CN112557925A - Lithium ion battery SOC estimation method and device - Google Patents
Lithium ion battery SOC estimation method and device Download PDFInfo
- Publication number
- CN112557925A CN112557925A CN202011257741.XA CN202011257741A CN112557925A CN 112557925 A CN112557925 A CN 112557925A CN 202011257741 A CN202011257741 A CN 202011257741A CN 112557925 A CN112557925 A CN 112557925A
- Authority
- CN
- China
- Prior art keywords
- battery
- model
- soc
- lithium ion
- equivalent circuit
- 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.)
- Granted
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]
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
- Tests Of Electric Status Of Batteries (AREA)
Abstract
The embodiment of the invention provides a method and a device for estimating the SOC of a lithium ion battery, wherein the method comprises the following steps: determining the mapping relation between each model parameter in the battery equivalent circuit model and the temperature and SOC value; establishing a battery discrete state space model according to the battery equivalent circuit model and the mapping relation between each model parameter and the temperature and SOC value; the SOC value of the battery discrete state space model is estimated through the iterative extended Kalman filtering algorithm, the SOC value of the lithium ion battery is determined, multiple iterations are adopted at each time step, the deviation caused by the adoption of first-order Taylor approximation can be effectively reduced, and the algorithm precision is improved.
Description
Technical Field
The invention relates to the technical field of battery management, in particular to a method and a device for estimating the SOC of a lithium ion battery.
Background
The estimation of the battery SOC (State of Charge, Chinese) is an important part of a battery management system of an electric vehicle, and because the estimation of the battery SOC is comprehensively influenced by many factors (such as charging and discharging multiplying power, temperature, cycle life, self-discharge and the like), the estimation accuracy of the SOC in practical application is difficult to ensure. The battery SOC estimation of the battery management system of the electric automobile directly influences the control of the electric automobile. The inaccuracy of the estimation of the SOC of the battery may limit the use of the electric vehicle and make it difficult to perform its best performance; on the other hand, the battery can be abused or even thermally out of control, and great potential safety hazard is brought to the use of the electric automobile. The SOC is accurately estimated, so that the SOC is maintained in a reasonable area, and the power battery is prevented from being overcharged and overdischarged, and the SOC estimation method has important significance for accurately estimating the residual electric quantity of the power battery.
The existing technology can solve the problems of error accumulation and the like in the SOC estimation process, but cannot completely cover the working temperature of the battery, and meanwhile, flexible iteration can not be flexibly carried out according to the error of the current estimation time.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a method and a device for estimating the SOC of a lithium ion battery.
In a first aspect, an embodiment of the present invention provides a method for estimating an SOC of a lithium ion battery, including:
determining the mapping relation between each model parameter in the battery equivalent circuit model and the temperature and SOC value;
establishing a battery discrete state space model according to a battery equivalent circuit model and the mapping relation between each model parameter and the temperature and SOC value;
and estimating the SOC value of the battery discrete state space model through an iterative extended Kalman filtering algorithm to determine the SOC value of the lithium ion battery.
Optionally, the establishing a discrete state space model of the battery according to the battery equivalent circuit model and the mapping relationship between the model parameters and the temperature and the SOC value includes:
and (3) running at least two Kalman filters in parallel, establishing a battery discrete state space equation and a system measurement updating equation by combining the battery equivalent circuit model and the mapping relation between each model parameter and the temperature and SOC value, and defining a measurement matrix.
Optionally, the estimating the SOC value of the discrete state space model of the battery by using the iterative extended kalman filter algorithm to determine the SOC value of the lithium ion battery includes:
initializing an iterative extended Kalman filtering algorithm at an initial moment, and determining an initial state estimation value and an initial state error covariance matrix at the initial moment;
and running all Kalman filtering in parallel, circularly executing a plurality of computing operations, executing the iterative extended Kalman filtering algorithm for a plurality of times in each computing operation, updating the time and measuring and updating the iteration when executing the iterative extended Kalman filtering algorithm for each time until the iterative extended Kalman filtering algorithm reaches a cut-off condition, finishing the computing operation and determining the SOC value of the lithium ion battery.
Optionally, the determining a mapping relationship between each model parameter in the battery equivalent circuit model and the temperature and SOC value includes:
establishing a battery equivalent circuit model of a lithium ion battery, and establishing a characteristic equation of the battery equivalent circuit model;
performing pulse discharge operation based on the battery equivalent circuit model, and determining model parameter values under each operation;
establishing an initial mapping relation expression of the model parameters and the temperature and SOC values, determining undetermined coefficients in the mapping relation expression of the model parameters and the temperature and SOC values through a curve fitting method, and determining the mapping relation of the model parameters and the temperature and SOC values according to the undetermined coefficients.
In a second aspect, an embodiment of the present invention provides a lithium ion battery SOC estimation device, including:
the determining module is used for determining the mapping relation between each model parameter in the battery equivalent circuit model and the temperature and SOC value;
the building module is used for building a battery discrete state space model according to a battery equivalent circuit model and the mapping relation between each model parameter and the temperature and SOC value;
the estimation module is used for estimating the SOC value of the battery discrete state space model through an iterative extended Kalman filtering algorithm to determine the SOC value of the lithium ion battery,
optionally, the building module is specifically configured to:
and (3) running at least two Kalman filters in parallel, establishing a battery discrete state space equation and a system measurement updating equation by combining the battery equivalent circuit model and the mapping relation between each model parameter and the temperature and SOC value, and defining a measurement matrix.
Optionally, the estimation module is specifically configured to:
initializing an iterative extended Kalman filtering algorithm at an initial moment, and determining an initial state estimation value and an initial state error covariance matrix at the initial moment;
and running all Kalman filtering in parallel, circularly executing a plurality of computing operations, executing the iterative extended Kalman filtering algorithm for a plurality of times in each computing operation, updating the time and measuring and updating the iteration when executing the iterative extended Kalman filtering algorithm for each time until the iterative extended Kalman filtering algorithm reaches a cut-off condition, finishing the computing operation and determining the SOC value of the lithium ion battery.
Optionally, the determining module is specifically configured to:
establishing a battery equivalent circuit model of a lithium ion battery, and establishing a characteristic equation of the battery equivalent circuit model;
performing pulse discharge operation based on the battery equivalent circuit model, and determining model parameter values under each operation;
establishing an initial mapping relation expression of the model parameters and the temperature and SOC values, determining undetermined coefficients in the mapping relation expression of the model parameters and the temperature and SOC values through a curve fitting method, and determining the mapping relation of the model parameters and the temperature and SOC values according to the undetermined coefficients.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the method for estimating the SOC of the lithium ion battery as described above.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the lithium ion battery SOC estimation method as described above.
According to the method and the device for estimating the SOC of the lithium ion battery, the mapping relation between the battery equivalent circuit model and each model parameter as well as the temperature and the SOC value is established, so that the established battery discrete state space model is widely applicable to the lithium ion battery, then the SOC value of the battery discrete state space model is estimated through the iterative extended Kalman filtering algorithm, the SOC value of the lithium ion battery is determined, multiple iterations are adopted at each time step, the deviation caused by adopting first-order Taylor approximation can be effectively reduced, and the algorithm precision is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of an embodiment of a method for estimating SOC of a lithium ion battery according to the present invention;
FIG. 2 is a schematic diagram of an equivalent circuit model established based on a lithium ion battery according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart illustrating the estimation of the SOC value of the discrete state space model of the battery according to the embodiment of the present invention;
fig. 4 is a schematic structural diagram of a lithium ion battery SOC estimation device provided in an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 shows a schematic flow chart of a method for estimating SOC of a lithium ion battery according to an embodiment of the present invention, and referring to fig. 1, the method includes:
s11, determining the mapping relation between each model parameter in the battery equivalent circuit model and the temperature and SOC value;
s12, establishing a battery discrete state space model according to the battery equivalent circuit model and the mapping relation between each model parameter and the temperature and SOC value;
and S13, estimating the SOC value of the battery discrete state space model through an iterative extended Kalman filtering algorithm, and determining the SOC value of the lithium ion battery.
With respect to step S11-step S13, it should be noted that, in the embodiment of the present invention, lithium ions are addedThe battery performs SOC (remaining battery capacity) estimation, and it is necessary to perform circuit equivalent design on the lithium ion battery so that the estimation of the lithium ion battery is set in the process of detecting the circuit. The battery equivalent circuit model is shown in fig. 2 (two stages are taken as an example, but the order is not limited to two stages), and in fig. 2, the battery equivalent circuit includes an ohmic resistor R connected in series0And two RC units, each RC unit is composed of a resistor and a capacitor which are connected in parallel.
In the circuit detection process, each model parameter in the circuit model can be determined, and the model parameter has a certain interaction relation with the temperature and the SOC value. For this reason, the mapping relation between each model parameter in the battery equivalent circuit model and the temperature and SOC value needs to be determined.
In this embodiment, the estimation of the SOC value of the lithium ion battery may be performed by using a certain estimation model. Here, a battery discrete state space model is established according to a battery equivalent circuit model and a mapping relation between each model parameter and a temperature and an SOC value. The battery discrete state space model can realize the estimation of the SOC value of the lithium ion battery.
In the estimation process, the SOC value of the battery discrete state space model is estimated through an iterative extended Kalman filtering algorithm, and the SOC value of the lithium ion battery is determined. Kalman filtering (Kalman filtering) is an algorithm that uses a linear system state equation to optimally estimate the state of a system by inputting and outputting observation data through the system. The optimal estimation can also be seen as a filtering process, since the observed data includes the effects of noise and interference in the system. In the embodiment, the SOC value of the battery discrete state space model is estimated by adopting the Kalman filtering algorithm of iterative expansion, and multiple iterations are adopted at each time step, so that the deviation caused by adopting first-order Taylor approximation can be effectively reduced, and the algorithm precision is improved.
According to the method for estimating the SOC of the lithium ion battery, the built battery discrete state space model is widely applicable to the lithium ion battery by building the mapping relation between the battery equivalent circuit model and each model parameter as well as the temperature and the SOC value, then the SOC value of the battery discrete state space model is estimated by the iterative extended Kalman filtering algorithm to determine the SOC value of the lithium ion battery, so that multiple iterations are adopted at each time step, the deviation caused by adopting first-order Taylor approximation can be effectively reduced, and the algorithm precision is improved.
In a further embodiment of the method according to the above embodiment, a process of establishing a discrete state space model of a battery according to a mapping relationship between a battery equivalent circuit model and each model parameter and a temperature and an SOC value is mainly explained, which is specifically as follows:
and (3) running at least two Kalman filters in parallel, establishing a battery discrete state space equation and a system measurement updating equation by combining the battery equivalent circuit model and the mapping relation between each model parameter and the temperature and SOC value, and defining a measurement matrix.
In contrast, it should be noted that at least two kalman filters are operated in parallel, taking M optimal kalman filters as an example, based on the kalman filter principle, and by combining the battery equivalent circuit model and the mapping relationship between each model parameter and the SOC value, a battery discrete state space equation of lithium ions is established, and parameters are all expressed in a matrixing form:
xk＝Fk-1xk-1+Gk-1uk-1 (1)
wherein:
x＝[U0,U1,U2,SOC]T (2)
u＝[I,I,I,I]T (3)
U0is the ohm internal resistance R in figure 20The voltage at both ends, at, is the sampling interval,Ccapis the rated capacity of the battery.
Besides establishing a battery discrete state space equation of lithium ions, a system measurement update equation needs to be established, and a measurement matrix needs to be defined.
The established system measurement updating equation is as follows:
zk＝hk+vk＝UOCV(T,SOC)-U0-U1-U2+vk (6)
defining the measurement matrix as:
U0is the ohm internal resistance R in figure 20Voltage across,. DELTA.t, sampling interval, CcapIs the rated capacity of the battery.
Besides establishing a battery discrete state space equation of lithium ions, a system measurement update equation needs to be established, and a measurement matrix needs to be defined.
In a further embodiment of the method according to the above embodiment, explanation is mainly given to a processing procedure of estimating the SOC value of the discrete state space model of the battery by using the iterative extended kalman filter algorithm to determine the SOC value of the lithium ion battery, specifically as follows:
and initializing the iterative extended Kalman filtering algorithm at the initial moment, and determining an initial state estimation value and an initial state error covariance matrix at the initial moment.
And running all Kalman filtering in parallel, circularly executing a plurality of computing operations, executing the iterative extended Kalman filtering algorithm for a plurality of times in each computing operation, updating the time and measuring and updating the iteration when executing the iterative extended Kalman filtering algorithm for each time until the iterative extended Kalman filtering algorithm reaches a cut-off condition, finishing the computing operation and determining the SOC value of the lithium ion battery.
In this regard, it should be noted that, in the embodiment of the present invention, as shown in fig. 3, a schematic flowchart of estimating the SOC value of the discrete state space model of the battery is shown. Referring to fig. 3, at the beginning of the estimation process, the iterative extended kalman filter algorithm at the initial time is initialized, which may be represented by the following relation:
wherein Λ represents the estimate,
in order to be an initial state estimation value,
is an initial state error covariance matrix, x0Are model parameter values.
When the loop time k is 1, the loop calculation operation is started, and the loop k is continued to be 1,2 and … …, and the following steps are continuously executed: the loop performs a plurality of calculation operations, the number of loops i is 1,2, … …, M, and the specific number of loops may be set according to actual requirements, which is not limited herein. When the loop calculation operation is executed each time, the iterative extended kalman filter algorithm is executed for many times, and each loop calculation is updated in execution time, for example, the time for executing the ith iterative extended kalman filter in fig. 3 is updated as follows:
initializing iterative extended Kalman filtering as follows:
each time a loop calculation operation is performed, a measurement update is performed, such as the measurement update performed the ith kalman algorithm in fig. 3.
The meaning of each parameter in the above equations (10) to (17) is a general meaning commonly used in the kalman filter algorithm.
And ending the calculation operation until the iterative extended Kalman filtering algorithm reaches a cut-off condition, and determining the SOC value of the lithium ion battery.
In a further embodiment of the method according to the above embodiment, a process of determining a mapping relationship between each model parameter and a temperature and an SOC value in a battery equivalent circuit model is mainly explained, specifically as follows:
establishing a battery equivalent circuit model of the lithium ion battery, and establishing a characteristic equation of the battery equivalent circuit model;
performing pulse discharge operation based on the battery equivalent circuit model, and determining model parameter values under each operation;
establishing an initial mapping relation expression of the model parameters and the temperature and SOC values, determining undetermined coefficients in the mapping relation expression of the model parameters and the temperature and SOC values through a curve fitting method, and determining the mapping relation of the model parameters and the temperature and SOC values according to the undetermined coefficients.
In contrast, in the embodiment of the present invention, a battery equivalent circuit model (as shown in fig. 2) of the lithium ion battery is first established, and the equivalent circuit includes the ohmic resistor R connected in series0And two RC units, each RC unit is composed of a resistor and a capacitor which are connected in parallel. Determining terminal voltage U and open circuit voltage U in battery equivalent circuit modelOCVCharacteristic relationship between them. Aiming at the second-order battery equivalent circuit model shown in FIG. 2, a characteristic equation of the lithium ion battery model is established to describe the terminal voltage U and the open-circuit voltage UOCVThe characteristic equation is as follows:
U0＝IR0 (20)
U＝UOCV-U0-U1-U2 (21)
wherein, U0Is the ohmic internal resistance R0Terminal voltage at both ends, U1～U2Is the voltage across the corresponding two RC cells, I is the current.
Solving equations (18) - (21), the expression of the equivalent circuit terminal voltage can be obtained as:
wherein, U1(0) And U2(0) Are respectively a meterAt the beginning of the time, the voltage across the two RC units is at its initial value.
According to the current equivalent circuit, a pulse discharge test is carried out, the pulse discharge is an existing test method, and the pulse discharge time, the current and the like are all the existing specifications (for example, according to a Freedom battery test manual). Selecting pulse discharge SOC interval, wherein SOC is at least 20 points within the range of 0-1, the temperature range is-10-55 ℃, performing pulse discharge test every 5-10 ℃ when the temperature is lower than 10 ℃, and performing pulse discharge test every 5-15 ℃ when the temperature T is higher than 10 ℃. When the temperature is lower than 10 ℃, the standing balance time after pulse discharge is longer than 3h, and when the temperature is higher than 10 ℃, the standing balance time after pulse discharge is longer than 1 h. When pulse discharge tests at different temperatures are performed, the capacity standard of the SOC is adjusted to be the capacity that can be discharged by the battery at the current temperature, not the rated capacity at normal temperature.
As can be seen from the structure of fig. 2, when the battery is left standing for a sufficiently long time after pulse discharge, the terminal voltage of the battery is an open-circuit voltage Uocv。
As can be seen from the structure of FIG. 2, the voltage change at the end of the pulse discharge is completely determined by the ohmic internal resistance R0And (4) generating. Therefore, ohmic internal resistance R0Obtained using the following formula:
in the formula of ULThe voltage abrupt change at the end of the pulse discharge is denoted as I, and the pulse discharge current value is denoted as I.
As can be seen from the circuit configuration of fig. 2, after the pulse discharge is completed, the voltages at both ends of the ohmic internal resistance become zero, but the voltages at both ends of the three RC units do not become zero. The voltage characteristic equation is therefore:
r at corresponding temperature and SOC can be obtained by nonlinear fitting1，C1，R2，C2Value, buildingAnd (3) establishing a two-dimensional relation of model parameters with temperature and SOC.
f(x,y)＝p00+p10x+p01y+p20x2+p11xy+p02y2+p30x3+p21x2y+p12xy2+p03y3+p40x4+p31x3y+p22x2y2+p13xy3+p04y4+p50x5+p41x4y+p32x3y2+p23x2y3+p14xy4+p05y5
(f＝Uocv,R0,R1,R2,C1,C2)
(x＝T,y＝SOC)
Determining undetermined coefficients (e.g., p) in an expression based on a surface fitting method00，p10Etc.).
And if the undetermined system is determined, determining the mapping relation between each model parameter and the temperature and SOC value according to the undetermined coefficient.
According to the method for estimating the SOC of the lithium ion battery, the built battery discrete state space model is widely applicable to the lithium ion battery by building the mapping relation between the battery equivalent circuit model and each model parameter as well as the temperature and the SOC value, then the SOC value of the battery discrete state space model is estimated by the iterative extended Kalman filtering algorithm, and the SOC value of the lithium ion battery is determined, so that multiple iterations are adopted at each time step, the deviation caused by adopting the first-order Taylor approximation can be effectively reduced, and the algorithm precision is improved.
Fig. 4 shows a schematic structural diagram of an SOC estimation apparatus for a lithium ion battery according to an embodiment of the present invention, referring to fig. 4, the apparatus includes a determination module 41, a construction module 42, and an estimation module 43, where:
the determining module 41 is configured to determine a mapping relationship between each model parameter in the battery equivalent circuit model and the temperature and SOC value;
the building module 42 is used for building a battery discrete state space model according to the battery equivalent circuit model and the mapping relation between the model parameters and the temperature and SOC value;
and the estimation module 43 is configured to estimate the SOC value of the discrete state space model of the battery by using an iterative extended kalman filter algorithm, so as to determine the SOC value of the lithium ion battery.
In a further embodiment of the apparatus of the above embodiment, the building module is specifically configured to:
and (3) running at least two Kalman filters in parallel, establishing a battery discrete state space equation and a system measurement updating equation by combining the battery equivalent circuit model and the mapping relation between each model parameter and the temperature and SOC value, and defining a measurement matrix.
In a further embodiment of the apparatus of the above embodiment, the estimating module is specifically configured to:
initializing an iterative extended Kalman filtering algorithm at an initial moment, and determining an initial state estimation value and an initial state error covariance matrix at the initial moment;
and running all Kalman filtering in parallel, circularly executing a plurality of computing operations, executing the iterative extended Kalman filtering algorithm for a plurality of times in each computing operation, updating the time and measuring and updating the iteration when executing the iterative extended Kalman filtering algorithm for each time until the iterative extended Kalman filtering algorithm reaches a cut-off condition, finishing the computing operation and determining the SOC value of the lithium ion battery.
In a further embodiment of the apparatus of the above embodiment, the determining module is specifically configured to:
establishing a battery equivalent circuit model of a lithium ion battery, and establishing a characteristic equation of the battery equivalent circuit model;
performing pulse discharge operation based on the battery equivalent circuit model, and determining model parameter values under each operation;
establishing an initial mapping relation expression of the model parameters and the temperature and SOC values, determining undetermined coefficients in the mapping relation expression of the model parameters and the temperature and SOC values through a curve fitting method, and determining the mapping relation of the model parameters and the temperature and SOC values according to the undetermined coefficients.
Since the principle of the apparatus according to the embodiment of the present invention is the same as that of the method according to the above embodiment, further details are not described herein for further explanation.
It should be noted that, in the embodiment of the present invention, the relevant functional module may be implemented by a hardware processor (hardware processor).
The lithium ion battery SOC estimation device provided in the above embodiment enables the application of the established battery discrete state space model to the lithium ion battery to be wide by establishing the mapping relationship between the battery equivalent circuit model and each model parameter and the temperature and SOC value, and then estimates the SOC value of the battery discrete state space model by the iterative extended kalman filter algorithm to determine the SOC value of the lithium ion battery, so that multiple iterations are adopted at each time step, and the deviation caused by adopting the first-order taylor approximation can be effectively reduced, thereby improving the algorithm precision.
Fig. 5 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 5: a processor (processor)51, a communication Interface (communication Interface)52, a memory (memory)53 and a communication bus 54, wherein the processor 51, the communication Interface 52 and the memory 53 complete communication with each other through the communication bus 54. The processor 51 may call logic instructions in the memory 53 to perform the following method: determining the mapping relation between each model parameter in the battery equivalent circuit model and the temperature and SOC value; establishing a battery discrete state space model according to a battery equivalent circuit model and the mapping relation between each model parameter and the temperature and SOC value; and estimating the SOC value of the battery discrete state space model through an iterative extended Kalman filtering algorithm to determine the SOC value of the lithium ion battery.
In addition, the logic instructions in the memory 53 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Embodiments of the present invention further provide a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the method provided in the foregoing embodiments when executed by a processor, and the method includes: determining the mapping relation between each model parameter in the battery equivalent circuit model and the temperature and SOC value; establishing a battery discrete state space model according to a battery equivalent circuit model and the mapping relation between each model parameter and the temperature and SOC value; and estimating the SOC value of the battery discrete state space model through an iterative extended Kalman filtering algorithm to determine the SOC value of the lithium ion battery.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A lithium ion battery SOC estimation method is characterized by comprising the following steps:
determining the mapping relation between each model parameter in the battery equivalent circuit model and the temperature and SOC value;
establishing a battery discrete state space model according to a battery equivalent circuit model and the mapping relation between each model parameter and the temperature and SOC value;
and estimating the SOC value of the battery discrete state space model through an iterative extended Kalman filtering algorithm to determine the SOC value of the lithium ion battery.
2. The method for estimating the SOC of the lithium ion battery according to claim 1, wherein the establishing a discrete state space model of the battery according to the mapping relationship between the equivalent circuit model of the battery and the model parameters and the temperature and the SOC value comprises:
and (3) running at least two Kalman filters in parallel, establishing a battery discrete state space equation and a system measurement updating equation by combining the battery equivalent circuit model and the mapping relation between each model parameter and the temperature and SOC value, and defining a measurement matrix.
3. The method of claim 2, wherein the determining the SOC value of the lithium ion battery by estimating the SOC value of the discrete state space model of the battery through the iterative extended kalman filter algorithm comprises:
initializing an iterative extended Kalman filtering algorithm at an initial moment, and determining an initial state estimation value and an initial state error covariance matrix at the initial moment;
and running all Kalman filtering in parallel, circularly executing a plurality of computing operations, executing the iterative extended Kalman filtering algorithm for a plurality of times in each computing operation, updating the time and measuring and updating the iteration when executing the iterative extended Kalman filtering algorithm for each time until the iterative extended Kalman filtering algorithm reaches a cut-off condition, finishing the computing operation and determining the SOC value of the lithium ion battery.
4. The method of claim 1, wherein the determining the mapping relationship between the model parameters and the temperature and SOC values in the equivalent circuit model of the battery comprises:
establishing a battery equivalent circuit model of a lithium ion battery, and establishing a characteristic equation of the battery equivalent circuit model;
performing pulse discharge operation based on the battery equivalent circuit model, and determining model parameter values under each operation;
establishing an initial mapping relation expression of the model parameters and the temperature and SOC values, determining undetermined coefficients in the mapping relation expression of the model parameters and the temperature and SOC values through a curve fitting method, and determining the mapping relation of the model parameters and the temperature and SOC values according to the undetermined coefficients.
5. A lithium ion battery SOC estimation device, comprising:
the determining module is used for determining the mapping relation between each model parameter in the battery equivalent circuit model and the temperature and SOC value;
the building module is used for building a battery discrete state space model according to a battery equivalent circuit model and the mapping relation between each model parameter and the temperature and SOC value;
and the estimation module is used for estimating the SOC value of the battery discrete state space model through an iterative extended Kalman filtering algorithm to determine the SOC value of the lithium ion battery.
6. The lithium ion battery SOC estimation device of claim 5, wherein the construction module is specifically configured to:
and (3) running at least two Kalman filters in parallel, establishing a battery discrete state space equation and a system measurement updating equation by combining the battery equivalent circuit model and the mapping relation between each model parameter and the temperature and SOC value, and defining a measurement matrix.
7. The lithium ion battery SOC estimation device of claim 6, wherein the estimation module is specifically configured to:
initializing an iterative extended Kalman filtering algorithm at an initial moment, and determining an initial state estimation value and an initial state error covariance matrix at the initial moment;
and running all Kalman filtering in parallel, circularly executing a plurality of computing operations, executing the iterative extended Kalman filtering algorithm for a plurality of times in each computing operation, updating the time and measuring and updating the iteration when executing the iterative extended Kalman filtering algorithm for each time until the iterative extended Kalman filtering algorithm reaches a cut-off condition, finishing the computing operation and determining the SOC value of the lithium ion battery.
8. The apparatus of claim 5, wherein the determining module is specifically configured to:
establishing a battery equivalent circuit model of a lithium ion battery, and establishing a characteristic equation of the battery equivalent circuit model;
performing pulse discharge operation based on the battery equivalent circuit model, and determining model parameter values under each operation;
establishing an initial mapping relation expression of the model parameters and the temperature and SOC values, determining undetermined coefficients in the mapping relation expression of the model parameters and the temperature and SOC values through a curve fitting method, and determining the mapping relation of the model parameters and the temperature and SOC values according to the undetermined coefficients.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the lithium ion battery SOC estimation method of any of claims 1 to 4 when executing the computer program.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the lithium ion battery SOC estimation method of any of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011257741.XA CN112557925B (en) | 2020-11-11 | 2020-11-11 | Lithium ion battery SOC estimation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011257741.XA CN112557925B (en) | 2020-11-11 | 2020-11-11 | Lithium ion battery SOC estimation method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112557925A true CN112557925A (en) | 2021-03-26 |
| CN112557925B CN112557925B (en) | 2023-05-05 |
Family
ID=75042167
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011257741.XA Active CN112557925B (en) | 2020-11-11 | 2020-11-11 | Lithium ion battery SOC estimation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112557925B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114611443A (en) * | 2022-02-21 | 2022-06-10 | 浙江大学 | On-chip filter reverse design method based on equivalent circuit space mapping |
| CN115469230A (en) * | 2022-09-27 | 2022-12-13 | 华电内蒙古能源有限公司 | OCV-SOC online estimation method and device, computer equipment and storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0438887A2 (en) * | 1990-01-26 | 1991-07-31 | AT&T Corp. | Kalman filter-based optimizer and controller |
| US20060097698A1 (en) * | 2004-11-11 | 2006-05-11 | Plett Gregory L | Method and system for cell equalization using state of charge |
| CN102944848A (en) * | 2012-11-21 | 2013-02-27 | 广东省自动化研究所 | Real-time evaluation method for remaining capacity of power batteries and device thereof |
| CN106054081A (en) * | 2016-06-17 | 2016-10-26 | 合肥工业大学智能制造技术研究院 | Lithium battery modeling method for SOC (State of Charge) estimation of electric vehicle power battery |
| CN106340304A (en) * | 2016-09-23 | 2017-01-18 | 桂林航天工业学院 | Online speech enhancement method for non-stationary noise environment |
| CN108594135A (en) * | 2018-06-28 | 2018-09-28 | 南京理工大学 | A kind of SOC estimation method for the control of lithium battery balance charge/discharge |
| CN109870651A (en) * | 2019-01-22 | 2019-06-11 | 重庆邮电大学 | A kind of electric automobile power battery system SOC and SOH joint estimation on line method |
| CN110687462A (en) * | 2019-11-04 | 2020-01-14 | 北京理工大学 | Power battery SOC and capacity full life cycle joint estimation method |
| CN111239608A (en) * | 2019-11-21 | 2020-06-05 | 国联汽车动力电池研究院有限责任公司 | Battery SOC estimation method and device based on extended Kalman filtering |
-
2020
- 2020-11-11 CN CN202011257741.XA patent/CN112557925B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0438887A2 (en) * | 1990-01-26 | 1991-07-31 | AT&T Corp. | Kalman filter-based optimizer and controller |
| US20060097698A1 (en) * | 2004-11-11 | 2006-05-11 | Plett Gregory L | Method and system for cell equalization using state of charge |
| CN101065876A (en) * | 2004-11-11 | 2007-10-31 | 株式会社Lg化学 | Method and system for cell equalization using state of charge |
| CN102944848A (en) * | 2012-11-21 | 2013-02-27 | 广东省自动化研究所 | Real-time evaluation method for remaining capacity of power batteries and device thereof |
| CN106054081A (en) * | 2016-06-17 | 2016-10-26 | 合肥工业大学智能制造技术研究院 | Lithium battery modeling method for SOC (State of Charge) estimation of electric vehicle power battery |
| CN106340304A (en) * | 2016-09-23 | 2017-01-18 | 桂林航天工业学院 | Online speech enhancement method for non-stationary noise environment |
| CN108594135A (en) * | 2018-06-28 | 2018-09-28 | 南京理工大学 | A kind of SOC estimation method for the control of lithium battery balance charge/discharge |
| CN109870651A (en) * | 2019-01-22 | 2019-06-11 | 重庆邮电大学 | A kind of electric automobile power battery system SOC and SOH joint estimation on line method |
| CN110687462A (en) * | 2019-11-04 | 2020-01-14 | 北京理工大学 | Power battery SOC and capacity full life cycle joint estimation method |
| CN111239608A (en) * | 2019-11-21 | 2020-06-05 | 国联汽车动力电池研究院有限责任公司 | Battery SOC estimation method and device based on extended Kalman filtering |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114611443A (en) * | 2022-02-21 | 2022-06-10 | 浙江大学 | On-chip filter reverse design method based on equivalent circuit space mapping |
| CN115469230A (en) * | 2022-09-27 | 2022-12-13 | 华电内蒙古能源有限公司 | OCV-SOC online estimation method and device, computer equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112557925B (en) | 2023-05-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Murnane et al. | A closer look at state of charge (SOC) and state of health (SOH) estimation techniques for batteries | |
| US20230152380A1 (en) | Method and apparatus for determining state of charge and battery management system | |
| CN107003359B (en) | Method for automatically estimating capacity of battery cell of battery pack | |
| CA2588856C (en) | Method and system for battery state and parameter estimation | |
| CN111913109B (en) | Method and device for predicting peak power of battery | |
| CN110058160B (en) | Lithium battery health state prediction method based on square root extended Kalman filtering | |
| Li et al. | A novel parameter and state-of-charge determining method of lithium-ion battery for electric vehicles | |
| Zhang et al. | State-of-charge estimation based on microcontroller-implemented sigma-point Kalman filter in a modular cell balancing system for Lithium-Ion battery packs | |
| CN112327183B (en) | Lithium ion battery SOC estimation method and device | |
| CN110568373A (en) | Lithium battery health state evaluation method, system, terminal and storage medium | |
| CN112433154B (en) | FFRLS and EKF-based lithium ion battery SOC estimation algorithm | |
| KR101996943B1 (en) | Apparatus and method for estimating of battery state-of-charge | |
| JP6455914B2 (en) | Storage power remaining amount estimation device, method for estimating remaining power storage amount of storage battery, and computer program | |
| CN111624496B (en) | Method and system for jointly estimating parameters and SOC (state of charge) of anti-noise lithium ion battery model | |
| CN111142025A (en) | Battery SOC estimation method and device, storage medium and electric vehicle | |
| CN112557925B (en) | Lithium ion battery SOC estimation method and device | |
| CN114114038A (en) | Lithium battery SOC and available capacity joint estimation method under full-life and full-temperature conditions | |
| CN109061482B (en) | Battery health degree prediction method and device | |
| Ramezani-al et al. | A novel combined online method for SOC estimation of a Li-Ion battery with practical and industrial considerations | |
| CN113466725A (en) | Method and device for determining state of charge of battery, storage medium and electronic equipment | |
| CN113484762A (en) | Battery state of health estimation method, device, equipment and storage medium | |
| CN114740385A (en) | Self-adaptive lithium ion battery state of charge estimation method | |
| CN110927582B (en) | Lithium battery SOC estimation method based on multiple sigma points | |
| CN117110891A (en) | Calculation method and calculation device for lithium ion battery state of charge estimation value | |
| CN112213644A (en) | Battery state of charge estimation method and battery management system |
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 |