Key characteristic parameter estimation method of lithium battery for braking energy recovery of mining electric drive dump truck

Abstract

The invention discloses a key parameter estimation method of a lithium battery for braking energy recovery of a mining electric drive dumper, which comprises the following steps of: using a fractional order equivalent circuit model (FOM); the multi-time-scale cyclic estimation of the key characteristic parameters of the lithium battery of the braking energy recovery system of the mining electric transmission dump truck is realized by fusing the dual adaptive unscented Kalman algorithm DAUKF, and an accurate data basis is provided for efficient and safe recovery of braking energy. According to the method, the defects of an existing lithium battery characteristic parameter estimation method are improved, and rapid and accurate estimation of the lithium battery characteristic parameters under extreme working conditions such as frequent charging and discharging and large-current impact is achieved. And the safety and the stability of the braking energy recovery system of the mining electric transmission dump truck are improved. The method can solve the technical problem that the existing method for estimating the key characteristic parameters of the lithium battery mainly aims at the lithium battery in a common working environment, but is not suitable for estimating the key parameters of the lithium battery for recovering the braking energy of the mining electric drive dump truck.

Description

Technical Field

[0001] The present invention belongs to the field of novel energy storage technology and high-end intelligent manufacturing technology, and more specifically, relates to a method for estimating key parameters (KBP) of a lithium battery for braking energy recovery of an electric drive dump truck for mining. Background Art

[0002] Electric drive dump trucks for mining are key equipment for global resource mining and large-scale infrastructure construction. They undertake heavy transportation tasks in large open-pit coal mines in my country, the Three Gorges Dam on the Yangtze River and other large-scale projects. Electric drive dump trucks for mining in my country mostly work on upper and lower broken sections, with extremely high braking energy consumption, severe wear and tear, and poor operating conditions. In order to avoid the consumption of braking energy in the form of heat, it is proposed to use supercapacitors and lithium batteries as a hybrid energy storage system to recover the braking energy of the mining car. Lithium batteries are often selected as lithium batteries for this energy storage system due to their significant advantages such as high energy density, low self-discharge rate and long life. The key characteristic parameters of lithium batteries include the state of charge (SOC) and the state of health (SOH). The accurate estimation of these parameters is very important for the safe and stable operation of lithium batteries, which is convenient for the accurate allocation of the energy ratio of the hybrid energy storage system, and thus has important significance for the safe operation of the entire braking energy recovery system.

[0003] At present, the estimation methods for the key characteristic parameters of lithium batteries for brake energy recovery are mainly divided into three categories: one is the estimation method based on experiments, which includes the ampere-hour integration method, the open circuit voltage method, etc. The ampere-hour integration method is based on the integration of the current change and time when the lithium battery is working to express the change in charge, so as to estimate the current SOC of the lithium battery and then use the algorithm to estimate the SOH. The open circuit voltage method is based on the correlation between the open circuit voltage (OCV) of the battery in a static state and the SOC to estimate the SOC and then use the algorithm to estimate the SOH; the second is the model-based estimation method, which constructs relevant lithium battery models such as equivalent circuit models and electrochemical models, identifies relevant parameters of the model, and realizes the estimation of the key characteristic parameters of the lithium battery; the third is the data-driven estimation method, which relies on the mapping relationship between the input and output of the battery system and trains a large number of data sets to estimate the key characteristic parameters of the battery.

[0004] However, the above-mentioned estimation methods of key characteristic parameters of lithium batteries all have some defects that cannot be ignored: First, although the ampere-hour integration method is simple to calculate, it has a large cumulative error and relies on the initial value of SOC for estimation, but the initial value of SOC is difficult to obtain accurately, which leads to low estimation accuracy; Second, the open circuit voltage method is simple to operate, but the battery must be kept stationary for more than ten hours when measuring the open circuit voltage. This method cannot meet the actual working conditions of the battery and cannot estimate the key characteristic parameters of the lithium battery in real time; Third, for the model-based estimation method, the model is divided into an internal characteristic model and an external characteristic model. Although the internal characteristic model (mainly an electrochemical model) can better fit the internal chemical reaction of the lithium battery, the calculation is very complicated, and the external characteristic model (mainly an internal resistance model) is not suitable for the estimation of key characteristic parameters of lithium batteries. , Thevenin model, PNGV model, etc.) If a simple model is used, the estimation accuracy is not high, and if a complex model is used, it is difficult to build and the parameter identification is difficult; Fourth, the estimation method based on data-driven needs to obtain a large amount of data in advance for training. Over-reliance on data sets without considering the battery model will lead to overfitting; Fifth, the existing estimation methods for the key characteristic parameters of lithium batteries are mainly aimed at lithium batteries in ordinary working environments, but are not suitable for the key parameter estimation of lithium batteries for braking energy recovery of electric drive dump trucks for mining (this is because electric drive dump trucks for mining mostly work on uphill and downhill sections, with frequent charging and discharging and large size. When performing braking energy recovery, lithium batteries usually face large current shocks and extremely high braking energy consumption. The working environment is harsh, facing extreme temperature differences of -20℃ to 40℃ and dusty environments). Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a method for estimating key parameters of a lithium battery for braking energy recovery of an electric drive dump truck for mining, the purpose of which is to solve the technical problems that the existing ampere-hour integration method has a large accumulated error and relies on the initial value of SOC for estimation, but the initial value of SOC is difficult to obtain accurately, which leads to low estimation accuracy, and the existing open circuit voltage method must keep the battery stationary for more than ten hours when measuring the open circuit voltage, which cannot meet the actual working conditions of the battery and cannot estimate the key characteristic parameters of the lithium battery for braking energy recovery in real time, and the existing model-based estimation method is very complicated to calculate when using an internal characteristic model, and the estimation accuracy is not high when using an external characteristic model, and the parameter identification is difficult, and the existing data-driven estimation method needs to obtain a large amount of data in advance for training, and excessive reliance on the data set without considering the battery model will lead to overfitting, and the existing method for estimating key characteristic parameters of lithium batteries for braking energy recovery is mainly aimed at lithium batteries for braking energy recovery in ordinary working environments, but is not suitable for the key parameter estimation of lithium batteries for braking energy recovery of electric drive dump trucks for mining.

[0006] To achieve the above object, according to one aspect of the present invention, a method for estimating key characteristic parameters of a lithium battery for braking energy recovery of an electric drive dump truck for mining is provided, comprising the following steps:

[0007] (1) Simulate and model the lithium battery for braking energy recovery using the SIMULINK tool in MATLAB software to obtain a fractional-order equivalent circuit model;

[0008] (2) The fractional-order equivalent circuit model obtained in step (1) is processed using Kirchhoff's current law and Kirchhoff's voltage law to obtain an electrical principle equation and an output equation of the fractional-order equivalent circuit model.

[0009] (3) The Greenwald-Letnikoff formula is used to process the electrical principle equation and output equation of the fractional-order equivalent circuit model established in step (2) to obtain the state space equation and observation equation defined in fractional order.

[0010] (4) The state equation and observation equation defined by the fractional order obtained in step (3) are discretized to obtain the discretized state equation and observation equation respectively.

[0011] (5) using an online parameter identification method to process the discretized state equation and observation equation obtained in step (4) respectively to obtain parameter data in the fractional-order equivalent circuit;

[0012] (6) Input the parameter data obtained by online parameter identification in step (5) into the fractional-order adaptive unscented Kalman algorithm FAUKF for iterative calculation to obtain the SOC at time t t Estimated value and ohmic internal resistance parameter R of the battery fractional-order equivalent circuit model at time t 0,t , the algorithm flow chart is as follows Figure 4 As shown:

[0013] (7) Determine whether the number of iterations in step (6) is greater than 60. If so, proceed to step (8); otherwise, return to step (6).

[0014] (8) Obtain multiple SOC estimation values ​​of the lithium battery for braking energy recovery obtained after 60 iterations and the ohmic internal resistance parameter R of the fractional-order equivalent circuit model obtained after the 60th iteration 0,t .

[0015] (9) The ohmic internal resistance parameter R of the battery fractional-order equivalent circuit model at time t obtained according to step (8) 0,t , and obtain the estimated state of health SOH value of the lithium battery used for braking energy recovery through the FAUKF algorithm.

[0016] (10) Displaying the multiple SOC estimation values ​​of the lithium battery for braking energy recovery obtained in step (8) and the SOH estimation value obtained in step (9) to the user of the electric drive dump truck for mining.

[0017] Preferably, the electrical principle equation of the fractional-order equivalent circuit model is:

[0018]

[0019] Among them, U1(t) and U2(t) respectively represent the voltage drops of the two RC loops at time t in the fractional-order equivalent circuit model at the current moment; R1 represents the electrochemical polarization resistance in the fractional-order equivalent circuit model; CPE1 represents the electrochemical polarization capacitance in the fractional-order equivalent circuit model; R2 is the concentration polarization resistance in the fractional-order equivalent circuit model; CPE2 is the concentration polarization capacitance in the fractional-order equivalent circuit model; I t is the working voltage of the fractional-order equivalent circuit model; n1 and n2 are the fractional orders of the electrochemical polarization capacitance and the concentration polarization capacitance in the fractional-order equivalent circuit model, and their values ​​range from 0 to 1; The SOC expression of the charge state of the lithium battery for braking energy recovery at the next moment, SOC t is the SOC and SOC of the equivalent circuit model at the current moment t+1 is the SOC of the equivalent circuit model at the next moment; T s is the system sampling period of the equivalent circuit model; Q n is the battery capacity of the equivalent circuit model.

[0020] The output equation of the fractional-order equivalent circuit model is:

[0021] U0(t)=U oc (SOC)-R0I(t)-U1(t)-U2(t)

[0022] Where U0(t) represents the terminal voltage of the fractional-order equivalent circuit model at the current moment, U oc (SOC) represents the open circuit voltage of the fractional-order equivalent circuit model, R0 represents the ohmic internal resistance of the fractional-order equivalent circuit model, and U1(t) and U2(t) are the two RC loop voltage drops of the fractional-order equivalent circuit model at the current moment.

[0023] Preferably, specifically, the fractional derivative definition formula is:

[0024]

[0025] Where D n Fractional differential operators representing the state space equations and the observation equations; D nf(t) represents the calculus of f(t); n is the order of the fractional equivalent circuit model, and its value range is [0,1]; n! is the factorial of the order of the fractional equivalent circuit model; T s is the system sampling period of the fractional-order equivalent circuit model, the value range is [0,1], and 1s is preferred; L is the memory length of the fractional-order equivalent circuit model, and L=60; i is the number of sampling times; are the Newton binomial coefficients.

[0026] The state space equation and observation equation defined in fractional order in step (3) are shown in the following formulas:

[0027]

[0028] Among them, [U 1,k U 2,k SOC k ] T is the input of the state-space equation, which represents the voltage drop of loop 1, the voltage drop of loop 2 and the state of charge of the fractional-order equivalent circuit model at the current moment; [U 1,k+1 U 2,k+1 SOC k+1 ] T represents the voltage drop of loop 1, the voltage drop of loop 2 and the state of charge of the fractional-order equivalent circuit model at the next moment; U oc (SOC k ) is the function of the open circuit voltage with respect to the state of charge; I k is the working current of the fractional-order equivalent circuit model at the current moment; ω k is the system noise of the fractional-order equivalent circuit model at time k, which is caused by the uncertainty of the fractional-order equivalent circuit model; υ k is the measurement noise of the fractional-order equivalent circuit model at time k, which is caused by the measurement error of the fractional-order equivalent circuit model.

[0029] Preferably, the state equation and observation equation after discretization in step (4) are as shown in the following formula:

[0030]

[0031] Among them, x k is the system input of the fractional-order equivalent circuit model at time k, and x k-1 is the system input quantity of the fractional-order equivalent circuit model at time k-1; I k-1 is the operating current of the fractional-order equivalent circuit model at time k-1; ω k-1 is the system noise of the fractional-order equivalent circuit model at time k-1; kis the system output of the fractional-order equivalent circuit model at time k; C = [-1 -1 0]; υ k is the measurement noise of the fractional-order equivalent circuit model at time k.

[0032] Preferably, the online parameter identification method in step (5) adopts a recursive least squares method based on minimum forgetting factor combined with a genetic algorithm, wherein the data to be identified of the fractional-order equivalent circuit includes the ohmic internal resistance R0, electrochemical polarization resistance R1, electrochemical polarization capacitance CPE1, concentration polarization resistance R2, concentration polarization capacitance CPE2, and fractional orders n1 and n2 of the fractional-order equivalent circuit model.

[0033] Preferably, step (5) specifically includes the following sub-steps:

[0034] (5-1) Perform Laplace transform on the discrete state equation and observation equation obtained in step (4) to obtain the frequency domain expression of the fractional order model, as shown in formula (7).

[0035]

[0036] Among them, Y(k) is the transfer function of the fractional-order equivalent circuit model in the time domain; Y(s) is the transfer function of the fractional-order equivalent circuit model in the frequency domain; s is the independent variable of the transfer function, which is equivalent to the time state in the frequency domain; U oc (s) is the open circuit voltage of the fractional-order equivalent circuit model in the frequency domain; U o (s) is the terminal voltage of the fractional-order equivalent circuit model in the frequency domain; I(s) is the operating current of the fractional-order equivalent circuit model in the frequency domain.

[0037] (5-2) Use the bilinear transformation method to process the frequency domain expression of the fractional-order model obtained in step (5-1) to obtain the transformation form G(z) of the fractional-order model in the z domain:

[0038]

[0039] Among them, a1, a2, a3, a4, and a5 are the parameter identification data of the fractional-order equivalent circuit model, and the mathematical relationship between them and the data to be identified of the fractional-order equivalent circuit model is as follows: By using the mathematical relationship between the parameter identification data of the fractional-order equivalent circuit model and the data to be identified of the fractional-order equivalent circuit model, the data to be identified of the parameters of the fractional-order equivalent circuit model can be obtained.

[0041] (5-3) Simplify the transformed form of the fractional-order model obtained in step (5-2) in the z-domain to obtain the recursive equation Y(k) of the fractional-order equivalent circuit model.

[0042] Y(k)=a1Y(k-1)+a2Y(k-2)+a3I(k)+a4I(k-1)+a5I(k-2)

[0043] (5-4) Initialize the parameter identification column vector θ(k) and covariance P(k) at time k obtained in step (5-3) to obtain the parameter identification column vector θ(0) and covariance P(0) at time k=0 after initialization.

[0044] (5-5) Determine whether k is equal to 60. If so, go to step (5-10); otherwise, go to step (5-6).

[0045] (5-6) Use the initialized parameter identification column vector θ(0) and covariance P(0) obtained in step (5-5) to obtain the gain matrix K(k):

[0046]

[0047] Wherein K(k) is the estimated gain at time k; P(k-1) is the covariance at time k-1; λ is the forgetting factor, and its value range is [0.90, 1.00]. Considering the application scenarios of the present invention, it is preferably 0.955.

[0048] (5-7) The parameter identification column vector θ(k) in the recursive equation at time k is updated according to the gain matrix obtained in step (5-6) to obtain the updated parameter identification column vector θ(k) in the recursive equation at time k.

[0049]

[0050] Among them, e(k) is the sampling error at time k.

[0051] (5-8) Obtain the parameter data to be identified of the fractional-order equivalent circuit model based on the parameter identification column vector θ(k) in the updated k-time recursive equation obtained in step (5-7).

[0052] (5-9) The covariance P(k) is updated according to the parameter data to be identified of the fractional-order equivalent circuit model obtained in step (5-8) to obtain an updated covariance P(k), and then the process returns to step (5-5):

[0053]

[0054] Wherein, P(k) is the updated covariance at time k; I is a 5-order scalar matrix, and the value range of the elements on the diagonal is [10e5, 10e6], preferably 10e6.

[0055] (5-10) Binary encoding the parameter data to be identified of the fractional-order equivalent circuit model obtained in step (5-8) and the fractional orders n1 and n2 to obtain a gene population expressed in a coded form, wherein n1 and n2 are both 1 at the kth moment;

[0056] (5-11) Obtaining the population fitness based on the gene population expressed in coded form obtained in step (5-10):

[0057]

[0058] Where J(i) is the fitness of the population when the population size is i, and i∈[1,N]; N is the population size, preferably N=100; A(i) is the predicted output value U in the fractional-order equivalent circuit model y (y) and the actual measured output value U c (y) is the sum of the squares of the differences, A max is the optimization target of the genetic algorithm. In the present invention, A max =0.005; U y (j) is the predicted output value at the jth iteration; U c (j) is the actual measured output value at the jth iteration, and j∈[1,M]; M is the number of iterations, preferably M=60;.

[0059] (5-12) According to the population fitness obtained in step (5-11), the population is subjected to hybridization and mutation processing to obtain a processed population:

[0060]

[0061] Among them, P z (i) is the hybridization probability of the population when the population size is i; K z is the hybridization factor of the population, its value range is (0.6, 0.9), and is preferably 0.7; P b (i) is the probability of population mutation when the population size is i; K b is the variation factor, and its value range is [0.01, 0.1], preferably 0.05; A ave is the average fitness value in the population.

[0062] (5-13) Decode the population processed in step (5-12) to obtain a decoded population, which is the parameter data to be identified of the fractional-order equivalent circuit model and the fractional orders n1 and n2.

[0063] (5-14) Determine whether the number of iterations j is greater than 60. If so, proceed to step (5-15). Otherwise, set j=j+1 and return to step (5-11).

[0064] (5-15) Output parameter data in the fractional-order equivalent circuit, including the ohmic internal resistance R0, electrochemical polarization resistance R1, electrochemical polarization capacitance CPE1, concentration polarization resistance R2, concentration polarization capacitance CPE2, and fractional orders n1 and n2 of the fractional-order equivalent circuit model.

[0065] Preferably, step (5) comprises the following sub-steps:

[0066] (9-1) The ohmic internal resistance parameter R of the battery fractional-order equivalent circuit model at time t obtained according to step (8) 0,t Obtain the discretized state-space equations and observation equations for estimating the ohmic internal resistance:

[0067] R 0,t+1 =R 0,t +r t

[0068] U 0,t =U oc,t (SOC)-R 0,t IU 1,t -U 2,t +t t

[0069] Among them, R 0,t+1 is the ohmic internal resistance of the fractional-order equivalent circuit model at time t+1; R 0,t is the ohmic internal resistance of the fractional-order equivalent circuit model at time t; r t is the state noise of the state space equation estimating the ohmic internal resistance at time t; t is the observation noise of the observation equation for estimating the ohmic internal resistance at time t.

[0070] (9-2) Initialize the discretized state space equation and observation equation for estimating the ohmic internal resistance obtained in step (9-1).

[0071] Specifically, this step is to initialize the ohmic internal resistance of the fractional-order equivalent circuit model to R0 obtained by parameter identification in step (5); and to initialize the initial ohmic internal resistance covariance of the fractional-order equivalent circuit model to 0.

[0072] (9-3) Perform untraceable transformation (UT) on the discrete state space equation and observation equation initialized in step (9-2) to obtain multiple sigma points.

[0073] (9-4) Apply the multiple sigma points obtained in step (9-3) to the fractional-order equivalent circuit model obtained in step (1) to obtain the predicted value of the ohmic internal resistance of the fractional-order equivalent circuit model at time t Variance prediction value P of the state space equation x, the mean weight corresponding to each sigma point and the covariance weights for the discrete state space equations

[0074] (9-5) The mean weight corresponding to the sigma point obtained in step (9-4) Covariance Weights for Discrete State Space Equations Get the observed value of the observation equation at time t+1 and the observed variance predicted value P y .

[0075] (9-6) The predicted value of the ohmic internal resistance of the fractional-order equivalent circuit model at time t obtained according to step (9-4) Variance prediction value P of discrete state space equation x , the mean weight corresponding to the sigma point Covariance Weights for Discrete State Space Equations And the observed value of the observation equation at time t+1 obtained in step (9-5) and the observed variance predicted value P y , get the input-output cross covariance matrix P xy , the gain K of the Kalman algorithm at time t t ; The covariance matrix P of the Kalman algorithm at time t t .

[0076] (9-7) The input-output cross covariance matrix P obtained according to step (9-6) xy , the gain K of the Kalman algorithm at time t t ; The covariance matrix P of the Kalman algorithm at time t t , obtain the noise covariance Q of the discrete state space equation at time t+1 t+1 and the observation noise covariance R t+1 .

[0077] (9-8) The noise covariance Q of the discrete state space equation at time t+1 obtained using step (9-7) t+1 and the observation noise covariance R t+1 Get the predicted value of the ohmic internal resistance of the fractional-order equivalent circuit model at time t+1

[0078] (9-9) The predicted value of the ohmic internal resistance of the fractional-order equivalent circuit model at time t+1 obtained in step (9-8) Get the estimated SOH value SOH of the lithium battery for braking energy recovery at time t+1 t+1 .

[0079] Preferably, the UT transformation process of step (9-3) is:

[0080]

[0081] in, is the mth sigma point at time t, representing the mathematical expectation of the state vector at time t; is the initial predicted value of the ohmic internal resistance of the fractional-order equivalent circuit model; is the predicted value of the ohmic internal resistance of the fractional-order equivalent circuit model at time t; m is the dimension of the state variable of the discrete state space equation after initialization in step (9-2); M is the length of the state vector of the discrete state space equation after initialization in step (9-2), and the value is determined by the number of state vectors. In the present invention, M is taken as 3; γ is the scale parameter of the traceless transformation; α is the divergence factor of the traceless transformation, which is used to determine the sampling distribution of the sigma point, and the value range is 0.0001-1, preferably 0.005; β is the adjustment parameter of the traceless transformation, which is used to provide other constraints on the sampling points, and the value is 0.

[0082] Step (9-4) uses the following calculation formula:

[0083]

[0084] Where χ is the weight coefficient, χ=2; Q t is the noise covariance of the discrete state space equation at time t.

[0085] Preferably, step (9-5) updates the observed value and the observed variance predicted value P y The process is as follows:

[0086]

[0087] in, is the observed value of the observation equation at the updated time t+1; y t is the actual observed value of the observation equation at time t; R t is the observation noise covariance of the observation equation at time t.

[0088] The update equation of step (9-6) is:

[0089]

[0090] in, is the transpose of the input-output cross covariance matrix.

[0091] The implementation process of step (9-7) is shown as follows:

[0092]

[0093] Among them, G k is the adaptive variance of the moving window method; Z is the adaptive moving window.

[0094] The calculation formula for the predicted SOH value of the lithium battery for braking energy recovery at time t+1 in step (9-9) is as follows:

[0095]

[0096] Among them, SOH t+1 is the estimated SOH value of the lithium battery for braking energy recovery at time t+1; R N is the maximum available resistance of the lithium battery for braking energy recovery, is the rated ohmic internal resistance of the lithium battery for braking energy recovery; R e It is the healthy termination resistor of the lithium battery for braking energy recovery. It is usually stipulated that the capacity of the lithium battery for braking energy recovery is less than 80% of the rated capacity and the battery needs to be scrapped compulsorily. e The size of is the ohmic internal resistance of the lithium battery for braking energy recovery when the capacity of the lithium battery for braking energy recovery is reduced to 80% of the rated capacity.

[0097] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0098] (1) Since the present invention adopts steps (1) to (4) to establish a fractional-order equivalent circuit model, it can better fit the nonlinear system of the lithium battery for braking energy recovery in the braking energy recovery system compared with the direct and simple calculation of the ampere-hour integration method;

[0099] (2) Since the present invention adopts step (5), once the mining electric drive dump truck is started and runs uninterruptedly, the lithium battery for braking energy recovery in the braking energy recovery system is in a rapidly changing nonlinear time-varying system. The recursive least squares method based on the minimum forgetting factor can reduce the influence of old data, track the changes of the battery system more quickly, and the parameter identification is more in line with the system operation characteristics. At the same time, the genetic algorithm is used to identify the fractional order, avoiding the problem that the recursive least squares method based on the minimum forgetting factor cannot identify the fractional order online;

[0100] (3) Since the present invention adopts steps (6) to (8), the charge state of the lithium battery for braking energy recovery is estimated in real time on a short time scale, which is consistent with the working conditions of the lithium battery for braking energy recovery, thereby ensuring the safe and stable operation of the lithium battery for braking energy recovery;

[0101] (4) Since the present invention adopts step (9), and since the health state of the lithium battery for braking energy recovery does not change significantly, the charge state of the lithium battery for braking energy recovery is estimated 60 times, and then the health state of the lithium battery for braking energy recovery is estimated once within a long time scale, thereby reducing the number of calculations while ensuring the accuracy of the estimation;

[0102] (5) Since the present invention adopts steps (6) to (9), the key characteristic parameters SOC and SOH of the lithium battery for braking energy recovery are jointly estimated in long-short time scales through two FAUKF algorithms, and the estimated result of SOC is used as the input of SOH estimation, which reduces calculation redundancy, and the SOC and SOH measurement data are updated and corrected with each other, which can be monitored in real time under harsh operating conditions to ensure safe and stable operation of the system.

[0103] (6) Since the present invention adopts step (10), the user can pay attention to the operating status of the lithium battery for braking energy recovery in real time, promptly perceive battery failure and replace aging batteries, thereby ensuring driving safety and improving economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] Figure 1 It is a flow chart of a key parameter estimation method of a lithium battery for braking energy recovery of a mining electric drive dump truck according to the present invention;

[0105] Figure 2 is a schematic diagram of a fractional-order equivalent circuit model of the present invention;

[0106] Figure 3 is a detailed flow chart of step (5) in the method of the present invention;

[0107] Figure 4 is a detailed flow chart of step (6) in the method of the present invention;

[0108] Figure 5 It is a detailed flow chart of step (9) in the method of the present invention. DETAILED DESCRIPTION

[0109] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0110] The present invention provides a key parameter estimation method for a lithium battery for braking energy recovery of an electric drive dump truck for mining. The estimation method uses a fractional order model (FOM) and integrates a double adaptive unscented Kalman filter (DAUKF) to achieve multi-time scale cyclic estimation of key characteristic parameters of a lithium battery for braking energy recovery of a braking energy recovery system of an electric drive dump truck for mining, thereby ensuring safe and stable operation of the braking energy recovery system of the electric drive dump truck for mining.

[0111] like Figure 1 As shown, the present invention provides a method for estimating key characteristic parameters of a lithium battery for braking energy recovery of a mining electric drive dump truck, comprising the following steps:

[0112] (1) The SIMULINK tool in MATLAB software is used to simulate and model the lithium battery for braking energy recovery to obtain a fractional-order equivalent circuit model, such as Figure 2 shown.

[0113] Specifically, the lithium battery for braking energy recovery of the present invention uses a 6-DG-190 manganese acid lithium battery for braking energy recovery, and its specific parameter indicators are shown in the following Table 1:

[0114] Table 1 Parameters of lithium batteries for manganese acid brake energy recovery

[0115]

[0116]

[0117] (2) The fractional-order equivalent circuit model obtained in step (1) is processed using Kirchhoff's current law and Kirchhoff's voltage law to obtain an electrical principle equation and an output equation of the fractional-order equivalent circuit model.

[0118] Specifically, the electrical principle equation of the fractional-order equivalent circuit model is shown in formula (1):

[0119]

[0120] Wherein, U1(t) and U2(t) represent the voltage drops of the two RC loops at time t in the fractional-order equivalent circuit model, respectively; R1 represents the electrochemical polarization resistance in the fractional-order equivalent circuit model; CPE1 represents the electrochemical polarization capacitance in the fractional-order equivalent circuit model; R2 represents the concentration polarization resistance in the fractional-order equivalent circuit model; CPE2 represents the concentration polarization capacitance in the fractional-order equivalent circuit model; I tis the working voltage of the fractional-order equivalent circuit model; n1 and n2 are the fractional orders of the electrochemical polarization capacitance and the concentration polarization capacitance in the fractional-order equivalent circuit model, and their values ​​range from 0 to 1; The state of charge (SOC) of the lithium battery used for braking energy recovery is expressed as follows: t is the SOC and SOC of the equivalent circuit model at the current moment t+1 is the SOC of the equivalent circuit model at the next moment; T s is the system sampling period of the equivalent circuit model; Q n is the battery capacity of the equivalent circuit model.

[0121] The output equation of the fractional-order equivalent circuit model is shown in formula (2):

[0122] U0(t)=U oc (SOC)-R0I(t)-U1(t)-U2(t) (2)

[0123] Where U0(t) represents the terminal voltage of the fractional-order equivalent circuit model, U oc (SOC) represents the open circuit voltage of the fractional-order equivalent circuit model, R0 represents the ohmic internal resistance of the fractional-order equivalent circuit model, and U1(t) and U2(t) are the two RC loop voltage drops of the fractional-order equivalent circuit model.

[0124] (3) The electrical principle equation and output equation of the fractional-order equivalent circuit model established in step (2) are processed using the Grunwald-Letnikov formula (GL fractional-order derivative definition formula) to obtain the state space equation and observation equation defined in fractional order.

[0125] Specifically, the definition formula of fractional derivative is shown in formula (3):

[0126]

[0127] Where D n Fractional differential operators representing the state space equations and the observation equations; D n f(t) represents the calculus of f(t); n is the order of the fractional equivalent circuit model, and its value range is [0,1]; n! is the factorial of the order of the fractional equivalent circuit model; T s is the system sampling period of the fractional-order equivalent circuit model, the value range is [0,1], and 1s is preferred; L is the memory length of the fractional-order equivalent circuit model, and L=60; i is the number of sampling times; are the Newton binomial coefficients.

[0128] The state space equation and observation equation defined in fractional order in this step are shown in the following formula (4) and formula (5) respectively:

[0129]

[0130] Among them, [U 1,k U 2,k SOC k ] T is the input of the state-space equation, which represents the voltage drop of loop 1, the voltage drop of loop 2 and the state of charge of the fractional-order equivalent circuit model at the current moment; [U 1,k+1 U 2,k+1 SOC k+1 ] T represents the voltage drop of loop 1, the voltage drop of loop 2 and the state of charge of the fractional-order equivalent circuit model at the next moment; U oc (SOC k ) is the function of the open circuit voltage with respect to the state of charge; I k is the working current of the fractional-order equivalent circuit model at the current moment; ω k is the system noise of the fractional-order equivalent circuit model at time k, which is caused by the uncertainty of the fractional-order equivalent circuit model; υ k is the measurement noise of the fractional-order equivalent circuit model at time k, which is caused by the measurement error of the fractional-order equivalent circuit model.

[0131] (4) The state equation and observation equation defined by the fractional order obtained in step (3) are discretized to obtain the discretized state equation and observation equation respectively.

[0132] Specifically, the state equation and observation equation after discretization are shown in the following formula (6):

[0133]

[0134] Among them, x k is the system input of the fractional-order equivalent circuit model at time k, and x k-1 is the system input quantity of the fractional-order equivalent circuit model at time k-1; I k-1 is the operating current of the fractional-order equivalent circuit model at time k-1; ω k-1 is the system noise of the fractional-order equivalent circuit model at time k-1; k is the system output of the fractional-order equivalent circuit model at time k; C = [-1 -1 0]; υ k is the measurement noise of the fractional-order equivalent circuit model at time k.

[0135] The advantage of the above steps (1) to (4) is that a fractional-order equivalent circuit model is established, which can better fit the nonlinear system of the lithium battery for braking energy recovery compared to other models.

[0136] (5) Use the online parameter identification method to process the discretized state equation and observation equation obtained in step (4) respectively to obtain parameter data in the fractional-order equivalent circuit, such as Figure 3 shown.

[0137] Specifically, the online parameter identification method adopts the recursive least squares method based on the minimum forgetting factor combined with the genetic algorithm, where the data to be identified of the fractional-order equivalent circuit includes the ohmic internal resistance R0, electrochemical polarization resistance R1, electrochemical polarization capacitance CPE1, concentration polarization resistance R2, concentration polarization capacitance CPE2, and fractional orders n1 and n2 of the fractional-order equivalent circuit model.

[0138] This step specifically includes the following sub-steps:

[0139] (5-1) Perform Laplace transform on the discrete state equation and observation equation obtained in step (4) to obtain the frequency domain expression of the fractional order model, as shown in formula (7).

[0140]

[0141] Among them, Y(k) is the transfer function of the fractional-order equivalent circuit model in the time domain; Y(s) is the transfer function of the fractional-order equivalent circuit model in the frequency domain; s is the independent variable of the transfer function, which is equivalent to the time state in the frequency domain; U oc (s) is the open circuit voltage of the fractional-order equivalent circuit model in the frequency domain; U o (s) is the terminal voltage of the fractional-order equivalent circuit model in the frequency domain; I(s) is the operating current of the fractional-order equivalent circuit model in the frequency domain.

[0142] (5-2) Use the bilinear transformation method to process the frequency domain expression of the fractional-order model obtained in step (5-1) to obtain the transformation form G(z) of the fractional-order model in the z domain.

[0143] Specifically, T s is the sampling period of the system, and T s =1s, the transformed fractional-order circuit model is shown in formula (8).

[0144]

[0145] Among them, a1, a2, a3, a4, and a5 are the parameter identification data of the fractional-order equivalent circuit model, and the mathematical relationship between them and the data to be identified of the fractional-order equivalent circuit model is as follows: By using the mathematical relationship between the parameter identification data of the fractional-order equivalent circuit model and the data to be identified of the fractional-order equivalent circuit model, the data to be identified of the parameters of the fractional-order equivalent circuit model can be obtained.

[0147] (5-3) Simplify the transformed form of the fractional-order model obtained in step (5-2) in the z-domain to obtain the recursive equation Y(k) of the fractional-order equivalent circuit model.

[0148] Specifically, the parameter identification using the recursive least squares method based on the minimum forgetting factor requires simplifying the fractional-order equivalent circuit model equation to y(k) = φ(k) T The form of θ(k), where φ(k) = [Y(k-1), Y(k-2), I(k), I(k-1), I(k-2)] is the data vector at time k; θ(k) = [a1, a2, a3, a4, a5] T , is the parameter identification column vector at time k.

[0149] The recursive equation obtained in this step is shown in formula (9).

[0150] Y(k)=a1Y(k-1)+a2Y(k-2)+a3I(k)+a4I(k-1)+a5I(k-2) (9)

[0151] (5-4) Initialize the parameter identification column vector θ(k) and covariance P(k) at time k obtained in step (5-3) to obtain the parameter identification column vector θ(0) and covariance P(0) at time k=0 after initialization.

[0152] Specifically, θ(0)=[0]; P(0) takes a 5-order scalar matrix, and the elements on the diagonal take values ​​of 10e6.

[0153] (5-5) Determine whether k is equal to 60. If so, go to step (5-10); otherwise, go to step (5-6).

[0154] (5-6) Use the initialized parameter identification column vector θ(0) and covariance P(0) obtained in step (5-5) to obtain the gain matrix K(k).

[0155] The gain matrix update equation is shown in formula (10):

[0156]

[0157] Wherein K(k) is the estimated gain at time k; P(k-1) is the covariance at time k-1; λ is the forgetting factor, and its value range is [0.90, 1.00]. Considering the application scenarios of the present invention, it is preferably 0.955.

[0158] (5-7) The parameter identification column vector θ(k) in the recursive equation at time k is updated according to the gain matrix obtained in step (5-6) to obtain the updated parameter identification column vector θ(k) in the recursive equation at time k.

[0159]

[0160] Among them, e(k) is the sampling error at time k.

[0161] (5-8) Obtain the parameter data to be identified of the fractional-order equivalent circuit model based on the parameter identification column vector θ(k) in the updated k-time recursive equation obtained in step (5-7).

[0162] This step specifically uses the above step (5-2) θ(k) = [a1, a2, a3, a4, a5] T The mathematical relationship between the parameter data R0, R1, CPE1, R2, and CPE2 to be identified of the fractional-order equivalent circuit model is used to perform mathematical calculations to obtain the parameter data to be identified of the fractional-order equivalent circuit model.

[0163] (5-9) updating the covariance P(k) according to the to-be-identified parameter data of the fractional-order equivalent circuit model obtained in step (5-8) to obtain an updated covariance P(k), and then returning to step (5-5);

[0164] Specifically, the update equation is shown in formula (12):

[0165]

[0166] Wherein, P(k) is the updated covariance at time k; I is a 5-order scalar matrix, and the value range of the elements on the diagonal is [10e5, 10e6], preferably 10e6.

[0167] (5-10) Binary encode the parameter data to be identified of the fractional-order equivalent circuit model obtained in step (5-8) and the fractional orders n1 and n2 (n1 and n2 are both 1 at the kth moment) to obtain a gene population expressed in a coded form.

[0168] (5-11) Obtaining population fitness based on the gene population expressed in coded form obtained in step (5-10).

[0169] Specifically, the population fitness is calculated using the following formula (13):

[0170]

[0171] Where J(i) is the fitness of the population when the population size is i, and i∈[1,N]; N is the population size, preferably N=100; A(i) is the predicted output value U in the fractional-order equivalent circuit model y (y) and the actual measured output value U c (y) is the sum of the squares of the differences, A max is the optimization target of the genetic algorithm. In the present invention, A max =0.005; U y (j) is the predicted output value at the jth iteration; U c (j) is the actual measured output value at the jth iteration, and j∈[1,M]; M is the number of iterations, preferably M=60;.

[0172] (5-12) According to the population fitness obtained in step (5-11), the population is subjected to hybridization and mutation processing to obtain a processed population.

[0173] Specifically, the hybridization and mutation processing process is shown in formula (14).

[0174]

[0175] Among them, P z (i) is the hybridization probability of the population when the population size is i; K z is the hybridization factor of the population, its value range is (0.6, 0.9), and is preferably 0.7; P b (i) is the probability of population mutation when the population size is i; K b is the variation factor, and its value range is [0.01, 0.1], preferably 0.05; A ave is the average fitness value in the population.

[0176] (5-13) Decode the population processed in step (5-12) to obtain a decoded population, which is the parameter data to be identified of the fractional-order equivalent circuit model and the fractional orders n1 and n2.

[0177] Specifically, the decoding operation in this step is performed using a binary decoding method, and an inverse transformation is performed to convert the binary-coded genes into a decimal-coded population, namely, the parameter data to be identified of the fractional-order equivalent circuit model and the fractional orders n1 and n2.

[0178] (5-14) Determine whether the number of iterations j is greater than 60. If so, proceed to step (5-15). Otherwise, set j=j+1 and return to step (5-11).

[0179] (5-15) Output parameter data in the fractional-order equivalent circuit, including the ohmic internal resistance R0, electrochemical polarization resistance R1, electrochemical polarization capacitance CPE1, concentration polarization resistance R2, concentration polarization capacitance CPE2, and fractional orders n1 and n2 of the fractional-order equivalent circuit model.

[0180] The advantage of this step (5) is that the recursive least squares method based on the minimum forgetting factor is used to reduce the influence of old data, the changes of the battery system are tracked faster, and the parameter identification is more in line with the system operation characteristics. At the same time, the genetic algorithm is used to identify the fractional order, avoiding the problem that the recursive least squares method based on the minimum forgetting factor cannot identify the fractional order online.

[0181] (6) Input the parameter data obtained by online parameter identification in step (5) into the Fractional Order Model Adaptive Unscented Kalman Filter (FAUKF) for iterative calculation to obtain the SOC at time t. t Estimated value and ohmic internal resistance parameter R of the battery fractional-order equivalent circuit model at time t 0,t , the algorithm flow chart is as follows Figure 4 As shown:

[0182] (7) Determine whether the number of iterations in step (6) is greater than 60. If so, proceed to step (8); otherwise, return to step (6).

[0183] (8) Obtain multiple SOC estimation values ​​of the lithium battery for braking energy recovery obtained after 60 iterations and the ohmic internal resistance parameter R of the fractional-order equivalent circuit model obtained after the 60th iteration 0,t .

[0184] Specifically, the SOC estimation of the lithium battery for braking energy recovery for 60 times is defined as a time scale. When outputting, the estimated SOC value of the lithium battery for braking energy recovery obtained by the 60 times estimation and the ohmic internal resistance parameter R of the battery fractional equivalent circuit model obtained by the latest estimation are output. 0,t .

[0185] The advantage of the above steps (6) to (8) is that the charge state of the lithium battery for braking energy recovery is estimated in real time within a short time scale, which is consistent with the working conditions of the lithium battery for braking energy recovery and ensures the safe and stable operation of the lithium battery for braking energy recovery.

[0186] (9) The ohmic internal resistance parameter R of the battery fractional-order equivalent circuit model at time t obtained according to step (8) 0,t, and obtain the estimated value of the health state (SOH) of the lithium battery for braking energy recovery through the FAUKF algorithm. The flow chart is as follows Figure 5 shown.

[0187] Specifically, this step characterizes the battery health status by transforming the internal resistance of the battery system, and converts the ohmic internal resistance parameter R of the battery fractional-order equivalent circuit model at time t estimated in step (8) into 0,t The FAUKF algorithm is used as a parameter to iteratively calculate and estimate the health state of the lithium battery for braking energy recovery, so as to achieve coordinated real-time estimation of the charge state and health state of the lithium battery for braking energy recovery on micro and macro time scales.

[0188] This step includes the following sub-steps:

[0189] (9-1) The ohmic internal resistance parameter R of the battery fractional-order equivalent circuit model at time t obtained according to step (8) 0,t Obtain the discretized state-space equations and observation equations for estimating the ohmic internal resistance.

[0190] The discretized state space equation for estimating the ohmic internal resistance is shown in formula (20), and the observation equation is shown in formula (21):

[0191] R 0,t+1 =R 0,t +r t (20)

[0192] U 0,t =U oc,t (SOC)-R 0,t IU 1,t -U 2,t +t t (twenty one)

[0193] Among them, R 0,t+1 is the ohmic internal resistance of the fractional-order equivalent circuit model at time t+1; R 0,t is the ohmic internal resistance of the fractional-order equivalent circuit model at time t; r t is the state noise of the state space equation estimating the ohmic internal resistance at time t; t is the observation noise of the observation equation for estimating the ohmic internal resistance at time t.

[0194] (9-2) Initialize the discretized state space equation and observation equation for estimating the ohmic internal resistance obtained in step (9-1).

[0195] Specifically, this step is to initialize the ohmic internal resistance of the fractional-order equivalent circuit model to R0 obtained by parameter identification in step (5); and to initialize the initial ohmic internal resistance covariance of the fractional-order equivalent circuit model to 0.

[0196] (9-3) Perform unscented transformation (UT) on the discrete state space equation and observation equation initialized in step (9-2) to obtain multiple unscented points (sigma points).

[0197] The UT transformation process is shown in the following formula (22):

[0198]

[0199] in, is the mth sigma point at time t, representing the mathematical expectation of the state vector at time t; is the initial predicted value of the ohmic internal resistance of the fractional-order equivalent circuit model; is the predicted value of the ohmic internal resistance of the fractional-order equivalent circuit model at time t; m is the dimension of the state variable of the discrete state space equation after initialization in step (9-2); M is the length of the state vector of the discrete state space equation after initialization in step (9-2), and the value is determined by the number of state vectors. In the present invention, M is taken as 3; γ is the scale parameter of the traceless transformation; α is the divergence factor of the traceless transformation, which is used to determine the sampling distribution of the sigma point, and the value range is 0.0001-1, preferably 0.005; β is the adjustment parameter of the traceless transformation, which is used to provide other constraints on the sampling points, and the value is 0.

[0200] (9-4) Apply the multiple sigma points obtained in step (9-3) to the fractional-order equivalent circuit model obtained in step (1) to obtain the predicted value of the ohmic internal resistance of the fractional-order equivalent circuit model at time t Variance prediction value P of the state space equation x , the mean weight corresponding to each sigma point and the covariance weights for the discrete state space equations

[0201] Specifically, the relevant calculation formula is shown in formula (23):

[0202]

[0203] Where χ is the weight coefficient, χ=2; Q t is the noise covariance of the discrete state space equation at time t.

[0204] (9-5) The mean weight corresponding to the sigma point obtained in step (9-4) Covariance Weights for Discrete State Space Equations Get the observed value of the observation equation at time t+1 and the observed variance predicted value P y.

[0205] Update observations and the observed variance predicted value P y This process is shown in formula (24):

[0206]

[0207] in, is the observed value of the observation equation at the updated time t+1; y t is the actual observed value of the observation equation at time t; R t is the observation noise covariance of the observation equation at time t.

[0208] (9-6) The predicted value of the ohmic internal resistance of the fractional-order equivalent circuit model at time t obtained according to step (9-4) Variance prediction value P of discrete state space equation x , the mean weight corresponding to the sigma point Covariance Weights for Discrete State Space Equations And the observed value of the observation equation at time t+1 obtained in step (9-5) and the observed variance predicted value P y , get the input-output cross covariance matrix P xy , the gain K of the Kalman algorithm at time t t ; The covariance matrix P of the Kalman algorithm at time t t .

[0209] The update equation is shown in formula (25):

[0210]

[0211] in, is the transpose of the input-output cross covariance matrix.

[0212] (9-7) The input-output cross covariance matrix P obtained according to step (9-6) xy , the gain K of the Kalman algorithm at time t t ; The covariance matrix P of the Kalman algorithm at time t t , obtain the noise covariance Q of the discrete state space equation at time t+1 t+1 and the observation noise covariance R t+1 .

[0213] Specifically, the moving window method is used to update the noise to achieve adaptive adjustment of the noise covariance of the state space equation and the observation noise covariance of the observation equation, and the process equation is shown in formula (26):

[0214]

[0215] Among them, G k is the adaptive variance of the moving window method; Z is the adaptive moving window.

[0216] (9-8) The noise covariance Q of the discrete state space equation at time t+1 obtained using step (9-7) t+1 and the observation noise covariance R t+1 Get the predicted value of the ohmic internal resistance of the fractional-order equivalent circuit model at time t+1

[0217] Specifically, the noise covariance Q of the discrete state space equation at time t+1 obtained in step (9-7) is t+1 and the observation noise covariance R t+1 Substitute into formulas (23), (24) and (25) to calculate Then calculate The calculation process is shown in formula (27).

[0218]

[0219] (9-9) The predicted value of the ohmic internal resistance of the fractional-order equivalent circuit model at time t+1 obtained in step (9-8) Get the estimated SOH value SOH of the lithium battery for braking energy recovery at time t+1 t+1 .

[0220] The calculation formula for the predicted SOH value of the lithium battery for braking energy recovery at time t+1 is shown in formula (28):

[0221]

[0222] Among them, SOH t+1 is the estimated SOH value of the lithium battery for braking energy recovery at time t+1; R N is the maximum available resistance of the lithium battery for braking energy recovery, is the rated ohmic internal resistance of the lithium battery for braking energy recovery; R e It is the healthy termination resistor of the lithium battery for braking energy recovery. It is usually stipulated that the capacity of the lithium battery for braking energy recovery is less than 80% of the rated capacity and the battery needs to be scrapped compulsorily. e The size of is the ohmic internal resistance of the lithium battery for braking energy recovery when the capacity of the lithium battery for braking energy recovery is reduced to 80% of the rated capacity.

[0223] The advantages of the above steps (6) to (9) are that the key characteristic parameters SOC and SOH of the lithium battery for braking energy recovery are jointly estimated on a long-short time scale through two FAUKF algorithms, and the estimated result of SOC is used as the input of SOH estimation, thereby reducing computational redundancy. The SOC and SOH measurement data are updated and corrected with each other, and real-time monitoring can be performed under harsh operating conditions to ensure safe and stable operation of the system.

[0224] (10) Displaying the multiple SOC estimation values ​​of the lithium battery for braking energy recovery obtained in step (8) and the SOH estimation value obtained in step (9) to the user of the electric drive dump truck for mining.

[0225] The advantage of this step (10) is that the user of the mining electric drive dump truck can pay real-time attention to the operating status of the lithium battery used for brake energy recovery, promptly detect battery failure and replace aging batteries, thereby ensuring driving safety and improving economic benefits.

[0226] In summary, through the above description of the present invention, the main advantages of the present invention include:

[0227] 1. Establish a second-order RC equivalent circuit model based on the fractional-order model, which can better fit the nonlinear system of the lithium battery used for braking energy recovery in the braking energy recovery system compared with other models;

[0228] 2. The recursive least square method based on the minimum forgetting factor can reduce the impact of old data, track the changes of the battery system more quickly, and the parameter identification is more in line with the system operation characteristics. At the same time, the genetic algorithm is used to identify the fractional order, avoiding the problem that the recursive least square method based on the minimum forgetting factor cannot identify the fractional order online;

[0229] 3. The adaptive unscented Kalman filter algorithm based on the fractional order model is used to estimate the state of charge of the lithium battery for braking energy recovery in real time on a short time scale to ensure the safe and stable operation of the lithium battery for braking energy recovery;

[0230] 4. Since the health status of the lithium battery used for braking energy recovery does not change significantly, the charge status of the lithium battery used for braking energy recovery is estimated 60 times in a short time scale, and then the health status is estimated once in a long time scale, which reduces the number of calculations while ensuring the accuracy of the estimation;

[0231] 5. The key characteristic parameters SOC and SOH of the lithium battery for braking energy recovery are jointly estimated in long-short time scale cycles through two FAUKF algorithms. The estimated result of SOC is used as the input of SOH estimation to reduce computational redundancy. The SOC and SOH measurement data are updated and corrected with each other, which can be monitored in real time under harsh operating conditions to ensure safe and stable operation of the system.

[0232] 6. Users of mining electric drive dump trucks can pay real-time attention to the operating status of the lithium battery used for brake energy recovery, promptly detect battery failures and replace aging batteries to ensure driving safety and improve economic benefits.

[0233] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for estimating key characteristic parameters of a lithium battery for braking energy recovery of a mining electric drive dump truck, characterized in that: The following steps are involved: (1) Simulate and model the lithium battery for braking energy recovery using the SIMULINK tool in MATLAB software to obtain a fractional-order equivalent circuit model; (2) The fractional-order equivalent circuit model obtained in step (1) is processed using Kirchhoff's current law and Kirchhoff's voltage law to obtain an electrical principle equation and an output equation of the fractional-order equivalent circuit model. (3) The Greenwald-Letnikoff formula is used to process the electrical principle equation and output equation of the fractional-order equivalent circuit model established in step (2) to obtain the state space equation and observation equation defined in fractional order. (4) The state equation and observation equation defined by the fractional order obtained in step (3) are discretized to obtain the discretized state equation and observation equation respectively. (5) using an online parameter identification method to process the discretized state equation and observation equation obtained in step (4) respectively to obtain parameter data in the fractional-order equivalent circuit; (6) Input the parameter data obtained by online parameter identification in step (5) into the fractional-order adaptive unscented Kalman algorithm FAUKF for iterative calculation to obtain the SOC at time t t Estimated value and ohmic internal resistance parameter R of the battery fractional-order equivalent circuit model at time t 0,t , the algorithm flow chart is shown in Figure 4: (7) Determine whether the number of iterations in step (6) is greater than 60. If so, proceed to step (8); otherwise, return to step (6). (8) Obtain multiple SOC estimation values ​​of the lithium battery for braking energy recovery obtained after 60 iterations and the ohmic internal resistance parameter R of the fractional-order equivalent circuit model obtained after the 60th iteration 0，t . (9) The ohmic internal resistance parameter R of the battery fractional-order equivalent circuit model at time t obtained according to step (8) 0，t , and obtain the estimated state of health SOH value of the lithium battery used for braking energy recovery through the FAUKF algorithm. (10) Displaying the multiple SOC estimation values ​​of the lithium battery for braking energy recovery obtained in step (8) and the SOH estimation value obtained in step (9) to the user of the electric drive dump truck for mining.

2. The method for estimating key characteristic parameters of a lithium battery for braking energy recovery of a mining electric drive dump truck according to claim 1, characterized in that: The electrical principle equation of the fractional-order equivalent circuit model is: Among them, U1(t) and U2(t) respectively represent the voltage drops of the two RC loops at time t in the fractional-order equivalent circuit model at the current moment; R1 represents the electrochemical polarization resistance in the fractional-order equivalent circuit model; CPE1 represents the electrochemical polarization capacitance in the fractional-order equivalent circuit model; R2 is the concentration polarization resistance in the fractional-order equivalent circuit model; CPE2 is the concentration polarization capacitance in the fractional-order equivalent circuit model; I t is the working voltage of the fractional-order equivalent circuit model; n1 and n2 are the fractional orders of the electrochemical polarization capacitance and the concentration polarization capacitance in the fractional-order equivalent circuit model, and their values ​​range from 0 to 1; The SOC expression of the charge state of the lithium battery for braking energy recovery at the next moment, SOC t is the SOC and SOC of the equivalent circuit model at the current moment t+1 is the SOC of the equivalent circuit model at the next moment; T s is the system sampling period of the equivalent circuit model; Q n is the battery capacity of the equivalent circuit model. The output equation of the fractional-order equivalent circuit model is: U0(t)=U oc (SOC)-R0I(t)-U1(t)-U2(t) Where U0(t) represents the terminal voltage of the fractional-order equivalent circuit model at the current moment, U oc (SOC) represents the open circuit voltage of the fractional-order equivalent circuit model, R0 represents the ohmic internal resistance of the fractional-order equivalent circuit model, and U1(t) and U2(t) are the two RC loop voltage drops of the fractional-order equivalent circuit model at the current moment.

3. The method for estimating key characteristic parameters of a lithium battery for braking energy recovery of a mining electric drive dump truck according to claim 1 or 2, characterized in that: Specifically, the definition formula of fractional derivative is: Where D n Fractional differential operators representing the state space equations and the observation equations; D n f(t) represents the calculus of f(t); n is the order of the fractional equivalent circuit model, and its value range is [0, 1]; n! is the factorial of the order of the fractional equivalent circuit model; T s is the system sampling period of the fractional-order equivalent circuit model, the value range is [0, 1], and 1s is preferred; L is the memory length of the fractional-order equivalent circuit model, and L=60; i is the number of sampling times; is the Newton binomial coefficient. The state space equation and observation equation defined in fractional order in step (3) are shown in the following formulas: Among them, [U 1，k U 2，k SOC k ] T is the input of the state-space equation, which represents the voltage drop of loop 1, the voltage drop of loop 2 and the state of charge of the fractional-order equivalent circuit model at the current moment; [U 1，k+1 U 2，k+1 SOC k+1 ] T represents the voltage drop of loop 1, the voltage drop of loop 2 and the state of charge of the fractional-order equivalent circuit model at the next moment; U oc (SOC k ) is the function of the open circuit voltage with respect to the state of charge; I k is the working current of the fractional-order equivalent circuit model at the current moment; ω k is the system noise of the fractional-order equivalent circuit model at time k, which is caused by the uncertainty of the fractional-order equivalent circuit model. k is the measurement noise of the fractional-order equivalent circuit model at time k, which is caused by the measurement error of the fractional-order equivalent circuit model.

4. The method for estimating key characteristic parameters of a lithium battery for braking energy recovery of an electric mining dump truck according to any one of claims 1 to 3, characterized in that: The state equation and observation equation after discretization in step (4) are shown in the following formula: Among them, x k is the system input of the fractional-order equivalent circuit model at time k, and x k-1 is the system input quantity of the fractional-order equivalent circuit model at time k-1; I k-1 is the operating current of the fractional-order equivalent circuit model at time k-1; ω k-1 is the system noise of the fractional-order equivalent circuit model at time k-1; k is the system output of the fractional-order equivalent circuit model at time k; C = [-1 -1 0]; v k is the measurement noise of the fractional-order equivalent circuit model at time k.

5. The method for estimating key characteristic parameters of a lithium battery for braking energy recovery of an electric mining dump truck according to claim 4, characterized in that: The online parameter identification method in step (5) adopts a recursive least squares method based on minimum forgetting factor and a genetic algorithm, wherein the data to be identified of the fractional-order equivalent circuit includes the ohmic internal resistance R0, the electrochemical polarization resistance R1, the electrochemical polarization capacitance CPE1, the concentration polarization resistance R2, the concentration polarization capacitance CPE2, and the fractional orders n1 and n2 of the fractional-order equivalent circuit model.

6. The method for estimating key characteristic parameters of a lithium battery for braking energy recovery of an electric mining dump truck according to claim 5, characterized in that: Step (5) specifically includes the following sub-steps: (5-1) Perform Laplace transform on the discrete state equation and observation equation obtained in step (4) to obtain the frequency domain expression of the fractional order model, as shown in formula (7). Among them, Y(k) is the transfer function of the fractional-order equivalent circuit model in the time domain; Y(s) is the transfer function of the fractional-order equivalent circuit model in the frequency domain; s is the independent variable of the transfer function, which is equivalent to the time state in the frequency domain; U oc (s) is the open circuit voltage of the fractional-order equivalent circuit model in the frequency domain; U o (s) is the terminal voltage of the fractional-order equivalent circuit model in the frequency domain; I(s) is the operating current of the fractional-order equivalent circuit model in the frequency domain. (5-2) Use the bilinear transformation method to process the frequency domain expression of the fractional-order model obtained in step (5-1) to obtain the transformation form G(z) of the fractional-order model in the z domain: Among them, a1, a2, a3, a4, and a5 are the parameter identification data of the fractional-order equivalent circuit model, and the mathematical relationship between them and the data to be identified of the fractional-order equivalent circuit model is as follows: By using the mathematical relationship between the parameter identification data of the fractional-order equivalent circuit model and the data to be identified of the fractional-order equivalent circuit model, the data to be identified of the parameters of the fractional-order equivalent circuit model can be obtained. (5-3) Simplify the transformed form of the fractional-order model obtained in step (5-2) in the z-domain to obtain the recursive equation Y(k) of the fractional-order equivalent circuit model. Y(k)=a1Y(k-1)+a2Y(k-2)+a3I(k)+a4I(k-1)+a5i(k-2) (5-4) Initialize the parameter identification column vector θ(k) and covariance P(k) at time k obtained in step (5-3) to obtain the parameter identification column vector θ(0) and covariance P(0) at time k=0 after initialization. (5-5) Determine whether k is equal to 60. If so, go to step (5-10); otherwise, go to step (5-6). (5-6) Use the initialized parameter identification column vector θ(0) and covariance P(0) obtained in step (5-5) to obtain the gain matrix K(k): Wherein K(k) is the estimated gain at time k; P(k-1) is the covariance at time k-1; λ is the forgetting factor, and its value range is [0.90, 1.00]. Considering the application scenarios of the present invention, it is preferably 0.

955. (5-7) The parameter identification column vector θ(k) in the recursive equation at time k is updated according to the gain matrix obtained in step (5-6) to obtain the updated parameter identification column vector θ(k) in the recursive equation at time k. Among them, e(k) is the sampling error at time k. (5-8) Obtain the parameter data to be identified of the fractional-order equivalent circuit model based on the parameter identification column vector θ(k) in the updated k-time recursive equation obtained in step (5-7). (5-9) The covariance P(k) is updated according to the parameter data to be identified of the fractional-order equivalent circuit model obtained in step (5-8) to obtain an updated covariance P(k), and then the process returns to step (5-5): Among them, P(k) is the updated covariance at time k, I is a 5-order scalar matrix, and the value range of the elements on the diagonal is [10e5, 10e6], preferably 10e6. (5-10) Binary encoding the parameter data to be identified of the fractional-order equivalent circuit model obtained in step (5-8) and the fractional orders n1 and n2 to obtain a gene population expressed in a coded form, wherein n1 and n2 are both 1 at the kth moment; (5-11) Obtaining the population fitness based on the gene population expressed in coded form obtained in step (5-10): Where J(i) is the fitness of the population when the population size is i, and i∈[1,N]; N is the population size, preferably N=100; A(i) is the predicted output value U in the fractional-order equivalent circuit model y (y) and the actual measured output value U c (y) is the sum of the squares of the differences, A max is the optimization target of the genetic algorithm. In the present invention, A max =0.005; U y (j) is the predicted output value at the jth iteration; U c (j) is the actual measured output value at the jth iteration, and j∈[1,M]; M is the number of iterations, preferably M=60;. (5-12) According to the population fitness obtained in step (5-11), the population is subjected to hybridization and mutation processing to obtain a processed population: Among them, P z (i) is the hybridization probability of the population when the population size is i; K z is the hybridization factor of the population, its value range is (0.6, 0.9), and is preferably 0.7; P b (i) is the probability of population mutation when the population size is i; K b is the variation factor, and its value range is [0.01, 0.1], preferably 0.05; A ave is the average fitness value in the population. (5-13) Decode the population processed in step (5-12) to obtain a decoded population, which is the parameter data to be identified of the fractional-order equivalent circuit model and the fractional orders n1 and n2. (5-14) Determine whether the number of iterations is greater than 60. If so, proceed to step (5-15). Otherwise, set j=j+1 and return to step (5-11). (5-15) Output parameter data in the fractional-order equivalent circuit, including the ohmic internal resistance R0, electrochemical polarization resistance R1, electrochemical polarization capacitance CPE1, concentration polarization resistance R2, concentration polarization capacitance CPE2, and fractional orders n1 and n2 of the fractional-order equivalent circuit model.

7. The method for estimating key characteristic parameters of a lithium battery for braking energy recovery of an electric mining dump truck according to claim 6, characterized in that: Step (5) includes the following sub-steps: (9-1) The ohmic internal resistance parameter R of the battery fractional-order equivalent circuit model at time t obtained according to step (8) 0，t Obtain the discretized state-space equations and observation equations for estimating the ohmic internal resistance: R 0，t+1 =R 0，t +r t The 0，t =U oc，t (SOC)-R 0，t IU 1，t -U 2，t +t t Among them, R 0,t+1 is the ohmic internal resistance of the fractional-order equivalent circuit model at time t+1; R 0，t is the ohmic internal resistance of the fractional-order equivalent circuit model at time t; r t is the state noise of the state space equation estimating the ohmic internal resistance at time t; t is the observation noise of the observation equation for estimating the ohmic internal resistance at time t. (9-2) Initialize the discretized state space equation and observation equation for estimating the ohmic internal resistance obtained in step (9-1). Specifically, this step is to initialize the ohmic internal resistance of the fractional-order equivalent circuit model to R0 obtained by parameter identification in step (5); and to initialize the initial ohmic internal resistance covariance of the fractional-order equivalent circuit model to 0. (9-3) Perform untraceable transformation (UT) on the discrete state space equation and observation equation initialized in step (9-2) to obtain multiple sigma points. (9-4) Apply the multiple sigma points obtained in step (9-3) to the fractional-order equivalent circuit model obtained in step (1) to obtain the predicted value of the ohmic internal resistance of the fractional-order equivalent circuit model at time t Variance prediction value P of the state space equation x , the mean weight corresponding to each sigma point and the covariance weights for the discrete state space equations (9-5) The mean weight corresponding to the sigma point obtained in step (9-4) Covariance Weights for Discrete State Space Equations Get the observed value of the observation equation at time t+1 and the observed variance predicted value P y . (9-6) The predicted value of the ohmic internal resistance of the fractional-order equivalent circuit model at time t obtained according to step (9-4) Variance prediction value P of discrete state space equation x , the mean weight corresponding to the sigma point Covariance Weights for Discrete State Space Equations And the observed value of the observation equation at time t+1 obtained in step (9-5) and the observed variance predicted value P y , get the input-output cross covariance matrix P xy , the gain K of the Kalman algorithm at time t t ; The covariance matrix P of the Kalman algorithm at time t t . (9-7) The input-output cross covariance matrix P obtained according to step (9-6) xy , the gain K of the Kalman algorithm at time t t ; The covariance matrix P of the Kalman algorithm at time t t , obtain the noise covariance Q of the discrete state space equation at time t+1 t+1 and the observation noise covariance R t+1 . (9-8) The noise covariance Q of the discrete state space equation at time t+1 obtained using step (9-7) t+1 and the observation noise covariance R t+1 Get the predicted value of the ohmic internal resistance of the fractional-order equivalent circuit model at time t+1 (9-9) The predicted value of the ohmic internal resistance of the fractional-order equivalent circuit model at time t+1 obtained in step (9-8) Get the estimated SOH value SOH of the lithium battery for braking energy recovery at time t+1 t+ 1.

8. The method for estimating key characteristic parameters of a lithium battery for braking energy recovery of an electric mining dump truck according to claim 7, characterized in that: The UT transformation process of step (9-3) is: in, is the mth sigma point at time t, representing the mathematical expectation of the state vector at time t; is the initial predicted value of the ohmic internal resistance of the fractional-order equivalent circuit model; is the predicted value of the ohmic internal resistance of the fractional-order equivalent circuit model at time t; m is the dimension of the state variable of the discrete state space equation after initialization in step (9-2); M is the length of the state vector of the discrete state space equation after initialization in step (9-2), and the value is determined by the number of state vectors. In the present invention, M is taken as 3; γ is the scale parameter of the traceless transformation; α is the divergence factor of the traceless transformation, which is used to determine the sampling distribution of the sigma point, and the value range is 0.0001-1, preferably 0.005; β is the adjustment parameter of the traceless transformation, which is used to provide other constraints on the sampling points, and the value is 0. Step (9-4) uses the following calculation formula: Where χ is the weight coefficient, χ=2; Q t is the noise covariance of the discrete state space equation at time t.

9. The method for estimating key characteristic parameters of a lithium battery for braking energy recovery of an electric mining dump truck according to claim 8, characterized in that: Step (9-5) Update observations and the observed variance predicted value P y The process is as follows: in, is the observed value of the observation equation at the updated time t+1; y t is the actual observed value of the observation equation at time t; R t is the observation noise covariance of the observation equation at time t. The update equation of step (9-6) is: in, is the transpose of the input-output cross covariance matrix. The implementation process of step (9-7) is shown as follows: Among them, G k is the adaptive variance of the moving window method; Z is the adaptive moving window. The calculation formula for the predicted SOH value of the lithium battery for braking energy recovery at time t+1 in step (9-9) is as follows: Among them, SOH t+1 is the estimated SOH value of the lithium battery for braking energy recovery at time t+1; R N is the maximum available resistance of the lithium battery for braking energy recovery, is the rated ohmic internal resistance of the lithium battery for braking energy recovery; R e It is the healthy termination resistor of the lithium battery for braking energy recovery. It is usually stipulated that the capacity of the lithium battery for braking energy recovery is less than 80% of the rated capacity and the battery needs to be scrapped compulsorily. e The size of is the ohmic internal resistance of the lithium battery for braking energy recovery when the capacity of the lithium battery for braking energy recovery is reduced to 80% of the rated capacity.

Images