CN112964997B - Unmanned aerial vehicle lithium ion battery peak power self-adaptive estimation method - Google Patents
Unmanned aerial vehicle lithium ion battery peak power self-adaptive estimation method Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle lithium ion battery peak power self-adaptive estimation method, which belongs to the technical field of new energy measurement and control, and comprises the steps of establishing a lithium ion battery equivalent model and carrying out online parameter identification on the model, representing the working state of a lithium ion battery through the equivalent circuit model, carrying out online estimation on the open-circuit voltage and other model parameters of the lithium ion battery by using the equivalent circuit model, estimating the SOC and the model polarization voltage of the lithium ion battery through a self-adaptive extended Kalman algorithm by adopting a recursive calculation mode, and recursively calculating the peak power which can be continuously reached within a period of time through the state parameters obtained by estimation and the required prediction time, so that the defects of the existing lithium ion battery and battery pack peak power estimation method are overcome, and the purpose of accurately estimating the peak power in the application of the lithium ion battery is solved.
Description
Technical Field
The invention belongs to the technical field of new energy measurement and control, and particularly relates to a method for adaptively estimating peak power of a lithium ion battery of an unmanned aerial vehicle.
Background
In the whole life cycle of lithium ion battery group, BMS's main effect is the safe handling of guarantee lithium cell, peak power's control and regulation will influence power take off's effect and security, the current lithium cell of peak power characterization lasts the maximum power of output, the security of lithium ion battery and user equipment is being concerned with, to large-scale unmanned aerial vehicle, accurate peak power estimation can accurate control unmanned aerial vehicle's flight height, also can accurate control unmanned aerial vehicle heavy burden flight limit simultaneously, avoid the accident that output is not enough to bring, not only can be very big promotion lithium ion battery's safety in utilization, also can improve the availability factor.
In the application of a large unmanned aerial vehicle, the peak value estimation of a lithium ion battery is very important for BMS management; the peak power estimation technology in the BMS is still in a starting stage, so that the problem of poor accuracy of the peak power prediction of the lithium ion battery exists; for a lithium ion battery, accurate peak power estimation is related to the reliability of the BMS and the safety of the unmanned aerial vehicle; the accurate estimation of the peak power value can also avoid the problem of overcharge or overdischarge caused in the use process of the lithium ion battery, and the overcharge or overdischarge can seriously affect the safety of the lithium ion battery, can cause the irreversible chemical reaction inside the lithium ion battery, namely affect the use safety and also affect the service life of the lithium ion battery.
Aiming at the research of peak power estimation, China, Japan and the United States establish own test specifications which are a United States Advanced Battery Consortium power test method in the United States, Japanese Japan Electric Vehicle power measurement standard and relevant test specifications related to power Battery power test in the '863' project in China, although more test specifications are provided, the test specifications are only suitable for offline test and can not meet the requirement of online estimation; relevant research institutions and universities, such as the massachusetts institute of science, state university of bingzhou, southern card university, british litz university, robert university of british, national renewable energy house, leidend energy company of america, english-fleshing science and technology company of germany, qinghua university, beijing aerospace university, beijing university of science and technology, beijing university of transportation, Chongqing university, China university of science and technology, and Harbin university, etc., have developed a great deal of research and conducted intensive research on state estimation of lithium ion batteries; many periodicals at home and abroad, such as Journal of Power Sources, Applied Energy, IEEE Transactions on Power Systems, Power technology and the like, establish highly targeted columns for relevant research result display; at present, relevant researchers at home and abroad make great research progress aiming at the problem of peak power estimation; common peak power estimation methods include an HPPC method, a model-based method, a multi-constraint method, and a neural network and machine learning-based method.
In the conventional BMS application of the lithium ion battery pack, the HPPC-based lithium ion battery power estimation method carries out table look-up estimation on peak power according to parameter values at different moments by establishing an interpolation table, needs a large amount of experiments to construct the interpolation table, is complex in use working condition of a power lithium ion battery, and has large errors under different use working conditions; the initial polarization voltage of the model is unknown by a model-based method, and an estimation error exists; the neural network and machine learning based method requires a large amount of experimental data, training of the experimental data under different working conditions is strictly required, and the method occupies a large amount of calculation capacity of the BMS in use.
Disclosure of Invention
In view of the above, in order to solve the above problems in the prior art, the present invention provides an adaptive estimation method for peak power of a lithium ion battery of an unmanned aerial vehicle, so as to overcome the shortcomings of the existing estimation methods for peak power of a lithium ion battery and a battery pack, and solve the problem of accurate estimation of peak power in application of a lithium ion battery.
The technical scheme adopted by the invention is as follows: an unmanned aerial vehicle lithium ion battery peak power self-adaptive estimation method comprises the following steps:
s1: characterizing the working state of the lithium ion battery through a lithium ion battery model;
s2: performing iterative calculation on the polarization voltage and the SOC value of the lithium ion battery through a self-adaptive extended Kalman algorithm, and estimating the open-circuit voltage of the lithium ion battery at the current moment on line according to the SOC value at the current moment;
s3: and estimating the peak power of the lithium ion battery by a recursive estimation method according to the lithium ion battery model parameters, the polarization voltage and the open-circuit voltage.
Further, the method for iteratively calculating the polarization voltage and the SOC value is as follows:
s201: setting an initial value of SOC, identifying parameters of a lithium ion battery model on line, and discretizing the lithium ion battery model through a discrete model;
s202: estimating discrete model parameters by adopting a recursive least square algorithm and separating the discrete model parameters;
s203: calculating to obtain lithium ion battery model parameters according to the obtained discrete model parameters;
s204: calculating a state prediction equation of the lithium ion battery according to the lithium ion battery model parameters;
s205: calculating Kalman gain and system residual error by a state prediction equation;
s206: and calculating the SOC value and the polarization voltage at the moment k according to the Kalman gain and the system residual error.
Further, the initial value of the SOC is obtained according to manual assignment or according to the state of the lithium ion battery.
And further, updating the covariance of the measurement noise and the covariance of the system noise according to the system residual error, substituting the updated covariance into the state prediction equation, and updating the state prediction equation.
And further, updating the covariance of the measurement noise and the covariance of the system noise according to the system residual error, and updating the initial value of the SOC through a battery state space model.
Further, the battery state space model is constructed by a state equation and an observation equation, variables of the state equation comprise the SOC value and the covariance of system noise, and variables of the observation equation comprise the covariance of output closed-circuit voltage and measurement noise.
Further, the method for estimating the peak power of the lithium ion battery comprises the following steps:
s301: acquiring the SOC value, the polarization voltage and the open-circuit voltage at the moment k;
s302: carrying out recursive prediction on the state of the lithium ion battery after N sampling moments, and carrying out nonlinear processing on the open-circuit voltage after N sampling moments during recursive prediction;
s303: estimating the peak current of the lithium ion battery after N sampling moments;
s304: and estimating the peak power of the lithium ion battery after N sampling moments according to the peak current of the lithium ion battery.
The invention has the beneficial effects that:
1. by adopting the unmanned aerial vehicle lithium ion battery peak power self-adaptive estimation method provided by the invention, the power application requirement and the working characteristic experimental analysis of the lithium ion battery pack are based, the modern control theory research thought is combined, and the peak power estimation method based on the self-adaptive extended Kalman filtering algorithm and the equivalent circuit model has stronger applicability; aiming at the accurate estimation target of the peak power of the lithium ion battery, the defect that the existing estimation method has unknown polarization voltage is overcome; the SOC value and the polarization voltage are accurately estimated by adopting the adaptive extended Kalman filter, the estimation precision of the open-circuit voltage is improved, the error of a peak power estimation method in the existing method is reduced, the accurate estimation of the lithium ion battery and the grouped peak power thereof is realized, and the reliability of the peak power estimation is improved; meanwhile, the method can provide reference for the establishment of lithium ion battery pack peak power estimation models and the calculation of peak power values in different application scenes, and has the advantages of simplicity in calculation, good adaptability and high precision.
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Fig. 1 is a calculation flow chart of an unmanned aerial vehicle lithium ion battery peak power adaptive estimation method provided by the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar modules or modules having the same or similar functionality throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application. On the contrary, the embodiments of the application include all changes, modifications and equivalents coming within the spirit and terms of the claims appended hereto.
Example 1
The method is mainly used for solving the peak value estimation of the lithium ion battery aiming at whether the lithium ion battery can meet the current maximum power of equipment during power output, and the method is based on online parameter identification, a self-adaptive extended Kalman algorithm and a recursive peak power estimation method based on an equivalent circuit model to realize the iterative computation process of the peak power; in the prediction calculation process of the peak power estimation, solving the problem of nonlinear conversion by using Taylor formula expansion, and realizing the polarization voltage estimation by an adaptive extended Kalman filtering algorithm; and through an iterative calculation process, the estimation of the peak power of the lithium ion battery pack is realized.
In order to better embody the method, the lithium ion battery is only taken as an example in the present embodiment, but it should be well known to those skilled in the art that various lithium ion battery adaptive estimation methods based on equivalent model parameter online estimation can be implemented according to the technical idea of the method, as shown in fig. 1, the estimation method mainly includes the following steps:
s1: the working state of the lithium ion battery is represented through a lithium ion battery model so as to conveniently estimate the peak power by using a self-adaptive extended Kalman filtering algorithm and a recursive peak power estimation method; wherein, the lithium ion battery model is also the Thevenin equivalent circuit model of the lithium ion battery. Parameter identification is carried out on the Thevenin equivalent circuit model by using a recursive least square algorithm, and nonlinear characteristics of the lithium ion battery are described based on Taylor series expansion, so that peak power estimation is carried out by using a self-adaptive extended Kalman filtering algorithm and a recursive peak power estimation method in a follow-up method.
S2: combining a battery state space model of the lithium ion battery, performing iterative calculation on the polarization voltage and the SOC value of the lithium ion battery through a self-adaptive extended Kalman algorithm, and estimating the open-circuit voltage U of the lithium ion battery at the current moment on line according to the SOC value at the current momentOCFor open circuit voltage UOCIn practical application, the open-circuit voltage U is obtained through experimental measurementOCAfter a plurality of experiments, the open-circuit voltage U is obtainedOCAnd SOC value, in the method, the open-circuit voltage U at the current time is calculated according to the SOC value at the current time based on the relationOC. In the step, the iterative calculation of the SOC value and the polarization voltage is realized by combining a state space model of the lithium ion battery and based on the iterative calculation of the adaptive extended Kalman filter algorithm.
S201: acquiring a set SOC initial value according to artificial assignment or the state of the lithium ion battery; identifying parameters of the lithium ion battery model on line, and discretizing the lithium ion battery model through a discrete model; the characterization for the discrete model is as follows:
wherein, UOC(s) is the open circuit voltage after Ralsberg transform, UL(s) is the measured voltage after Ralstonia transform, Ua(s) is the difference between the two, s represents the s domain, and v is the observation noise of the lithium ion battery model. For establishing an open-circuit voltage U by means of the formula (1)OCMeasuring voltage ULInternal resistance R of lithium ion battery model1Polarization resistance R of lithium ion battery modelPThe relation between the current I(s) of the lithium ion battery model, the time constant tau of the polarization part and the measurement noise matrix v is obtained by substituting the open-circuit voltage UOCAnd measuring the voltage ULCalculating to obtain current I(s);
substituting the calculated current i(s) into equation (2), equation (2) is as follows:
y(k)＝a1y(k-1)+b0u(k)+b1u(k-1)+v(k) (2)
wherein, a1，b0，b1Discretizing parameters of the lithium ion battery model, wherein u (k) and u (k +1) are currents of the lithium ion battery model at the time k and the time k +1 respectively, and v (k) is a measurement noise matrix; calculating a y value at the time k through the formula (2), namely y (k), wherein y (k-1) is the observed value of the lithium ion battery system at the time k and the observed value of the lithium ion battery system at the time k-1 respectively;
the Thevenin model is used as an equivalent model of the lithium ion battery, and the provided online parameter identification method can solve the problem that the power lithium ion battery needs certain prior data when parameter estimation is carried out.
S202: estimating discrete model parameters by adopting a recursive least square algorithm and separating the discrete model parameters, wherein the discrete model parameters are mainly separated by the following formula:
in the formula (3), x (k) is a variable of the parameter to be identified, and θ is a discrete model parameter to be identified after the dispersion; a is1，b0，b1Discretizing parameters of the lithium ion battery model; u (k) and u (k +1) are the current of the lithium ion battery model at the time k and the time k +1 respectively; y (k) and y (k-1) are the system observed values at the time k and the time k-1, respectively; in the calculation of the formula (3), it is necessary to substitute the values of y (k) and y (k-1) calculated in the formula (2).
Equation (4) is used to identify θ in equation (3); in the formula (4), I is an identity matrix, and x (k) is a variable of the parameter to be identified, and has the same meaning as in the formula (3); the discrete model parameters to be identified are calculated by the formula (4), wherein γ is used to represent the inverse matrix when p (k) is obtained, and p (k) is the error covariance matrix.
In the formula (5), a1，b0，b1，b2Discretizing parameters of the lithium ion battery model; r1Internal resistance, R, for lithium ion battery modelPPolarization resistance of lithium ion battery model, T sampling time, CPIs the polarization capacitance of the lithium ion battery model;
s203: calculating to obtain lithium ion battery model parameters according to the obtained discrete model parameters, namely the obtained lithium ion battery model parameters are the internal resistance R of the lithium ion battery model respectively1Polarization resistance R of lithium ion battery modelPAnd Cp. The discrete model is obtained through Laplace transformation and bilinear transformation, parameters of the transformed discrete model are calculated through a formula (1) and a formula (2), and parameters of the lithium ion battery model are calculated through a formula (3) to a formula (5).
S204: calculating a state prediction equation of the lithium ion battery according to the lithium ion battery model parameters, wherein a battery state space model of the battery is calculated mainly through the lithium ion battery model parameters, and the state prediction equation is directly obtained according to the battery state equation, as shown in a formula (7);
when the method is used for tracking the output voltage of the lithium ion battery, the average estimation error is 0.005V, and the maximum estimation error is 0.021V; under complex working conditions, the estimation error of the peak power of the lithium ion battery is within 5 percent; the state equation and the expression of the observation equation are constructed by taking the SOC and the polarization voltage as variables in the state equation thereof and measuring the voltage as variables of the observation equation, and in the present embodiment, the state space model for the battery is as follows:
in the formula (6), SOC (k) is a state variable which is the SOC value at the time k; u shapeL(k) For observing the variable, is U at time kLValue, and UL(k) A voltage signal output for taking into account the influence of the measurement error v (k); the battery state space model is linearly transformed by Taylor expansion, and has random vector SOC (k) with Gaussian white noise w (k) and observation variable U with Gaussian white noise v (k) for k values at different momentsL(k) Constituting a discrete time nonlinear system.
Aiming at different time k, the SOC estimation process comprises a random state variable SOC fused with white Gaussian noise w (k) and an observed random variable U fused with white Gaussian noise v (k) by the battery state space modelL(k) (ii) a f (—) is a nonlinear equation of state for describing the SOC state of the lithium ion battery; g (, x) is a non-linear observation equation that characterizes the output closed circuit voltage (measured voltage). In this embodiment, the variance of the system noise matrix w (k) is described using Q, and the variance of the measurement noise matrix v (k) is described using R; under the influence of random noise, the target can be accurately estimated aiming at the peak power of the lithium ion battery.
The equation for state prediction is as follows:
X(k|k-1)＝A(k)X(k-1)+Bu(k) (7)
P(k|k-1)＝AP(k-1)AT+Q(k) (8)
in the formula (7), the calculated X (k-1) is a state matrix of the system at the time k-1, and X (k) is [ soc (k); u shapeP(k)]SOC is state of charge and UPIs the polarization voltage of the lithium ion battery model; x (k | k-1) is a predicted system state matrix;
in equation (8), P (k-1) is calculated as the system covariance matrix at time k-1, and P (k | k-1) is the predicted system covariance matrix.
A (k) is a matrix of bodies, a ═ 1, 0; 0, exp (t/τ)]In the matrix of the shape body, comprising: polarization voltage U of lithium ion battery modelPAnd state of charge SOC;
b is a control matrix of the system, and [ - η t/Q [ ]0；RP*(1-exp(t/τ))]，Q0Is the current capacity of the battery;
p (k) is the covariance matrix of the system;
q (k) is the system noise of the system;
s205: calculating Kalman gain and system residual error by a state prediction equation; the calculation formula of the Kalman gain K (k) and the system residual error epsilon (k) is as follows:
K(k)＝P(k|k-1)CT(CP(k|k-1)CT+R(k))-1 (9)
ε(k)＝y(k)-CX(k|k-1)-Du(k) (10)
in formula (9) and formula (10), k (k) is the calculated kalman compensation gain, and r (k) is the observation noise of the system; epsilon (k) is a system residual error, y (k) is an observed value of the system, C is a measurement matrix of the system, u (k) is the current of the lithium ion battery model at the moment k, and D is the internal resistance R1 of the lithium ion battery model;
a. updating the covariance of the measured noise and the covariance of the system noise according to the system residual error, substituting the updated covariance into a state prediction equation, and updating the state prediction equation; in this embodiment, the state matrix X (kk-1) of the system and the covariance matrix P (kk-1) of the system, which are calculated from the state prediction equation, are substituted into the formula (9) and the formula (10), and the kalman gain k (k) and the system residual epsilon (k) can be calculated.
b. As can be seen from the above formula (6), the battery state space model contains a random vector SOC (k) with white Gaussian noise w (k) and an observed variable U with white Gaussian noise v (k)L(k) Thereby forming the structure. Updating the covariance of the measurement noise and the covariance of the system noise according to the system residual error, updating the initial value of the SOC through a battery state space model, which can be known from FIG. 1, and performing iterative loop calculation according to the updated initial value; the battery state space model is constructed by a state equation and an observation equation, variables of the state equation comprise an SOC value and covariance of system noise, and variables of the observation equation comprise covariance of output closed-circuit voltage and measurement noise.
In the above, the covariance of the measurement noise and the covariance of the system noise are updated according to the system residual, and the formula adopted by the update is as follows:
wherein d (k) is a forgetting factor, so that noise estimation deviation caused by large measurement error fluctuation is avoided; y to (k) are system residuals after state prediction.
S206: calculating an SOC value and a polarization voltage at the moment k according to Kalman gain K (k) and system residual error epsilon (k), wherein the SOC value and the polarization voltage at the moment k are characterized as follows:
X(k)＝X(k|k-1)+K(k)ε(k) (13)
in the formula (11), kalman gain k (k) and system residual epsilon (k) and state matrix X (kk-1) of the system are calculated in the above formula, so that state matrix X (k) can be calculated, where X (k) is the SOC value and polarization voltage at time k.
S3: according to the lithium ion battery model parameters, the polarization voltage and the open-circuit voltage, estimating the peak power of the lithium ion battery by a recursive estimation method, wherein the estimation method of the peak power of the lithium ion battery comprises the following steps:
s301: acquiring the SOC value, the polarization voltage and the open-circuit voltage at the moment k;
s302: and (3) carrying out recursive prediction on the state of the lithium ion battery after N sampling moments, wherein the recursive prediction formula is as follows:
in equation (14), X (k + N) is a state matrix after N sampling times, and reflects the SOC value and the polarization voltage after N sampling times; a. theN(k) Refers to a matrix of the shape after N sampling moments; x (k) is the state matrix at time k; i (k) is the current at time k; b (k)Is the control matrix at time k;
and the non-linear processing is carried out on the open-circuit voltage after N sampling moments in the recursive prediction; the formula for nonlinear speech processing is as follows:
in the formula (15), for UOC(SOC(k),Q0) And U isOC(SOC(k),Q0) For linearizing the open-circuit voltage after processing, U calculated therefromOC(SOC(k),Q0) Substituted into equation (14); q0Is the variance initial value of the system noise matrix w (k), and eta is the coulombic efficiency (discharge efficiency); Δ t is the sampling time.
S303: estimating the peak current of the lithium ion battery after N sampling moments, wherein the estimated formula is as follows:
s304: by adopting a recursion estimation method, the estimation of the peak power at the current moment and the continuous maximum power of the internal energy in a period of time in the future is realized to estimate the peak power of the lithium ion battery after N sampling moments, and the estimation formula is as follows:
in this formula, UL,min、UL,maxRespectively a minimum value and a maximum value of the measured voltage,
and
each is calculated by the above formula (16).
In the process of estimating the peak power of the lithium ion battery, iteration is carried out through the series of formulas, the output X (k) is the estimated SOC and the polarization voltage value, and P is the peak power after the N sampling moments.
According to the unmanned aerial vehicle lithium ion battery peak power self-adaptive estimation method, aiming at the accurate peak power estimation target of the lithium ion battery, the estimation precision, the calculation complexity and the algorithm stability are comprehensively considered, the lithium ion peak power self-adaptive estimation method based on the equivalent model parameter on-line estimation is provided, the iterative calculation of the lithium ion battery peak power estimation is realized by combining the establishment of an SOC estimation model and the establishment of the peak power estimation on the basis of fully considering the lithium ion battery work, and a basis is provided for the lithium ion battery peak power estimation and the real-time monitoring of the work state.
According to the unmanned aerial vehicle lithium ion battery peak power self-adaptive estimation method provided by the embodiment, the model parameters of the lithium ion battery are obtained through online calculation in a least square online parameter identification mode, the polarization voltage and the SOC are corrected and estimated by using a self-adaptive extended Kalman filtering algorithm, and the peak power at the current moment and the maximum peak power which can be continued in a period of time in the future are estimated by using the self-adaptive extended Kalman filtering algorithm and a recursive model estimation method. The method combines the establishment of peak power estimation to realize the mathematical expression of the lithium ion battery pack on the basis of fully considering the lithium ion batteries and grouping work, and constructs the lithium ion peak power self-adaptive estimation based on the on-line estimation of equivalent model parameters.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present application, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (5)
1. An unmanned aerial vehicle lithium ion battery peak power self-adaptive estimation method is characterized by comprising the following steps:
s1: characterizing the working state of the lithium ion battery through a lithium ion battery model;
s2: performing iterative calculation on the polarization voltage and the SOC value of the lithium ion battery through a self-adaptive extended Kalman algorithm, and estimating the open-circuit voltage of the lithium ion battery at the current moment on line according to the SOC value at the current moment;
s3: estimating the peak power of the lithium ion battery by a recursive estimation method according to the lithium ion battery model parameters, the polarization voltage and the open-circuit voltage;
the method for iterative calculation of the polarization voltage and the SOC value is as follows:
s201: setting an initial value of SOC, identifying parameters of a lithium ion battery model on line, and discretizing the lithium ion battery model through a discrete model;
s202: estimating discrete model parameters by adopting a recursive least square algorithm and separating the discrete model parameters;
s203: calculating to obtain lithium ion battery model parameters according to the obtained discrete model parameters;
s204: calculating a state prediction equation of the lithium ion battery according to the lithium ion battery model parameters;
s205: calculating Kalman gain and system residual error by a state prediction equation;
s206: calculating an SOC value and a polarization voltage at the moment k according to Kalman gain and system residual error;
the method for estimating the peak power of the lithium ion battery comprises the following steps:
s301: acquiring the SOC value, the polarization voltage and the open-circuit voltage at the moment k;
s302: carrying out recursive prediction on the state of the lithium ion battery after N sampling moments, and carrying out nonlinear processing on the open-circuit voltage after N sampling moments during recursive prediction;
s303: estimating the peak current of the lithium ion battery after N sampling moments;
s304: and estimating the peak power of the lithium ion battery after N sampling moments according to the peak current of the lithium ion battery.
2. The adaptive estimation method for peak power of the lithium ion battery of the unmanned aerial vehicle according to claim 1, wherein the initial value of the SOC is obtained according to artificial assignment or according to a state of the lithium ion battery.
3. The adaptive estimation method for peak power of the lithium ion battery of the unmanned aerial vehicle according to claim 1, wherein the covariance of the measurement noise and the covariance of the system noise are updated according to the system residual error, and the updated covariance is substituted into the state prediction equation and the state prediction equation is updated.
4. The adaptive estimation method for peak power of the lithium ion battery of the unmanned aerial vehicle according to claim 1, wherein the covariance of the measurement noise and the covariance of the system noise are updated according to the system residual error, and the initial value of the SOC is updated through a battery state space model.
5. The adaptive estimation method for peak power of the lithium ion battery of the unmanned aerial vehicle of claim 4, wherein the battery state space model is constructed by a state equation and an observation equation, variables of the state equation comprise a SOC value and a covariance of system noise, and variables of the observation equation comprise a covariance of an output closed-circuit voltage and a measurement noise.
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