CN112881916A - Method and system for predicting health state and remaining usable life of lithium battery - Google Patents
Method and system for predicting health state and remaining usable life of lithium battery Download PDFInfo
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 264
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 264
- 230000036541 health Effects 0.000 title claims abstract description 55
- 238000000034 method Methods 0.000 title claims abstract description 55
- 239000002245 particle Substances 0.000 claims abstract description 29
- 230000008569 process Effects 0.000 claims abstract description 24
- 238000004364 calculation method Methods 0.000 claims abstract description 10
- 238000007599 discharging Methods 0.000 claims description 40
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- 238000012360 testing method Methods 0.000 description 16
- 230000006870 function Effects 0.000 description 7
- 238000002474 experimental method Methods 0.000 description 3
- 230000003862 health status Effects 0.000 description 3
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 2
- 238000012952 Resampling Methods 0.000 description 2
- 230000032683 aging Effects 0.000 description 2
- 229910001416 lithium ion Inorganic materials 0.000 description 2
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- 125000004122 cyclic group Chemical group 0.000 description 1
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- 238000005315 distribution function Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 239000000178 monomer Substances 0.000 description 1
- 239000007773 negative electrode material Substances 0.000 description 1
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- 239000007774 positive electrode material Substances 0.000 description 1
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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Abstract
The invention discloses a method and a system for predicting the health state and the remaining usable life of a lithium battery, which relate to the technical field of lithium battery performance evaluation and comprise the steps of constructing a first lithium battery remaining capacity prediction model by adopting a particle filter algorithm according to the charge-discharge cycle data of the lithium battery; constructing a second lithium battery residual capacity prediction model by adopting a Gaussian process regression algorithm according to the charge-discharge cycle data of the lithium battery; judging whether the comprehensive residual capacity corresponding to the current cycle number is greater than a failure capacity threshold value or not; the comprehensive residual capacity is obtained by calculation according to a lithium battery comprehensive residual capacity prediction model; the lithium battery comprehensive residual capacity prediction model is constructed according to the first lithium battery residual capacity prediction model and the second lithium battery residual capacity prediction model; if yes, returning to the judging step; and if the health state of the lithium battery is determined to be the failure state, determining the current cycle number as the remaining usable life of the lithium battery. The invention can improve the prediction precision and shorten the detection period of the lithium battery.
Description
Technical Field
The invention relates to the technical field of lithium battery performance evaluation, in particular to a method and a system for predicting the health state and the remaining usable life of a lithium battery.
Background
In order to ensure safe and reliable running of the electric vehicle, a Battery Management System (BMS) must accurately acquire the use state information of the on-vehicle power battery pack in real time. Among them, the battery aging prediction problem is a key problem in a battery management system, and the aging state of a battery can be expressed by the state of health (SOH) of the battery. One important reason for studying the SOH of a battery is that the Remaining Usable Life (RUL) of the battery can be predicted on the basis thereof. On the premise of the known battery RUL, the operation condition of the electric automobile can be adjusted according to actual needs to prolong the service life of the battery, and the battery pack can be replaced in advance before the service life of the battery is ended so as to avoid influencing the normal running and safety of the electric automobile.
The health state and the remaining usable life of a lithium ion battery (lithium battery for short) are important performance indexes of the BMS, and no matter research on a positive electrode material or a negative electrode material or a test on a single battery pack, a cycle performance test needs to be performed in a laboratory to determine the health state and the cycle life. When the cycle performance of the battery is tested, the charge and discharge mode of the battery is mainly determined, and the cycle performance of the battery is characterized and tested by periodically cycling until the battery capacity is reduced to a specified value (usually 80% of rated capacity) and taking the charge and discharge times of the battery as the Remaining Usable Life (RUL) or comparing the remaining capacity of the battery after the same cycle. The evaluation method of the health state and the remaining usable life of the lithium battery also largely determines the performance of the electric vehicle. Due to the fact that a lithium battery system is complex and the performance is very complex, the health state and the remaining usable life of the lithium battery cannot be accurately predicted by the single method.
Disclosure of Invention
The invention aims to provide a method and a system for predicting the health state and the remaining usable life of a lithium battery, so as to quickly, effectively and accurately evaluate the health state and the remaining usable life of the lithium battery, shorten the detection period of the lithium battery and reduce the experiment cost.
In order to achieve the purpose, the invention provides the following scheme:
a method for predicting the health state and the remaining usable life of a lithium battery comprises the following steps:
acquiring charging and discharging cycle data of lithium batteries at different temperatures;
according to all the lithium battery charging and discharging cycle data, a particle filter algorithm is adopted to construct a first lithium battery residual capacity prediction model at different temperatures;
according to all the lithium battery charging and discharging cycle data, a Gaussian process regression algorithm is adopted to construct a second lithium battery residual capacity prediction model at different temperatures;
according to the first lithium battery residual capacity prediction model and the second lithium battery residual capacity prediction model, building a lithium battery comprehensive residual capacity prediction model at different temperatures;
respectively judging whether the comprehensive residual capacity corresponding to the current cycle times at different temperatures is greater than a failure capacity threshold value; the comprehensive residual capacity corresponding to the current cycle number is obtained by calculation according to the comprehensive residual capacity prediction model of the lithium battery;
if so, determining that the health state of the lithium battery corresponding to the current cycle number is in a non-failure state, adding 1 to the current cycle number, and then returning to the step of respectively judging whether the comprehensive residual capacity corresponding to the current cycle number at different temperatures is greater than a failure capacity threshold value;
if not, determining that the health state of the lithium battery corresponding to the current cycle number is the failure state, determining the current cycle number as the remaining usable life of the lithium battery, and further determining the remaining usable life of the lithium battery at different temperatures.
Optionally, the acquiring charge-discharge cycle data of the lithium battery at different temperatures specifically includes:
step (1): placing the lithium battery in a thermostat, and setting the temperature of the thermostat;
step (2): charging the lithium battery with a constant current of 1C to an upper cut-off voltage, then charging the lithium battery with a constant voltage of the upper cut-off voltage until the current is reduced to 0.05C, stopping charging, and standing for 5 min;
and (3): discharging the lithium battery to a lower cut-off voltage in a constant current manner by using a current of 1C, and repeating the steps (2) - (3) for n/2 times; wherein n is the minimum number of cycles that a desired lithium battery needs to achieve;
and (4): and (4) placing a new lithium battery in the thermostat, resetting the temperature of the thermostat, and repeating the steps (2) to (4) so as to obtain the charging and discharging cycle data of the lithium battery at different temperatures.
Optionally, the expression of the first lithium battery residual capacity prediction model is as follows:
f1(k)＝ak·exp(bk·k)+ck·k2+dk
wherein f is1(k) The residual capacity of the first lithium battery is the cycle number k;
ak、bk、ck、dkrespectively, the model parameter a when the cycle number is kk-1、bk-1、ck-1、dk-1Respectively, the model parameter v when the cycle number is k-11,k、v2,k、v3,k、v4,kThe noise is represented by the number of cycles k.
Optionally, the constructing a second lithium battery residual capacity prediction model at different temperatures by using a gaussian process regression algorithm according to all the lithium battery charge and discharge cycle data specifically includes:
calculating a kernel matrix according to a Gaussian kernel function;
determining the kernel matrix as a covariance matrix of a joint Gaussian distribution;
and constructing a second lithium battery residual capacity prediction model at different temperatures by adopting a Gaussian process regression algorithm according to all the lithium battery charging and discharging cycle data and the covariance matrix.
Optionally, the expression of the lithium battery comprehensive residual capacity prediction model is as follows:
f(k)＝xf1(k)+(1-x)f2(k)
wherein, f (k) represents the comprehensive residual capacity of the lithium battery when the cycle number is k; f. of1(k) Representing the residual capacity of the first lithium battery when the cycle number is k; f. of2(k) Indicates the number of cyclesThe remaining capacity of the second lithium battery when the number is k; x represents weight, and x is more than or equal to 0 and less than or equal to 1.
A system for predicting the state of health and remaining useful life of a lithium battery, comprising:
the lithium battery charging and discharging cycle data acquisition module is used for acquiring charging and discharging cycle data of the lithium battery at different temperatures;
the first lithium battery residual capacity prediction model construction module is used for constructing first lithium battery residual capacity prediction models at different temperatures by adopting a particle filter algorithm according to all the lithium battery charging and discharging cycle data;
the second lithium battery residual capacity prediction model construction module is used for constructing second lithium battery residual capacity prediction models at different temperatures by adopting a Gaussian process regression algorithm according to all the lithium battery charging and discharging cycle data;
the lithium battery comprehensive residual capacity prediction model construction module is used for constructing lithium battery comprehensive residual capacity prediction models at different temperatures according to the first lithium battery residual capacity prediction model and the second lithium battery residual capacity prediction model;
the judging module is used for respectively judging whether the comprehensive residual capacity corresponding to the current cycle times at different temperatures is greater than the failure capacity threshold value; the comprehensive residual capacity corresponding to the current cycle number is obtained by calculation according to the comprehensive residual capacity prediction model of the lithium battery;
the health state determining and returning module is used for determining that the health state of the lithium battery corresponding to the current cycle number is in a non-failure state when the comprehensive residual capacity corresponding to the current cycle number is larger than the failure capacity threshold, adding 1 to the current cycle number, and then returning to the judging module;
and the health state and remaining usable life determining module is used for determining the health state of the lithium battery corresponding to the current cycle number as a failure state when the comprehensive remaining capacity corresponding to the current cycle number is less than or equal to the failure capacity threshold, determining the current cycle number as the remaining usable life of the lithium battery, and further determining the remaining usable life of the lithium battery at different temperatures.
Optionally, an expression of the first lithium battery residual capacity prediction model in the first lithium battery residual capacity prediction model building module is as follows:
f1(k)＝ak·exp(bk·k)+ck·k2+dk
wherein f is1(k) The residual capacity of the first lithium battery is the cycle number k;
ak、bk、ck、dkrespectively, the model parameter a when the cycle number is kk-1、bk-1、ck-1、dk-1Respectively, the model parameter v when the cycle number is k-11,k、v2,k、v3,k、v4,kThe noise is represented by the number of cycles k.
Optionally, the second lithium battery residual capacity prediction model building module specifically includes:
the kernel matrix calculation unit is used for calculating a kernel matrix according to the Gaussian kernel function;
a covariance matrix determination unit configured to determine the kernel matrix as a covariance matrix of a joint gaussian distribution;
and the second lithium battery residual capacity prediction model construction unit is used for constructing second lithium battery residual capacity prediction models at different temperatures by adopting a Gaussian process regression algorithm according to all the lithium battery charging and discharging cycle data and the covariance matrix.
Optionally, the expression of the lithium battery comprehensive residual capacity prediction model in the lithium battery comprehensive residual capacity prediction model building module is as follows:
f(k)＝xf1(k)+(1-x)f2(k)
wherein, f (k) represents the comprehensive residual capacity of the lithium battery when the cycle number is k; f. of1(k) Representing the residual capacity of the first lithium battery when the cycle number is k; f. of2(k) Representing the residual capacity of the second lithium battery when the cycle number is k; x represents a weight, and 0≤x≤1。
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the method predicts the health state and the remaining usable life of the lithium battery through a particle filter algorithm and a Gaussian process regression algorithm, namely, the method comprehensively predicts the health state and the remaining usable life of the lithium battery by adopting different methods and different weight modes so as to improve the prediction precision and shorten the period of battery selection and allocation of the whole automobile factory in order to reduce the test times.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a method for predicting the state of health and remaining useful life of a lithium battery according to the present invention;
FIG. 2 is a schematic diagram of a system for predicting the state of health and remaining useful life of a lithium battery according to the present invention;
FIG. 3 is a schematic flow chart illustrating an exemplary method for predicting the health and remaining useful life of a lithium battery according to the present invention;
FIG. 4 is a schematic view of a lithium battery testing apparatus according to the present invention;
FIG. 5 is a graph of capacity prediction of 0.5C rate cycle test for a 0.9Ah battery of the present invention;
FIG. 6 is a graph showing the prediction of the capacity of the Ah battery with a capacity of 1.1 according to the present invention in the 1C rate cycle test.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for predicting the health state and the remaining usable life of a lithium battery, so as to quickly, effectively and accurately evaluate the health state and the remaining usable life of the lithium battery, shorten the detection period of the lithium battery and reduce the experiment cost.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
In the invention, the health state and the remaining usable life of the lithium battery do not depend on a certain unique method, but are comprehensively predicted by different methods in different weight modes so as to improve the prediction precision, and in order to reduce the test times and shorten the period of selecting and matching batteries in the whole automobile factory, the method for rapidly detecting the health state and the remaining usable life of the lithium battery is also adopted. In addition, the test environment of the lithium battery has certain influence on the charge and discharge performance of the lithium battery, so the invention also considers the influence of temperature. In conclusion, the method provided by the invention can be used for rapidly, effectively and accurately evaluating the health state and the remaining usable life of the lithium battery, shortening the detection period of the lithium battery and reducing the experiment cost.
Example one
As shown in fig. 1, the present embodiment provides a method for predicting the state of health and remaining useful life of a lithium battery, including the following steps.
Step 101: and acquiring the charge-discharge cycle data of the lithium battery at different temperatures.
Step 102: and constructing a first lithium battery residual capacity prediction model at different temperatures by adopting a particle filter algorithm according to all the lithium battery charging and discharging cycle data.
Step 103: and constructing a second lithium battery residual capacity prediction model at different temperatures by adopting a Gaussian process regression algorithm according to all the lithium battery charging and discharging cycle data.
Step 104: and constructing a comprehensive residual capacity prediction model of the lithium batteries at different temperatures according to the residual capacity prediction model of the first lithium battery and the residual capacity prediction model of the second lithium battery.
Step 105: respectively judging whether the comprehensive residual capacity corresponding to the current cycle times at different temperatures is greater than a failure capacity threshold value; the comprehensive residual capacity corresponding to the current cycle number is obtained by calculation according to the comprehensive residual capacity prediction model of the lithium battery; if yes, go to step 106; otherwise, go to step 107.
Step 106: and determining that the health state of the lithium battery corresponding to the current cycle number is in an inefficacy state, adding 1 to the current cycle number, and returning to the step 105.
Step 107: and determining the health state of the lithium battery corresponding to the current cycle number as a failure state, determining the current cycle number as the remaining usable life of the lithium battery, and further determining the remaining usable life of the lithium battery at different temperatures.
Wherein, step 101 specifically includes:
step (1): the lithium battery is placed in a thermostat, and the thermostat temperature is set.
Step (2): and (3) charging the lithium battery with a constant current of 1C to an upper cut-off voltage, then charging the lithium battery with a constant voltage by the cut-off voltage until the current is reduced to 0.05C, stopping charging, and standing for 5 min.
And (3): discharging the lithium battery to a lower cut-off voltage in a constant current manner by using a current of 1C, and repeating the steps (2) - (3) for n/2 times; where n is the minimum number of cycles that a desired lithium battery needs to achieve.
And (4): and (4) placing a new lithium battery in the thermostat, resetting the temperature of the thermostat, and repeating the steps (2) to (4) so as to obtain the charging and discharging cycle data of the lithium battery at different temperatures.
The expression of the first lithium battery residual capacity prediction model is as follows:
f1(k)＝ak·exp(bk·k)+ck·k2+dk
wherein f is1(k) The residual capacity of the first lithium battery is the cycle number k;
ak、bk、ck、dkrespectively, the model parameter a when the cycle number is kk-1、bk-1、ck-1、dk-1Respectively, the model parameter v when the cycle number is k-11,k、v2,k、v3,k、v4,kThe noise is represented by the number of cycles k.
The specific process of step 102 is as described in embodiment three, and will not be described repeatedly here.
Step 103 specifically comprises:
the kernel matrix is computed from the gaussian kernel function.
Determining the kernel matrix as a covariance matrix of a joint gaussian distribution.
And constructing a second lithium battery residual capacity prediction model at different temperatures by adopting a Gaussian process regression algorithm according to all the lithium battery charging and discharging cycle data and the covariance matrix.
The specific process of step 103 is as described in embodiment three, and the description is not repeated here.
The expression of the lithium battery comprehensive residual capacity prediction model is as follows:
f(k)＝xf1(k)+(1-x)f2(k)
wherein, f (k) represents the comprehensive residual capacity of the lithium battery when the cycle number is k; f. of1(k) Representing the residual capacity of the first lithium battery when the cycle number is k; f. of2(k) Representing the residual capacity of the second lithium battery when the cycle number is k; x represents weight, and x is more than or equal to 0 and less than or equal to 1.
Example two
As shown in fig. 2, the present embodiment provides a system for predicting a health state and a remaining useful life of a lithium battery, including:
the lithium battery charging and discharging cycle data acquisition module 201 is used for acquiring charging and discharging cycle data of lithium batteries at different temperatures.
The first lithium battery residual capacity prediction model construction module 202 is configured to construct a first lithium battery residual capacity prediction model at different temperatures by using a particle filter algorithm according to all the lithium battery charging and discharging cycle data.
And the second lithium battery residual capacity prediction model construction module 203 is used for constructing second lithium battery residual capacity prediction models at different temperatures by adopting a Gaussian process regression algorithm according to all the lithium battery charging and discharging cycle data.
And the lithium battery comprehensive residual capacity prediction model construction module 204 is configured to construct a lithium battery comprehensive residual capacity prediction model at different temperatures according to the first lithium battery residual capacity prediction model and the second lithium battery residual capacity prediction model.
A judging module 205, configured to respectively judge whether the comprehensive remaining capacity corresponding to the current cycle times at different temperatures is greater than a failure capacity threshold; and calculating the comprehensive residual capacity corresponding to the current cycle number according to the comprehensive residual capacity prediction model of the lithium battery.
And a health status determining and returning module 206, configured to determine that the health status of the lithium battery corresponding to the current cycle number is in a non-failure status, add 1 to the current cycle number, and then return to the determining module when the comprehensive remaining capacity corresponding to the current cycle number is greater than the failure capacity threshold.
The health state and remaining usable life determining module 207 is configured to determine that the health state of the lithium battery corresponding to the current cycle number is the failure state, determine the current cycle number as the remaining usable life of the lithium battery, and further determine the remaining usable life of the lithium battery at different temperatures, when the comprehensive remaining capacity corresponding to the current cycle number is less than or equal to the failure capacity threshold.
The expression of the first lithium battery residual capacity prediction model in the first lithium battery residual capacity prediction model building module 202 is as follows:
f1(k)＝ak·exp(bk·k)+ck·k2+dk
wherein f is1(k) The residual capacity of the first lithium battery is the cycle number k;
ak、bk、ck、dkrespectively, the model parameter a when the cycle number is kk-1、bk-1、ck-1、dk-1Respectively, the model parameter v when the cycle number is k-11,k、v2,k、v3,k、v4,kThe noise is represented by the number of cycles k.
The second lithium battery residual capacity prediction model construction module 203 specifically includes:
and the kernel matrix calculation unit is used for calculating a kernel matrix according to the Gaussian kernel function.
And the covariance matrix determining unit is used for determining the kernel matrix as a covariance matrix of the joint Gaussian distribution.
And the second lithium battery residual capacity prediction model construction unit is used for constructing second lithium battery residual capacity prediction models at different temperatures by adopting a Gaussian process regression algorithm according to all the lithium battery charging and discharging cycle data and the covariance matrix.
The expression of the lithium battery comprehensive residual capacity prediction model in the lithium battery comprehensive residual capacity prediction model building module 204 is as follows:
f(k)＝xf1(k)+(1-x)f2(k)
wherein, f (k) represents the comprehensive residual capacity of the lithium battery when the cycle number is k; f. of1(k) Representing the residual capacity of the first lithium battery when the cycle number is k; f. of2(k) Representing the residual capacity of the second lithium battery when the cycle number is k; x represents weight, and x is more than or equal to 0 and less than or equal to 1.
EXAMPLE III
As shown in fig. 3, the method for predicting the health status and the remaining useful life of the lithium battery provided in this embodiment includes the following steps.
The first step is as follows: a lithium ion battery (hereinafter referred to as a lithium battery) charge-discharge cycle life test is prepared to obtain charge-discharge cycle data of the lithium battery at different temperatures. The lithium battery test equipment is shown in figure 4.
(1) Test equipment: arbin and other monomer charging and discharging equipment and a constant temperature box.
(2) And (3) testing environment: 0 ℃ and 25 ℃ and 45 ℃.
(3) The testing steps are as follows: (1) placing the lithium battery in a thermostat, and setting the temperature of the thermostat; (2) charging the lithium battery with a constant current of 1C to an upper cut-off voltage, then charging with a constant voltage of the upper cut-off voltage until the current is reduced to 0.05C, stopping charging, and standing for 5 min; (3) discharging the lithium battery to a lower cut-off voltage in a constant current manner by using a current of 1C, and repeating the charging and discharging steps for n/2 times; wherein n is the minimum number of cycles that a desired lithium battery needs to achieve; (4) and (3) placing the lithium battery in a thermostat, resetting the temperature of the thermostat, and repeating the steps (2) to (4) so as to obtain the charging and discharging cycle data of the lithium battery at the temperature of 0 ℃, the charging and discharging cycle data of the lithium battery at the temperature of 25 ℃ and the charging and discharging cycle data of the lithium battery at the temperature of 45 ℃.
The second step is that: calculating a first lithium battery residual capacity prediction model at different temperatures by adopting a particle filter algorithm according to the charge-discharge cycle data of the lithium batteries at different temperatures; the method specifically comprises the following steps:
the first lithium battery residual capacity prediction model is shown as the following formula:
f1(k)＝ak·exp(bk·k)+ck·k2+dk(1)；
wherein f is1(k) The residual capacity of the first lithium battery with the cycle number k obtained by using a particle filter algorithm, a, b, c and d are exponential and polynomial capacity model parameters, v1，v2，v3，v4Is noise. Wherein the noise follows a normal distribution, the mean and variance of which are given at initialization.
The particle filter algorithm comprises the following specific steps:
the state variables at loop number k are: x is the number ofk＝[ak bk ck dk]。
Initialization setting: when k is 0, the state variable x is first determinedkInitial value of state and initial prior probability density p (x)r,0) Wherein the initial prior probability density follows a gaussian distribution; then obtaining N particles randomly according to the initial prior probability density
For k equal to 1, the following steps are performed:
a. importance sampling: generating N predicted sample particles when the new cycle number is k according to the formula (2):
b. updating the weight: setting the capacity value measured by the test as the observed value y of the capacity when the current cycle number k isr,kComparing the observed value y of capacityr,kSubstituting the predicted sampling particles at the current cycle number k into a capacity predicted value f obtained in formula (1)1(k) And thus the weight of each particle. The larger the weight value is, the closer the predicted value and the observed value are, the smaller the weight value is, the farther the predicted value and the observed value are, and the weight is calculated by the following formula:
wherein q (x)i r,k|xi r,0:k-1,yr,1:k) To the probability density of importance, p (x)i r,k|xi r,k-1) Is the state transition probability density, p (y)r,k|xi r,k) In order to observe the likelihood probability density distribution function,
the weight of the ith particle is the weight of the ith particle when the cycle number is k.
To facilitate comparison of the weight values of each particle, the particle weights are normalized to:
wherein,
is the weight of the ith particle when the normalized cycle number is k.
c. Resampling: and (3) resampling according to the weight of the particles, copying the particles with larger weight, wherein the larger the weight is, the more the copying times are, the smaller the weight is, the fewer the copying times are, and the particles with undersize weight are directly removed. Wherein, the copied particles are used to replace the removed particles so as to ensure that the total number of the particles is not changed in the process.
d. And (3) state estimation: using the state variable x of the N resampled particlesi r,kAnd their corresponding weights
The system state variables when the number of calculation cycles is k are:
wherein,
is the state variable x when the cycle number is kr,kAn estimate of (d).
By using
And covering the initial value of the state, and randomly obtaining new N particles according to the prior probability density.
e. Repeating iteration: repeating the steps a to d until k equals to the loop number n/2 when k equals to 2, 3, and obtaining the state variable x when the loop number is n/2r,kSubstituting the formula (1) to obtain the predicted capacity value f of the subsequent cycle number1(k)。
The third step: and constructing a second lithium battery residual capacity prediction model at different temperatures by adopting a Gaussian process regression algorithm according to the charging and discharging cycle data of all lithium batteries.
The Gaussian process regression algorithm comprises the following steps:
a. and selecting a proper kernel function and calculating a kernel matrix.
The embodiment adopts the most widely applied RBF kernel (gaussian kernel, also called radial basis function) as the kernel function, which is as follows:
wherein α is a hyper-parameter, l is a parameter determined by learning, and then f can be conveniently calculated by learning a suitable kernel only in a supervised learning manner in the embodiment2(k) Covariance matrix of f2(k) The residual capacity of the second lithium battery of the kth cycle is obtained by applying a Gaussian process regression algorithm.
b. The kernel matrix is used as a covariance matrix of the joint gaussian distribution and is combined with historical data (the historical data is lithium battery charge and discharge cycle data) to calculate the conditional probability distribution.
An estimated value of a point to be estimated is represented, and a prior distribution of the estimated value is determined as a Gaussian distribution.
Known as f2(k)～N(μ,K),f2(K) N (μ, K (K, K)), the prior of its joint probability distribution can be calculated as:
wherein, K**Is f2(k) a covariance matrix according to equation (3): k**＝k(k*,k*),K*＝k(k,k*)。
The conditional probability distribution is then calculated using the priors of the joint probability distribution.
c. And performing linear regression prediction according to the conditional probability distribution to obtain a second lithium battery residual capacity prediction model.
According to p (f)2) A prior distribution (alternatively referred to as gaussian distribution) and the conditional probability distribution p (f) calculated above2,f2Using Bayesian equation, p (f) is calculated2Posterior probability of), which is calculated as follows:
thus, for f2Estimation of x: f. of2*～(μ′,K′)，
Thereby outputting the predicted value f of capacity2(k)。
The fourth step: according to the first lithium battery residual capacity prediction model and the second lithium battery residual capacity prediction model, a lithium battery comprehensive residual capacity prediction model under different temperatures is constructed, namely, a capacity prediction value obtained by a particle filter algorithm and a capacity prediction value obtained by a Gaussian process regression algorithm are weighted, and finally obtained comprehensive residual capacity f (k) ═ xf is output1(k)+(1-x)f2(k) In that respect Wherein x is the weight of different algorithms, and x is more than or equal to 0 and less than or equal to 1. And calculating the health state, the residual usable life and the prediction error of the lithium battery by using the comprehensive residual capacity.
The algorithm provided in this example is used to predict the capacity of a battery with a capacity of 0.9Ah at a rate of 0.5C cycle test and to predict the capacity of a battery with a capacity of 1.1Ah at a rate of 1C cycle test, as shown in fig. 5 and 6.
The fifth step: comparing the comprehensive residual capacity obtained in the step with a failure capacity threshold value to judge whether the lithium battery fails, if the comprehensive residual capacity is larger than the failure capacity threshold value, not failing, and then continuing a cyclic prediction process by using the obtained data; and if the comprehensive residual capacity is less than the failure capacity threshold value after circulation, the lithium battery fails, and the circulation frequency at the moment is the residual usable life of the lithium battery.
Calculating the comprehensive residual capacity of the lithium battery under each prediction cycle at different temperatures in sequence until f (k)end)＝xf1(kend)+(1-x)f2(kend)≤0.8Cinit，CinitIs nominal capacity，0.8CinitIs a fail capacity threshold; when the comprehensive residual capacity is smaller than the failure capacity threshold value, the lithium battery is required to be replaced, and the cycle number k at the moment is obtainedendI.e. the number of charge-discharge cycles k when the lithium battery decays to 80% of the nominal capacityendI.e. the Remaining Useful Life (RUL) of the lithium battery.
The invention provides a method for predicting the health state and the remaining usable life of a lithium battery in the field of application of new energy technology of automobiles, in particular relates to a method for evaluating the health state and the remaining usable life of the lithium battery in a laboratory, and has the advantages of simplicity, easiness, test frequency reduction, wide application range, strong practicability and capability of evaluating the health state and the remaining usable life of various lithium batteries.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (9)
1. A method for predicting the health state and the remaining usable life of a lithium battery is characterized by comprising the following steps:
acquiring charging and discharging cycle data of lithium batteries at different temperatures;
according to all the lithium battery charging and discharging cycle data, a particle filter algorithm is adopted to construct a first lithium battery residual capacity prediction model at different temperatures;
according to all the lithium battery charging and discharging cycle data, a Gaussian process regression algorithm is adopted to construct a second lithium battery residual capacity prediction model at different temperatures;
according to the first lithium battery residual capacity prediction model and the second lithium battery residual capacity prediction model, building a lithium battery comprehensive residual capacity prediction model at different temperatures;
respectively judging whether the comprehensive residual capacity corresponding to the current cycle times at different temperatures is greater than a failure capacity threshold value; the comprehensive residual capacity corresponding to the current cycle number is obtained by calculation according to the comprehensive residual capacity prediction model of the lithium battery;
if so, determining that the health state of the lithium battery corresponding to the current cycle number is in a non-failure state, adding 1 to the current cycle number, and then returning to the step of respectively judging whether the comprehensive residual capacity corresponding to the current cycle number at different temperatures is greater than a failure capacity threshold value;
if not, determining that the health state of the lithium battery corresponding to the current cycle number is the failure state, determining the current cycle number as the remaining usable life of the lithium battery, and further determining the remaining usable life of the lithium battery at different temperatures.
2. The method for predicting the health state and the remaining usable life of a lithium battery according to claim 1, wherein the obtaining of the charge-discharge cycle data of the lithium battery at different temperatures specifically comprises:
step (1): placing the lithium battery in a thermostat, and setting the temperature of the thermostat;
step (2): charging the lithium battery with a constant current of 1C to an upper cut-off voltage, then charging the lithium battery with a constant voltage of the upper cut-off voltage until the current is reduced to 0.05C, stopping charging, and standing for 5 min;
and (3): discharging the lithium battery to a lower cut-off voltage in a constant current manner by using a current of 1C, and repeating the steps (2) - (3) for n/2 times; wherein n is the minimum number of cycles that a desired lithium battery needs to achieve;
and (4): and (4) placing a new lithium battery in the thermostat, resetting the temperature of the thermostat, and repeating the steps (2) to (4) so as to obtain the charging and discharging cycle data of the lithium battery at different temperatures.
3. The method for predicting the state of health and the remaining useful life of a lithium battery as claimed in claim 1, wherein the expression of the first lithium battery remaining capacity prediction model is as follows:
f1(k)＝ak·exp(bk·k)+ck·k2+dk
wherein f is1(k) The residual capacity of the first lithium battery is the cycle number k;
ak、bk、ck、dkrespectively, the model parameter a when the cycle number is kk-1、bk-1、ck-1、dk-1Respectively, the model parameter v when the cycle number is k-11,k、v2,k、v3,k、v4,kThe noise is represented by the number of cycles k.
4. The method for predicting the health state and the remaining usable life of a lithium battery according to claim 1, wherein a gaussian process regression algorithm is adopted to construct a second lithium battery remaining capacity prediction model at different temperatures according to all the lithium battery charging and discharging cycle data, and the method specifically comprises the following steps:
calculating a kernel matrix according to a Gaussian kernel function;
determining the kernel matrix as a covariance matrix of a joint Gaussian distribution;
and constructing a second lithium battery residual capacity prediction model at different temperatures by adopting a Gaussian process regression algorithm according to all the lithium battery charging and discharging cycle data and the covariance matrix.
5. The method for predicting the health state and the remaining usable life of a lithium battery as claimed in claim 1, wherein the expression of the comprehensive lithium battery remaining capacity prediction model is as follows:
f(k)＝xf1(k)+(1-x)f2(k)
wherein, f (k) represents the comprehensive residual capacity of the lithium battery when the cycle number is k; f. of1(k) Representing the residual capacity of the first lithium battery when the cycle number is k; f. of2(k) Representing the residual capacity of the second lithium battery when the cycle number is k; x represents weight, and x is more than or equal to 0 and less than or equal to 1.
6. A system for predicting the state of health and remaining useful life of a lithium battery, comprising:
the lithium battery charging and discharging cycle data acquisition module is used for acquiring charging and discharging cycle data of the lithium battery at different temperatures;
the first lithium battery residual capacity prediction model construction module is used for constructing first lithium battery residual capacity prediction models at different temperatures by adopting a particle filter algorithm according to all the lithium battery charging and discharging cycle data;
the second lithium battery residual capacity prediction model construction module is used for constructing second lithium battery residual capacity prediction models at different temperatures by adopting a Gaussian process regression algorithm according to all the lithium battery charging and discharging cycle data;
the lithium battery comprehensive residual capacity prediction model construction module is used for constructing lithium battery comprehensive residual capacity prediction models at different temperatures according to the first lithium battery residual capacity prediction model and the second lithium battery residual capacity prediction model;
the judging module is used for respectively judging whether the comprehensive residual capacity corresponding to the current cycle times at different temperatures is greater than the failure capacity threshold value; the comprehensive residual capacity corresponding to the current cycle number is obtained by calculation according to the comprehensive residual capacity prediction model of the lithium battery;
the health state determining and returning module is used for determining that the health state of the lithium battery corresponding to the current cycle number is in a non-failure state when the comprehensive residual capacity corresponding to the current cycle number is larger than the failure capacity threshold, adding 1 to the current cycle number, and then returning to the judging module;
and the health state and remaining usable life determining module is used for determining the health state of the lithium battery corresponding to the current cycle number as a failure state when the comprehensive remaining capacity corresponding to the current cycle number is less than or equal to the failure capacity threshold, determining the current cycle number as the remaining usable life of the lithium battery, and further determining the remaining usable life of the lithium battery at different temperatures.
7. The system for predicting the state of health and the remaining useful life of a lithium battery as claimed in claim 6, wherein the expression of the first lithium battery remaining capacity prediction model in the first lithium battery remaining capacity prediction model building module is as follows:
f1(k)＝ak·exp(bk·k)+ck·k2+dk
wherein f is1(k) The residual capacity of the first lithium battery is the cycle number k;
ak、bk、ck、dkrespectively, the model parameter a when the cycle number is kk-1、bk-1、ck-1、dk-1Respectively, the model parameter v when the cycle number is k-11,k、v2,k、v3,k、v4,kThe noise is represented by the number of cycles k.
8. The system for predicting the state of health and the remaining useful life of a lithium battery as claimed in claim 6, wherein the second lithium battery remaining capacity prediction model building module specifically comprises:
the kernel matrix calculation unit is used for calculating a kernel matrix according to the Gaussian kernel function;
a covariance matrix determination unit configured to determine the kernel matrix as a covariance matrix of a joint gaussian distribution;
and the second lithium battery residual capacity prediction model construction unit is used for constructing second lithium battery residual capacity prediction models at different temperatures by adopting a Gaussian process regression algorithm according to all the lithium battery charging and discharging cycle data and the covariance matrix.
9. The system for predicting the state of health and the remaining useful life of a lithium battery as claimed in claim 6, wherein the expression of the lithium battery comprehensive residual capacity prediction model in the lithium battery comprehensive residual capacity prediction model construction module is as follows:
f(k)＝xf1(k)+(1-x)f2(k)
wherein, f (k) represents the comprehensive residual capacity of the lithium battery when the cycle number is k; f. of1(k) Representing the residual capacity of the first lithium battery when the cycle number is k; f. of2(k) Representing the residual capacity of the second lithium battery when the cycle number is k; x represents weight, and x is more than or equal to 0 and less than or equal to 1.
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