CN112379297B - Battery system life prediction method, device, equipment and storage medium - Google Patents
Battery system life prediction method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN112379297B CN112379297B CN202011138935.8A CN202011138935A CN112379297B CN 112379297 B CN112379297 B CN 112379297B CN 202011138935 A CN202011138935 A CN 202011138935A CN 112379297 B CN112379297 B CN 112379297B
- Authority
- CN
- China
- Prior art keywords
- battery
- battery system
- data sets
- life prediction
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 64
- 238000012360 testing method Methods 0.000 claims abstract description 81
- 230000032683 aging Effects 0.000 claims abstract description 24
- 230000008569 process Effects 0.000 claims description 19
- 230000036541 health Effects 0.000 claims description 17
- 230000008859 change Effects 0.000 claims description 16
- 238000007599 discharging Methods 0.000 claims description 10
- 239000002131 composite material Substances 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 6
- 230000004913 activation Effects 0.000 claims description 4
- 230000006870 function Effects 0.000 claims description 4
- 239000002184 metal Substances 0.000 claims description 4
- 125000004122 cyclic group Chemical group 0.000 claims description 3
- 230000002427 irreversible effect Effects 0.000 claims description 3
- 230000002441 reversible effect Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 description 11
- 230000009286 beneficial effect Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 239000003990 capacitor Substances 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000003862 health status Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000007723 transport mechanism Effects 0.000 description 1
Abstract
The invention discloses a battery system life prediction method, a device, equipment and a storage medium, and belongs to the field of batteries. The battery system life prediction method comprises the following steps: performing cycle life test on a battery pack in a battery system to obtain a plurality of first data sets; performing storage life test on the battery pack to obtain a plurality of second data sets; carrying out final life prediction on the battery system according to the first data sets, the second data sets and the life prediction model of the battery system to obtain life prediction data of the battery system; the battery system life prediction model comprises a battery electric model, a battery thermal model, a battery pressure model and a battery aging model. The battery system life prediction method can eliminate the influence of the use environment on prediction data, and improves the accuracy of battery system life prediction.
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 lifetime of a battery system.
Background
With the development of electric tools (such as electric trains and electric bicycles), it is important to predict the health status and life of a battery system, and currently, the life prediction method of the battery system cannot eliminate the interference caused by the difference of the service environments to the life prediction of the battery system, and affects the accuracy of the life prediction of the battery system.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides a battery system life prediction method, which can predict the life of the battery system according to the physical characteristics of the battery, eliminate the influence of the use environment on prediction data and improve the accuracy of the life prediction of the battery system.
The invention also provides a battery system life prediction device with the battery system life prediction method.
The invention also provides battery system life prediction equipment with the battery system life prediction method.
The invention also provides a computer readable storage medium with the battery system life prediction method.
A battery system lifetime prediction method according to an embodiment of the first aspect of the present invention includes:
performing cycle life test on a battery pack in a battery system to obtain a plurality of first data sets;
Performing storage life test on the battery pack to obtain a plurality of second data sets;
Performing final life prediction on 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;
The battery system life prediction model comprises a battery electric model, a battery thermal model, a battery pressure model and a battery aging model.
The battery system life prediction method provided by the embodiment of the invention has at least the following beneficial effects: according to the battery system life prediction method, the cycle life test and the storage life test are carried out on the battery pack, the battery system life prediction model is established by utilizing the physical characteristics of the battery, the final life prediction is carried out on the battery system according to the data obtained by the test and the battery system life prediction model, the influence of the use environment on the predicted data can be eliminated, and the accuracy of the battery system life prediction is improved.
According to some embodiments of the invention, the first data set includes a third data set and a fourth data set, and the performing the cycle life test on the battery pack in the battery system obtains a plurality of first data sets, including:
Performing standard cycle test on the 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 a storage life test on the battery pack results in a number of second data sets, including:
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 electric model is an n-order equivalent circuit model, the first data set includes an open circuit voltage OCV, an internal resistance R, and an initial state of charge SOC 0 of the target battery, the second data set includes a factory rated capacity C 0 of the target battery, and the final life prediction is performed on 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, including:
And calculating to obtain the terminal voltage Ut, the state of charge SOC, the current I, the equivalent resistance R 1 and the voltage U R1 at two ends of the equivalent resistance R 1 of the target battery according to the first data sets, the second data sets and the equivalent circuit of the battery electric model.
According to some embodiments of the 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 final life prediction is performed on 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, including:
Calculating the temperature T of the target battery according to the first data sets and a first formula of the battery thermal model;
The first formula is: m Cp dT/dt= Qirev + Qrev + Qtran;
Wherein Qirev is the irreversible heat of the target battery, qirev =i 2R+R1*(I-dUR1/dt)2;
qrev is the reversible heat of the target battery, 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 cyclic process temperature difference Δt, a battery gasket stiffness cs, an elastic coefficient ks of a battery gasket, an initial thickness L of a battery metal case, and a thickness variation β of the target battery during charging and discharging, and the predicting a 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 a pressure value Force of the target battery according to the first data sets and a second formula of the battery pressure model;
The second formula is: force=f (Δt) ×f (SOC) ×f (Ah) =ks (1-cs×Δt) (β×l×Δt) ×ah n.
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 is performed on 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, 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 the battery aging model;
the third formula is: soh=1-Qloss, the Qloss being a capacity loss of the target battery;
Wherein Qloss = Qcycleloss + Qcalendarloss, qcycleloss is the cycle life of the target battery and Qcalendarloss 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 lifetime prediction device 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 a 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 life of the battery system according to the first data sets, the second data sets and the life prediction model of the battery system to obtain life prediction data of the battery system.
The battery system life prediction device provided by the embodiment of the invention has at least the following beneficial effects: according to the battery system life prediction device, the first acquisition module and the second acquisition module perform cycle life test and storage life test on the battery pack to obtain a plurality of data sets, the battery system life prediction model is established by utilizing the physical characteristics of the battery, and the prediction module performs final life prediction on the battery system life according to the data of the plurality of data sets and the battery system life prediction model, so that life prediction data is obtained, the influence of the use environment on a prediction result can be eliminated, and the accuracy of battery system life prediction is improved.
According to some embodiments of the invention, the prediction module comprises:
a third acquisition module for acquiring a plurality of initial data sets of the battery system;
And the calculation module is used for carrying out life prediction on the battery system according to the initial data sets, the first data sets, the second data sets and the battery system life prediction model to obtain a life prediction result of the battery system.
A battery system lifetime 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 battery system life prediction method as in the embodiment of the first aspect described above when executing the computer program.
The battery system life prediction device according to the embodiment of the invention has at least the following beneficial effects: according to the battery system life prediction device, the battery system life prediction method of the first aspect is used for carrying out cycle life test and storage life test on the battery pack, the battery system life prediction model is established by utilizing the physical characteristics of the battery, and final life prediction is carried out on the battery system according to the data obtained by the test and the battery system life prediction model, so that the influence of the use environment on the predicted data is eliminated, and the accuracy of the battery system life prediction device on the battery system life prediction is improved.
A computer-readable storage medium according to an embodiment of the fourth aspect of the present invention stores computer-executable instructions for causing a computer to execute the battery system lifetime prediction method according to the embodiment of the first aspect described above.
The computer-readable storage medium according to the embodiment of the invention has at least the following advantageous effects: the computer-readable storage medium enables the computer to execute the battery system life prediction method of the first aspect by sending the computer-executable instructions to perform cycle life test and storage life test on the battery pack, the physical characteristics of the battery are utilized to establish a battery system life prediction model, and final life prediction is performed on the battery system according to the data obtained by the test and the battery system life prediction model, so that the influence of the use environment on predicted data is eliminated, and the accuracy of battery system life prediction 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 foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow chart of a battery system life prediction method according to one embodiment of the present invention;
FIG. 2 is a flowchart of a battery system life prediction method according to another embodiment of the present invention;
FIG. 3 is a circuit configuration diagram of a battery electrical model of a battery system life prediction method according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of life prediction of a battery system life prediction method according to another embodiment of the present invention;
fig. 5 is a schematic structural view of a battery system life prediction apparatus according to an embodiment of the present invention;
Reference numerals: 400. a battery system; 410. a battery electric 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
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It should be noted that although functional block division is performed in a device diagram and a logic sequence is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the block division in the device, or in the flowchart. The terms first, second and the like in the description and in the claims and in the above-described figures, 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 battery system life prediction method according to an embodiment of the present invention includes:
S101, performing cycle life test on a battery pack in a battery system to obtain a plurality of first data sets;
S102, performing storage life test on the battery pack to obtain a plurality of second data sets;
s103, carrying out final life prediction on the battery system according to the first data sets, the second data sets and the battery system life prediction model to obtain life prediction data of the battery system;
the battery system 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 formed by connecting a plurality of target batteries in series or in parallel, one or a plurality of target batteries are selected, a battery system life prediction model corresponding to the target batteries is established by utilizing the physical characteristics of the batteries, the battery pack in the battery system is subjected to cycle life test, namely the life of charge and discharge cycles of the batteries is detected, a plurality of first data sets are obtained, and 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 carrying out storage life test on the battery pack to obtain a plurality of second data sets, wherein the second data sets comprise pressure change, thickness change, rated capacity and the like of the battery in the storage process, and then carrying out 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 which are obtained through the test to obtain life prediction data of the battery system. The battery system life prediction model comprises a battery electric model, a battery thermal model, a battery pressure model and a battery aging model, wherein the battery system life prediction models are mutually influenced, and the health state of a target battery can be reflected on the whole, so that the health state of the battery system is reflected on the whole.
According to the battery system life prediction method, the cycle life test and the storage life test are carried out on the battery pack, the battery system life prediction model is established by utilizing the physical characteristics of the battery, the final life prediction is carried out on the battery system according to the data obtained by the test and the battery system life prediction model, the influence of the use environment on the predicted data can be eliminated, and the accuracy of the battery system life prediction is improved.
Referring to fig. 2, in some embodiments, the first data set includes a third data set and a fourth data set, and step S101, performing a cycle life test on a battery pack in the battery system, to obtain a plurality of first data sets includes:
s201, performing standard cycle test on a target battery of the battery pack to obtain a plurality of third data sets;
s202, performing composite pulse current test on the target battery to obtain a plurality of fourth data sets.
When the cycle life test is carried out on the target battery, the front and back sides of the surface of the battery core of the target battery are connected with pressure sensors, and the electrode posts of the battery core and the surface of the battery core are connected with thermocouples; the precision of the pressure sensor and the thermocouple can be selected according to actual conditions, and the flexibility is high.
In some embodiments, a set of cycle temperatures is preset in the standard cycle test process of the target battery, the cycle temperatures at least include two different temperature values (the temperature values can be taken according to actual conditions), then the charge and discharge conditions of the target battery at the set temperature are recorded until the service life of the target battery is terminated, that is, the capacity of the target battery is smaller than a preset threshold value, and a plurality of third data sets are recorded and obtained, including the pressure, the temperature, the electric quantity, the voltage and the battery thickness change, the accumulated discharge electric quantity, the battery quality, the ambient temperature and the like of the target battery in the cycle test process. For example, the cycle temperature may be set at 45 ℃, 25 ℃, 10 ℃, the target battery capacity threshold is selected to be 80% of the battery rated capacity, and then the target battery is subjected to a charge-discharge test, resulting in several 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 performing cycle life test on the target battery, composite pulse battery test is performed on the target battery every certain charge and discharge times, so that the power performance of the target battery is obtained, and a plurality of fourth data sets including data such as open circuit voltage and direct current internal resistance of the target battery are obtained. For example, the time interval of the composite pulse battery test is set to be one test per 200 charges and discharges of the target battery to acquire the related data.
In some embodiments, step S102, performing a storage life test on the battery pack, resulting in a number of second data sets, includes:
and carrying out standard capacity test on the target battery to obtain a plurality of second data sets.
In the process of carrying out storage life test on the target battery, a group of charge state values (battery residual capacity) are preset, wherein the charge state values at least comprise one charge state value, a plurality of target batteries with corresponding charge states are selected, the plurality of target batteries are stored at preset circulation temperatures (the circulation temperatures correspond to the temperatures of carrying out the circulation life test on the batteries), standard capacity test and charge state adjustment are carried out on the target batteries at certain intervals, the process of the storage life test of the battery lasts for a certain time, and a plurality of second data sets comprising pressure change, thickness change and rated capacity of the target batteries are recorded and obtained in the process.
It should be noted that, the preset state of charge value, interval time and duration may be set according to practical situations, for example, for selecting a target battery with SOC of 97%, 50% and 10%, storing the target batteries with three states of charge at 45 ℃,25 ℃,10 ℃, and performing standard capacity test and state of charge adjustment every 30 days, where 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 in the life test of the target battery, and include parameters of the target battery itself, environmental parameters, and the like, such as an elasticity coefficient of the battery pad, rigidity of the battery pad, environmental temperature, and the like.
In some embodiments, the battery electric model is an n-order equivalent circuit model, the first data set includes an open circuit voltage OCV, an internal resistance R, and an initial state of charge SOC 0 of the target battery, the second data set includes a factory rated capacity C 0 of the target battery, and step S103, final life prediction is performed on the battery system according to the first data sets, the second data sets, and the battery system life prediction model to obtain life prediction data of the battery system, including:
According to the first data sets, the second data sets and the equivalent circuit of the battery electric model, the terminal voltage Ut, the state of charge SOC, the current I, the equivalent resistance R 1 and the voltage U R1 at two ends of the equivalent resistance R 1 of the target battery are obtained through calculation.
The battery electric model is an n-order equivalent circuit model, can meet the calculation of the battery state of charge under various conditions, has good applicability, calculates the terminal voltage Ut, the state of charge SOC, the current I, the equivalent resistance R 1 and the voltage U R1 at two ends of the equivalent resistance R 1 of the target battery according to a plurality of first data sets, a plurality of second data sets and an equivalent circuit of the battery electric model, and can well utilize the electrical characteristics of the battery to predict the final service life of the battery system.
Referring to fig. 3, taking a battery electric model as a first-order RC equivalent circuit model as an example, in the process of predicting the life of a battery system by using the battery electric model, composite pulse battery test data included in a plurality of first data sets, that is, open circuit voltage OCV and internal resistance R of a target battery, are obtained, wherein the open circuit voltage is a function related to 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 resistance R and the first loop are connected in series in the battery loop, and an open-circuit voltage OCV, the internal resistance R, the first resistor R1 and the first capacitor C1 are used as inputs, so that a battery terminal voltage Ut and a voltage U R1 at two ends of an equivalent resistor R 1 can be obtained through the battery electric model.
In the battery electric model, an ampere-hour integration method is adopted to calculate the state of charge (SOC) of the battery, namely SOC= { SOC 0*C0+∫(η*I)dt}/(SOH*C0), wherein SOC 0 is the initial state of charge of the target battery, C 0 is the factory rated capacity of the target battery, 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 state of charge obtained by the formula is the state of charge SOC of the target battery, and the calculation is simple and the time is saved.
The calculation of the state of charge of the battery may also be performed by, but not limited to, an open circuit voltage method, an ampere-hour integration method, an internal resistance method, a neural network, a kalman filter method, and the like.
In some embodiments, the first data set includes a battery mass m of the target battery, a battery specific heat capacity Cp, a heat exchange coefficient h, an ambient temperature Tamb, and a battery heat exchange area S, and step S103, performing final life prediction on the battery system according to the first data sets, the second data sets, and the battery system life prediction model 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 battery thermal model;
The first formula is: m Cp dT/dt= Qirev + Qrev + Qtran;
Wherein Qirev is the irreversible heat of the target battery, qirev =i 2R+R1*(I-dUR1/dt)2;
qrev is the reversible heat of the target battery, qrev =i×t×dcv/dT (SOC);
Qtran is the heat exchange of the target cell, 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 cyclic process temperature difference Δt, a battery gasket stiffness cs, an elastic coefficient ks of the battery gasket, an initial thickness L of the battery metal shell, and a thickness variation β of the target battery during charging and discharging, and step S103, performing final life prediction on 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 a pressure value Force of the target battery according to a plurality of first data sets and a second formula of the battery pressure model;
The second formula is: force=f (Δt) ×f (SOC) ×f (Ah) =ks (1-cs×Δt) (β×l×Δt) ×ah) n
It should be noted that β is a thickness change of the target battery during charging and discharging, and may be regarded as a SOC function (i.e., β=f (SOC)) of the target battery during charging and discharging. Therefore, the first pressure value Force of the target battery can be obtained by calculation according to the series of data and the second formula, so that the service life of the battery system can be predicted by well utilizing the surface pressure characteristics of the battery, the interference of different pressure environments on the service life prediction of the battery system is eliminated, and the accuracy of the service life prediction 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, performing final life prediction on the battery system according to the first data sets, the second data sets, and the 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 a third formula of the battery aging model, wherein the third formula comprises a plurality of first data sets and a plurality of second data sets;
the third formula is: soh=1-Qloss, qloss is the capacity loss of the target battery;
Wherein Qloss = Qcycleloss + Qcalendarloss, qcycleloss is the cycle life of the target battery, qcalendarloss is the calendar life of the target battery;
Qcycleloss＝α*exp(-Ea/(273+T))*(Ah)z;Qcalendarloss＝A*f(SOC)*(day)n。
According to a plurality of first data sets obtained by the battery cycle life test, a plurality of second data sets obtained by the battery storage life test, a plurality of data calculated by the battery electric model and a third formula, the state of health SOH of the target battery is calculated, and the state of health SOH of the target battery is calculated through the battery aging model, so that the aging degree of the target battery can be intuitively reflected, the final life prediction is carried out on the battery system as a whole, and the accuracy of 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. Considering the capacity loss Qloss of the battery system as a superposition of the battery cycle life Qcycleloss and the battery calendar life Qcalendarloss, namely Qloss = Qcycleloss + Qcalendarloss, it should be noted that the cycle life of the target battery refers to the life of the target battery in charge-discharge cycles, 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:
Qcycleloss = α x exp (-Ea/(273+t)) (Ah) z; 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 all coefficients, alpha is a battery coefficient related to depth of discharge (DOD), charge-discharge multiplying power and the like, and Ea is the activation energy;
The calendar life calculation formula of the target battery is:
Qcalendarloss＝A*f(SOC)*(day)n；
The difference between the constant n, the constant a, and the natural logarithm exp in the calculation of the health states of different target batteries is calculated by an initial cycle life Qcycleloss 0 and an initial calendar life Qcalendarloss 0 obtained by performing a life cycle test and a storage life test on a plurality of target batteries.
The cycle life, calendar life and capacity loss of the battery system of the target battery can be calculated through the formula, and then the state of health SOH of the target battery is obtained according to a third formula SOH=1-Qloss, so that the aging degree of the target battery is obtained, and the aging degree of the battery system can be predicted and evaluated through overall evaluation of the aging degree of a plurality of target batteries of the battery pack of the battery system.
Referring to fig. 4, in some embodiments, initial data such as initial open circuit voltage OCV 0, initial internal resistance R 0, rated capacity C 0 of the battery pack in the battery system is obtained from a bar code of the factory battery or the battery system, and is recorded and stored in the first data set for ready access. Meanwhile, a cycle life test and a storage life test are carried out on a battery pack in a battery system to obtain a plurality of first data sets and a plurality of second data sets, such as battery quality m, battery specific heat capacity Cp, heat exchange coefficient h, thickness change beta of a target battery in a charging and discharging process, initial state of charge SOC 0 and the like, the obtained parameters such as initial open-circuit voltage OCV 0, initial internal resistance R 0, rated capacity C 0 and the like are used as input condition parameters of a battery electric model, calculation processing is carried out in the battery electric model, dynamic change parameters of the target battery, namely new battery state of charge SOC, new open-circuit voltage OCV, internal resistance R and the like, and then the temperature T, the pressure value Force and the state of health SOH of the target battery are calculated in the battery thermal model, the battery pressure model and the battery aging model according to the updated dynamic change parameters and the data in the first data sets and the second data sets, so that final life prediction data of the battery system are further obtained.
Because 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, the interaction results are that the service life SOH of the battery system is the whole appearance of the service life SOH of the target batteries, each target battery is provided with a corresponding battery system service life prediction model, 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, certain differences (such as temperature T, state of charge SOC, internal resistance R and the like) can exist in battery model parameters of different target batteries, the temperature, pressure, state of charge and the like of the target batteries 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 batteries can be predicted by calculating the changes of the parameters.
For example, as shown in fig. 4, initial open circuit voltage OCV 0, initial internal resistance R 0, rated capacity C 0, and other initial data of the battery pack are obtained from the battery system 400, a target battery 450 is selected to establish a battery system prediction model corresponding to the initial open circuit voltage OCV 0, initial internal resistance R 0, rated capacity C 0, the initial open circuit voltage OCV, the initial internal resistance R 0, rated capacity C 0, and other initial data are selected, the battery system prediction model includes a battery electric model 410, a battery pressure model 420, a battery thermal model 430, and a battery aging model 440, wherein the battery electric model 410 uses the initial open circuit voltage OCV 0, the initial internal resistance R 0, the rated capacity C 0, and the like as input parameters, a new battery state of charge SOC, a new open circuit voltage OCV, an internal resistance R, and a current I of the target battery 450 are provided to the battery thermal model 430 by the battery electric model 410 as input parameters, according to the battery pressure model 420 and the battery thermal model 430, a pressure value Force and a temperature T are obtained respectively, the battery pressure model 420 provides the pressure value Force as an input parameter to the battery aging model 440, the battery thermal model 430 provides the temperature T as an input parameter to the battery electric model 410, the battery pressure model 420 and the battery aging model 440, the battery aging model 440 obtains the health state SOH of the target battery through calculation, and provides the health state SOH to the battery electric model 410, the battery pressure model 420 and the battery thermal model 430, it should be noted that the mutual influence is caused by the mutual transmission of parameters such as the state of charge SOC, the temperature T, the pressure value Force and the health state SOH between the models, the dynamic change of a certain parameter value causes the change of other parameters, so that the service life condition of the target battery can be reflected on the whole, and the state of health of the battery system is accurately reflected on the whole, and the final service life of the battery system is 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 one of ordinary skill in the art without departing from the spirit of the present invention.
Second aspect, referring to fig. 5, a battery system lifetime prediction apparatus according to an embodiment of the present invention includes:
A first obtaining module 510, configured to perform a cycle life test on a battery pack in a battery system, to obtain a plurality of first data sets;
A second obtaining module 520, configured to perform a storage life test on the battery pack to obtain a plurality of second data sets;
The prediction module 530 is configured to predict a final lifetime of the battery system according to the first data sets, the second data sets, and the lifetime prediction model of the battery system, so as to obtain lifetime prediction data of the battery system.
Selecting one or more target batteries, establishing a battery system life prediction model corresponding to the target batteries by utilizing physical characteristics of the batteries, and performing cycle life test on a battery pack in the battery system by a first acquisition module 510, namely detecting the life of a battery in charge-discharge cycle to obtain a plurality of first data sets, wherein the first data sets comprise pressure, temperature, electric quantity, battery thickness change condition, battery open-circuit voltage, internal resistance, battery quality, battery specific heat capacity and the like of the target batteries in the test process; the second obtaining module 520 performs a storage life test on the battery pack to obtain a plurality of second data sets, wherein the second data sets comprise pressure changes, thickness changes, 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 by the test to obtain life prediction data of the battery system. The battery system life prediction model comprises a battery electric model, a battery thermal model, a battery pressure model and a battery aging model, wherein the battery system life prediction models are mutually influenced, and the health state of a target battery can be reflected on the whole, so that the health state of the battery system is reflected on the whole.
According to the battery system life prediction device, the first acquisition module 510 and the second acquisition module 520 are used for carrying out cycle life test and storage life test on the battery pack to obtain a plurality of data sets, the battery system life prediction model is established by utilizing the physical characteristics of the battery, and the prediction module 530 is used for carrying out final life prediction on the battery system according to the data of the plurality of data sets and the battery system life prediction model, so that life prediction data are obtained, the influence of the use environment on the prediction data can be eliminated, and the accuracy of the battery system life prediction device on the battery system life prediction is improved.
In a third aspect, an embodiment of the present invention provides a battery system lifetime prediction apparatus, 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.
According to the battery system life prediction device, the battery system life prediction method of the first aspect is used for carrying out cycle life test and storage life test on the battery pack, the battery system life prediction model is established by utilizing the physical characteristics of the battery, and final life prediction is carried out on the battery system according to the data obtained by the test and the battery system life prediction model, so that the influence of the use environment on the predicted data is eliminated, and the accuracy of the battery system life prediction device on the battery system life prediction is improved.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the battery system life prediction method of the first aspect.
The computer-readable storage medium enables the computer to execute the battery system life prediction method of the first aspect by sending the computer-executable instructions to perform cycle life test and storage life test on the battery pack, the physical characteristics of the battery are utilized to establish a battery system life prediction model, and final life prediction is performed on the battery system according to the data obtained by the test and the battery system life prediction model, so that the influence of the use environment on predicted data is eliminated, and the accuracy of battery system life prediction is improved.
The above described apparatus embodiments 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 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 this embodiment.
Those 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 both 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 known to those skilled 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 be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, 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.
While the preferred embodiment of the present application has been described in detail, the present application is not limited to the above embodiment, and various equivalent modifications and substitutions can be made by those skilled in the art without departing from the spirit of the present application, and these equivalent modifications and substitutions are intended to be included in the scope of the present application as defined in the appended claims.
Claims (9)
1. A battery system life prediction method, comprising:
performing cycle life test on a battery pack in a battery system to obtain a plurality of first data sets;
Performing storage life test on the battery pack to obtain a plurality of second data sets;
Performing final life prediction on 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;
The battery system life prediction model comprises a battery electric model, a battery thermal model, a battery pressure model and a battery aging model, wherein the battery electric model is an n-order equivalent circuit model;
The first data set comprises accumulated discharge electric quantity Ah of a target battery of the battery pack, a temperature difference delta T in a circulation process, battery gasket rigidity cs, an elastic coefficient ks of a battery gasket, initial thickness L of a battery metal shell and thickness change beta of the target battery in a charging and discharging process; and predicting the final life of the battery system according to the first data sets, the second data sets and a life prediction model of the battery system to obtain life prediction data of the battery system, wherein the method comprises the following steps:
Calculating a pressure value Force of the target battery according to the first data sets and a second formula of the battery pressure model;
the second formula is: force=f (Δt) ×f (SOC) ×f (Ah) =ks (1-cs×Δt) (β×l×Δt) ×ahn;
wherein, the SOC is the state of charge of the target battery; n in Ahn is a constant; beta is the thickness variation of the target battery during charging and discharging, and beta is the state of charge SOC function of the target battery during charging and discharging, i.e., beta=f (SOC).
2. The battery system life prediction method according to claim 1, wherein the first data set includes a third data set and a fourth data set, and the performing the cycle life test on the battery pack in the battery system to obtain a plurality of first data sets includes:
Performing standard cycle test on the 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 battery system life prediction method according to claim 2, wherein said performing a storage life test on said battery pack results in a plurality of second data sets, comprising:
and carrying out standard capacity test on the target battery to obtain a plurality of second data sets.
4. The battery system life prediction method according to claim 3, wherein the battery electric model is an n-order equivalent circuit model, the first data set includes an open circuit voltage OCV, an internal resistance R, a battery coulomb efficiency η, and an initial state of charge SOC 0 of the target battery, the second data set includes a factory rated capacity C 0 of the target battery, and the final life prediction is performed 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:
And calculating to obtain the terminal voltage Ut, the state of charge SOC, the current I, the equivalent resistance R 1 and the voltage U R1 at two ends of the equivalent resistance R 1 of the target battery according to the first data sets, the second data sets and the equivalent circuit of the battery electric model.
5. The battery system life prediction method 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 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 battery thermal model;
The first formula is: m Cp dT/dt= Qirev + Qrev + Qtran;
Wherein Qirev is the irreversible heat of the target battery, qirev =i 2R+R1*(I-dUR1/dt)2;
qrev is the reversible heat of the target battery, qrev =i×t×dcv/dT (SOC);
qtran is the heat exchange of the target cell, qtran =h×s (Tamb-T);
Wherein t is time; dT/dT refers to the rate at which the temperature T of the target battery changes over time, dU R1/dT refers to the rate at which the voltage U R1 across the equivalent resistor R 1 changes over time.
6. The battery system life prediction method according to claim 1, wherein 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 predicting the final life of the battery system according to the plurality of first data sets, the plurality of 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, the Qloss being a capacity loss of the target battery;
Wherein Qloss = Qcycleloss + Qcalendarloss, qcycleloss is the cycle life of the target battery and Qcalendarloss is the calendar life of the target battery;
Qcycleloss＝α*exp(-Ea/(273+T))*(Ah)z;Qcalendarloss＝A*f(SOC)*(day)n；
Where T is the temperature of the target battery, z is a constant, day is a date in the calendar, and calendar life Qcalendarloss of the target battery is a function of the date in the calendar as a function of day.
7. A battery system life prediction apparatus, comprising:
the first acquisition module is used for carrying out cycle life test on a 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;
the prediction module is used for predicting the final life of the battery system according to the first data sets, the second data sets and the battery system life prediction model to obtain life prediction data of the battery system;
The battery system life prediction model comprises a battery electric model, a battery thermal model, a battery pressure model and a battery aging model, wherein the battery electric model is an n-order equivalent circuit model;
The first data set includes an accumulated discharge electric quantity Ah of a target battery of the battery pack, a cyclic process temperature difference Δt, a battery gasket stiffness cs, an elastic coefficient ks of a battery gasket, an initial thickness L of a battery metal shell, and a thickness variation β of the target battery in a charge-discharge process, and the final life prediction is performed on 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, including:
Calculating a pressure value Force of the target battery according to the first data sets and a second formula of the battery pressure model;
the second formula is: force=f (Δt) ×f (SOC) ×f (Ah) =ks (1-cs×Δt) (β×l×Δt) ×ahn;
wherein, the SOC is the state of charge of the target battery; n in Ahn is a constant; beta is the thickness variation of the target battery during charging and discharging, and beta is the state of charge SOC function of the target battery during charging and discharging, i.e., beta=f (SOC).
8. A battery system life prediction apparatus characterized by 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 one of claims 1 to 6 when the computer program is executed.
9. A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the battery system life prediction method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011138935.8A CN112379297B (en) | 2020-10-22 | Battery system life prediction method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011138935.8A CN112379297B (en) | 2020-10-22 | Battery system life prediction method, device, equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112379297A CN112379297A (en) | 2021-02-19 |
| CN112379297B true CN112379297B (en) | 2024-06-11 |
Family
ID=
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103308864A (en) * | 2013-07-09 | 2013-09-18 | 中国人民解放军国防科学技术大学 | Method for estimating secondary cell SOH value and testing residual service life |
| CN107202960A (en) * | 2017-05-25 | 2017-09-26 | 安徽江淮汽车集团股份有限公司 | Electrokinetic cell life-span prediction method |
| CN107255786A (en) * | 2017-05-18 | 2017-10-17 | 中山职业技术学院 | A kind of ferric phosphate lithium cell LOC models |
| WO2019199219A1 (en) * | 2018-04-09 | 2019-10-17 | Scania Cv Ab | Methods and control units for determining an extended state of health of a component and for control of a component |
| CN110901470A (en) * | 2019-11-29 | 2020-03-24 | 安徽江淮汽车集团股份有限公司 | Method, device and equipment for predicting service life of battery of electric vehicle and storage medium |
| CN111062137A (en) * | 2019-12-23 | 2020-04-24 | 荣盛盟固利新能源科技有限公司 | Lithium ion battery performance prediction model and construction method and application thereof |
| CN111239630A (en) * | 2020-03-09 | 2020-06-05 | 江苏中兴派能电池有限公司 | Energy storage battery service life prediction method and management system |
| CN111426952A (en) * | 2019-01-10 | 2020-07-17 | 郑州宇通客车股份有限公司 | Lithium ion battery life prediction method |
| CN111610448A (en) * | 2020-06-01 | 2020-09-01 | 北京理工大学 | Lithium ion battery life prediction method applying digital twinning technology |
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103308864A (en) * | 2013-07-09 | 2013-09-18 | 中国人民解放军国防科学技术大学 | Method for estimating secondary cell SOH value and testing residual service life |
| CN107255786A (en) * | 2017-05-18 | 2017-10-17 | 中山职业技术学院 | A kind of ferric phosphate lithium cell LOC models |
| CN107202960A (en) * | 2017-05-25 | 2017-09-26 | 安徽江淮汽车集团股份有限公司 | Electrokinetic cell life-span prediction method |
| WO2019199219A1 (en) * | 2018-04-09 | 2019-10-17 | Scania Cv Ab | Methods and control units for determining an extended state of health of a component and for control of a component |
| CN111426952A (en) * | 2019-01-10 | 2020-07-17 | 郑州宇通客车股份有限公司 | Lithium ion battery life prediction method |
| CN110901470A (en) * | 2019-11-29 | 2020-03-24 | 安徽江淮汽车集团股份有限公司 | Method, device and equipment for predicting service life of battery of electric vehicle and storage medium |
| CN111062137A (en) * | 2019-12-23 | 2020-04-24 | 荣盛盟固利新能源科技有限公司 | Lithium ion battery performance prediction model and construction method and application thereof |
| CN111239630A (en) * | 2020-03-09 | 2020-06-05 | 江苏中兴派能电池有限公司 | Energy storage battery service life prediction method and management system |
| CN111610448A (en) * | 2020-06-01 | 2020-09-01 | 北京理工大学 | Lithium ion battery life prediction method applying digital twinning technology |
Non-Patent Citations (3)
| Title |
|---|
| 史永胜 ; 张耀忠 ; 洪元涛 ; 刘聪 ; .基于电-热-老化耦合模型的锂离子电池优化充电策略.电子器件.2020,(第05期),1078-1084. * |
| 王锋.基于COMSOL模拟的铅酸电池自放电机理研究.《南通航运职业技术学院学报》.2020,第19卷(第3期),60-65. * |
| 郑君.基于改进单粒子模型的锂离子电池参数获取与老化分析.《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》.2016,(第02期),C042-1553. * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Baccouche et al. | Implementation of an improved coulomb-counting algorithm based on a piecewise soc-ocv relationship for soc estimation of li-ionbattery | |
| CN109856559B (en) | Lithium battery cycle life prediction method | |
| CN105005000B (en) | The system and method for sensing battery capacity | |
| CN102998626B (en) | System and method for determining battery state-of-health | |
| Yang et al. | Online estimation of capacity fade and power fade of lithium-ion batteries based on input–output response technique | |
| JP6428957B2 (en) | Degradation estimation apparatus, degradation estimation method, and computer program | |
| CN111463513B (en) | Method and device for estimating full charge capacity of lithium battery | |
| CN110376536B (en) | SOH detection method and device for battery system, computer equipment and storage medium | |
| JP7394983B2 (en) | Method and apparatus for determining the state of charge and health of a rechargeable battery | |
| US11428747B2 (en) | Degradation estimating device, degradation estimating method, and computer program | |
| JP2016102674A (en) | Battery pack abnormality determination device | |
| CN109613445A (en) | A kind of method and apparatus of the health status of estimated driving force battery | |
| KR20220034543A (en) | Method for estimating state of charge of battery | |
| CN110806540B (en) | Battery cell test data processing method, device and system and storage medium | |
| US9702941B2 (en) | Method and devices for making available information for the purpose of performing maintenance and servicing of a battery | |
| JP6494431B2 (en) | Deterioration diagnosis device for electricity storage devices | |
| CN116670524A (en) | Method and device for determining state of charge (SOC) of battery pack | |
| CN112379297B (en) | Battery system life prediction method, device, equipment and storage medium | |
| CN112255558A (en) | Method and device for calculating battery calendar life attenuation | |
| Miftahullatif et al. | Novel state-of-health prediction method for lithium-ion batteries in battery storage system by using voltage variation at rest period after discharge | |
| CN112379297A (en) | Battery system service life prediction method, device, equipment and storage medium | |
| CN110651194B (en) | Apparatus and method for estimating SOC of battery | |
| CN115372848A (en) | Method, device, equipment and medium for detecting self-discharge performance of battery | |
| CN110365066B (en) | Lithium ion power battery pack balance control method, device, medium and equipment | |
| CN117890798B (en) | Battery remaining energy state estimation method and related device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: 518000 1-2 Floor, Building A, Xinwangda Industrial Park, No. 18 Tangjianan Road, Gongming Street, Guangming New District, Shenzhen City, Guangdong Province Applicant after: Xinwangda Power Technology Co.,Ltd. Address before: 518000 Xinwangda Industrial Park, No.18, Tangjia south, Gongming street, Guangming New District, Shenzhen City, Guangdong Province Applicant before: SUNWODA ELECTRIC VEHICLE BATTERY Co.,Ltd. |
|
| GR01 | Patent grant |