CN109061505B - A kind of detection method of lithium battery SOH - Google Patents
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Abstract
本发明公开了一种锂电池SOH的检测方法，包括以下步骤：1）建立锂电池的等效电路模型，包括由开路电压Uoc、极化电容Cp、欧姆内阻Ro依次串联组成的支路，所述开路电压Uoc两端并联有自放电电阻Rd，极化电容Cp两端并联有极化内阻Rp，得到锂电池的等效电路模型的方程；2）根据锂电池的等效电路模型方程，建立电池SOC、欧姆内阻Ro、自放电内阻Rd的离散系统状态空间方程和观测方程；采用扩展卡尔曼滤波算法进行迭代计算，得到电池SOC、欧姆内阻Ro、自放电内阻Rd的估算值；3）将电池SOC、欧姆内阻Ro、自放电内阻Rd的估算值和锂电池的循环次数H作为输入变量输入神经网络模型，估算得到锂电池SOH。
The invention discloses a method for detecting the SOH of a lithium battery, comprising the following steps: 1) establishing an equivalent circuit model of the lithium battery, including a branch circuit consisting of an open circuit voltage Uoc, a polarization capacitance Cp, and an ohmic internal resistance Ro in series; Two ends of the open circuit voltage Uoc are connected in parallel with a self-discharge resistance Rd, and two ends of the polarization capacitor Cp are connected in parallel with a polarization internal resistance Rp, so as to obtain the equation of the equivalent circuit model of the lithium battery; 2) According to the equivalent circuit model equation of the lithium battery , establish the discrete system state space equation and observation equation of battery SOC, ohmic internal resistance Ro, and self-discharge internal resistance Rd; use the extended Kalman filter algorithm for iterative calculation to obtain the battery SOC, ohmic internal resistance Ro, and self-discharge internal resistance Rd Estimated value; 3) The estimated value of battery SOC, ohmic internal resistance Ro, self-discharge internal resistance Rd and the number of cycles H of the lithium battery are input into the neural network model as input variables, and the lithium battery SOH is estimated.
Description
Technical Field
The invention relates to the technical field of lithium battery detection, in particular to a method for detecting SOH of a lithium battery.
Background
Along with the development of cities and the improvement of living standard, the demand of society on fossil energy is continuously increased, and the adverse effect of the fossil energy on the environment is increasingly prominent. In order to deal with the continuously intensified energy crisis and environmental pollution, the research and development and popularization of new energy are carried forward; the power lithium ion battery is one of new energy sources, as a complex electrochemical system, the state of health (SOH) of the lithium ion battery gradually degrades with use, and it is very important to accurately grasp the state of SOH of the lithium ion battery in the use process of the lithium ion battery. However, in the actual use process, parameters for characterizing the lithium battery SOH are difficult to directly test, and the lithium battery SOH diagnosis is a great problem in the world.
Currently, there are two main methods for measuring the SOH of a lithium battery: firstly, the ratio of the maximum available capacity to the rated maximum available capacity of the lithium battery in the use process of the lithium battery; and secondly, the ratio of the impedance of the lithium battery in the using process to the impedance of the lithium battery in the health state. In the first method, the lithium battery needs to be charged to a full-charge state by adopting constant current and constant voltage under a relatively stable condition, the lithium battery is discharged to a cut-off voltage by adopting a constant current mode, multiple groups of measurements are obtained after multiple cycles, and finally an average value is taken as the current available capacity of the lithium battery. In the second method, the change amplitude of the impedance value before and after the lithium battery is aged is small, and methods for acquiring the impedance value mainly comprise a pulse method and an electrochemical impedance spectroscopy method, are easily influenced by the environment and are few in practical use.
At present, there are two methods for estimating the SOH of a lithium battery, one of which is an SOH method based on voltage measurement estimation: calculating the change of the voltage of the lithium battery in the charging process by using the measured voltage value, and further estimating the relative SOH of the lithium battery; the method excessively depends on voltage value measurement, and if the error of the measured value is large, the SOH value of the lithium battery has an error, so that an accumulated error is caused. Secondly, an estimation method for estimating the SOH of the lithium battery based on the capacity of the lithium battery comprises the following steps: the method comprises the steps of firstly determining the initial capacity of a lithium battery, charging the lithium battery until the lithium battery is in a cut-off state, calculating the charged capacity according to charging current time, calculating the capacity of the lithium battery when the lithium battery is cut off, and obtaining the SOH of the lithium battery by utilizing the ratio of the capacity of the lithium battery when the lithium battery is cut off to the rated capacity; according to the method, the lithium battery capacity is calculated by using the current, but an error exists in the current measuring process, a larger error exists in the final cut-off lithium battery capacity, and meanwhile, a measuring error also exists when the initial capacity of the lithium battery is determined, so that the estimation of the SOH of the lithium battery is not facilitated.
In summary, the current method for detecting the SOH of the lithium battery has great disadvantages.
Disclosure of Invention
The invention aims to solve the technical problem of providing a lithium battery SOH detection method, which adopts a plurality of measurement parameters capable of representing the lithium battery SOH to estimate, and greatly improves the estimation precision by using a Kalman filtering algorithm and a neural network algorithm.
The invention is realized by the following technical scheme:
a detection method for lithium battery SOH comprises the following steps:
1) establishing an equivalent circuit model of a lithium battery, wherein the equivalent circuit model comprises a branch consisting of an open-circuit voltage Uoc, a polarization capacitor Cp and an ohmic internal resistance Ro which are sequentially connected in series, the two ends of the branch represent terminal voltage U of the lithium battery, the two ends of the open-circuit voltage Uoc are connected in parallel with a self-discharge resistor Rd, the two ends of the polarization capacitor Cp are connected in parallel with a polarization internal resistance Rp, and an equation of the equivalent circuit model of the lithium battery is obtained:
wherein Up is the voltage at two ends of the polarization capacitor Cp;
2) establishing a discrete system state space equation and an observation equation of the battery SOC, the ohmic internal resistance Ro and the self-discharge internal resistance Rd according to an equivalent circuit model equation of the lithium battery; iterative calculation is carried out by adopting an extended Kalman filtering algorithm to obtain estimated values of the battery SOC, the ohmic internal resistance Ro and the self-discharge internal resistance Rd;
3) and inputting the estimated values of the battery SOC, the ohmic internal resistance Ro and the self-discharge internal resistance Rd and the cycle number H of the lithium battery as input variables into a neural network model, and estimating to obtain the SOH of the lithium battery.
The invention further adopts the scheme that an Elman neural network model is adopted in the step 3); the Elman neural network adds a carrying layer in the hidden of the feed-forward network, and the carrying layer is used as an operator for one-step time delay to achieve the purpose of memory, so that the system has the capability of adapting to time-varying characteristics, can directly and dynamically reflect the characteristics of a dynamic process system, and improves the estimation accuracy of a nonlinear system.
The further scheme of the invention is that step 2) adopts an ampere-hour integration method to obtain a state space equation of the battery SOC:
in the formula, mu is the coulombic efficiency of the lithium battery, Qt is the rated capacity of the lithium battery at the moment t, and SOC0 is the initial residual capacity of the lithium battery;
and (3) discretizing the formula (3) by using EKF estimation to obtain a discrete system state space equation of the battery SOC:
wherein k is a certain time node after discrete processing, and delta t is the current charging or discharging time;
establishing an observation equation according to an equivalent circuit model equation of the lithium battery:
vk is the noise in the model represented by the observation equation;
the further scheme of the invention is that step 2) establishes a discrete system state space equation of ohmic internal resistance Ro and self-discharge internal resistance Rd:
wherein k is a certain time node after discrete processing;
the observation equation:
where vk is the noise in the model represented by the observation equation.
Compared with the prior art, the invention has the advantages that:
adding a self-discharge resistor R into a Thevenin equivalent circuit modeldThe characteristic of the lithium ion battery is better simulated, the variable is iteratively calculated by using the efficient and simple extended Kalman filtering algorithm, the SOH of the lithium ion battery is obtained by estimation, and the reliability is higher.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is an equivalent circuit diagram of a lithium battery.
FIG. 3 is a diagram of an Elman neural network model.
FIG. 4 is a graph of cycle number versus capacity.
Fig. 5 is a graph of ohmic internal resistance versus battery capacity.
Fig. 6 is a graph of the number of cycles versus the battery capacity under different self-discharges.
Detailed Description
As shown in fig. 4, as the number of cycles increases, the capacity of the lithium battery decreases, and the capacity have a linear relationship; as shown in fig. 5, the ohmic internal resistance of the lithium battery gradually increases with the increase of the service time, and at this time, the ohmic internal resistance can be one of the key factors for estimating the capacity of the lithium battery; as shown in fig. 6, the difference of the initial self-discharge resistance of the lithium battery has a great influence on the SOH and SOH variation of the lithium battery, and the self-discharge resistance of the lithium battery slowly decays with the increase of the cycle period. The self-discharge resistance of a lithium battery is inversely proportional to the self-discharge current and decays as the cycle life of the lithium battery increases, and therefore, the self-discharge resistance factor of the battery is not negligible in the estimation of the SOH of the lithium battery.
The method for detecting the lithium battery SOH as shown in FIG. 1 comprises the following steps:
1) establishing an equivalent circuit model of the lithium battery as shown in fig. 2, wherein the equivalent circuit model comprises a branch circuit formed by sequentially connecting an open-circuit voltage Uoc, a polarization capacitor Cp and an ohmic internal resistance Ro in series, the two ends of the branch circuit represent the terminal voltage U of the lithium battery, the two ends of the open-circuit voltage Uoc are connected in parallel with a self-discharge resistor Rd, the two ends of the polarization capacitor Cp are connected in parallel with a polarization internal resistance Rp, and an equation of the equivalent circuit model of the lithium battery is obtained:
wherein Up is the voltage at two ends of the polarization capacitor Cp;
2) establishing a discrete system state space equation and an observation equation of the battery SOC, the ohmic internal resistance Ro and the self-discharge internal resistance Rd according to an equivalent circuit model equation of the lithium battery; iterative calculation is carried out by adopting an extended Kalman filtering algorithm to obtain estimated values of the battery SOC, the ohmic internal resistance Ro and the self-discharge internal resistance Rd; the method comprises the following specific steps:
the state of charge of the battery, also called the remaining capacity, refers to the ratio of the available capacity of the battery at the current moment to the rated capacity of the battery at the moment, and the expression is as follows:
in the formula QnIs the current residual capacity, Q, of the lithium battery at the moment ttIs a lithium batteryRated capacity at time t;
obtaining a state space equation of the battery SOC by adopting an ampere-hour integration method:
in the formula, mu is the coulomb efficiency of the lithium battery, and SOC0 is the initial residual capacity of the lithium battery;
and (3) discretizing the formula (3) by using EKF estimation to obtain a discrete system state space equation of the battery SOC:
wherein k is a certain time node after discrete processing, and delta t is the current charging or discharging time;
establishing an observation equation according to an equivalent circuit model equation of the lithium battery:
vk is the noise in the model;
the open circuit voltages of 5 lithium batteries and the battery SOC are fitted into a polynomial:
Uoc＝K0+K1SOC+K2SOC2+K3SOC3+K4SOC4+K5SOC5 (6)
k1, K2, K3, K4 and K5 respectively represent parameters of each battery, the voltage of each battery has a fixed relation with the SOC, and the voltages and the SOC can be mutually represented by fitting;
substituting equation (6) for equation (5) yields the measurement equation:
the general forms of EKF were used for the expression of formula (4) and formula (7), respectively:
xk+1＝f(xk，uk)+wk (8)
yk＝g(xk，uk)+vk (9)
in the formula f (x)k，uk)＝Axk+Buk，g(xk，uk)＝Cxk+Duk，ukFor the input signal, xkIs a status signal, ykWk is the noise in the model for the output signal;
the following results are obtained from equations (4), (7), (8) and (9):
A＝1 (10)
initialization:
k＝0 (13)
Wk＝E(w*wT) (16)
Vk＝E(v*vT) (17)
time update equation for discrete systems:
measurement update equation for discrete systems:
obtaining the value x of the SOC of the battery through the estimation of EKFk；
Establishing a discrete system state space equation of ohmic internal resistance Ro and self-discharge internal resistance Rd:
wherein k is a certain time node after discrete processing;
the observation equation:
where vk is the noise in the model.
The estimation of the ohmic internal resistance Ro is:
when k is 0, the parameters of ohmic internal resistance are as follows:
xk＝Rk (26)
A＝1 (28)
Wk＝E(r*rT) (31)
Vk＝E(v*vT) (32)
the time updating equation and the measurement updating equation of the discrete system are in the same expression (18) to expression (22) to obtain the ohmic internal resistance R of the lithium batteryo。
The estimation of the self-discharge internal resistance Rd is:
A＝1 (35)
Wk＝E(n*nT) (38)
Vk＝E(v*vT) (39)
the self-discharge internal resistance Rd of the lithium battery is obtained by the time updating equation and the measurement updating equation of the system in the same formulas (18) to (22).
3) And inputting the estimated values of the battery SOC, the ohmic internal resistance Ro and the self-discharge internal resistance Rd and the cycle number H of the lithium battery as input variables into the Elman neural network model, and estimating to obtain the SOH of the lithium battery.
The specific process is as follows:
and respectively taking n groups of four parameters including the estimated values of the battery SOC, the ohmic internal resistance Ro and the self-discharge internal resistance Rd and the cycle number H of the lithium battery, wherein the data of each group are acquired at the same moment. 1 st time node [ SOC1，Ro1，Rd1，H1]2 nd time node [ SOC2，Ro2，Rd2，H2].. nth time node [ SOCn，Ron，Rdn，Hn]. Using the normalization equation:
the battery state [ SOC ] of the ith time nodei，Roi，Rdi，Hi]1, 2, … …, n, normalized to: [ SOCi′，R′oi，R′di，Hi]I ═ 1, 2, … …, n; taking the normalization of ohmic internal resistance as an example, the normalization equation is:
wherein Rmax and Rmin represent the maximum value and the minimum value of the n time node voltage parameters, respectively. The obtained parameters are used as input variables x (k) of the neural network, and the lithium battery state of health (SOH) value corresponding to each moment is used as a sample output value yd(k)。
As can be seen from fig. 5, the Elman neural network not only has an input layer, a hidden layer and an output layer, but also has a special association layer of the link units, and memorizes the output values before the middle layer and feeds them back to the input layer in a delayed manner. Here, the input layer input is x (k), and the input of the hidden layer is x0(k) Output is
The correlation layer output is yc(k) The output layer output is y (k). The following formula can be obtained:
x0(k)＝W1x(k)+W3yc(k)+θ1 (40)
yc(k)＝o(k-1)＝f(x0(k-1)) (41)
in the formula, theta1、θ2Threshold values for the intermediate layer and the output layer; w1、W2、W3Respectively are the connection weights between the input layer and the hidden layer, between the middle layer and the output layer and between the middle layer and the associated layer; f (x) is a Sigmiod-type activation function, i.e., a hyperbolic activation function.
The method comprises the following steps: the neural network is trained using N sets of training samples, where the training set is [ x (k), y (k) ] (k ═ 1, 2, 3.. N). The neural network training output value can be calculated from the expressions (40), (41) and (42). The single sample error is:
the total error of a single training is:
in the formula, yd(k) And y (k) are the sample output value and the training output value, respectively.
After the system error is obtained through calculation, the network weight W is calculated1、W2、W3And (6) carrying out correction.
To W1(weight between input layer and intermediate layer) to correct:
to W2(the weight between the output layer and the intermediate layer) is corrected to obtainAnd (3) correction:
to W3(the weight between the middle layer and the associated layer) is corrected to obtain a correction:
according to the principle of a dynamic reverse error transfer algorithm, a steepest descent method is adopted, and a connection weight is adjusted to enable an error E function to reach a minimum value at the fastest speed. The basic formula of weight correction is as follows:
where ρ is the learning rate.
The Elman network weight correction formula obtained by integrating the above formula is as follows:
obtaining a weight:
Wi'＝Wi+ΔWi (i＝1、2、3) (54)
and calculating to obtain a training error E through training of the training sample. And if the error meets the test requirement, stopping training, and assigning the weight obtained by training to the neural network model so as to be used for actual work. Otherwise, continuing training until the requirements are met.
Claims (2)
1. A detection method for lithium battery SOH is characterized by comprising the following steps:
1) establishing an equivalent circuit model of a lithium battery, wherein the equivalent circuit model comprises a branch consisting of an open-circuit voltage Uoc, a polarization capacitor Cp and an ohmic internal resistance Ro which are sequentially connected in series, the two ends of the branch represent terminal voltage U of the lithium battery, the two ends of the open-circuit voltage Uoc are connected in parallel with a self-discharge resistor Rd, the two ends of the polarization capacitor Cp are connected in parallel with a polarization internal resistance Rp, and an equation of the equivalent circuit model of the lithium battery is obtained:
wherein Up is the voltage at two ends of the polarization capacitor Cp;
2) establishing a discrete system state space equation and an observation equation of the battery SOC, the ohmic internal resistance Ro and the self-discharge internal resistance Rd according to an equivalent circuit model equation of the lithium battery; iterative calculation is carried out by adopting an extended Kalman filtering algorithm to obtain estimated values of the battery SOC, the ohmic internal resistance Ro and the self-discharge internal resistance Rd;
obtaining a state space equation of the battery SOC by adopting an ampere-hour integration method:
in the formula, mu is the coulombic efficiency of the lithium battery, Qt is the rated capacity and SOC of the lithium battery at the moment t0The initial residual capacity of the lithium battery is obtained;
and (3) carrying out discretization treatment by using an extended Kalman filtering algorithm to obtain a discrete system state space equation of the battery SOC:
wherein k is a certain time node after discrete processing, and delta t is the current charging or discharging time;
establishing an observation equation according to an equivalent circuit model equation of the lithium battery:
in the formula vkIs the noise in the model;
the open circuit voltages of 5 lithium batteries and the battery SOC are fitted into a polynomial:
Uoc＝K0+K1SOC+K2SOC2+K3SOC3+K4SOC4+K5SOC5 (6)
k1, K2, K3, K4 and K5 respectively represent parameters of each battery, the voltage of each battery has a fixed relation with the SOC, and the voltages and the SOC can be mutually represented by fitting;
substituting equation (6) for equation (5) yields the observation equation:
establishing a discrete system state space equation of ohmic internal resistance Ro and self-discharge internal resistance Rd:
wherein k is a certain time node after discrete processing;
the observation equation:
in the formula vkNoise in the model represented by the observation equation;
3) and inputting the estimated values of the battery SOC, the ohmic internal resistance Ro and the self-discharge internal resistance Rd and the cycle number H of the lithium battery as input variables into a neural network model, and estimating to obtain the SOH of the lithium battery.
2. The method for detecting the SOH of the lithium battery as claimed in claim 1, wherein:
and 3) adding a carrying layer in a hidden layer of the feed-forward network by adopting an Elman neural network model to serve as a one-step time-delay operator to achieve the aim of memorizing, so that the system has the capability of adapting to time-varying characteristics, can directly and dynamically reflect the characteristics of a dynamic process system, and improves the estimation precision of a nonlinear system.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109725266B (en) * | 2018-12-29 | 2021-05-11 | 蜂巢能源科技有限公司 | Method and device for calculating SOH (state of health) of battery |
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| CN111736085B (en) * | 2020-07-07 | 2023-11-10 | 中国检验检疫科学研究院 | Lithium ion battery health state estimation method based on electrochemical impedance spectrum |
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| US11422199B1 (en) | 2021-06-17 | 2022-08-23 | Hong Kong Applied Science and Technology Research Institute Company Limited | State of health evaluation of retired lithium-ion batteries and battery modules |
| CN115267578B (en) * | 2022-07-14 | 2025-08-22 | 广东美的白色家电技术创新中心有限公司 | Estimation method, device, electronic device and storage medium |
| CN116679211B (en) * | 2023-05-18 | 2024-07-12 | 淮阴工学院 | A method for predicting the health status of lithium batteries |
| CN118425787B (en) * | 2024-05-27 | 2025-02-07 | 淮阴工学院 | A battery SOC estimation method and system based on unscented Kalman filtering |
| CN118625027A (en) * | 2024-06-24 | 2024-09-10 | 深圳市美隆电子有限公司 | A method and system for testing capacitance performance of a capacitor |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7554296B2 (en) * | 2005-02-14 | 2009-06-30 | Denso Corporation | Method and apparatus for detecting charged state of secondary battery based on neural network calculation |
| JP4623448B2 (en) * | 2005-04-20 | 2011-02-02 | 株式会社デンソー | Secondary battery remaining capacity calculation method |
| CN102062841A (en) * | 2009-11-11 | 2011-05-18 | 北汽福田汽车股份有限公司 | Estimation method and system of state of charge (SOC) of power battery |
| CN103941195A (en) * | 2014-05-05 | 2014-07-23 | 山东大学 | Method for battery SOC estimation based on small model error criterion expanding Kalman filter |
| CN104181470A (en) * | 2014-09-10 | 2014-12-03 | 山东大学 | Battery state-of-charge (SOC) estimation method based on nonlinear prediction extended Kalman filtering |
| CN104537166A (en) * | 2014-12-19 | 2015-04-22 | 中国汽车技术研究中心 | Equivalent circuit model method for power battery |
| CN105203969A (en) * | 2015-10-23 | 2015-12-30 | 南昌航空大学 | Modification-based state-of-charge estimation method for RC battery model |
| CN106443471A (en) * | 2016-09-20 | 2017-02-22 | 首都师范大学 | System on chip (SOC) estimation method for lithium ion battery and hardware implementation of estimation method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104237791A (en) * | 2013-06-20 | 2014-12-24 | 电子科技大学 | Lithium battery charge state estimation method, battery management system and battery system |
-
2018
- 2018-08-28 CN CN201810991631.2A patent/CN109061505B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7554296B2 (en) * | 2005-02-14 | 2009-06-30 | Denso Corporation | Method and apparatus for detecting charged state of secondary battery based on neural network calculation |
| JP4623448B2 (en) * | 2005-04-20 | 2011-02-02 | 株式会社デンソー | Secondary battery remaining capacity calculation method |
| CN102062841A (en) * | 2009-11-11 | 2011-05-18 | 北汽福田汽车股份有限公司 | Estimation method and system of state of charge (SOC) of power battery |
| CN103941195A (en) * | 2014-05-05 | 2014-07-23 | 山东大学 | Method for battery SOC estimation based on small model error criterion expanding Kalman filter |
| CN104181470A (en) * | 2014-09-10 | 2014-12-03 | 山东大学 | Battery state-of-charge (SOC) estimation method based on nonlinear prediction extended Kalman filtering |
| CN104537166A (en) * | 2014-12-19 | 2015-04-22 | 中国汽车技术研究中心 | Equivalent circuit model method for power battery |
| CN105203969A (en) * | 2015-10-23 | 2015-12-30 | 南昌航空大学 | Modification-based state-of-charge estimation method for RC battery model |
| CN106443471A (en) * | 2016-09-20 | 2017-02-22 | 首都师范大学 | System on chip (SOC) estimation method for lithium ion battery and hardware implementation of estimation method |
Non-Patent Citations (3)
| Title |
|---|
| "动力锂离子电池组SOH估计方法研究";薛辉;《中国优秀硕士学位论文全文数据库（工程科技Ⅱ辑）》;20130915(第9期);C042-121 * |
| "电动汽车动力电池SOH在线实时估计算法研究";汤露曦;《中国优秀硕士学位论文全文数据库（工程科技Ⅱ辑）》;20151015(第10期);C035-59 * |
| "锂离子电池建模与故障预测方法研究";张卓识;《中国优秀硕士学位论文全文数据库（工程科技II辑）》;20160715(第7期);C042-481 * |
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