishes a battery system service life prediction model by using the physical characteristics of the battery, and performs final service life prediction on a battery system through the prediction module 530 according to the data of the data sets and the battery system service life prediction model, so that service life prediction data is obtained, the influence of the service environment on the prediction data can be eliminated, and the accuracy of the battery system service life prediction device on the battery system service life prediction is improved.
In a third aspect, an embodiment of the present invention provides a device for predicting a life of a battery system, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the battery system life prediction method of the first aspect when executing the computer program.
The battery system service life prediction device performs cycle service life test and storage service life test on the battery pack by implementing the battery system service life prediction method of the first aspect, establishes a battery system service life prediction model by using the physical characteristics of the battery, and performs final service life prediction on the battery system according to the data obtained by the test and the battery system service life prediction model, thereby eliminating the influence of the service environment on the prediction data and improving the accuracy of the battery system service life prediction device.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where computer-executable instructions are stored, and the computer-executable instructions are configured to cause a computer to execute the method for predicting the life of a battery system according to the first aspect.
The computer-readable storage medium enables a computer to execute the battery system service life prediction method of the first aspect to perform cycle service life test and storage service life test on the battery pack by sending computer-executable instructions, establishes a battery system service life prediction model by using the physical characteristics of the battery, and performs final service life prediction on the battery system according to the data obtained by the test and the battery system service life prediction model, so that the influence of the service environment on the predicted data is eliminated, and the accuracy of the service life prediction of the battery system is improved.
The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
While the preferred embodiments of the present invention have been described, the present invention is not limited to the above embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and such equivalent modifications or substitutions are included in the scope of the present invention defined by the claims.
Claims (10)
1. A method for predicting the life of a battery system, comprising:
carrying out cycle life test on a battery pack in a battery system to obtain a plurality of first data sets;
testing the storage life of the battery pack to obtain a plurality of second data sets;
performing final service life prediction on the battery system according to the plurality of first data sets, the plurality of second data sets and a battery system service life prediction model to obtain service life prediction data of the battery system;
the battery system service life prediction model comprises a battery electric model, a battery thermal model, a battery pressure model and a battery aging model.
2. The method of claim 1, wherein the first data set comprises a third data set and a fourth data set, and performing a cycle life test on a battery pack in the battery system to obtain a plurality of first data sets comprises:
performing standard cycle test on a target battery of the battery pack to obtain a plurality of third data sets;
and carrying out composite pulse current test on the target battery to obtain a plurality of fourth data sets.
3. The method of claim 2, wherein the performing a storage life test on the battery pack to obtain a plurality of second data sets comprises:
and carrying out standard capacity test on the target battery to obtain a plurality of second data sets.
4. The method of claim 3, wherein the battery electrical model is an n-order equivalent circuit model, and the first data set comprises an open-circuit voltage OCV, an internal resistance R, a battery coulombic efficiency η, and an initial state of charge SOC of the target battery0The second data set comprises the factory rated capacity C of the target battery0The predicting the final service life of the battery system according to the plurality of first data sets, the plurality of second data sets and the battery system service life prediction model to obtain service life prediction data of the battery system includes:
calculating to obtain the terminal voltage Ut, the state of charge SOC, the current I and the equivalent resistance R of the target battery according to the first data sets, the second data sets and the equivalent circuit of the battery electric model1And an equivalent resistance R1Voltage U acrossR1。
5. The method for predicting the service life of a battery system according to claim 4, wherein the first data set includes a battery mass m, a battery specific heat capacity Cp, a heat exchange coefficient h, an ambient temperature Tamb, and a battery heat exchange area s of the target battery, and the step of performing final service life prediction on the battery system according to the first data sets, the second data sets, and a battery system service life prediction model to obtain service life prediction data of the battery system comprises:
calculating the temperature T of the target battery according to the first data sets and a first formula of the thermal model of the battery;
the first formula is: m Cp dT/dT Qirev + Qrev + Qtran;
wherein Qirev is irreversible heat of the target battery, and Qirev is I2R+R1*(I-dUR1/dt)2；
Qrev is the reversible heat of the target cell, Qrev ═ I × T × dcv/dt (soc);
qtran is the heat exchange of the target cell, Qtran ═ h ═ s (Tamb-T).
6. The method for predicting the service life of the battery system according to claim 5, wherein the first data set includes an accumulated discharge electric quantity Ah of the target battery, a cycle process temperature difference Δ T, a battery gasket stiffness cs, a battery gasket elastic coefficient ks, a battery metal shell initial thickness L, and a thickness change β of the target battery during charging and discharging, and the final service life prediction of the battery system according to the first data sets, the second data sets, and a battery system service life prediction model to obtain the service life prediction data of the battery system includes:
calculating a pressure value Force of the target battery according to the plurality of first data sets and a second formula of the battery pressure model;
the second formula is: force ═ f (Δ T)/(soc)/(Ah) · (1-cs · Δ T) (β × L · Δ T) · Ahn。
7. The method for predicting the life of the battery system according to claim 6, wherein the first data set includes a battery coefficient α, an activation energy Ea, and an accumulated discharge power Ah of the target battery, the second data set includes a constant n, a constant a, and a natural logarithm exp, and the final life prediction of the battery system according to the first data sets, the second data sets, and a battery system life prediction model to obtain the life prediction data of the battery system comprises:
calculating the state of health (SOH) of the target battery according to the first data sets, the second data sets and a third formula of the battery aging model;
the third formula is: SOH 1-Qloss, said Qloss being the capacity loss of said target battery;
wherein Qloss is Qcycloss + Qcalendarlos, Qcycloss is the cycle life of the target battery, and Qcalendarlos is the calendar life of the target battery;
Qcycleloss＝α*exp(-Ea/(273+T))*(Ah)z；Qcalendarloss＝A*f(SOC)*(day)n。
8. a battery system life prediction apparatus, comprising:
the first acquisition module is used for carrying out cycle life test on the battery pack in the battery system to obtain a plurality of first data sets;
the second acquisition module is used for carrying out storage life test on the battery pack to obtain a plurality of second data sets;
and the prediction module is used for predicting the final service life of the battery system according to the plurality of first data sets, the plurality of second data sets and the service life prediction model of the battery system to obtain service life prediction data of the battery system.
9. A battery system life prediction apparatus, comprising:
a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the battery system life prediction method of any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the method of battery system life prediction of any of claims 1 to 7.
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