CN116338464A - Construction method of power battery combination life model and battery life assessment method - Google Patents

Construction method of power battery combination life model and battery life assessment method Download PDF

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CN116338464A
CN116338464A CN202310130645.6A CN202310130645A CN116338464A CN 116338464 A CN116338464 A CN 116338464A CN 202310130645 A CN202310130645 A CN 202310130645A CN 116338464 A CN116338464 A CN 116338464A
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model
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power battery
calendar
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李双双
覃升
曹智敏
李毅崑
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Guangzhou Juwan Technology Research Co ltd
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Abstract

The invention discloses a construction method of a power battery combination life model and a battery life assessment method, wherein the construction method comprises the following steps: constructing a preliminary cycle life model; the preliminary cycle life model is used for calculating the attenuation rate of the energy of the power battery, and comprises a polynomial semi-empirical model taking the capacity as a life characteristic quantity and an exponential semi-empirical model taking the internal resistance as the life characteristic quantity, and the energy of the power battery is taken as a dependent variable; constructing a power battery combined life model through a preliminary cycle life model and a calendar life model; the calendar life model is a power function semi-empirical model based on time and is used for calculating the calendar capacity loss percentage of the power battery. The invention uses the capacity and the internal resistance as the characteristic quantity of the preliminary cycle life model, and uses the battery electric quantity as the dependent variable, and the service life decay of the battery is reflected from the electrical property and the chemical property; the battery life attenuation is described by using two semi-empirical models and a calendar life model together, so that the accuracy of model prediction is improved.

Description

Construction method of power battery combination life model and battery life assessment method
Technical Field
The invention belongs to the technical field of battery life prediction, and particularly relates to a construction method of a power battery combination life model and a battery life assessment method.
Background
As one of the most core parts of the electric automobile, the performance of the power battery is closely related to the performance of the whole automobile, and the service life of the power battery also determines the service life of the whole automobile. The capacity of the vehicle-mounted power battery is gradually reduced in the use process, and the vehicle-mounted power battery is regulated according to industry standards: when the capacity of the power battery for a vehicle reaches 80% of the initial capacity or the internal resistance increases to 150% of the initial internal resistance, the power battery needs to be replaced. However, the early replacement of the battery can reduce the comprehensive economical performance of the vehicle, and the too late replacement can affect the dynamic performance and the safety of the vehicle, so that the best balance point of the economical efficiency and the safety reliability can be certainly found if the life decay law of the power battery can be accurately predicted.
At present, three main modeling methods for lithium ion lifetime models are: electrochemical-based modeling methods, empirical formula-based modeling methods, and data-driven based modeling methods. The modeling method based on the empirical formula is widely applied to the aspect of life prediction of the power battery. The specific method comprises the following steps: according to a semi-empirical model of battery life attenuation, factors influencing battery life attenuation such as charge-discharge multiplying power, working temperature and discharge depth are mostly taken as independent variables, life characteristic quantities such as capacity, internal resistance and power are taken as dependent variables, and a regression algorithm is adopted to fit parameters in the semi-empirical model according to battery life test results.
Related patents of a current battery life model, such as a battery life assessment method, with the bulletin number of CN112731164A, firstly establish a calendar life model and a cycle life model according to constant current charge-discharge cycle life test data under laboratory conditions, then determine operation simulation working condition parameters according to user demand curves, respectively calculate the total capacity loss rate at the end of each prediction time unit in a plurality of preset prediction time units through the calendar life model and the cycle life model and the operation simulation working condition parameters, and when calculating the total capacity loss rate of each prediction time unit, correct the current total capacity loss rate through the total capacity retention rate of the last prediction time unit, and comprehensively predict and assess the life condition of the battery by adopting a calculation method of calendar life and cycle life mutual coupling. According to the invention, life attenuation caused by circulation and shelving is comprehensively considered, the model parameter fitting working condition is as close as possible to a real vehicle, the model practicability is good, and the defect that the related patents of the current life model mostly do not consider the running environment of the real vehicle and the storage life loss is overcome. There are still two disadvantages: firstly, only the capacity is used as a life characteristic quantity in a model; secondly, the semi-empirical model only has one type, a single life characteristic quantity and an empirical model formula cannot accurately describe a complex chemical system of the power battery, and the model precision is not high.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a construction method of a power battery combined life model and a battery life assessment method, wherein the construction method uses capacity and internal resistance together as characteristic quantity of a primary cycle life combined model, and uses electric quantity related to the capacity and the internal resistance as dependent variable of the primary cycle life combined model, and simultaneously reflects the degradation of the battery life from electrical property and internal chemical property; the battery life attenuation is described by using two semi-empirical models and a calendar life model together, so that the accuracy of model prediction is improved.
The first aim of the invention is to provide a method for constructing a life model of a power battery combination.
A second object of the present invention is to provide a method for estimating the lifetime of a power battery.
The first object of the present invention can be achieved by adopting the following technical scheme:
constructing a preliminary cycle life model; the preliminary cycle life model is used for calculating the attenuation rate of the energy of the power battery, and comprises a polynomial semi-empirical model taking the capacity as a life characteristic quantity and an exponential semi-empirical model taking the internal resistance as the life characteristic quantity, and the energy of the power battery is taken as a dependent variable;
constructing a power battery combined life model through a preliminary cycle life model and a calendar life model; the power battery combination life model is used for calculating the energy attenuation rate of the power battery; the calendar life model is a power function semi-empirical model based on time and is used for calculating the calendar capacity loss percentage of the power battery.
Further, the power battery combination life model is as follows:
Q loss,％，energy ＝e 1 *Q loss,％,cyc,energy +e 2 *Q loss,％,calendar +e 3 (1)
wherein e 1 ,e 2 ,e 3 Is of constant coefficient, Q loss,％，energy Decay rate, Q, representing the initial full charge discharge energy of a power battery loss,％,cyc,energy Represents the attenuation rate, Q of the energy of the power battery loss,％,calendar The calendar capacity loss percentage of the power cell is shown.
Further, the preliminary cycle life model is:
Q loss,％,cyc,energy ＝d 1 *Q loss,％,cyc,cap +d 2 *Q loss,％,cyc,R +d 3 (2)
wherein d 1 ,d 2 ,d 3 Is a constant; q (Q) loss,％,cy,ccap The cycle life decay rate, Q, which represents the capacity as a characteristic quantity loss,％,cyc,R The cycle life decay rate is represented by the internal resistance as a characteristic amount.
Further, the polynomial semi-empirical model using capacity as the life characteristic quantity is as follows:
Q loss,％,cyc,cap ＝b 1 k 2 +b 2 k+b 3 (3)
wherein b 1 ,b 2 ,b 3 Is a constant coefficient; k is charge and dischargeCycle times;
the polynomial semi-empirical model with capacity as life characteristic quantity is determined through the following process:
performing a cyclic charge-discharge life test on the power battery to obtain a capacity value of the power battery corresponding to a cyclic life stage;
performing regression analysis on the polynomial semi-empirical model with capacity as life characteristic quantity by using the capacity value, wherein the regression analysis comprises the following steps:
Q loss,％,cyc,cap is calculated according to the capacity value; k is the charge-discharge cycle number in the cycle charge-discharge life test;
sum k and corresponding Q loss,％,cyc,cap Substituting the value of (a) into the formula (3), and fitting the formula (3) by adopting a least square method to obtain b 1 、b 2 、b 3 。
Further, the exponential semi-empirical model using the internal resistance as the lifetime characteristic quantity is as follows:
Figure BDA0004083704150000031
wherein a is 1 Is a constant coefficient, a 2 Is a constant related to the depth of discharge DOD, the ambient temperature T and the charge-discharge rate Ratio; ah is the total throughput of charges in Ah units; the unit of the ambient temperature T is °c;
the exponential semi-empirical model taking internal resistance as a life characteristic quantity is determined through the following process:
performing a cyclic charge-discharge life test on the power battery, and obtaining a capacity value of the power battery corresponding to a cyclic life stage according to cyclic life test data; the cycle life test data also comprises the environmental temperature, the charge-discharge multiplying power and the discharge depth in the cycle charge-discharge process;
and carrying out regression analysis on the exponential semi-empirical model taking the internal resistance as the life characteristic quantity by utilizing the cycle life test data and the corresponding internal resistance value, wherein the regression analysis comprises the following steps:
the cycle life test data and corresponding Q loss,％,cyc,R Substitution of the value of (2)Equation (4), taking natural logarithm from two sides of equation (4), and then using least square method to compare constant coefficient a 1 、a 2 Fitting is carried out; wherein Q is loss,％,cyc,R Is calculated from the internal resistance value.
Further, the preliminary cycle life model is determined by:
after determining a polynomial semi-empirical model with capacity as a life characteristic quantity and an exponential semi-empirical model with internal resistance as a life characteristic quantity, performing regression analysis on the preliminary cycle life model by using battery energy in a cycle charge-discharge life test as a dependent variable, including:
Q loss,％,cyc，energy is calculated according to the battery energy value;
d 1 ,d 2 ,d 3 according to Q loss,％,cyc，energy 、Q loss,％,cyc,R And Q loss,％,cyc,cap Regression analysis is performed on the values of (2).
Further, the calendar semi-empirical model is:
Figure BDA0004083704150000041
wherein Q is loss,％,calendar Representing the percentage of calendar capacity loss for the battery; r represents a universal gas constant, T represents the absolute temperature of the ambient temperature, and the unit is K; ea represents the activation energy of the power battery, and the unit is J/mol; c 1 Representing the pre-coefficients; t represents the rest time in "days".
Further, the calendar semi-empirical model is determined by the following process:
carrying out a shelving test on the power battery, and acquiring a calendar capacity value of the power battery in a corresponding storage stage according to shelving test data;
regression analysis of the calendar semi-empirical model using the rest test data and calendar capacity values, including:
the Q is loss,％,calendar Is calculated from the calendar capacity value；
Using the lay-up test data and corresponding Q loss,％,calendar Fitting the values of equation (5), comprising:
first, according to the shelf test data, the relationship between the battery capacity loss and the shelf time is analyzed, including:
taking natural logarithms from both sides of the formula (5) simultaneously to obtain:
Figure BDA0004083704150000042
then MATLAB is used to fit the slope of the straight line to be c 2 ；
Based on c 2 And (3) analyzing the relation between the logarithm of the battery capacity loss value and 1/T at different rest times: at different rest times, the slopes are equal, i.e., -Ea/R is a constant; ea is obtained according to the slope fitting of the straight line, c is obtained according to the intercept fitting of the straight line 1 。
Further, the power battery combination life model is determined through the following process:
after determining a preliminary cycle life model and a calendar semi-empirical model, performing regression analysis on the power battery combined life model by using initial full charge discharge energy of a power battery in a whole-vehicle endurance test of an electric vehicle as a dependent variable, wherein the regression analysis comprises the following steps:
Q loss,％，energy is calculated from the initial full charge discharge energy;
Q loss,％,cyc,energy and Q loss,％,calendar The value of the (2) is obtained by calculation according to experimental data in the durability test of the whole electric automobile;
will Q loss,％，energy 、Q loss,％,cyc,energy And Q loss,％,calendar The value of (2) is substituted into the formula (1) and is fitted by a linear least square method to obtain e 1 ,e 2 ,e 3 ；
Will e 1 ,e 2 ,e 3 Substituting the service life model into the formula (1) to obtain the final power battery combination service life model.
The second object of the invention can be achieved by adopting the following technical scheme:
the power battery life estimation method is realized based on the construction method, and the method comprises the following steps:
according to the obtained measured data of the power battery to be estimated, calculating the attenuation rate of the energy of the power battery to be estimated by using the preliminary cycle life model, and calculating the calendar capacity loss percentage of the power battery to be estimated by using the calendar life model;
and calculating the energy attenuation rate of the power battery to be estimated by using the power battery combination life model according to the attenuation rate and the calendar capacity loss percentage.
Compared with the prior art, the invention has the following beneficial effects:
according to the method for constructing the life model of the power battery combination, the life attenuation caused by circulation and shelving is comprehensively considered, the model parameter fitting working condition is from a real vehicle, and the model practicability is high. The capacity and the internal resistance are used as the characteristic quantity of the primary cycle life combination model, the electric quantity which is related to the capacity and the internal resistance is used as the dependent variable of the primary cycle life combination model, and the service life decay of the battery is reflected in the aspects of electrical property and internal chemical property; the battery life attenuation is described by using two semi-empirical models and a calendar life model together, so that the situation that a single model describes a complex system of the battery is avoided, and the accuracy of model prediction is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for constructing a life model of a power battery pack according to an embodiment of the present invention.
FIG. 2 is a graph of partial real vehicle endurance test data validating accuracy of a combined life model in accordance with an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making any inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention. It should be understood that the description of the specific embodiments is intended for purposes of illustration only and is not intended to limit the scope of the present application.
Examples:
according to the method for constructing the power battery combined life model, the combined life model takes the battery energy as a dependent variable, the influence of capacity attenuation and internal resistance increase on the external characteristics of the battery is reflected, and the accuracy of battery life prediction is improved. The combined life model comprises a cyclic life and a calendar life, wherein the dependent variable of the cyclic life combined model is the power battery electric quantity attenuation rate, the cyclic life combined model comprises a polynomial semi-empirical model taking capacity as a life characteristic quantity and an exponential semi-empirical model taking internal resistance as a life characteristic quantity, and the calendar life model is a power function semi-empirical model taking time as a base.
As shown in fig. 1, the method for constructing a life model of a power battery assembly provided in this embodiment includes: firstly, referring to the running environment of the electric automobile, preparing a cyclic charge-discharge life test and shelving test matrix, and carrying out a reference performance test in the cyclic and shelving process, wherein: the capacity test results in the cyclic charge and discharge and reference performance tests thereof are used for carrying out regression analysis on the capacity semi-empirical model; the HPPC (hybrid power pulse characteristic) test result in the cyclic charge and discharge and reference performance test is used for carrying out regression analysis on the internal resistance semi-empirical model; the battery energy value of the capacity test in the cyclic charge and discharge and reference performance test is used as a dependent variable to carry out regression analysis on the cyclic life combination model; the calendar service life database carries out regression analysis on the calendar semi-empirical model; secondly, acquiring durability test data of the whole vehicle, wherein the data comprise running data and rest data of the whole vehicle, and carrying out regression analysis on a life model by one part of the durability test data so as to obtain a combined life model; and part of the model accuracy is used for verifying the model accuracy.
Specifically, the implementation steps of the method for constructing the life model of the power battery combination provided in the embodiment are as follows:
(1) And establishing a preliminary cycle life model.
The related literature data at the present stage show that the temperature, the charge-discharge multiplying power and the discharge depth are the most main factors influencing the cycle life of the battery, so that the service life test scheme is designed by adopting the mode of operation of the electric vehicle in combination with the actual operation environment of the electric vehicle, taking the operation working condition of the private vehicle as the use scene and taking the NCM523 power battery produced by a certain battery manufacturer in China as a test object, as shown in the table 1:
table 1NCM523 power cell design life test protocol for test subjects
Figure BDA0004083704150000071
(1-1) an exponential semi-empirical model having internal resistance as a lifetime characteristic amount.
After test data are arranged, the data 01 to 09 are used for fitting an exponential semi-empirical model taking internal resistance as life characteristic quantity, namely:
Figure BDA0004083704150000072
wherein Q is loss,％,cyc,R The cycle life attenuation rate with the internal resistance as the characteristic quantity is calculated according to the internal resistance value obtained in the cycle charge-discharge reference performance test, and specifically comprises the following steps: the ratio of the increase in the internal resistance value relative to the initial internal resistance value to the initial internal resistance, i.e. Q loss,％,cyc,R An increase amount of the internal resistance value relative to the initial internal resistance value/initial internal resistance; a, a 1 Is normalCoefficient, a 2 Is a constant related to the depth of discharge DOD, the ambient temperature T and the charge-discharge multiplying power Ratio, and the unit of T is the temperature; ah represents the total throughput of charge in Ah.
First, the natural logarithm ln is taken for both sides of the formula, i.e
Figure BDA0004083704150000073
Then the least square method is adopted to calculate the coefficient a in the model 1 、a 2 Fitting was performed.
The obtained coefficient a 1 、a 2 Substituting the data into the right side of the formula (1) to obtain an exponential semi-empirical model taking the internal resistance as a life characteristic quantity.
(1-2) polynomial semi-empirical model with capacity as life characteristic quantity.
Fitting a polynomial semi-empirical model with capacity as a life characteristic quantity, namely:
Q loss,％,cy,ccap ＝b 1 k 2 +b 2 k+b 3 (2)
wherein Q is loss,％,cy,ccap The cycle life attenuation rate with the capacity as the characteristic quantity is calculated according to the capacity value obtained in the cycle charge-discharge reference performance test, and specifically comprises the following steps: the ratio of the amount of change in each capacity value relative to the initial capacity value to the initial capacity, i.e. Q loss,％,cyc,R Change amount of each capacity value relative to initial capacity value/initial capacity; b 1 ,b 2, b 3 K is the number of charge and discharge cycles, which is a constant coefficient of the model; fitting the model by using a least square method according to the characteristics of the model to obtain b 1 、b 2 、b 3 。
(1-3) a cycle life preliminary model.
Determining a cyclic life preliminary model according to an exponential semi-empirical model taking internal resistance as a life characteristic quantity and a polynomial semi-empirical model taking capacity as a life characteristic quantity, namely:
Q loss,％,cyc，energy ＝d 1 *Q loss,％,cyc,cap +d 2 * Q loss,％,cyc,R +d 3 (3)
wherein Q is loss,％,cyc，Energy Representing the battery electric quantity attenuation rate, wherein the data is derived from battery energy values of capacity tests in a cyclic charge-discharge reference performance test, and d 1 ,d 2 ,d 3 The constant is obtained by carrying out regression analysis by taking the battery energy attenuation rate of the capacity test in the cyclic charge and discharge and the reference performance test thereof as a dependent variable and combining a cyclic life test database.
(2) And establishing a calendar life model.
During battery rest, time and temperature are two important parameters affecting the calendar life of the power battery, and an exponential relationship exists between time and calendar life characteristics. Thus, the calendar life model is built using the Arrhenius equation, namely:
Figure BDA0004083704150000081
in which Q loss,％,calendar Representing the calendar capacity loss percentage of the battery, and calculating according to the calendar capacity value obtained in the reference performance test in the rest test, wherein the calendar capacity loss percentage is specifically: the ratio of the change amount of the calendar capacity of each reference performance test relative to the initial calendar capacity; r represents a universal gas constant, i.e. 8.31 J.mol -1 ·K -1 The method comprises the steps of carrying out a first treatment on the surface of the T represents absolute temperature, and is expressed in units of K, and the relation between the absolute temperature and the ambient temperature in the database is as follows: t (K) =ambient temperature (°c) +273.15; the method comprises the steps of carrying out a first treatment on the surface of the Ea represents the activation energy of the power battery, and the unit is J/mol; c 1 Representing the pre-coefficients; t represents the rest time, in "days".
Fitting an exponential semi-empirical model with calendar capacity as a life characteristic quantity using data 10-12, comprising: first, according to the shelf test data, the relationship between the battery capacity loss and the time days was analyzed. To find the relation between the two more intuitively, the natural logarithm is taken from both sides of the model to obtain the formula
Figure BDA0004083704150000091
Natural logarithm of visible battery capacity lossThe linear relation between the value and the logarithmic value of time days is obtained, and the slope of the straight line fitted by MATLAB is c 2 。
Reference c 2 Analyzing the relationship between the logarithm of the battery capacity loss value and 1/T at different rest time days, as shown in ln Q loss,％,calendar Proportional to 1/T, the slopes are equal at different rest times, i.e., -Ea/R is a constant, ea is obtained from a straight line slope fit, and c is obtained from a straight line intercept fit 1 。
(3) And establishing a battery life model according to the preliminary cycle life model and the calendar life model.
According to the preliminary cycle life model in the step (1) and the calendar life model in the step (2), establishing a life model of the battery as follows:
Q loss,％，energy ＝e 1 *Q loss,％,cy,cenergy+ e 2 *Q loss,％,caelndar +e 3 (5)
in the formula e 1 ,e 2 ,e 3 The constant coefficient is obtained by the following method:
firstly, dividing real vehicle endurance test data into t according to fixed time intervals 0 ～t 10 For 11 time periods, counting the initial full charge discharge energy, the total charge throughput Ah, the static time length and the ambient temperature in static state of the battery in each time interval, and using t 0 ～t 4 Data in 5 time periods are obtained by adopting linear least square fitting 1 ,e 2 ,e 3 。
Using t 5 ～t 10 The total 6 segments of data verify the model precision, the result is shown in figure 2, wherein the model prediction and the real vehicle statistical relative error analysis are shown in table 2, the maximum relative error is 0.26%, the model precision is higher, and the attenuation condition under the real vehicle running condition can be accurately estimated. Wherein the model calculation results are life decay rate, which is a normal phenomenon due to the increase in initial energy usage of the power cell in the example, and negative values of decay rate, but for more detailed and more specific data, FIG. 2 uses discharge energy retention rate to describeThe battery is attenuated, and the relation between the energy attenuation rate and the energy retention rate is as follows: energy decay rate + energy retention rate = 100%.
TABLE 2 statistical and model predictive relative error analysis of real vehicles
Time period Real vehicle statistics Model prediction Relative error
t5 97.83％ 97.96％ 0.13％
t6 98.08％ 98.32％ 0.24％
t7 97.43％ 97.65％ 0.23％
t8 96.55％ 96.78％ 0.24％
t9 95.87％ 96.12％ 0.26％
t10 94.91％ 95.16％ 0.26％
The embodiment also provides a method for estimating the service life of the power battery, which comprises the following steps:
according to the obtained measured data of the power battery to be estimated, calculating the attenuation rate of the energy of the power battery to be estimated by using the preliminary cycle life model, and calculating the calendar capacity loss percentage of the power battery to be estimated by using the calendar life model;
and calculating the energy attenuation rate of the power battery to be estimated by using the power battery combination life model according to the attenuation rate and the calendar capacity loss percentage.
Specifically, the measured data includes the number of charge and discharge cycles, the static time length, the ambient temperature at the time of static state, and the rest time;
substituting the ambient temperature and the total charge throughput into an exponential semi-empirical model (i.e., determining the coefficient a 1 、a 2 In the formula (1)), a cycle life attenuation rate is obtained with the internal resistance as a characteristic quantity;
substituting the charge-discharge cycle times into a polynomial semi-empirical model (i.e. determining coefficient b) 1 ,b 2 ,b 3 In the formula (2)), the cycle life attenuation rate with the capacity as a characteristic amount is obtained;
further, the cycle life attenuation rate characterized by the internal resistance and the cycle life attenuation rate characterized by the capacity are substituted into the cycle life preliminary model (i.e., the determination constant d 1 ,d 2 ,d 3 In the formula (3)), the battery power attenuation rate is obtained;
substituting ambient temperature and shelf time into a calendar life model (i.e., determining coefficient c 1 ，c 2 Equation (4)) to obtain the calendar capacity loss percentage of the battery;
further, the battery charge decay rate and the calendar capacity loss percentage of the battery are substituted into the battery life model (i.e., the coefficient e is determined 1 ,e 2 ,e 3 In the formula (5)), the energy attenuation rate of the power battery to be estimated is obtained.
Those skilled in the art will appreciate that all or part of the steps in a method implementing the above embodiments may be implemented by a program to instruct related hardware, and the corresponding program may be stored in a computer readable storage medium. It should be noted that although the method operations of the above embodiments are depicted in the drawings in a particular order, this does not require or imply that the operations must be performed in that particular order or that all illustrated operations be performed in order to achieve desirable results. Rather, the depicted steps may change the order of execution. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform.
The above-mentioned embodiments are only preferred embodiments of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art can make equivalent substitutions or modifications according to the technical solution and the inventive concept of the present invention within the scope of the present invention disclosed in the present invention patent, and all those skilled in the art belong to the protection scope of the present invention.

Claims (10)

1. The method for constructing the life model of the power battery combination is characterized by comprising the following steps of:
constructing a preliminary cycle life model; the preliminary cycle life model is used for calculating the attenuation rate of the energy of the power battery, and comprises a polynomial semi-empirical model taking the capacity as a life characteristic quantity and an exponential semi-empirical model taking the internal resistance as the life characteristic quantity, and the energy of the power battery is taken as a dependent variable;
constructing a power battery combined life model through a preliminary cycle life model and a calendar life model; the power battery combination life model is used for calculating the energy attenuation rate of the power battery; the calendar life model is a power function semi-empirical model based on time and is used for calculating the calendar capacity loss percentage of the power battery.
2. The method of claim 1, wherein the power cell combination lifetime model is:
Q loss,％，energy ＝e 1 *Q loss,％,cyc,energy +e 2 *Q loss,％,calendar +e 3 (1)
wherein e 1 ,e 2 ,e 3 Is of constant coefficient, Q loss,％，energy Decay rate, Q, representing the initial full charge discharge energy of a power battery loss,％,cyc,energy Represents the attenuation rate, Q of the energy of the power battery loss,％,calendar The calendar capacity loss percentage of the power cell is shown.
3. The construction method according to any one of claims 1 or 2, wherein the preliminary cycle life model is:
Figure FDA0004083704120000011
wherein d 1 ,d 2 ,d 3 Is a constant; q (Q) loss,％,cyc,cap The cycle life decay rate, Q, which represents the capacity as a characteristic quantity loss,％,cyc,R The cycle life decay rate is represented by the internal resistance as a characteristic amount.
4. The construction method according to claim 3, wherein the polynomial semi-empirical model with capacity as a lifetime feature is:
Q loss,％,cyc,cap ＝b 1 k 2 +b 2 k+b 3 (3)
wherein b 1 ,b 2 ,b 3 Is a constant coefficient; k is the number of charge and discharge cycles;
the polynomial semi-empirical model with capacity as life characteristic quantity is determined through the following process:
performing a cyclic charge-discharge life test on the power battery to obtain a capacity value of the power battery corresponding to a cyclic life stage;
performing regression analysis on the polynomial semi-empirical model with capacity as life characteristic quantity by using the capacity value, wherein the regression analysis comprises the following steps:
Q loss,％,cyc,cap is calculated according to the capacity value; k is the charge-discharge cycle number in the cycle charge-discharge life test;
sum k and corresponding Q loss,％,cyc,cap Substituting the value of (a) into the formula (3), and fitting the formula (3) by adopting a least square method to obtain b 1 、b 2 、b 3 。
5. The construction method according to claim 3, wherein the exponential semi-empirical model with internal resistance as a lifetime characteristic is:
Figure FDA0004083704120000021
wherein a is 1 Is a constant coefficient, a 2 Is a constant related to the depth of discharge DOD, the ambient temperature T and the charge-discharge rate Ratio; ah is the total throughput of charges in Ah units; the unit of the ambient temperature T is °c;
the exponential semi-empirical model taking internal resistance as a life characteristic quantity is determined through the following process:
performing a cyclic charge-discharge life test on the power battery, and obtaining a capacity value of the power battery corresponding to a cyclic life stage according to cyclic life test data; the cycle life test data also comprises the environmental temperature, the charge-discharge multiplying power and the discharge depth in the cycle charge-discharge process;
and carrying out regression analysis on the exponential semi-empirical model taking the internal resistance as the life characteristic quantity by utilizing the cycle life test data and the corresponding internal resistance value, wherein the regression analysis comprises the following steps:
the cycle life test data and corresponding Q loss,％,cyc,R Substituting the value of (a) into the formula (4), taking natural logarithms from two sides of the formula (4), and then adopting a least square method to compare the constant coefficient a 1 、a 2 Fitting is carried out; wherein Q is loss,％,cyc,R Is calculated from the internal resistance value.
6. A method of constructing as claimed in claim 3 wherein said preliminary cycle life model is determined by:
after determining a polynomial semi-empirical model with capacity as a life characteristic quantity and an exponential semi-empirical model with internal resistance as a life characteristic quantity, performing regression analysis on the preliminary cycle life model by using battery energy in a cycle charge-discharge life test as a dependent variable, including:
Q loss,％,cyc，energy is calculated according to the battery energy value;
d 1 ,d 2 ,d 3 according to Q loss,％,cyc，energy 、Q loss,％,cyc,R And Q loss,％,cyc,cap Regression analysis is performed on the values of (2).
7. The method of construction according to any one of claims 1 or 2, wherein the calendar semi-empirical model is:
Figure FDA0004083704120000031
wherein Q is loss,％,calendar Representing the percentage of calendar capacity loss for the battery; r represents a universal gas constant, T represents the absolute temperature of the ambient temperature, and the unit is K; ea represents the activation energy of the power battery, and the unit is J/mol; c 1 Representing the pre-coefficients; t represents the rest time in "days".
8. The method of claim 7, wherein the calendar semi-empirical model is determined by:
carrying out a shelving test on the power battery, and acquiring a calendar capacity value of the power battery in a corresponding storage stage according to shelving test data;
regression analysis of the calendar semi-empirical model using the rest test data and calendar capacity values, including:
the Q is loss,％,calendar Is calculated according to the calendar capacity value;
using the lay-up test data and corresponding Q loss,％,calendar Fitting the values of equation (5), comprising:
first, according to the shelf test data, the relationship between the battery capacity loss and the shelf time is analyzed, including:
taking natural logarithms from both sides of the formula (5) simultaneously to obtain:
Figure FDA0004083704120000032
then MATLAB is used to fit the slope of the straight line to be c 2 ；
Based on c 2 And (3) analyzing the relation between the logarithm of the battery capacity loss value and 1/T at different rest times: at different rest times, the slopes are equal, i.e., -Ea/R is a constant; ea is obtained according to the slope fitting of the straight line, c is obtained according to the intercept fitting of the straight line 1 。
9. The construction method according to claim 2, wherein the power cell combination lifetime model is determined by:
after determining a preliminary cycle life model and a calendar semi-empirical model, performing regression analysis on the power battery combined life model by using initial full charge discharge energy of a power battery in a whole-vehicle endurance test of an electric vehicle as a dependent variable, wherein the regression analysis comprises the following steps:
Q loss,％，energy is calculated from the initial full charge discharge energy;
Q loss,％,cyc,energy and Q loss,％,calendar The value of the (2) is obtained by calculation according to experimental data in the durability test of the whole electric automobile;
will Q loss,％，energy 、Q loss,％,cyc,energy And Q loss,％,calendar The value of (2) is substituted into the formula (1) and is fitted by a linear least square method to obtain e 1 ,e 2 ,e 3 ；
Will e 1 ,e 2 ,e 3 Substituting the service life model into the formula (1) to obtain the final power battery combination service life model.
10. A method of estimating the lifetime of a power battery, implemented on the basis of the construction method according to any one of claims 1-9, characterized in that the method comprises:
according to the obtained measured data of the power battery to be estimated, calculating the attenuation rate of the energy of the power battery to be estimated by using the preliminary cycle life model, and calculating the calendar capacity loss percentage of the power battery to be estimated by using the calendar life model;
and calculating the energy attenuation rate of the power battery to be estimated by using the power battery combination life model according to the attenuation rate and the calendar capacity loss percentage.
CN202310130645.6A 2023-02-16 2023-02-16 Construction method of power battery combination life model and battery life assessment method Pending CN116338464A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116653701A (en) * 2023-08-02 2023-08-29 江苏开沃汽车有限公司 Power battery full life cycle safety control method, system, equipment and medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116653701A (en) * 2023-08-02 2023-08-29 江苏开沃汽车有限公司 Power battery full life cycle safety control method, system, equipment and medium
CN116653701B (en) * 2023-08-02 2023-10-20 江苏开沃汽车有限公司 Power battery full life cycle safety control method, system, equipment and medium

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