CN110632528B - Lithium battery SOH estimation method based on internal resistance detection - Google Patents
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Abstract
The invention discloses a lithium battery SOH estimation method based on internal resistance detection, which is characterized in that the internal resistance of a lithium battery is detected by a direct current discharge method, related health factor characteristic parameters are obtained, the characteristic parameters can effectively represent the change trend of the health state of the battery, an RBF neural network model of the health factor of the battery and the actual health state is established, the establishment of a complex equivalent circuit model is avoided, and the accuracy and the generalization of SOH estimation can be balanced.
Description
Technical Field
The invention relates to the technical field of lithium batteries, in particular to a lithium battery SOH estimation method based on internal resistance detection.
Background
Lithium batteries inevitably undergo aging during use, which is reflected in the fading of battery capacity and the increase of internal resistance. At present, the health state estimation function of the battery is gradually added to the lithium battery of the mobile phone and the power battery pack of the electric automobile. The State of Health (SOH) of a lithium battery represents the capacity of the current battery for storing electric energy and energy relative to a fully new battery, and is an index for quantitatively describing the performance State of the battery. The SOH parameter may affect various aspects of battery SOC estimation, battery charge and discharge strategy setup, battery pack equalization control, and battery fault diagnosis. Therefore, accurate SOH estimation is of great importance to extend battery life and to ensure battery safety.
For SOH estimation of lithium batteries, there are several major methods at present:
the experimental test method comprises the following steps: the experimental test method analyzes the aging behavior of the battery through a large amount of data, requires related experimental equipment for measurement, and is greatly different from a test environment and the actual operation condition of the battery, and some methods are difficult to apply on line.
An equivalent circuit model based method: the equivalent circuit model of the lithium battery is composed of basic circuit elements such as basic resistance, inductance, capacitance and voltage source, and has been widely used as an estimation of SOH due to its advantages of small calculation amount and simplicity in online application. After identifying the internal resistance, capacity, OCV, and some other characteristic parameters, the SOH of the battery can be obtained by searching the functional relationship between the characteristic parameters and the corresponding characteristic parameters. The equivalent circuit model method has high precision and strong robustness and is easy to realize in different types of batteries; the method has the disadvantages of needing a large amount of experimental verification, greatly influencing the precision by the model and having larger calculation amount of the algorithm.
The data driving method comprises the following steps: a data-driven method is used for establishing a black box estimation model to realize SOH estimation according to the change of characterization parameters related to battery aging in the battery aging process, and the method does not depend on predetermined system parameters or the relation with the physical performance of the battery. The data driving method needs less preliminary testing on the battery, and has higher estimation precision on the slowly-changing battery health parameters; the method has the disadvantages of high requirements on the operation efficiency and generalization performance of the algorithm and high tolerance to a large amount of complete sampling data under high frequency.
Disclosure of Invention
The invention aims to solve the problem that the existing lithium battery SOH estimation is difficult to simultaneously consider the aspects of realizability and precision, and provides a lithium battery SOH estimation method based on internal resistance detection.
In order to solve the problems, the invention is realized by the following technical scheme:
a lithium battery SOH estimation method based on internal resistance detection specifically comprises the following steps:
step 1: carrying out a cyclic charge and discharge experiment on the single sample battery, and acquiring internal resistance data in the battery aging process by a direct current discharge detection method;
step 2: establishing a nonlinear regression curve of the change of the internal resistance of the battery through a PSO-SVR algorithm based on the internal resistance data acquired in the step 1;
step 3, fitting the nonlinear regression curve of the change of the internal resistance of the battery, and respectively calibrating the minimum value point and the maximum value point in the fitted nonlinear regression curve of the change of the internal resistance of the battery as the initial internal resistance R of the batteryNewAnd an end-of-life internal resistance REOLAnd calculating to obtain the SOH of the battery under different health statesHI：
In the formula, RNewIs the initial internal resistance of the battery, REOLTo end-of-life internal resistance; rNowIs the internal resistance of the battery in the current state;
step 4, the SOH of the battery health factorHIAs an input characteristic, the actual health state of the battery is used as an output, and an RBF neural network model with a determined structure and parameters is obtained by training a data set of the battery health factor and the actual health state in the whole aging stage;
step 5, in the actual estimation process, based on the steps 1-3, firstly detecting the internal resistance of the current battery through direct current discharge, then constructing a nonlinear regression curve by using the detected internal resistance, fitting the nonlinear regression curve, and then calculating the battery health factor in the current state by using a formula I;
and 6, taking the battery health factor in the current state obtained in the step 5 as the input of the RBF neural network model obtained in the step 4, and realizing SOH estimation of the battery by using the RBF neural network model.
Compared with the prior art, the invention has the following characteristics:
1. the internal resistance of the lithium battery is detected by a direct current discharge method, and related health factor characteristic parameters are obtained, and the characteristic parameters can effectively represent the change trend of the health state of the battery.
2. The RBF neural network model of the battery health factor and the actual health state is established, the establishment of a complex equivalent circuit model is avoided, and the accuracy and the generalization of SOH estimation can be balanced.
Drawings
Fig. 1 is a flow chart of a lithium battery SOH estimation method based on internal resistance detection.
Fig. 2 is a flow chart of a lithium battery aging test.
Fig. 3 is a time point of internal resistance calculation by the dc discharge method.
FIG. 4 illustrates the SVR rationale.
FIG. 5 is a flow chart of the PSO-SVR algorithm.
FIG. 6 is a schematic diagram of a radial basis function neural network.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to specific examples.
A lithium battery SOH estimation method based on internal resistance detection is disclosed, as shown in figure 1, and comprises the following specific steps:
step 1: and carrying out a cyclic charge and discharge experiment on the single sample battery, and acquiring internal resistance data in the battery aging process by using a direct current discharge detection method.
Referring to fig. 2, aging cycle charge and discharge tests are performed on a single sample battery, that is, voltage and current data of the battery in each cycle process are collected, and the internal resistance and the actual capacity characteristics of the battery under 80% SOC after a certain cycle number are measured, and the specific process is as follows:
(1) and (3) battery aging cycle test: the charging process adopts a CC-CV (constant current-constant voltage) mode, the charging is carried out at a constant current of 1C until the charging cut-off voltage is reached, then the constant voltage charging is carried out at the voltage until the current is reduced to 0.05C, and after the static standing is carried out for 10min, the discharging process discharges at a constant current of 0.5C until the cut-off voltage is reached.
(2) Testing the battery characteristics: fully charging the battery by a CC-CV charging mode, and then discharging to cut-off voltage by constant current of 0.5C, wherein the discharged capacity in the discharging process is the current standard static capacity. The internal resistance test of the battery under the 80% SOC adopts a hybrid power pulse capability characteristic (HPPC) test method, and a test flow is formed by a continuous 10s discharging process with 1.00 multiplying factor relative to the maximum discharging current, a 40s standing process and a 10s charging process with 0.75 multiplying factor relative to the maximum charging current, as shown in figure 3. The charge-discharge internal resistance calculation formulas are as follows (1-1) and (1-2):
wherein R isdischargeAnd RchargeRespectively representing the internal resistance to discharge and the internal resistance to charge.
(3) And (3) judging the end of the battery aging cycle: the test ends when the measured static capacity drops to 80% of the initial static capacity.
Step 2: discrete battery charging internal resistance measurement points in the battery aging cycle process are selected, an internal resistance change curve is fitted, and a regression curve model of the battery internal resistance is determined through a PSO-SVR algorithm.
The internal resistance data collected in the whole aging process of the battery are discrete and have fluctuation, and a regression curve model of discrete points of the internal resistance of the battery needs to be established.
Support Vector Regression (SVR) as a hotspot and an important branch in the SVR is further developed in terms of processing Regression problems and shows good results. Given a training sample: d { (x)1,y1),(x2,y2),…,(xm,ym)}，
It is desirable to obtain a regression model such that the true values in the samples have the least error from the predicted values of the regression model, and the model can be described by equation (2-1):
wherein ω ═ ω (ω)1,ω2,…,ωm) The vector is a normal vector, and the vector is a vector,
and b is an offset term, and both omega and b are waiting coefficients of the model. In SVR, a certain error of f (x) within + - ε can be tolerated, ε being defined as an insensitive loss function, and the loss is calculated only if the absolute value of the error exceeds ε. It can be understood that intervals with the width of epsilon are constructed on both sides of the function with f (x) as the center, and if the predicted value is within the range of the interval and the center function, the prediction is also considered to be correct.
The support vector regression structure model has been determined as shown in fig. 4. In the process of mapping the sample input to the high-dimensional space, the SVR needs to select a proper inner product kernel function and a plurality of uncertain parameters including kernel width σ, penalty coefficient C and insensitive coefficient epsilon, and the selection of each parameter affects the accuracy and generalization capability of the model.
In the specific implementation process, a Gauss radial basis function is generally selected as an inner kernel function:
the invention adopts a particle swarm optimization algorithm to optimize the uncertain parameters and solve the optimal regression curve of the internal resistance of the battery. Suppose that in a D-dimensional space, eachThe particle group X ═ X1，…，xi，…，xD) And i is more than or equal to 1 and less than or equal to D and consists of N particles, wherein the position and the speed of the ith particle are respectively as follows:
Xi＝(xi1，xi2，…，xiD) i＝1,2,…,N (2-3)
vi＝(vi1，vi2，...，viD) i＝1,2,…,N (2-4)
the individual extremum represents the best position of a single particle so far, the global extremum represents the best position of the whole particle swarm, and the individual extremum and the global extremum of the ith particle can be respectively represented as:
pbest＝(pi1，pi2，…，piD) i＝1,2,…,N (2-5)
gbest＝(pg1，g2，…，pgD) (2-6)
each particle is updated as follows:
where k is the current number of iterations,
and
the position and velocity of the ith particle in d-dimension,
for the individual extremum of the ith particle in the d dimension,
global extremum in d dimension for whole particle swarm，c1And c2As an acceleration factor, wkFor inertial weighting, rand () represents a value range of [0,1 ]]A random function in between.
The method is realized by the following steps as shown in fig. 5:
1: initializing a population of particles, each particle position xiAnd velocity vi；
2: the fitness value of each particle is calculated, and the fitness function is taken as the mean square deviation
In the formula (2-9), f (x)i) Is the latest estimate of the ith sample, yiIs the true value of the ith sample, and N is the number of training samples;
3: for each particle, if the current fitness value is more matched, the current individual extremum is used for updating the historical individual extremum pibest. If the current individual extreme value is better than the global extreme value, the global extreme value g is updated by the current individual extreme valuebest；
4: updating the position x of each particle according to equations (2-7) and (2-8)iAnd velocity vi；
5: and if the iteration times meet the set requirement or the error range meets the requirement, ending the iteration. Otherwise, return to 2, continue iteration.
Step 3, marking the minimum value and the maximum value points in the fitted battery internal resistance regression curve as the initial internal resistance R of the batteryNewAnd an end-of-life internal resistance REOLAnd calculating to obtain the SOH of the battery at different cycle stagesHI。
Calculating the SOH of the battery by the formula (2-10)HI。
In the formula, RNewAnd REOLRespectively the initial internal resistance andend of life internal resistance; rNowIs the internal resistance of the battery in the current state. The obtained health factor is used as a characteristic parameter of the battery to estimate the SOH of the battery.
And step 3: taking the battery health factor as an input characteristic and the actual health state of the battery as an output, and training a data set of the battery health factor and the actual health state in the whole aging stage to obtain a determined RBF neural network model;
the SOH of the batteryHIAs the input characteristic of RBF neural network, the battery actual SOH is used as the output, and the health factor SOH is establishedHIAnd an RBF neural network model between the actual SOH and the RBF neural network model.
A Radial Basis Function (RBF) neural network is a three-layer network including an input layer, a single hidden layer, and an output layer, as shown in fig. 6. The mapping relation is as follows:
wherein X ═ X1, X2]TIs an input sample; (x) is an output vector; phi is ai(X) is the ith hidden layer basis function; omega0Weight of constant 1, ωiIs the ith basis function weight; k is the number of centers of the hidden layer basis functions; z is a radical ofiIs the center of the i-th layer of the gaussian function; deltaiIs the width of the ith basis function.
In the process of using the RBF neural network, uncertain parameters including root mean square error, radial basis function expansion speed, maximum neuron number and the like exist. The uncertain parameters can be optimized through the particle swarm optimization algorithm 2-1, and an RBF neural network model with a determined structure and parameters is obtained.
And 4, step 4: in the actual estimation process, detecting the internal resistance of the current battery through direct current discharge, and calculating the health factor of the battery in the current state;
in the actual estimation process, the internal resistance value obtained by detecting the internal resistance of the battery under the SOC of 80 percent, such as the internal resistance test method in the step 1-2, is used for calculating the corresponding health factor through the step 2.
And 5: and (3) taking the battery health factor as an input, and utilizing the RBF neural network model established in the step (3) to realize SOH estimation of the battery.
And using the health factor as an input, and finishing the estimation of the battery health state through the determined RBF neural network model.
The invention discloses a lithium battery SOH estimation method based on internal resistance detection, which comprises the steps of battery aging cycle test and data acquisition, establishment of a nonlinear regression curve of battery internal resistance change and health factor calculation, establishment of a RBF neural network model of the health factor and an actual health state, estimation of the battery health state by using the model and the like. Detecting internal resistance parameters in the aging cycle process of the lithium battery, and establishing historical data of internal resistance parameter change of the lithium battery; searching the optimal initial internal resistance and the optimal internal resistance parameter value at the end of the service life of the lithium battery through a fitting algorithm, and calculating according to a formula to obtain a health factor characteristic parameter; establishing an RBF neural network model by taking the health factor as input and the battery health state as output; and detecting the actual internal resistance of the battery, and inputting the calculated health factor into the determined neural network model to complete the estimation of the health state of the battery. The method simplifies the estimation of the SOH of the battery and ensures the accuracy of the SOH. According to the method, under the condition that an equivalent circuit of the lithium battery is not required to be established, the mapping relation between the health factor and the battery health state is obtained through the lithium battery internal resistance measurement and the method for calculating the health factor through the internal resistance, so that the complex data required by SOH estimation is simplified, and the stable estimation precision is achieved.
It should be noted that, although the above-mentioned embodiments of the present invention are illustrative, the present invention is not limited thereto, and thus the present invention is not limited to the above-mentioned embodiments. Other embodiments, which can be made by those skilled in the art in light of the teachings of the present invention, are considered to be within the scope of the present invention without departing from its principles.
Claims (1)
1. A lithium battery SOH estimation method based on internal resistance detection is characterized by comprising the following steps:
step 1, carrying out a cyclic charge-discharge experiment on a single sample battery, and acquiring internal resistance data in the battery aging process by a direct-current discharge detection method;
step 2, fitting a nonlinear regression curve of the change of the internal resistance of the battery through a PSO-SVR algorithm based on the internal resistance data acquired in the step 1;
step 3, respectively marking the minimum value point and the maximum value point in the fitted nonlinear regression curve of the change of the internal resistance of the battery as the initial internal resistance R of the batteryNewAnd an end-of-life internal resistance REOLAnd calculating to obtain the SOH of the battery under different health statesHI：
In the formula, RNewIs the initial internal resistance of the battery, REOLTo end-of-life internal resistance; rNowIs the internal resistance of the battery in the current state;
step 4, setting the battery health factor SOHHIAs an input characteristic, the actual health state of the battery is used as an output, and an RBF neural network model with a determined structure and parameters is obtained by training a data set of the battery health factor and the actual health state in the whole aging stage;
step 5, in the actual estimation process, based on the steps 1-3, detecting the internal resistance of the current battery through direct current discharge, fitting a nonlinear regression curve by using the detected internal resistance, and calculating a battery health factor in the current state by using a formula I after fitting;
and 6, taking the battery health factor in the current state obtained in the step 5 as the input of the RBF neural network model obtained in the step 4, and utilizing the RBF neural network model to realize SOH estimation of the battery.
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| CN113884913A (en) * | 2021-10-25 | 2022-01-04 | 招商局检测车辆技术研究院有限公司 | Method and device for establishing regression model of electrochemical characteristic impedance of lithium battery |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011214843A (en) * | 2010-03-31 | 2011-10-27 | Furukawa Electric Co Ltd:The | Device and method for estimating internal state of battery |
| CN102445663A (en) * | 2011-09-28 | 2012-05-09 | 哈尔滨工业大学 | Method for estimating battery health of electric automobile |
| CN102520361A (en) * | 2011-12-05 | 2012-06-27 | 惠州市亿能电子有限公司 | State of health (SOH) value assessment method of battery pack |
| KR101160545B1 (en) * | 2011-01-31 | 2012-06-27 | 주식회사티움리서치 | Apparatus for measuring state of health of rechargeable battery |
| CN104198949A (en) * | 2014-09-09 | 2014-12-10 | 上虞安卡拖车配件有限公司 | Battery health state detection method |
| WO2015031437A1 (en) * | 2013-08-27 | 2015-03-05 | The Regents Of The University Of Michigan | On-board state of health monitoring of batteries using incremental capacity analysis |
| CN104502847A (en) * | 2014-11-28 | 2015-04-08 | 华晨汽车集团控股有限公司 | Pre-estimate method for SOH (state of health) of power battery of electric car |
| CN108414938A (en) * | 2018-01-18 | 2018-08-17 | 武汉理工大学 | Batteries of electric automobile SOH online evaluation methods based on electric vehicle monitor supervision platform |
| CN109061505A (en) * | 2018-08-28 | 2018-12-21 | 淮阴工学院 | A kind of detection method of lithium battery SOH |
| CN109725266A (en) * | 2018-12-29 | 2019-05-07 | 蜂巢能源科技有限公司 | A kind of calculation method and device of cell health state SOH |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102680903B (en) * | 2012-05-11 | 2015-01-28 | 齐鲁工业大学 | Portable storage battery state detection system and method |
| CN106383316B (en) * | 2016-08-30 | 2018-07-10 | 郑州轻工业学院 | A kind of echelon utilizes lithium battery method of evaluating performance |
| CN108445406B (en) * | 2018-03-13 | 2021-05-25 | 桂林电子科技大学 | Power battery state of health estimation method |
-
2019
- 2019-11-04 CN CN201911066210.XA patent/CN110632528B/en not_active Expired - Fee Related
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011214843A (en) * | 2010-03-31 | 2011-10-27 | Furukawa Electric Co Ltd:The | Device and method for estimating internal state of battery |
| KR101160545B1 (en) * | 2011-01-31 | 2012-06-27 | 주식회사티움리서치 | Apparatus for measuring state of health of rechargeable battery |
| CN102445663A (en) * | 2011-09-28 | 2012-05-09 | 哈尔滨工业大学 | Method for estimating battery health of electric automobile |
| CN102520361A (en) * | 2011-12-05 | 2012-06-27 | 惠州市亿能电子有限公司 | State of health (SOH) value assessment method of battery pack |
| WO2015031437A1 (en) * | 2013-08-27 | 2015-03-05 | The Regents Of The University Of Michigan | On-board state of health monitoring of batteries using incremental capacity analysis |
| CN104198949A (en) * | 2014-09-09 | 2014-12-10 | 上虞安卡拖车配件有限公司 | Battery health state detection method |
| CN104502847A (en) * | 2014-11-28 | 2015-04-08 | 华晨汽车集团控股有限公司 | Pre-estimate method for SOH (state of health) of power battery of electric car |
| CN108414938A (en) * | 2018-01-18 | 2018-08-17 | 武汉理工大学 | Batteries of electric automobile SOH online evaluation methods based on electric vehicle monitor supervision platform |
| CN109061505A (en) * | 2018-08-28 | 2018-12-21 | 淮阴工学院 | A kind of detection method of lithium battery SOH |
| CN109725266A (en) * | 2018-12-29 | 2019-05-07 | 蜂巢能源科技有限公司 | A kind of calculation method and device of cell health state SOH |
Non-Patent Citations (2)
| Title |
|---|
| Remaining of Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression;J.Wei,etc;《IEEE Transactions on Industrial Electronics》;20171211;5634-5643 * |
| 动力电池SOH估计及故障预测;卢明哲;《中国优秀硕士学位论文全文数据库 工程科技II辑》;20160315;C042-1658 * |
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