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CN112986831A - Lithium ion battery life prediction method based on correlation coefficient particle filtering - Google Patents

Lithium ion battery life prediction method based on correlation coefficient particle filtering Download PDF

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CN112986831A
CN112986831A CN202110479300.2A CN202110479300A CN112986831A CN 112986831 A CN112986831 A CN 112986831A CN 202110479300 A CN202110479300 A CN 202110479300A CN 112986831 A CN112986831 A CN 112986831A
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capacity
ion battery
lithium
correlation coefficient
battery
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周勇
高迪驹
张松勇
王硕丰
顾伟
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Shanghai Maritime University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • 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]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • 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]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health

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Abstract

本发明公开了一种基于相关系数粒子滤波的锂离子电池寿命预测方法，包含：S1、设置预测起点及电池寿命阈值；S2、获取待预测锂离子电池的数据及其容量估计值

Figure DDA0003048574440000011
S3、建立锂离子电池容量指数衰减模型的状态空间，并进行参数估计；S4、设置采样粒子数目N，过程噪声方差σw和测量噪声方差σv以及重采样阈值
Figure DDA0003048574440000012
S5、将待预测锂离子电池的容量估计值
Figure DDA0003048574440000013
作为测量值代入锂离子电池容量指数衰减模型，基于相关系数粒子滤波算法，不断更新粒子权重，获取预测起点时状态后验估计；S6、基于容量衰减模型将状态后验估计迭代至寿命阈值，获取剩余寿命预测结果。其优点是：该方法将相关系数粒子滤波算法引入电池RUL预测，可以有效提高锂电池RUL预测精度。

Figure 202110479300

The invention discloses a lithium-ion battery life prediction method based on correlation coefficient particle filtering, comprising: S1, setting a prediction starting point and a battery life threshold; S2, acquiring data of the lithium-ion battery to be predicted and its capacity estimation value

Figure DDA0003048574440000011
S3. Establish the state space of the exponential decay model of lithium-ion battery capacity, and perform parameter estimation; S4. Set the number of sampling particles N, process noise variance σw , measurement noise variance σv , and resampling threshold
Figure DDA0003048574440000012
S5. Estimate the capacity of the lithium-ion battery to be predicted
Figure DDA0003048574440000013
Substitute the measured value into the lithium-ion battery capacity exponential decay model, and continuously update the particle weights based on the correlation coefficient particle filter algorithm to obtain the state posterior estimation at the starting point of prediction; S6, based on the capacity decay model, iterate the state posterior estimation to the life threshold, and obtain Remaining life prediction results. The advantage is that the method introduces the correlation coefficient particle filter algorithm into the battery RUL prediction, which can effectively improve the RUL prediction accuracy of the lithium battery.

Figure 202110479300

Description

Lithium ion battery life prediction method based on correlation coefficient particle filtering
Technical Field
The invention relates to the technical field of health prediction and diagnosis of a battery management system, in particular to a lithium ion battery service life prediction method based on correlation coefficient particle filtering.
Background
Lithium ion batteries have characteristics of high energy density, high open circuit voltage, wide temperature range, rapid charge and discharge, and high output power, and have been widely used in almost all industrial fields with energy supply. In many fields, lithium ion batteries have gradually become their key devices. However, unlike other rechargeable batteries, the performance of lithium ion batteries slowly degrades during use, which is manifested by a decrease in the capacity and an increase in the internal resistance of the lithium ion battery. The continuous use of the battery after the battery reaches the service life threshold value may bring a series of safety problems, and the accurate prediction of the remaining service life (RUL) of the battery is very important for ensuring the reliable and safe operation of the lithium ion battery, but the problem that the remaining capacity cannot be measured in real time exists in the RUL prediction of the lithium ion battery at present.
Disclosure of Invention
The invention aims to provide a lithium ion battery life prediction method based on correlation coefficient particle filtering, which aims at the problem that the capacity is difficult to directly measure in the RUL prediction of a lithium ion battery, and can estimate the residual capacity of the battery in real time by extracting measurable indirect parameters in a discharge period when the battery runs; in addition, the method introduces a relative particle filter algorithm into the battery RUL prediction aiming at the problems of particle degradation and sample shortage in the standard particle filter algorithm, and can effectively improve the RUL prediction precision of the lithium battery.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a lithium ion battery life prediction method based on correlation coefficient particle filtering is characterized by comprising the following steps:
s1, setting a prediction starting point and a battery life threshold;
s2, obtaining the data of the lithium ion battery to be predicted, and obtaining the capacity estimation value of the lithium ion battery to be predicted based on the lithium ion battery capacity estimation method
Figure BDA0003048574420000021
S3, establishing a state space of a lithium ion battery capacity index decay model, and performing parameter estimation;
s4, setting the number N of sampling particles and the process noise variance sigmawAnd measure the noise variance σvAnd a resampling threshold
Figure BDA0003048574420000022
S5, estimating the capacity of the lithium ion battery to be predicted
Figure BDA0003048574420000023
Substituting the measured value into a lithium ion battery capacity exponential decay model, continuously updating the weight of particles based on a correlation coefficient particle filter algorithm, and obtaining state posterior estimation when the starting point is predicted;
and S6, iterating the state posterior estimation to the life threshold value based on the capacity attenuation model, and obtaining the residual life prediction result.
Optionally, the step S2 includes:
s21, acquiring and analyzing data of the lithium ion battery during operation;
s22, extracting physical parameters capable of representing battery performance degradation in the data obtained in the step S21;
s23, reducing the dimensionality of the physical parameters extracted in the step S22 based on a principal component analysis method, and acquiring fusion health factors capable of representing battery performance degradation;
s24, taking the fusion health factor as an input parameter of the NARX neural network, and taking actually measured capacity data as an output parameter to obtain a relation model of the fusion health factor and the battery residual capacity;
s25, obtaining characteristic parameters of the lithium ion battery to be predicted before the prediction starting point, reducing the dimension, and using the characteristic parameters as the input of a relation model fusing the health factor and the battery residual capacity, thereby obtaining the capacity estimation value of the lithium ion battery to be predicted
Figure BDA0003048574420000024
Optionally, the physical parameters capable of characterizing the battery performance degradation in step S22 include:
the time when the voltage is reduced to the minimum peak value, the constant voltage drop discharge time, the time when the current at the load end and the output current are reduced to the minimum peak value, and the time when the temperature is increased to the maximum peak value.
Optionally, the state space for establishing the lithium ion battery capacity exponential decay model in step S3 includes:
the lithium ion battery capacity exponential decay model is as follows:
Qk＝a·exp(b·k)+c·exp(d·k) (1)
wherein a, b, c, d are parameters of the capacity exponential decay model, a is a first initial value of the capacity of the battery, c is a second initial value of the capacity of the battery, b is a first capacity decay rate, d is a second capacity decay rate, k is the number of charge-discharge cycles, QkThe residual capacity of the battery at the moment k is the residual capacity of the battery at the kth charge-discharge cycle;
the exponential decay model of the capacity of the lithium ion battery in the formula (1) can be converted into the following by polynomial operation:
Qk＝Qk-1·exp(b)+c·exp[d·(k-1)]·[1-exp(b-d)] (2)
the state space equation of the lithium ion battery capacity exponential decay model is as follows:
Figure BDA0003048574420000031
Figure BDA0003048574420000032
wherein wkIs the process noise at time k, vkMeasurement noise at time k, wk～N(0,σw) Denotes wkObedience is expected to be 0 and variance is σwNormal distribution of (v)k～N(0,σv) Denotes vkObedience is expected to be 0 and variance is σvNormal distribution of (2), Qk-1The residual capacity at the k-1 st charge-discharge cycle.
Optionally, the performing parameter estimation in step S3 includes:
taking the parameters in the lithium ion battery capacity exponential decay model as the system state to obtain a state space model x at the moment kk：
xk＝[ak,bk,ck,dk]
ak＝ak-1+wa wa～N(0,σa)
bk＝bk-1+wb wb～N(0,σb)
ck＝ck-1+wc wc～N(0,σc)
dk＝dk-1+wd wd～N(0,σd) (6)
Qk＝ak·exp(bk·k)+ck·exp(dk·k)+vk vk～N(0,σn)
Wherein, ak,bk,ck,dkA first battery capacity initial value, a second battery capacity initial value, a first capacity fade rate and a second capacity fade rate at time k, respectivelyk-1,bk-1,ck-1,dk-1Respectively corresponding parameter at time k-1, wa,wb,wc,wdAre respectively a constant;
fitting the full-period capacity data of the training lithium ion battery according to the lithium ion battery capacity exponential decay model, and taking the fitted parameters as initial parameters of the lithium ion battery to be predicted;
estimating the capacity of the lithium ion battery to be predicted
Figure BDA0003048574420000033
State space model x as time kkThe parameters are updated iteratively based on a particle filter algorithm, and the values of b, c and d after iteration are parameters in a lithium ion battery capacity exponential decay model.
Optionally, the step S5 includes:
s51, initialization: for i 1,2, N, from the distribution
Figure BDA0003048574420000041
Extracting N particles to form a particle set
Figure BDA0003048574420000042
S52, calculating the sample weight of the particle:
Figure BDA0003048574420000043
wherein z iskAs individual measurements;
s53, weight normalization:
Figure BDA0003048574420000044
s54, calculating the number N of effective particleseffSetting a resampling threshold NthIf the number of effective particles Neff＜NthThen resampling is carried out;
s55, actual measurement of construction systemValue sequence and sample estimation measurement matrix: for the actual measured value sequence, a length L sequence is taken
Figure BDA0003048574420000045
For a sequence of sample estimation measurements, take
Figure BDA0003048574420000046
Figure BDA0003048574420000047
S56, calculating a correlation coefficient: for the sequence ZkAnd
Figure BDA0003048574420000048
calculating the correlation coefficient cc;
s57, recalculating the weight of the particle sample: the range of the correlation coefficient cc is [ -1,1], and in order to transform it into the positive range, an exponential function with a parameter α is taken to process the correlation coefficient:
Figure BDA0003048574420000049
wherein, the parameter alpha is more than 0, the function is to adjust the discrete degree of the sample weight, and beta is the correlation coefficient after conversion;
the recalculated particle sample weights are:
Figure BDA00030485744200000410
and (4) normalizing the recalculated sample particle weight value according to the formula (8).
Compared with the prior art, the invention has the following advantages:
aiming at the problem that the capacity is difficult to directly measure in the RUL prediction of the lithium ion battery, the method can be used for establishing a relation model fusing a health factor and the residual capacity of the battery by analyzing measurable battery performance parameters in a discharge period during the operation of the battery so as to obtain the estimated value of the residual capacity of the lithium ion battery in real time; in addition, the method introduces a relative particle filter algorithm into the battery RUL prediction aiming at the problems of particle degradation and sample shortage in the standard particle filter algorithm, and can effectively improve the RUL prediction precision of the lithium battery.
Further, the method of the present invention can predict the RUL of the battery and give an uncertainty expression of the prediction result by substituting the obtained capacity estimation value as a measurement value into a correlation coefficient particle filter algorithm.
Drawings
FIG. 1 is a schematic diagram of a lithium ion battery life prediction method based on correlation coefficient particle filtering according to the present invention;
FIG. 2 is a graph of B5 cell and B6 cell capacity versus cycle number for an example of the present invention;
FIG. 3 is a diagram illustrating the capacity estimation result of a B6 battery according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating the RUL prediction result of a lithium ion battery based on a standard particle filtering algorithm according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a RUL prediction result of a lithium ion battery based on a correlation coefficient particle filter algorithm according to an embodiment of the present invention.
Detailed Description
The present invention will now be further described by way of the following detailed description of a preferred embodiment thereof, taken in conjunction with the accompanying drawings.
As shown in fig. 1, a schematic diagram of a lithium ion battery life prediction method based on correlation coefficient particle filtering according to the present invention is shown, and the method includes:
and S1, setting a prediction starting point and a battery life threshold.
S2, obtaining the data of the lithium ion battery to be predicted, and obtaining the capacity estimation value of the lithium ion battery to be predicted based on the lithium ion battery capacity estimation method
Figure BDA0003048574420000051
The step S2 includes:
and S21, acquiring and analyzing the data of the training lithium ion battery during operation.
And S22, extracting physical parameters capable of representing the battery performance degradation from the data obtained in the step S21.
Wherein the physical parameters capable of characterizing the battery performance degradation in step S22 include: the time when the voltage is reduced to the minimum peak value, the constant voltage drop discharge time, the time when the current at the load end and the output current are reduced to the minimum peak value, the time when the temperature is increased to the maximum peak value and the like.
And S23, reducing the dimensionality of the physical parameters extracted in the step S22 based on the principal component analysis method, and acquiring fusion health factors capable of representing battery performance degradation.
And S24, taking the fusion health factor as an input parameter of the NARX neural network, and taking actually measured capacity data as an output parameter to obtain a relation model of the fusion health factor and the battery residual capacity.
S25, obtaining characteristic parameters of the lithium ion battery to be predicted before the prediction starting point, reducing the dimension, and using the characteristic parameters as the input of a relation model fusing the health factor and the battery residual capacity, thereby obtaining the capacity estimation value of the lithium ion battery to be predicted
Figure BDA0003048574420000061
And S3, establishing a state space of the lithium ion battery capacity exponential decay model, and performing parameter estimation.
The state space for establishing the lithium ion battery capacity exponential decay model in the step S3 includes:
the lithium ion battery capacity exponential decay model is as follows:
Qk＝a·exp(b·k)+c·exp(d·k) (1)
wherein a, b, c, d are parameters of the capacity exponential decay model, a is a first initial value of the capacity of the battery, c is a second initial value of the capacity of the battery, b is a first capacity decay rate, d is a second capacity decay rate, k is the number of charge-discharge cycles, QkBattery at time kThe residual capacity is the residual capacity in the k-th charge-discharge cycle;
the exponential decay model of the capacity of the lithium ion battery in the formula (1) can be converted into the following by polynomial operation:
Qk＝Qk-1·exp(b)+c·exp[d·(k-1)]·[1-exp(b-d)] (2)
the state space equation of the lithium ion battery capacity exponential decay model is as follows:
Figure BDA0003048574420000062
Figure BDA0003048574420000063
wherein wkIs the process noise at time k, vkMeasurement noise at time k, wk～N(0,σw) Denotes wkObedience is expected to be 0 and variance is σwNormal distribution of (v)k～N(0,σv) Denotes vkObedience is expected to be 0 and variance is σvNormal distribution of (2), Qk-1The residual capacity at the k-1 st charge-discharge cycle.
Further, the performing of parameter estimation in step S3 includes:
taking the parameters in the lithium ion battery capacity exponential decay model as the system state to obtain a state space model x at the moment kk：
xk＝[ak,bk,ck,dk]
ak＝ak-1+wa wa～N(0,σa)
bk＝bk-1+wb wb～N(0,σb)
ck＝ck-1+wc wc～N(0,σc)
dk＝dk-1+wd wd～N(0,σd) (6)
Qk＝ak·exp(bk·k)+ck·exp(dk·k)+vk vk～N(0,σn)
Wherein, ak,bk,ck,dkA first battery capacity initial value, a second battery capacity initial value, a first capacity fade rate and a second capacity fade rate at time k, respectivelyk-1,bk-1,ck-1,dk-1Respectively corresponding parameter at time k-1, wa,wb,wc,wdAre constants (the constants are not necessarily equal).
And fitting the full-period capacity data of the training lithium ion battery according to the lithium ion battery capacity exponential decay model, and taking the fitted parameters as initial parameters of the lithium ion battery to be predicted.
Estimating the capacity of the lithium ion battery to be predicted
Figure BDA0003048574420000071
State space model x as time kkThe parameters are updated iteratively based on a particle filter algorithm, and the values of b, c and d after iteration are parameters in a lithium ion battery capacity exponential decay model.
S4, setting the number N of sampling particles and the process noise variance sigmawAnd measure the noise variance σvAnd a resampling threshold
Figure BDA0003048574420000072
S5, estimating the capacity of the lithium ion battery to be predicted
Figure BDA0003048574420000073
Substituting the measured value into a lithium ion battery capacity exponential decay model, continuously updating the particle weight based on a correlation coefficient particle filter algorithm, and obtaining the state posterior estimation at the starting point of prediction.
Specifically, the step S5 includes:
s51, initialization: for i 1,2, N, from the distribution
Figure BDA0003048574420000074
Extracting N particles to form a particle set
Figure BDA0003048574420000075
S52, calculating the sample weight of the particle:
Figure BDA0003048574420000076
wherein z iskAre individual measurements.
S53, weight normalization:
Figure BDA0003048574420000081
s54, calculating the number N of effective particleseffSetting a resampling threshold NthIf the number of effective particles Neff＜NthResampling is performed.
S55, constructing a system actual measurement value sequence and a sample estimation measurement value matrix: for the actual measured value sequence, a length L sequence is taken
Figure BDA0003048574420000082
For a sequence of sample estimation measurements, take
Figure BDA0003048574420000083
Figure BDA0003048574420000084
S56, calculating a correlation coefficient: for the sequence ZkAnd
Figure BDA0003048574420000085
the correlation coefficient cc is calculated.
S57, recalculating the weight of the particle sample: the range of the correlation coefficient cc is [ -1,1], and in order to transform it into the positive range, an exponential function with a parameter α is taken to process the correlation coefficient:
Figure BDA0003048574420000086
wherein, the parameter alpha is more than 0, the function is to adjust the discrete degree of the sample weight, and beta is the correlation coefficient after conversion;
the recalculated particle sample weights are:
Figure BDA0003048574420000087
and (4) normalizing the recalculated sample particle weight value according to the formula (8).
And S6, iterating the state posterior estimation to the life threshold value based on the capacity attenuation model, and obtaining the residual life prediction result.
In this embodiment, the validity of the method of the present invention is demonstrated by combining with an example, where the test set is test data obtained by performing an accelerated life test on a lithium ion battery by National Aeronautics and astronautics (NASA), and the data set includes test data of temperature, current, voltage, and the like during charging and discharging processes of four lithium ion batteries with numbers B5, B6, B7, and B18. The test samples had selected B5 and B6 cells. The B5 battery data are used as a training set to establish a relation model fused with the capacity of the health factor, so that the B6 battery capacity is estimated, and the B6 battery data are used for verification and RUL prediction.
As shown in fig. 2, the capacity of the B5 cell and the B6 cell were plotted against the number of cycles. The battery capacity generally shows a tendency to gradually decay, locally accompanied by a capacity recovery effect.
As shown in fig. 3, the result is the capacity estimation of the B6 battery. And establishing a relation model fusing the health factor and the residual capacity of the battery by taking the full life cycle data of the B5 battery as training data. Indirect parameters in the circulation process of the B6 battery are extracted, a main component analysis method is used for obtaining health factors, the health factors are used as input, the full life cycle capacity of the B6 battery is estimated, and the root mean square error of the estimation result is 0.0247. From the above results, it can be seen that the proposed lithium ion capacity estimation method has high accuracy and strong adaptability.
Fig. 4 shows the result of RUL prediction of a lithium ion battery using a standard particle filter algorithm. The prediction starting point is the 80 th cycle, the service life threshold value for judging whether the battery is failed is Q < 1.38, the particle number is selected to be 200, the actual failure time of the battery is the 113 th cycle, the predicted failure time is the 105 th cycle, and the prediction error is 8 cycle cycles.
Fig. 5 shows the result of RUL prediction of a lithium ion battery using the correlation coefficient particle filter algorithm according to the present invention. The prediction starting point is also selected as the 80 th cycle, the life threshold for judging whether the battery is failed is Q < 1.38, the particle number is selected to be 200, the actual failure time of the battery is the 113 th cycle, the predicted failure time is the 109 th cycle, and the prediction error is 4 cycle cycles. The simulation result can show that the method provided by the invention can effectively predict the service life of the lithium ion battery, the prediction error is within an acceptable range, and the particle filter algorithm based on the correlation coefficient can effectively improve the accuracy of the long-term prediction of the RUL.
In summary, according to the lithium ion battery life prediction method based on the correlation coefficient particle filter, aiming at the problem that the capacity is difficult to directly measure in the RUL prediction of the lithium ion battery, a relation model fusing a health factor and the battery residual capacity is established by analyzing measurable battery performance parameters in a discharge cycle during the operation of the battery, so as to obtain an estimated value of the lithium ion battery residual capacity in real time; in addition, the method introduces a relative particle filter algorithm into the battery RUL prediction aiming at the problems of particle degradation and sample shortage in the standard particle filter algorithm, and can effectively improve the RUL prediction precision of the lithium battery.
Further, the method of the present invention can predict the RUL of the battery and give an uncertainty expression of the prediction result by substituting the obtained capacity estimation value as a measurement value into a correlation coefficient particle filter algorithm.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.

Claims (6)

1.一种基于相关系数粒子滤波的锂离子电池寿命预测方法，其特征在于，包含：1. a lithium-ion battery life prediction method based on correlation coefficient particle filtering, is characterized in that, comprises: S1、设置预测起点及电池寿命阈值；S1. Set the prediction starting point and the battery life threshold; S2、获取待预测锂离子电池的数据，基于锂离子电池容量估计方法获取待预测锂离子电池的容量估计值
Figure FDA0003048574410000011
S2. Obtain the data of the lithium-ion battery to be predicted, and obtain the estimated capacity of the lithium-ion battery to be predicted based on the lithium-ion battery capacity estimation method
Figure FDA0003048574410000011
 S3、建立锂离子电池容量指数衰减模型的状态空间，并进行参数估计；S3. Establish the state space of the exponential decay model of lithium-ion battery capacity, and perform parameter estimation; S4、设置采样粒子数目N，过程噪声方差σw和测量噪声方差σv以及重采样阈值
Figure FDA0003048574410000012
S4. Set the number of sampling particles N, the process noise variance σw , the measurement noise variance σv , and the resampling threshold
Figure FDA0003048574410000012
 S5、将待预测锂离子电池的容量估计值
Figure FDA0003048574410000013
作为测量值代入锂离子电池容量指数衰减模型，基于相关系数粒子滤波算法，不断更新粒子权重，获取预测起点时状态后验估计；S5. Estimate the capacity of the lithium-ion battery to be predicted
Figure FDA0003048574410000013
As the measured value, it is substituted into the exponential decay model of the lithium-ion battery capacity, and the particle weight is continuously updated based on the correlation coefficient particle filter algorithm, and the posterior estimation of the state at the starting point of the prediction is obtained; S6、基于容量衰减模型将状态后验估计迭代至寿命阈值，获取剩余寿命预测结果。S6. Iterate the state posterior estimation to the life threshold based on the capacity decay model, and obtain the remaining life prediction result. 2.如权利要求1所述的基于相关系数粒子滤波的锂离子电池寿命预测方法，其特征在于，所述步骤S2包含：2. The lithium-ion battery life prediction method based on correlation coefficient particle filtering according to claim 1, wherein the step S2 comprises: S21、获取并分析训练锂离子电池运行时的数据；S21. Acquire and analyze the running data of the training lithium-ion battery; S22、提取步骤S21所得数据中能够表征电池性能退化的物理参数；S22, extracting physical parameters that can characterize the degradation of battery performance from the data obtained in step S21; S23、基于主成分分析方法降低步骤S22所提取的物理参数的维数，获取能够表征电池性能退化的融合健康因子；S23, reducing the dimension of the physical parameters extracted in step S22 based on the principal component analysis method, and obtaining a fusion health factor that can characterize the degradation of battery performance; S24、将融合健康因子作为NARX神经网络的输入参数，实际测量的容量数据作为输出参数，得到融合健康因子与电池剩余容量的关系模型；S24, using the fusion health factor as an input parameter of the NARX neural network, and the actual measured capacity data as an output parameter, to obtain a relationship model between the fusion health factor and the remaining battery capacity; S25、获取待预测锂离子电池在预测起点前的特征参数，降维后作为所述融合健康因子与电池剩余容量的关系模型的输入，从而获得待预测锂离子电池的容量估计值
Figure FDA0003048574410000014
S25. Obtain the characteristic parameters of the lithium-ion battery to be predicted before the prediction starting point, and use them as the input of the relationship model between the fusion health factor and the remaining capacity of the battery after dimension reduction, so as to obtain the estimated capacity of the lithium-ion battery to be predicted.
Figure FDA0003048574410000014
 3.如权利要求2所述的基于相关系数粒子滤波的锂离子电池寿命预测方法，其特征在于，所述步骤S22中的能够表征电池性能退化的物理参数包含：3. The lithium-ion battery life prediction method based on correlation coefficient particle filtering according to claim 2, wherein the physical parameters capable of characterizing battery performance degradation in the step S22 comprise: 电压降至最小峰值的时间，恒压降放电时间，负载端电流及输出电流降至最小峰值的时间，温度增长至最高峰值的时间。The time for the voltage to drop to the minimum peak value, the discharge time for constant voltage drop, the time for the load terminal current and output current to drop to the minimum peak value, and the time for the temperature to increase to the highest peak value. 4.如权利要求1所述的基于相关系数粒子滤波的锂离子电池寿命预测方法，其特征在于，所述步骤S3中的建立锂离子电池容量指数衰减模型的状态空间包含：4. The lithium-ion battery life prediction method based on correlation coefficient particle filtering as claimed in claim 1, wherein the state space for establishing the lithium-ion battery capacity exponential decay model in the step S3 comprises: 锂离子电池容量指数衰减模型为：The exponential decay model of lithium-ion battery capacity is: Qk＝a·exp(b·k)+c·exp(d·k) (1)Q k =a·exp(b·k)+c·exp(d·k) (1) 其中a,b,c,d为容量指数衰减模型的参数，a为第一电池容量初始值，c为第二电池容量初始值，b为第一容量衰减速率，d为第二容量衰减速率，k为充放电循环的次数，Qk为k时刻的电池剩余容量即第k次充放电循环时的剩余容量；where a,b,c,d are the parameters of the capacity exponential decay model, a is the initial value of the first battery capacity, c is the initial value of the second battery capacity, b is the first capacity decay rate, d is the second capacity decay rate, k is the number of charge-discharge cycles, and Q k is the remaining capacity of the battery at time k, that is, the remaining capacity at the kth charge-discharge cycle; 上述公式(1)的锂离子电池容量指数衰减模型经过多项式运算可以转换为：The exponential decay model of lithium-ion battery capacity in the above formula (1) can be converted into: Qk＝Qk-1·exp(b)+c·exp[d·(k-1)]·[1-exp(b-d)] (2)Q k =Q k-1 ·exp(b)+c·exp[d·(k-1)]·[1-exp(bd)] (2) 所述锂离子电池容量指数衰减模型的状态空间方程为：The state space equation of the lithium-ion battery capacity exponential decay model is:
Figure FDA0003048574410000021
Figure FDA0003048574410000021
Figure FDA0003048574410000022
Figure FDA0003048574410000022
 其中wk为k时刻的过程噪声，vk为k时刻的测量噪声，wk～N(0,σw)表示wk服从期望为0，方差为σw的正态分布，vk～N(0,σv)表示vk服从期望为0，方差为σv的正态分布，Qk-1为第k-1次充放电循环时的剩余容量。where w k is the process noise at time k, v k is the measurement noise at time k, w k ～N(0,σ w ) means w k obeys a normal distribution with expectation 0 and variance σ w , v k ～N (0,σ v ) means that v k obeys a normal distribution with an expectation of 0 and a variance of σ v , and Q k-1 is the remaining capacity at the k-1th charge-discharge cycle. 5.如权利要求4所述的基于相关系数粒子滤波的锂离子电池寿命预测方法，其特征在于，所述步骤S3中的进行参数估计包含：5. The lithium-ion battery life prediction method based on correlation coefficient particle filtering according to claim 4, wherein the parameter estimation in the step S3 comprises: 将所述锂离子电池容量指数衰减模型中的参数作为系统状态，得到k时刻的状态空间模型xk：Taking the parameters in the lithium-ion battery capacity exponential decay model as the system state, the state space model x k at time k is obtained: xk＝[ak,bk,ck,dk]x k =[ ak ,b k ,c k ,d k ] ak＝ak-1+wa wa～N(0,σa)a k =a k-1 +w a w a ～N(0,σ a ) bk＝bk-1+wb wb～N(0,σb)b k =b k-1 +w b w b ~N(0,σ b ) ck＝ck-1+wc wc～N(0,σc)c k =c k-1 +w c w c ~N(0,σ c ) dk＝dk-1+wd wd～N(0,σd) (6)d k =d k-1 +w d w d ~N(0,σ d ) (6) Qk＝ak·exp(bk·k)+ck·exp(dk·k)+vk vk～N(0,σn)Q k = ak ·exp(b k ·k)+c k ·exp(d k ·k)+v k v k ~N(0,σ n ) 其中，ak,bk,ck,dk分别为k时刻的第一电池容量初始值、第二电池容量初始值、第一容量衰减速率和第二容量衰减速率，ak-1,bk-1,ck-1,dk-1分别为k-1时刻的对应参数，wa,wb,wc,wd分别为常数；Among them, a k , b k , c k , d k are the initial value of the first battery capacity, the initial value of the second battery capacity, the first capacity decay rate and the second capacity decay rate at time k, respectively, a k-1 ,b k-1 , c k-1 , d k-1 are the corresponding parameters at time k-1, respectively, w a , w b , w c , and w d are constants respectively; 根据所述锂离子电池容量指数衰减模型对训练锂离子电池全周期容量数据进行拟合，将拟合的参数作为待预测锂离子电池的初始参数；Fitting the full-cycle capacity data of the training lithium-ion battery according to the lithium-ion battery capacity exponential decay model, and using the fitted parameters as the initial parameters of the lithium-ion battery to be predicted; 将待预测锂离子电池的容量估计值
Figure FDA0003048574410000031
作为k时刻的状态空间模型xk的测量值，基于粒子滤波算法对参数进行迭代更新，迭代后b,c,d的值为锂离子电池容量指数衰减模型中的参数。Estimate the capacity of the lithium-ion battery to be predicted
Figure FDA0003048574410000031
As the measured value of the state space model x k at time k, the parameters are iteratively updated based on the particle filter algorithm. After iteration, the values of b, c, and d are the parameters in the exponential decay model of lithium-ion battery capacity. 6.如权利要求5所述的基于相关系数粒子滤波的锂离子电池寿命预测方法，其特征在于，所述步骤S5包含：6. The lithium-ion battery life prediction method based on correlation coefficient particle filtering according to claim 5, wherein the step S5 comprises: S51、初始化：对于i＝1,2,...,N，从分布
Figure FDA0003048574410000032
抽取N个粒子，组成粒子集
Figure FDA0003048574410000033
S51, initialization: for i=1,2,...,N, from the distribution
Figure FDA0003048574410000032
Extract N particles to form a particle set
Figure FDA0003048574410000033
 S52、计算粒子的样本权值：S52. Calculate the sample weights of the particles:
Figure FDA0003048574410000034
Figure FDA0003048574410000034
 其中，zk为单独的测量值；where z k is an individual measurement; S53、权值归一化：S53, weight normalization:
Figure FDA0003048574410000035
Figure FDA0003048574410000035
 S54、计算有效粒子数目Neff，设置重采样阈值Nth，如果有效粒子数目Neff＜Nth则进行重采样；S54. Calculate the number of effective particles N eff , set a resampling threshold N th , and perform resampling if the number of effective particles N eff <N th ; S55、构造系统实际测量值序列及样本估计测量值矩阵：对于实际测量值序列，取长度为L的序列
Figure FDA0003048574410000036
对于样本估计测量值序列，取
Figure FDA0003048574410000037
S55. Construct the actual measurement value sequence of the system and the sample estimated measurement value matrix: for the actual measurement value sequence, take a sequence of length L
Figure FDA0003048574410000036
For a sample-estimated series of measurements, take
Figure FDA0003048574410000037
Figure FDA0003048574410000041
Figure FDA0003048574410000041
 S56、计算相关系数：对于序列Zk和
Figure FDA0003048574410000042
计算其相关系数cc；S56. Calculate the correlation coefficient: for the sequence Z k and
Figure FDA0003048574410000042
Calculate its correlation coefficient cc; S57、重新计算粒子样本权值：相关系数cc的取值范围为[-1,1]，为了将其变换到正数范围内，取一个带参数α的指数函数对相关系数进行处理：S57. Recalculate the weights of the particle samples: the value range of the correlation coefficient cc is [-1, 1]. In order to transform it into a positive range, an exponential function with parameter α is used to process the correlation coefficient:
Figure FDA0003048574410000043
Figure FDA0003048574410000043
 其中，参数α＞0，其作用是调整样本权值的离散程度，β为转化后的相关系数；Among them, the parameter α>0, its function is to adjust the degree of dispersion of the sample weights, and β is the transformed correlation coefficient; 重新计算的粒子样本权值为：The recalculated particle sample weights are:
Figure FDA0003048574410000044
Figure FDA0003048574410000044
 对重新计算的样本粒子权值按照公式(8)进行归一化处理。The recalculated sample particle weights are normalized according to formula (8).
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