CN110764003A - Lithium battery state of charge estimation method, device and system - Google Patents
Lithium battery state of charge estimation method, device and system Download PDFInfo
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 183
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Abstract
The invention provides a lithium battery state of charge estimation method, a device and a system, which relate to the technical field of battery management and comprise the steps of obtaining basic parameters of a lithium battery to be tested; establishing a dual-polarization model of the lithium battery, wherein the dual-polarization model comprises parameters to be identified; identifying the parameter to be identified by adopting an online recursive least square method; establishing a state equation and an observation equation of the state of charge of the lithium battery; estimating the state of charge of the lithium battery based on a particle filter algorithm and by combining an unscented Kalman filter algorithm; and outputting the estimation result. The lithium battery state of charge estimation method, device and system provided by the invention combine particle filtering and unscented Kalman filtering to realize accurate estimation of the lithium battery state of charge, and simultaneously have the advantages of fast convergence and strong robustness.
Description
Technical Field
The invention relates to the technical field of battery management, in particular to a lithium battery state of charge estimation method, device and system.
Background
The lithium battery is widely applied to the fields of electric automobiles and the like, realizes real-time accurate management of the lithium battery pack, guarantees the use safety of the battery and prolongs the service life of the battery under complex working conditions, and has important significance for development and popularization of industries such as electric automobiles and the like. The control strategy of battery management mainly depends on the estimation accuracy of the SOC (State of Charge) of the lithium battery. Therefore, current management research on batteries is critical to the real-time accurate estimation of SOC.
In the prior art, methods for estimating the state of charge of the lithium battery comprise an ampere-hour integration method, an open-circuit voltage method and a Kalman filtering series method, which have the characteristics, but most of the methods have the defects of low estimation precision and poor accuracy.
Aiming at the problems of low estimation precision and poor accuracy in the lithium battery state of charge estimation method, no effective solution is provided at present.
Disclosure of Invention
In view of the above, the present invention provides a method, an apparatus and a system for estimating a state of charge of a lithium battery, so as to alleviate the above technical problems.
In a first aspect, an embodiment of the present invention provides a method for estimating a state of charge of a lithium battery, where the method includes: acquiring basic parameters of a lithium battery to be tested; establishing a dual-polarization model of the lithium battery, wherein the dual-polarization model comprises parameters to be identified; identifying the parameter to be identified by adopting an online recursive least square method; establishing a state equation and an observation equation of the state of charge of the lithium battery; estimating the state of charge of the lithium battery based on a particle filter algorithm and by combining an unscented Kalman filter algorithm; and outputting an estimation result.
With reference to the first aspect, an embodiment of the present invention provides a first possible implementation manner of the first aspect, where the step of establishing a dual-polarization model of a lithium battery includes: establishing a dual-polarized equivalent circuit model of the lithium battery; and establishing a dual-polarization mathematical model of the lithium battery according to the dual-polarization equivalent circuit model.
With reference to the first aspect, an embodiment of the present invention provides a second possible implementation manner of the first aspect, where before identifying a parameter to be identified, the method further includes: performing a composite pulse test on the lithium battery according to preset experimental conditions to obtain the residual capacity and the average value of open-circuit voltage of the lithium battery; establishing a functional relation based on the residual capacity and the average value of the open-circuit voltage according to the residual capacity and the average value of the open-circuit voltage of the lithium battery, wherein the functional relation expression is as follows:
wherein, Uoc(SOC) is a function based on the remaining capacity and the mean value of the open circuit voltage, E0For the open circuit voltage, K, of the above-mentioned lithium battery when fully chargediAnd (i ═ 0, 1, 2 and 3) are the parameters to be identified.
With reference to the first aspect or the second possible implementation manner of the first aspect, an embodiment of the present invention provides a third possible implementation manner of the first aspect, where the identifying, by using an online recursive least squares method, the to-be-identified parameter includes: establishing a recursion formula of an online recursion least square method; according to the recurrence formula, combining the dual-polarization model of the lithium battery and the functional relation between the residual electric quantity and the average value of the open-circuit voltage, and identifying the parameter to be identified; and outputting the identification result of the parameter to be identified.
With reference to the first aspect or the second possible implementation manner of the first aspect, an embodiment of the present invention provides a fourth possible implementation manner of the first aspect, where the step of establishing a state equation and an observation equation of a state of charge of a lithium battery includes: establishing a state equation by taking the state of charge value of the lithium battery as a state variable; establishing an observation equation by taking the open-circuit voltage of the lithium battery as an observation variable; the state equation and the observation equation comprise identification results of the parameters to be identified.
With reference to the first aspect, an embodiment of the present invention provides a fifth possible implementation manner of the first aspect, where the estimating a state of charge of a lithium battery based on a particle filter algorithm and with reference to an unscented kalman filter algorithm includes: extracting particles according to the initial probability distribution function; carrying out importance sampling by applying an unscented Kalman filtering algorithm; calculating the posterior probability of the particles; judging whether the number of the effective particles after the importance sampling is smaller than a preset particle number threshold value; if so, resampling is carried out, and the charge state of the lithium battery is calculated according to the resampled sampling particles; and if not, calculating the state of charge of the lithium battery.
With reference to the fifth possible implementation manner of the first aspect, an embodiment of the present invention provides a sixth possible implementation manner of the first aspect, where the step of calculating the state of charge of the lithium battery further includes: judging whether the current computing time is the final computing time; if yes, outputting an estimation result; and if not, continuously calculating the state of charge of the lithium battery at the next moment.
In a second aspect, an embodiment of the present invention further provides a lithium battery state of charge estimation apparatus, where the apparatus includes: the acquisition module is used for acquiring basic parameters of the lithium battery to be tested; the modeling module is used for establishing a dual-polarization model of the lithium battery, wherein the dual-polarization model comprises parameters to be identified; the identification module is used for identifying the parameter to be identified by adopting an online recursive least square method; the equation building module is used for building a state equation and an observation equation of the state of charge of the lithium battery; the calculation module is used for estimating the state of charge of the lithium battery based on a particle filter algorithm and in combination with an unscented Kalman filter algorithm; and the output module is used for outputting the estimation result.
In a third aspect, an embodiment of the present invention further provides a lithium battery state of charge estimation system, where the system includes a memory and a processor, the memory is used to store a program that supports the processor to execute the foregoing method embodiments, and the processor is configured to execute the program stored in the memory.
In a fourth aspect, an embodiment of the present invention further provides a computer storage medium for storing computer software instructions for the foregoing apparatus embodiment.
The embodiment of the invention has the following beneficial effects:
according to the lithium battery state-of-charge estimation method, device and system provided by the embodiment of the invention, firstly, basic parameters of a lithium battery to be tested are obtained, then, a dual-polarization model of the lithium battery is established, parameters to be identified in the dual-polarization model are identified through an online recursive least square method, a state equation and an observation equation of the lithium battery state-of-charge are established according to the parameters, and finally, the state-of-charge of the lithium battery is estimated based on a particle filter algorithm and in combination with an unscented Kalman filter algorithm. According to the lithium battery state of charge estimation method, device and system provided by the embodiment of the invention, the particle filtering and unscented Kalman filtering are combined, so that the lithium battery state of charge is accurately estimated, and meanwhile, the method, device and system have the advantages of high convergence and strong robustness.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a method for estimating a state of charge of a lithium battery according to an embodiment of the present invention;
fig. 2 is a flowchart of another lithium battery state of charge estimation method according to an embodiment of the present invention;
fig. 3 is a model diagram of a dual-polarized equivalent circuit according to an embodiment of the present invention;
fig. 4 is a flowchart of another lithium battery state of charge estimation method according to an embodiment of the present invention;
fig. 5 is a flowchart of another lithium battery state of charge estimation method according to an embodiment of the present invention;
fig. 6 is a structural diagram of a lithium battery state of charge estimation device according to an embodiment of the present invention;
fig. 7 is a comparison graph of the effect of the lithium battery state of charge estimation method according to the embodiment of the present invention.
Icon: 51-an acquisition module; 52-a modeling module; 53-an identification module; 54-equation building block; 55-a calculation module; 56-output module.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent 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.
At present, the common lithium battery state of charge estimation methods mainly comprise an ampere-hour integral method, a neural network, a Kalman filtering series method and the like.
The principle of the ampere-hour integration method is that the electric quantity charged or discharged by the lithium battery within a specified time can be calculated by integrating the current in the charging and discharging processes of the lithium battery, and the SOC value in the current state can be calculated by dividing the electric quantity by the available capacity of the battery in the current state. The method is low in cost and convenient to measure, but an initial value of the SOC needs to be obtained by other methods, the measurement precision of the current directly influences the estimation precision of the SOC, and an accumulation error is easily generated in the integration process and cannot be eliminated, so that the ampere-hour integration method is difficult to apply in the occasions of electric automobiles.
Both the neural network and the support vector machine have good nonlinear mapping capability, but the main problems are that the method requires a large amount of training data, the application is relatively complex, and the quality of the training data greatly affects the estimation accuracy result. Now, mainly based on the theoretical level, a balance needs to be found.
The Kalman filtering series theory method is only suitable for a linear system at first, and then the theory is expanded to the nonlinear field by improving methods such as extended Kalman filtering, unscented Kalman filtering and the like, and the core idea is to perform optimal estimation on the state of the dynamic system in the least mean square sense. The application of Kalman filtering has the main characteristics that the robustness of the method is good, namely the error correction capability is strong, and the complexity of the hardware application is not as high as that of methods such as a neural network and the like, so that the defect that the estimation precision highly depends on the accuracy of a battery model is overcome.
Based on this, the lithium battery state of charge estimation method, device and system provided by the embodiment of the invention can effectively alleviate the technical problems.
For the convenience of understanding the present embodiment, a detailed description will be given to a state of charge estimation method for a lithium battery disclosed in the present embodiment.
In one embodiment, an embodiment of the present invention provides a method for estimating a state of charge of a lithium battery, referring to a flowchart of the method for estimating a state of charge of a lithium battery shown in fig. 1, where the method includes the following steps:
and S102, acquiring basic parameters of the lithium battery to be tested.
The basic parameters of the lithium battery to be tested comprise the rated capacity, the initial state, the discharge current and the like of the lithium battery.
And step S104, establishing a dual-polarization model of the lithium battery.
The dual-polarization model comprises parameters to be identified, the dual-polarization model can be an equivalent circuit model of a lithium battery and also can be a mathematical model, and the mathematical model can be in a state space equation form.
And S106, identifying the parameter to be identified by adopting an online recursive least square method.
In practical application, the parameter to be identified can be identified by calculation according to a recursion formula of an online recursion least square method.
And step S108, establishing a state equation and an observation equation of the state of charge of the lithium battery.
The state equation of the lithium battery charge state is used for describing the charge state of the lithium battery, and the observation equation of the lithium battery charge state is used for describing the observation value of the lithium battery charge state.
And step S110, estimating the state of charge of the lithium battery based on a particle filter algorithm and by combining an unscented Kalman filter algorithm.
In specific implementation, the lithium battery state of charge estimation method provided by the embodiment of the invention estimates the state of charge based on a particle filter algorithm, performs importance sampling by combining an unscented kalman filter algorithm, generates sampling particles and covariance thereof, updates the observed quantity, obtains the posterior probability of each sampling particle based on the observed quantity, and finally estimates the state of charge of the lithium battery by using each particle and the posterior probability thereof.
And step S112, outputting an estimation result.
According to the lithium battery state-of-charge estimation method provided by the embodiment of the invention, firstly, basic parameters of a lithium battery to be tested are obtained, then, a dual-polarization model of the lithium battery is established, the parameters to be identified in the dual-polarization model are identified through an online recursive least square method, a state equation and an observation equation of the state-of-charge of the lithium battery are established according to the parameters, and finally, the state-of-charge of the lithium battery is estimated based on a particle filter algorithm and in combination with an unscented Kalman filter algorithm. The lithium battery state of charge estimation method provided by the embodiment of the invention combines particle filtering and unscented Kalman filtering, realizes accurate estimation of the lithium battery state of charge, and has the advantages of fast convergence and strong robustness.
The embodiment of the present invention further provides another method for estimating a state of charge of a lithium battery, which is implemented on the basis of the method shown in fig. 1, and as shown in a flowchart of another method for estimating a state of charge of a lithium battery shown in fig. 2, the method specifically includes the following steps:
step S202, acquiring basic parameters of a lithium battery to be tested;
and step S204, establishing a dual-polarized equivalent circuit model of the lithium battery.
The dual-polarization equivalent circuit model is shown in fig. 3, wherein Uoc is the open circuit voltage of the lithium battery, Rd and Rc correspond to the ohmic internal resistances of the lithium battery in the discharging stage and the charging stage, I is the working current of the lithium battery, the discharging direction is positive, Ut is the terminal load voltage of the battery, Rm and Cm are the polarization resistance and the polarization capacitance of the battery and are used for representing the slow electrode reaction in the battery, Rn and Cn are the concentration resistance and the concentration capacitance of the battery and are used for representing the fast electrode reaction in the battery, Um is the voltages at two ends of Rm and Cm, Un is the voltages at two ends of Rn and Cn, and in addition, two equivalent capacitors are included. The model is used to simulate the voltage U at sudden changes in the current I and the fast changing process at a slow and stable process.
And S206, establishing a dual-polarized mathematical model of the lithium battery according to the dual-polarized equivalent circuit model.
During specific implementation, a bipolar mathematical model of the lithium battery can be established by kirchhoff's theorem corresponding to the dual-polarized equivalent circuit model, specifically, a state space equation is as follows:
rm and Cm are polarization resistance and polarization capacitance of the lithium battery, Rn and Cn are concentration resistance and concentration capacitance of the lithium battery, Um is voltage at two ends of Rm and Cm, Un is voltage at two ends of Rn and Cn, SOC is state of charge value of the lithium battery, R is charge value of the lithium battery, and R is charge value of the lithium battery0Is the ohmic internal resistance, U, of the lithium batteryoc(SOC) is an open-circuit voltage corresponding to the state of charge value of the lithium battery, C is the charge amount of the lithium battery, η is the energy conversion efficiency of the lithium battery, and i is the current of the lithium battery.
And S208, performing a composite pulse test on the lithium battery according to preset experimental conditions to obtain the residual capacity and the open-circuit voltage mean value of the lithium battery.
The open-circuit voltage Uoc of the lithium battery represents the electromotive force corresponding to the battery in a certain charge state, and has a nonlinear relation with the SOC of the battery, and the mathematical relation is important for battery modeling and battery performance control in a battery management system. The lithium battery is subjected to a 1C-rate composite Pulse experiment (HPPC), the dynamic characteristics of the battery can be tested, and further the function relation of OCV-SOC is obtained and model parameters are identified.
In specific implementation, the preset experimental conditions may include an experimental temperature of the lithium battery, and the environmental temperature of the battery is maintained at 25 ℃ by using a high-temperature and low-temperature experimental box. The voltage, current and temperature errors were 0.1%, 0.05%, ± 1 ℃ respectively. The preset experimental conditions further include a specific charging process of the composite pulse experiment, specifically: the method comprises the steps of firstly placing the capacity of a lithium battery to 0.1SOC, then standing for a long enough time, measuring the terminal voltage of the lithium battery to obtain a corresponding open-circuit voltage value, then carrying out 9-cycle HPPC experiments on the lithium battery (standing for 30min when the 1C multiplying power charging is carried out on the battery to increase the battery capacity by 0.1SOC, then discharging for 10s at the 1C multiplying power, standing for 40s, charging for 10s at the 1C multiplying power for 1min, and standing for 15min to wait for the next cycle) until the battery is fully charged.
Step S210, establishing a functional relation based on the residual capacity and the average value of the open-circuit voltage according to the residual capacity and the average value of the open-circuit voltage of the lithium battery.
Wherein, the functional relation expression is as follows:
wherein, Uoc(SOC) is a function based on the mean value of the remaining capacity and the open-circuit voltage, E0For open circuit voltage, K, of a fully charged lithium batteryiAnd (i ═ 0, 1, 2 and 3) are parameters to be identified.
Step S212, an online recursive least square method is adopted to identify the parameter to be identified.
Further, the above-mentioned step of adopting online recursion least square method to treat that the parameter of discerning discerns includes:
(1) establishing a recursion formula of an online recursion least square method;
(2) identifying the parameter to be identified according to a recurrence formula by combining a dual-polarization model of the lithium battery and a functional relation between the residual capacity and the mean value of the open-circuit voltage;
(3) and outputting the identification result of the parameter to be identified.
Wherein, the above recursion formula is:
in the formula,
is the parameter vector to be identified, K (-) is the gain, ξ (-) is the experimental data matrix, P (-) is the covariance matrix, y (K) is the output,
is the terminal voltage error, I is the identity matrix.
The identification steps are as follows:
a. setting an initial value
And P (0);
b. sampling the current output y (k) and the input u (k);
c. calculating K (k),
And P (k);
d. and (5) enabling k → k +1, returning to the step b, and continuing to loop.
And step S214, establishing a state equation and an observation equation of the state of charge of the lithium battery.
Further, the step of establishing the state equation and the observation equation of the state of charge of the lithium battery comprises:
(1) establishing a state equation by taking the charge state value of the lithium battery as a state variable;
(2) taking the open-circuit voltage of the lithium battery as an observation variable, and establishing an observation equation;
the state equation and the observation equation comprise identification results of the parameters to be identified.
In the concrete implementation, a state of charge (SOC) value of the lithium battery is used as a state variable, an open-circuit voltage of the lithium battery is used as an observation variable, and a state equation and an observation equation are established. Obtaining a discretization form by a state space equation of the lithium battery:
where Δ t is the sampling period, E0Is the maximum available capacity of the lithium battery under the condition of full charge, R is the internal resistance of the battery, KiIs obtained from OCV-SOC curve data, wkIs the systematic error, vkIs an observation error, w1k～N(0，Q1)，w2k～N(0，Q2)，w3k～N(0，Q3)，wk～N(0，Q)，Q＝diag{Q1，Q2，Q3And vk～N(0，R)。
Wherein, Xk+1＝＝HkXk+Ψkik+wkIs an equation of state, yk＝g(xk，ik，vk)＝Ut，kIs an observation equation. To facilitate the algorithm implementation, the state quantities are redefined as xk＝[xk Twk Tvk T]TThe state covariance is defined as Pk＝diag{PkQR}。
Step S216, estimating the state of charge of the lithium battery based on a particle filter algorithm and by combining an unscented Kalman filter algorithm;
in step S218, the estimation result is output.
The embodiment of the present invention further provides another method for estimating a state of charge of a lithium battery, which is implemented on the basis of the method shown in fig. 1, and as shown in a flowchart of another method for estimating a state of charge of a lithium battery shown in fig. 4, the method specifically includes the following steps:
step S302, acquiring basic parameters of a lithium battery to be tested;
step S304, establishing a dual-polarization model of the lithium battery;
step S306, identifying the parameter to be identified by adopting an online recursive least square method;
step S308, establishing a state equation and an observation equation of the state of charge of the lithium battery;
in step S310, particles are extracted according to the initial probability distribution function.
In specific implementation, initialization is performed first, so that k is equal to 0, and particles are obtained
Is formed by an initial probability distribution function p (x)0) Randomly generated, and the covariance between particles is defined as
And S312, applying an unscented Kalman filtering algorithm to perform importance sampling.
In a specific implementation, k is 1, 2
Generation of new particles using UKF algorithm
And covariance
The method comprises the following steps:
a. the sigma points of the particles are calculated, and 2r +1(r is 0, 1..) point sets are selected.
Where r is the dimension of the state quantity and λ is the composite scale parameter.
b. And (3) time updating:
c. updating the observed quantity:
in step S314, the posterior probability of the particle is calculated.
In concrete implementation, according to the observed quantity ykCalculating each particle
A posteriori probability q ofi：
Normalized, the method comprises the following steps:
thus, a posterior particle is obtained
Error covariance
And a corresponding probability, i ═ 1, 2.
Step S316, judging whether the number of the effective particles after the importance sampling is less than a preset particle number threshold value;
in practical application, the number of effective particles is calculated after samplingIf N is presentef＜Nth(particle number threshold value), if yes, step S322 is executed, and if no, step S318 is executed.
In step S318, resampling is performed.
In particular, by introducing the particle degradation metric, resampling at each step can be avoided.
And step S320, calculating the state of charge of the lithium battery.
Wherein,
in step S322, the estimation result is output.
The embodiment of the present invention further provides another lithium battery state of charge estimation method, which is implemented on the basis of the method shown in fig. 4, and as shown in a flowchart of another lithium battery state of charge estimation method shown in fig. 5, the method specifically includes the following steps:
step S402, acquiring basic parameters of a lithium battery to be tested;
step S404, establishing a dual-polarization model of the lithium battery;
step S406, identifying the parameter to be identified by adopting an online recursive least square method;
step S408, establishing a state equation and an observation equation of the state of charge of the lithium battery;
step S410, extracting particles according to the initial probability distribution function;
step S412, applying unscented Kalman filtering algorithm to perform importance sampling;
step S414, calculating the posterior probability of the particles;
step S416, judging whether the number of the effective particles after the importance sampling is smaller than a preset particle number threshold value;
if yes, go to step S418, if no, go to step S420.
Step S418, resampling is carried out;
step S420, calculating the state of charge of the lithium battery;
step S422, judging whether the current computing time is the final computing time;
if yes, go to step S424, if no, go to step S412.
In step S424, the estimation result is output.
According to the lithium battery state-of-charge estimation method provided by the embodiment of the invention, firstly, basic parameters of a lithium battery to be tested are obtained, then, a dual-polarization model of the lithium battery is established, the parameters to be identified in the dual-polarization model are identified through an online recursive least square method, a state equation and an observation equation of the state-of-charge of the lithium battery are established according to the parameters, and finally, the state-of-charge of the lithium battery is estimated based on a particle filter algorithm and in combination with an unscented Kalman filter algorithm. The lithium battery state of charge estimation method provided by the embodiment of the invention combines particle filtering and unscented Kalman filtering, realizes accurate estimation of the lithium battery state of charge, and has the advantages of fast convergence and strong robustness.
In the concrete implementation, in order to verify the performance superiority of the SOC of the lithium battery estimated by the lithium battery state of charge estimation method provided by the embodiment of the invention, a 3.7V/3.2Ah lithium battery is subjected to a relevant discharge test, and test data is used as simulation experiment input. In the simulation design, the number of particles N is set to be 200, the initial value of SOC of the lithium battery is set to be 0.7, the test working condition is an intermittent discharge test, the simulation time is set to be 2000s, the sampling frequency is set to be 2Hz, and the data such as the voltage and the current of the lithium battery are recorded in real time under the constant temperature condition (25 ℃).
The lithium battery SOC estimation method provided by the embodiment of the invention is used for estimating the lithium battery SOC by using the UPF and the EKF (extended kalman Filter), and is compared with the True State, and the experimental result is shown in fig. 7.
Obviously, the lithium battery state of charge estimation method UPF provided by the embodiment of the invention has a significantly improved estimation error maximum absolute value compared with other methods, and the estimation effect is more stable.
Corresponding to the method embodiment, the embodiment of the invention also provides a lithium battery state of charge estimation device, which is arranged on the terminal equipment, and the terminal equipment can be a computer comprising a host and a display screen. As shown in fig. 6, the apparatus includes:
the acquisition module 51 is used for acquiring basic parameters of the lithium battery to be tested;
the modeling module 52 is used for establishing a dual-polarization model of the lithium battery, wherein the dual-polarization model comprises parameters to be identified;
the identification module 53 is configured to identify the parameter to be identified by using an online recursive least square method;
the equation building module 54 is used for building a state equation and an observation equation of the state of charge of the lithium battery;
the calculation module 55 is used for estimating the state of charge of the lithium battery based on a particle filter algorithm and in combination with an unscented kalman filter algorithm;
and an output module 56 for outputting the estimation result.
The apparatus provided by the embodiment of the present invention has the same technical features as the method provided by the above embodiment, so that the same technical problems can be solved, and the same technical effects can be achieved. It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The embodiment of the invention also provides a lithium battery state of charge estimation system, which comprises a memory and a processor, wherein the memory is used for storing a program for supporting the processor to execute any one of the methods, and the processor is configured to execute the program stored in the memory.
The computer program product of the method, the device and the system for estimating the state of charge of the lithium battery provided by the embodiment of the invention comprises a computer readable storage medium storing program codes, wherein instructions included in the program codes can be used for executing the method described in the foregoing method embodiment, and specific implementation can refer to the method embodiment, and is not described herein again.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. 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.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A lithium battery state of charge estimation method, characterized in that the method comprises:
acquiring basic parameters of a lithium battery to be tested;
establishing a dual-polarization model of the lithium battery, wherein the dual-polarization model comprises parameters to be identified;
identifying the parameter to be identified by adopting an online recursive least square method;
establishing a state equation and an observation equation of the state of charge of the lithium battery;
estimating the state of charge of the lithium battery based on a particle filter algorithm and by combining an unscented Kalman filter algorithm;
and outputting the estimation result.
2. The method of claim 1, wherein the step of establishing a dual polarization model of the lithium battery comprises:
establishing a dual-polarized equivalent circuit model of the lithium battery;
and establishing a dual-polarization mathematical model of the lithium battery according to the dual-polarization equivalent circuit model.
3. The method according to claim 1, wherein before the identifying the parameter to be identified by using an online recursive least square method, the method further comprises:
performing a composite pulse test on the lithium battery according to preset experimental conditions to obtain the residual capacity and the average value of open-circuit voltage of the lithium battery;
establishing a functional relation based on the residual capacity and the open-circuit voltage mean value according to the residual capacity and the open-circuit voltage mean value of the lithium battery, wherein the functional relation expression is as follows:
wherein, Uoc(SOC) is a function based on the remaining capacity and the mean value of the open-circuit voltage, E0For the open circuit voltage, K, of the fully charged lithium batteryi(i ═ 0, 1, 2, and 3) is the parameter to be identified.
4. The method according to claim 1 or 3, wherein the step of identifying the parameter to be identified by using an online recursive least square method comprises:
establishing a recurrence formula of the online recurrence least square method;
according to the recurrence formula, in combination with a dual-polarization model of the lithium battery and a functional relation between the residual electric quantity and the average value of the open-circuit voltage, identifying the parameter to be identified;
and outputting the identification result of the parameter to be identified.
5. The method of claim 1 or 3, wherein the step of establishing the state of charge equation and the observation equation for the lithium battery comprises:
establishing the state equation by taking the state of charge value of the lithium battery as a state variable;
establishing the observation equation by taking the open-circuit voltage of the lithium battery as an observation variable;
wherein the state equation and the observation equation include the identification result of the parameter to be identified.
6. The method of claim 1, wherein the step of estimating the state of charge of the lithium battery based on a particle filter algorithm in combination with an unscented kalman filter algorithm comprises:
extracting particles according to the initial probability distribution function;
carrying out importance sampling by applying an unscented Kalman filtering algorithm;
calculating a posterior probability of the particle;
judging whether the number of the effective particles after the importance sampling is smaller than a preset particle number threshold value;
if so, resampling is carried out, and the state of charge of the lithium battery is calculated according to the resampled sampling particles;
and if not, calculating the state of charge of the lithium battery.
7. The method of claim 6, wherein the step of calculating the state of charge of the lithium battery further comprises:
judging whether the current computing time is the final computing time;
if yes, outputting the estimation result;
and if not, continuing to calculate the state of charge of the lithium battery at the next moment.
8. A lithium battery state of charge estimation device, the device comprising:
the acquisition module is used for acquiring basic parameters of the lithium battery to be tested;
the modeling module is used for establishing a dual-polarization model of the lithium battery, wherein the dual-polarization model comprises parameters to be identified;
the identification module is used for identifying the parameter to be identified by adopting an online recursive least square method;
the equation building module is used for building a state equation and an observation equation of the state of charge of the lithium battery;
the calculation module is used for estimating the state of charge of the lithium battery based on a particle filter algorithm and in combination with an unscented Kalman filter algorithm;
and the output module is used for outputting the estimation result.
9. A lithium battery state of charge estimation system, characterized in that the system comprises a memory for storing a program enabling a processor to perform the method according to any one of claims 1 to 8, and a processor configured to execute the program stored in the memory.
10. A computer storage medium storing computer software instructions for use by the apparatus of claim 8.
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