CN111983476B - Battery safety degree estimation method and device based on Kalman filtering method - Google Patents
Battery safety degree estimation method and device based on Kalman filtering method Download PDFInfo
- Publication number
- CN111983476B CN111983476B CN202010867304.3A CN202010867304A CN111983476B CN 111983476 B CN111983476 B CN 111983476B CN 202010867304 A CN202010867304 A CN 202010867304A CN 111983476 B CN111983476 B CN 111983476B
- Authority
- CN
- China
- Prior art keywords
- battery
- safety
- voltage
- safety degree
- value
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- 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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Tests Of Electric Status Of Batteries (AREA)
- Secondary Cells (AREA)
Abstract
The invention discloses a method and a device for evaluating the safety degree of a lithium ion power battery based on a Kalman filtering method, and belongs to the technical field of evaluation of the safety degree of the power battery. The method comprises the steps of establishing a lithium power battery model according to a battery model; estimating the safety degree of the battery; correcting the safety degree error by using a Kalman filtering method to obtain an accurate safety degree numerical value; the method can accurately estimate the safety degree value of the lithium battery, has the characteristics of high estimation precision, strong reliability, simple estimation model and the like, and can be widely applied to the technical field of power batteries of electric automobiles.
Description
Technical Field
The invention relates to the field of battery safety degree evaluation, in particular to a method and a device for evaluating the safety degree of a lithium ion power battery based on a Kalman filtering method.
Background
The deterioration of environmental pollution and the shortage of resources have increasingly urgent requirements on environmental protection and energy conservation, and in the development of new energy automobiles, batteries have the characteristics of high efficiency, safety and cleanness, so that new energy automobiles taking the batteries as main or auxiliary power sources become common research objects. With the frequent occurrence of accidents such as explosion, spontaneous combustion and the like of batteries in recent years, whether the batteries are safe or not is more and more emphasized. In the state of the battery, if the overvoltage and the overheating critical state are reached, the thermal runaway of the battery inevitably causes the occurrence of safety accidents, and the safety problem of the battery becomes an imminent problem to be solved by the new energy industry.
The lithium ion battery is a complex electrochemical system, the failure reason of the battery is complex, and the failure mode of the battery is influenced by a plurality of factors, such as the ambient temperature, the discharge depth, the charge and discharge current and the like. Even if the state parameters (such as voltage, current and temperature) of the battery can be measured in real time, the parameters such as internal resistance, capacity, SOC and the like of the battery can also be calculated through actually measuring the parameters, but the safety of the battery cannot be measured, the safety is a variable influenced by multiple factors at any time, and the guarantee of the safety is also a precondition for normal application of a battery system. The domestic main focus is on the aspect of a battery fault diagnosis method. The safety quantitative analysis of the battery is less, the fault diagnosis only judges the fault problem after the battery has a fault, and the fault diagnosis cannot prevent the battery from being in fault. In fact, the development of battery failure is a gradual process. For example, safety evaluation and quantitative index can be given during the use of the battery, which plays an important role in preventing battery accidents and ensuring user safety. How to realize real-time and accurate safety estimation is always a bottleneck problem in the design process of the lithium ion battery pack.
Disclosure of Invention
In order to solve the problems, the invention provides a method for evaluating the safety degree of a lithium ion power battery based on a Kalman filtering method according to influence factors of the lithium ion power battery in the using process.
The invention relates to a lithium ion power battery safety degree evaluation method based on a Kalman filtering method, which comprises the following steps:
a battery safety degree estimation method based on a Kalman filtering method is characterized by comprising the following steps:
establishing a lithium power battery model according to the battery model;
estimating the safety degree of the battery;
correcting the safety degree error by using a Kalman filtering method to obtain an accurate safety degree numerical value;
further, an equivalent circuit model is established, a cyclic intermittent charge and discharge experiment with different charge and discharge multiplying powers is carried out on the power battery, a voltage and temperature related experiment is carried out, a second-order RC circuit model is established according to a voltage rebound curve, and a model equation is established; a charge-discharge diagram, i.e., an estimated initial value, is obtained from a pulse experiment.
The method for establishing the lithium power battery model according to the battery model comprises the following steps:
establishing an equivalent circuit model;
carrying out cyclic intermittent charge and discharge experiments with different charge and discharge multiplying powers on the power battery, carrying out voltage and temperature experiments, and building a second-order RC circuit model;
the initial values were estimated from pulse experiments.
Further, the method for estimating the safety degree of the battery comprises the following steps:
obtaining a weight coefficient omega of the battery voltage 1 Weight coefficient omega of battery temperature 2 And a degree of attenuation K;
obtaining battery voltage safety coefficient S u Battery temperature safety coefficient S t And charging and discharging safety factor S c ；
Obtaining a battery safety value according to the following formula:
further, the weight coefficient omega of the battery voltage 1 And weight coefficient omega of battery temperature 2 The acquisition method comprises the following steps:
obtaining a characteristic value F of the battery voltage safety coefficient u And the corresponding variable total variance d (u);
by passing
Obtaining the contribution rate sigma of the battery voltage variance u ；
The contribution rate sigma of the cell voltage variance u Obtaining the weight coefficient omega of the battery voltage after normalization 1 。
Obtaining the characteristic value F of the battery temperature safety system t And the total variance d (t) of the corresponding variables;
by passing
Obtaining the contribution rate sigma of the temperature variance of the battery t ；
The contribution rate sigma of the battery temperature variance t Obtaining the weight coefficient omega of the battery voltage after normalization 2 。
Further, the attenuation K is obtained by the following formula:
wherein, R and C are the equivalent resistance and the equivalent capacitance in the second-order RC circuit respectively.
Further, the voltage safety factor S u Comprises the following steps:
in the formula of U s Is a standard operating voltage, U m Is a voltage threshold;
temperature safety factor S t Is as follows;
in the formula, T S Is a standard working temperature, T m Is a temperature threshold;
charging and discharging safety coefficient S c Is as follows;
in the formula of U S Is a standard operating voltage, U m Is a voltage threshold.
Further, model parameters are identified by using a circuit principle to obtain segmented off-line data, and an SOS (state of health) estimation idea is combined by applying an improved Kalman filtering algorithm and a least square algorithm to build an SOS on-line estimation system of the battery pack;
the idea of the kalman filter algorithm is used to estimate the battery SOS, which is an internal state variable of the system, considering the battery as a power system.
The method for correcting the safety degree error by using the Kalman filtering method to obtain the accurate safety degree value comprises the following steps:
calculate State variable Pre-estimate, noted
Calculating a battery terminal voltage;
comparing the actual value of the battery terminal voltage with the model output voltage value to obtain a voltage difference value;
calculating an error covariance matrix;
calculating a correction gain of Kalman filtering;
optimizing and estimating state variables;
the SOS value is output and returned to the state variable estimation value calculation step.
In another aspect, the present invention provides a battery safety estimation apparatus based on a kalman filter method, including:
the battery safety degree estimation method comprises the steps that an estimation module is used for estimating the safety degree of the current state of a battery according to the battery safety degree estimation method based on the Kalman filtering method in the first aspect of the invention;
the interval matching module is used for establishing a safety degree comparison table, wherein the safety degree comparison table is composed of a plurality of safety intervals, and the safety intervals correspond to the battery safety conditions at the current moment; and matching the safety degree value obtained by the estimation module with the safety interval to obtain the battery safety condition at the current moment.
And the display module is used for displaying the safety degree information of the battery in the current state.
As described above, the method for evaluating the safety of the lithium ion power battery based on the kalman filtering method provided by the present invention has the following effects:
1. the invention realizes the real-time quantitative estimation of the safety degree of the battery and is applied to the estimation of the safety degree of various batteries in various states.
2. According to the method, the internal parameters of the battery are estimated by adopting the second-order loop model, the steady-state characteristic and the transient-state characteristic of the battery can be well simulated, and meanwhile, the dynamic characteristic of the power battery is considered, so that the dynamic voltage prediction precision is higher.
3. The method combines the advantages of an ampere-hour integral method and an extended Kalman filtering method to obtain a more accurate SOS estimated value, thereby improving the estimation precision of the battery safety.
4. The battery voltage weight coefficient and the battery temperature weight coefficient are calculated by adopting a principal component analysis method, so that the mutual influence among variables is eliminated, the workload of parameter selection is reduced, and the accuracy and the efficiency of safety calculation are improved.
In conclusion, the lithium ion power battery safety degree evaluation method based on the Kalman filtering method is very suitable for safety degree evaluation of various batteries and has practicability.
Drawings
Fig. 1 is a flow chart illustrating the evaluation of the safety of a lithium ion power battery according to an embodiment of the present invention;
FIG. 2 is a diagram of battery charging and discharging according to an embodiment of the present invention;
FIG. 3 is a comparison curve before and after the battery open circuit voltage is set aside at a high temperature;
FIG. 4 is a test curve of internal resistance of the battery after high-temperature standing;
FIG. 5 is a second order RC circuit model according to one embodiment of the present invention;
FIG. 6 is a schematic flow diagram of an improved extended Kalman filtering algorithm in the SOS estimation method of Kalman filtering;
FIG. 7 is a graph showing factors that cause runaway of a high temperature of a battery;
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the drawings only show the components related to the present invention rather than the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
S1, establishing a lithium power battery model according to the battery model; the method specifically comprises the following steps:
an equivalent circuit model is established, wherein the circuit equivalent modeling is used for researching the relation among external characteristics of the battery such as terminal voltage, charging and discharging current, working temperature and the like, the static characteristics and the dynamic characteristics of the battery can be well represented, the power battery is subjected to cyclic intermittent charging and discharging experiments with different charging and discharging multiplying powers, the curve after the discharging reaction of the battery is balanced is shown in figure 2, the comparison curve before and after the open-circuit voltage of the battery is placed at high temperature is shown in figure 3, and the test curve of the internal resistance of the battery after the battery is placed at high temperature is shown in figure 4.
Carrying out cyclic intermittent charge and discharge experiments with different charge and discharge multiplying powers on the power battery, carrying out voltage and temperature experiments, and building a second-order RC circuit model; a second-order RC circuit model is built according to the curves, a model equation is built, and the built second-order equivalent circuit model is shown in FIG. 5.
The initial values were estimated from pulse experiments.
The model mainly comprises a battery structure module and an SOS calculation module. The parameters of the equivalent circuit elements of the battery in the model are changed in real time along with the charging and discharging processes, so that the common average value model is not applicable.
S2, estimating the battery safety degree by the equivalent model parameters;
s21, obtaining weight coefficient omega of battery voltage 1 Weight coefficient omega of battery temperature 2 And degree of attenuationK；
Weight coefficient ω of the battery voltage 1 And weight coefficient omega of battery temperature 2 The acquisition method comprises the following steps:
obtaining a weight coefficient omega of a battery voltage 1 The method specifically comprises the following steps:
obtaining a characteristic value F of the battery voltage safety factor data set { Su1, Su2, … } u And the corresponding variable total variance d (u);
by passing
Obtaining the contribution rate sigma of the battery voltage variance u ；
The contribution rate sigma of the cell voltage variance u Obtaining the weight coefficient omega of the battery voltage after normalization 1 。
Obtaining a weight coefficient omega of the battery temperature 2 The method specifically comprises the following steps:
obtaining a characteristic value F of the set of battery temperature safety system data { St1, St2, … } t And the total variance of the corresponding variables D (t);
by passing
Obtaining the contribution rate sigma of the temperature variance of the battery t ；
The contribution rate sigma of the battery temperature variance t Obtaining the weight coefficient omega of the battery voltage after normalization 2 。
The attenuation K is obtained by the following formula:
wherein, R and C are equivalent resistance and equivalent capacitance in the second-order RC circuit respectively.
S22, obtaining battery voltage safety coefficient S u Battery temperature safety coefficient S t And charging and discharging safety factor S c
The electricityPool voltage acquisition value and standard working voltage U s Comparing, combining with voltage threshold U m Obtaining the voltage safety factor S of the battery u ：
In the formula of U s Is a standard operating voltage, U m For the voltage threshold value, the voltage safety factor represents the working state of the voltage of the power battery, and the closer to 100, the safer the formula is.
Comparing the battery temperature acquisition value with a standard working temperature, and obtaining the temperature safety coefficient S of the battery by combining the temperature threshold value to obtain the safety coefficient of the temperature t ；
In the formula, T S Is a standard working temperature, T m For the temperature threshold value, the temperature safety factor represents the working state of the temperature of the power battery, and the closer to 100, the safer the formula is.
Charging and discharging safety coefficient S c Is as follows;
in the formula of U S Is a standard operating voltage, U m The charging and discharging safety coefficient represents the charging and discharging working state of the power battery for the voltage threshold, and the closer the formula is to 100, the safer the formula is.
S23, according to
Determining the safety degree of the battery;
in practical application, different standard voltages and standard temperatures are adopted for different power battery systems, and different voltage and temperature thresholds are selected according to different batteries, so that more effective battery safety can be obtained. By analyzing the differences between the voltage, temperature and standard values under the battery operating conditions, the SOS is one percent under the ideal operating state of the battery. Therefore, the more the value of the safety SOS of the power battery obtained through the test approaches to one hundred, the higher the safety of the battery at the moment is represented; the lower the value of the obtained power cell safety degree SOS is, the higher the probability of representing the danger of the power cell module under the condition is.
And S3, building an SOS online estimation system of the battery pack, and correcting the safety error by using a Kalman filtering method to obtain an accurate safety value.
Identifying model parameters by using a circuit principle to obtain segmented off-line data, and constructing an SOS (state of health) online estimation system of the battery pack by applying an improved Kalman filtering algorithm and a least square algorithm to jointly estimate the SOS; the concept of the kalman filter algorithm is used to estimate the battery SOS, which is considered to be a power system, and the SOS is an internal state variable of the system. The battery in the working condition is a highly complex nonlinear system, the nonlinear system needs to be linearized, and the method for estimating the linearization of the nonlinear system is an EKF algorithm.
The improved kalman improved extrapolated kalman filtering algorithm EKF is specifically as follows:
kalman filtering principle: the state estimate is modified using kalman gain by updating the state equation and the observation equation. The specific derivation process is as follows:
firstly, a discrete control system is introduced, and the state equation of the system is as follows:
X k+1 ＝A k X k +B k u k +w k ； (1)
in the above formula, X k Is an n-dimensional state vector, u, of the system at time k k As the control input vector of the system at the moment k, A is the system state transition matrix, B is the system input control matrix, w k Process noise;
y k ＝C k X k +D k +v k ； (2)
in the above formula, y k Is an observation vector at time k of the system, C k Is the observation matrix of the system, v k Noise was observed.
When the system is currently at the time k, according to the state space expression of the system, the state of the system transferred from the time k-1 to the time k can be predicted by the time k-1:
in the formula (3)
Is an estimated value of the state prediction of the system at the last moment
Is the state estimate, u, of the system at time k-1 k-1 Is the input quantity of the system at the moment k-1.
If represented by P
The covariance of (A) is based on the system transfer theory
In the formula (4)
Is and is
Corresponding covariance
Is and
the corresponding covariance is the covariance of the time system process, equations (3) and (4)Z-state prediction and covariance update for the system for the kalman filter estimator.
Estimation value of system state at k moment by using Kalman gain
Correction is carried out, the correction equation is as follows
In the formula, Kg is Kalman gain;
through the derivation process, the optimal estimated value of the system state at the k moment can be output
In order for the algorithm to loop and iterate continuously, the covariance of the system state at the time k needs to be updated when the observed value is converged
Wherein I is the identity matrix, and when the system shifts from time k to time, P is the identity matrix in formula (4)
Based on the transfer algorithm process, Kalman filtering estimation can proceed from the initial state to the last state autoregressive.
For a nonlinear real-time change system, a linearization step is added on the basis of KF, and during state estimation, real-time linear Taylor approximation is carried out on the estimated value of the system equation in the previous state; during prediction, linear Taylor similarity is carried out on the measurement equation at the corresponding prediction position, so that transition from a nonlinear function to linearity is realized, namely the improved Kalman filtering algorithm EKF.
The method for estimating the SOS value of the lithium iron phosphate by using the extended Kalman filtering algorithm comprises the following steps:
s31, calculating the state variable pre-estimation value
Set initial values to
And P (0);
s32, calculating a state variable pre-estimation value according to a state equation and an observation equation of the discrete system, and recording the state variable pre-estimation value as
The state equation and the observation equation of the discrete system are as follows:
order:
considering the influence of process noise wk and measurement noise vk, the state equation and observation equation for constructing the system are as follows:
the comparison can be carried out as follows:
C(x k+1 ，i k+1 )＝E[s(k+1)]-R 0 I(k+1)-U 1 (k+1)-U 2 (k+1)；
s33, calculating the battery terminal voltage according to the following formula;
U L (k)＝Z[X SOS (k)]-U 1 (k)-U 2 (k)-R i I(k)+v(k)；
s34, comparing the actual value of the battery terminal voltage with the terminal voltage value to obtain a voltage difference value:
s35, calculating an error covariance matrix;
P(k+1/k)＝A(k)P(k|k)A T (k)+Q k ；
P(k+1/k+1)
＝P(k+1|k)-P(k+1|k)H T (k+1)*[H(k+1)P(k+1|k)H T (k+1)+R k+1 ] -1 *H(k+1)P(k+1|k)；
in the formula (I), the compound is shown in the specification,
Q k ，R k respectively, of uncorrelated zero mean gaussian white noise w k ，v k The variance matrix of (2);
s36, calculating correction gain of Kalman filtering:
L(k+1)＝P(k+1|k)H T (k+1)*[H(k+1)P(k+1|k)H T (k+1)+R k+1 ] -1 ；
s37, state variable optimization estimation:
the correction gain L (k +1) is the weight occupied by the terminal voltage correction state variable, when the battery terminal voltage error value and the correction gain are large, the state variable optimization estimation value is also large, and the actual value isAt each sampling point by
The corrected value will not exceed-1% to 1%;
and S38, outputting a safety degree value and returning to the step S31.
The extended Kalman filter algorithm will compare the system state variable x (k) in each sampling time interval
And the error covariance matrix p (k) are estimated twice differently; first prediction estimate
The method is obtained by utilizing a state equation to calculate on the basis of a previous state estimation value; before the measurement of the observed data U (k +1) is finished, the prediction estimation is finished; second best estimate
The calculation is started only after the measurement of the observed data U (k +1) is finished, and after the numerical value of U (k +1) is obtained, the optimal estimation is carried out on the prediction estimation result
P (k +1| k) is corrected to finally obtain the optimal estimated value of the system
P(k+1|k+1)。
S4, establishing a safety degree comparison table, wherein the safety degree comparison table is composed of a plurality of safety intervals, and the safety intervals correspond to the battery safety conditions at the current moment; and matching the safety degree value obtained by the estimation module with the safety interval to obtain the battery safety condition at the current moment.
The safety degree of the battery in the embodiment is calculated in a range of 0-1, and the closer the safety degree is to 1, the safer the power battery is. The present embodiment builds the table 1 according to the existing battery database to prompt the current safety status of the battery used by the user, so as to avoid danger. As shown in Table 1, when the safety value of the battery is in the range of [0.8,1], it is shown that the battery is excellent in shape, can be used continuously, when the safety degree value of the battery is in the range of [0.6,0.8), the battery state is general at the moment, needs to be slightly noticed by a user, when the safety degree value of the battery is in the range of [0.4, 0.6), indicating that the battery is potentially dangerous, the user needs to pay more attention during the use process, when the safety degree value of the battery is in the range of [0.2, 0.4), the battery reaches the dangerous degree, the use is stopped and the battery is replaced, when the safety degree value of the battery is in the range of [0,0.2), the surface battery reaches a serious danger degree, which indicates that a burning explosion condition occurs or the battery is easy to cause burning and explosion, and at the moment, the battery is disassembled and properly transferred by adopting an emergency treatment mode according to actual needs.
TABLE 1, Battery safety degree correspondence table
| Safe phase | Range of safety values | Display unit |
| 1 | 0-0.2 | The battery reaches a serious danger level |
| 2 | 0.2-0.4 | The battery reaches a |
| 3 | 0.4-0.6 | Potential danger of battery |
| 4 | 0.6-0.8 | General state of the battery |
| 5 | 0.8-1 | Good battery state |
The embodiment provides a battery safety degree estimation device based on a Kalman filtering method, which comprises:
the estimation module is used for estimating the safety degree of the current state of the battery according to the battery safety degree estimation method based on the Kalman filtering method;
the interval matching module is used for establishing a safety degree comparison table, the safety degree comparison table is composed of a plurality of safety intervals, and the safety intervals correspond to the battery safety conditions at the current moment; matching the safety degree value obtained by the estimation module with the safety interval to obtain the battery safety condition at the current moment;
and the display module is used for displaying the safety degree information of the battery in the current state.
The safety degree estimation device in this embodiment is an electronic device, and includes a processor, a memory, and a display, where the memory has an instruction for implementing a method for estimating safety degree of a lithium ion power battery based on kalman filtering, and the processor is configured to call the instruction to execute the method for estimating a safety zone of a battery according to an embodiment of the present invention, and the processor in this embodiment may be a DSP or a single chip microcomputer.
In addition, when the instructions in the memory are implemented in the form of a software functional unit and sold or used as a separate product, the instructions may be stored in a computer-readable storage medium, that is, a technical solution of the present invention per se or a part contributing to the prior art or a part of the technical solution may be embodied in the form of a software product, where the computer software product is stored in a storage medium, and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method according to each embodiment of the present invention. And the aforementioned storage medium includes: u disk, removable hard disk, read only memory, random access memory, magnetic or optical disk, etc. for storing program codes.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (5)
1. A battery safety degree estimation method based on a Kalman filtering method is characterized by comprising the following steps:
s1, establishing a lithium power battery model according to the battery model;
s2, estimating the battery safety degree, comprising:
s21, obtaining weight coefficient omega of battery voltage 1 Weight coefficient omega of battery temperature 2 And a degree of attenuation K;
weight coefficient ω of the battery voltage 1 And weight coefficient omega of battery temperature 2 The acquisition method comprises the following steps:
obtaining a characteristic value F of the battery voltage safety coefficient u And the corresponding variable total variance D (u);
by passing
Obtaining the contribution rate sigma of the battery voltage variance u ；
The contribution rate sigma of the cell voltage variance u Obtaining the weight coefficient omega of the battery voltage after normalization 1 ；
Obtaining the characteristic value F of the battery temperature safety system t And the total variance d (t) of the corresponding variables;
by passing
Obtaining the contribution rate sigma of the temperature variance of the battery t ；
The contribution rate sigma of the battery temperature variance t Obtaining the weight coefficient omega of the battery voltage after normalization 2 ；
S22, obtaining battery voltage safety coefficient S u Battery temperature safety coefficient S t And charging and discharging safety factor S c ；
The voltage safety factor S u Comprises the following steps:
in the formula of U s Is a standard operating voltage, U m Is a voltage threshold;
temperature safety factor S t Is as follows;
in the formula, T S Is a standard working temperature, T m Is a temperature threshold;
charging and discharging safety coefficient S c Is as follows;
in the formula of U S Is a standard operating voltage, U m Is a voltage threshold;
s23, obtaining a battery safety degree value according to the following formula:
and S3, correcting the safety error by using a Kalman filtering method to obtain an accurate safety numerical value.
2. The battery safety degree estimation method based on the Kalman filtering method according to claim 1, characterized in that the step of establishing the lithium power battery model according to the battery model comprises the following steps:
establishing an equivalent circuit model;
carrying out cyclic intermittent charge and discharge experiments with different charge and discharge multiplying powers on the power battery, carrying out voltage and temperature experiments, and building a second-order RC circuit model;
the initial values were estimated from pulse experiments.
3. The battery safety degree estimation method based on the Kalman filtering method according to claim 1, characterized in that the method for correcting the safety degree error by the Kalman filtering method to obtain the accurate safety degree value comprises the following steps:
calculating a state variable pre-estimate, noted
Calculating a battery terminal voltage;
comparing the actual value of the battery terminal voltage with the model output voltage value to obtain a voltage difference value;
calculating an error covariance matrix;
calculating a correction gain of Kalman filtering;
optimizing and estimating state variables;
and outputting the SOS value and returning to the step of calculating the state variable pre-estimation value.
4. A battery safety degree estimation device based on a Kalman filtering method is characterized by comprising the following steps:
an estimation module, which is used for estimating the safety degree of the current state of the battery according to the battery safety degree estimation method based on the Kalman filtering method as claimed in any one of claims 1-3;
and the display module is used for displaying the safety degree information of the battery in the current state.
5. The battery safety degree estimation device based on the Kalman filtering method according to claim 4, characterized by comprising an interval matching module for establishing a safety degree comparison table, wherein the safety degree comparison table is composed of a plurality of safety intervals, and the safety intervals correspond to the battery safety conditions at the current moment; and matching the safety degree value obtained by the estimation module with the safety interval to obtain the battery safety condition at the current moment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010867304.3A CN111983476B (en) | 2020-08-24 | 2020-08-24 | Battery safety degree estimation method and device based on Kalman filtering method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010867304.3A CN111983476B (en) | 2020-08-24 | 2020-08-24 | Battery safety degree estimation method and device based on Kalman filtering method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111983476A CN111983476A (en) | 2020-11-24 |
| CN111983476B true CN111983476B (en) | 2022-08-30 |
Family
ID=73442822
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010867304.3A Active CN111983476B (en) | 2020-08-24 | 2020-08-24 | Battery safety degree estimation method and device based on Kalman filtering method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111983476B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203480000U (en) * | 2013-10-15 | 2014-03-12 | 浙江大学城市学院 | Detector for health status of power lithium battery for full electric vehicle |
| CN105021996A (en) * | 2015-08-04 | 2015-11-04 | 深圳拓普科新能源科技有限公司 | Battery SOH (section of health) estimation method of energy storage power station BMS (battery management system) |
| CN106093778A (en) * | 2016-05-30 | 2016-11-09 | 浙江南都电源动力股份有限公司 | Battery status Forecasting Methodology and system |
| CN106353687A (en) * | 2016-08-26 | 2017-01-25 | 中国电力科学研究院 | Assessment method of lithium battery health status |
| CN107271906A (en) * | 2017-05-31 | 2017-10-20 | 宁德时代新能源科技股份有限公司 | Battery pack health degree estimation method and device |
| CN109061516A (en) * | 2018-10-10 | 2018-12-21 | 哈尔滨理工大学 | A kind of cell health state appraisal procedure based on fuzzy probability Comprehensive Evaluation |
| CN109669134A (en) * | 2019-02-27 | 2019-04-23 | 浙江科技学院 | A kind of evaluation method of the SOC based on Kalman filtering method |
| CN111060834A (en) * | 2019-12-19 | 2020-04-24 | 中国汽车技术研究中心有限公司 | Power battery state of health estimation method |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR2943794B1 (en) * | 2009-03-24 | 2011-05-06 | Saft Groupe Sa | METHOD FOR DETERMINING THE HEALTH CONDITION OF A BATTERY |
| TWI708956B (en) * | 2018-03-08 | 2020-11-01 | 財團法人工業技術研究院 | Battery safety identifying method, method for setting hazard levels of battery internal short circuit and warning system using the same |
-
2020
- 2020-08-24 CN CN202010867304.3A patent/CN111983476B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203480000U (en) * | 2013-10-15 | 2014-03-12 | 浙江大学城市学院 | Detector for health status of power lithium battery for full electric vehicle |
| CN105021996A (en) * | 2015-08-04 | 2015-11-04 | 深圳拓普科新能源科技有限公司 | Battery SOH (section of health) estimation method of energy storage power station BMS (battery management system) |
| CN106093778A (en) * | 2016-05-30 | 2016-11-09 | 浙江南都电源动力股份有限公司 | Battery status Forecasting Methodology and system |
| CN106353687A (en) * | 2016-08-26 | 2017-01-25 | 中国电力科学研究院 | Assessment method of lithium battery health status |
| CN107271906A (en) * | 2017-05-31 | 2017-10-20 | 宁德时代新能源科技股份有限公司 | Battery pack health degree estimation method and device |
| CN109061516A (en) * | 2018-10-10 | 2018-12-21 | 哈尔滨理工大学 | A kind of cell health state appraisal procedure based on fuzzy probability Comprehensive Evaluation |
| CN109669134A (en) * | 2019-02-27 | 2019-04-23 | 浙江科技学院 | A kind of evaluation method of the SOC based on Kalman filtering method |
| CN111060834A (en) * | 2019-12-19 | 2020-04-24 | 中国汽车技术研究中心有限公司 | Power battery state of health estimation method |
Non-Patent Citations (2)
| Title |
|---|
| New Battery Model and State-of-Health Determination Through Subspace Parameter Estimation and State-Observe Techniques;C.R.Gould et al.;《IEEE Transactions on Vehicular Technology》;20091031;3905-3916 * |
| 基于改进卡尔曼滤波算法的电池健康度估算研究;周兴博;《中国优秀博硕士学位论文全文数据库（硕士） 工程科技II辑》;20170515;C042-483 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111983476A (en) | 2020-11-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dai et al. | A novel estimation method for the state of health of lithium-ion battery using prior knowledge-based neural network and Markov chain | |
| Cen et al. | Lithium‐ion battery SOC/SOH adaptive estimation via simplified single particle model | |
| Xiong et al. | A novel practical state of charge estimation method: an adaptive improved ampere‐hour method based on composite correction factor | |
| Xu et al. | A novel adaptive dual extended Kalman filtering algorithm for the Li‐ion battery state of charge and state of health co‐estimation | |
| Song et al. | A novel variable forgetting factor recursive least square algorithm to improve the anti-interference ability of battery model parameters identification | |
| CN111220920B (en) | Retired lithium ion battery state of charge calculation method based on H-infinity unscented Kalman filtering algorithm | |
| CN112580284B (en) | Hybrid capacitor equivalent circuit model and online parameter identification method | |
| CN111913109B (en) | Method and device for predicting peak power of battery | |
| Li et al. | A novel state estimation approach based on adaptive unscented Kalman filter for electric vehicles | |
| CN112379270B (en) | Rolling time domain estimation method for state of charge of power battery of electric automobile | |
| CN112630659A (en) | Lithium battery SOC estimation method based on improved BP-EKF algorithm | |
| Xu et al. | State of charge estimation for liquid metal battery based on an improved sliding mode observer | |
| Duan et al. | Online parameter identification and state of charge estimation of battery based on multitimescale adaptive double Kalman filter algorithm | |
| CN111983472B (en) | Lithium ion power battery safety degree estimation method and estimation device based on adaptive Kalman filtering | |
| Ren et al. | Novel co‐estimation strategy based on forgetting factor dual particle filter algorithm for the state of charge and state of health of the lithium‐ion battery | |
| Jiang et al. | An aging-aware soc estimation method for lithium-ion batteries using xgboost algorithm | |
| Miao et al. | A novel online model parameters identification method with anti‐interference characteristics for lithium‐ion batteries | |
| CN116047308A (en) | Lithium battery SOC estimation method based on PID control and DEKF | |
| CN115327415A (en) | Lithium battery SOC estimation method based on limited memory recursive least square algorithm | |
| Xu et al. | Novel reduced-order modeling method combined with three-particle nonlinear transform unscented Kalman filtering for the battery state-of-charge estimation | |
| Priya et al. | State of charge estimation of lithium‐ion battery based on extended Kalman filter and unscented Kalman filter techniques | |
| CN112946480B (en) | Lithium battery circuit model simplification method for improving SOC estimation real-time performance | |
| Zhu et al. | An improved proportional control forgetting factor recursive least square-Monte Carlo adaptive extended Kalman filtering algorithm for high-precision state-of-charge estimation of lithium-ion batteries | |
| CN111983471A (en) | Lithium ion power battery safety degree estimation method and estimation device based on double Kalman filtering | |
| CN111983476B (en) | Battery safety degree estimation method and device based on Kalman filtering method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |