CN116203432B - CSO optimization-based unscented Kalman filtering method for predicting battery state of charge - Google Patents
CSO optimization-based unscented Kalman filtering method for predicting battery state of charge Download PDFInfo
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
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Abstract
The invention discloses a CSO optimization-based unscented Kalman filtering method for predicting the state of charge of a battery, which comprises the following steps: s1: calculating relevant parameters affecting the prediction accuracy when predicting the state of charge (SOC) of the battery by adopting Unscented Kalman Filtering (UKF) algorithm; s2: testing the predicted battery to obtain UUUDS data of the urban road circulation condition; s3: adopting a criss-cross algorithm CSO to optimize an unscented Kalman filter UKF algorithm; s4: the optimized unscented Kalman filter UKF algorithm is applied to the prediction of the state of charge (SOC) of the battery to obtain a more accurate battery working state, so that the service life and the service efficiency of the battery are improved. The invention utilizes the advantages of fast convergence speed and strong global searching capability of the crisscross algorithm, can rapidly and accurately obtain the optimal UKF fitting parameters under different battery working condition data, avoids the parameters from sinking into local optimum, ensures the prediction precision of the battery state of charge to be as high as possible, and realizes the accurate tracking of the battery working state.
Description
Technical Field
The invention relates to the technical field of electric vehicle battery state monitoring, in particular to a method for predicting a battery state of charge based on CSO (continuous variable linear offset) optimization by unscented Kalman filtering.
Background
In order to realize accurate prediction of the State of Charge (SOC) of the battery, scholars at home and abroad propose a prediction scheme based on a Kalman Filter (KF). Since the state of charge of the battery is a nonlinear system, it is necessary to transform the nonlinear system into a linear system using an unscented transformation and then to perform kalman filtering, i.e., using an unscented kalman filtering algorithm (Unscented Kalman Filter, UKF). The unscented Kalman filtering algorithm needs parameter setting optimization to predict, and the traditional optimization algorithm comprises a genetic algorithm, a particle swarm algorithm and the like, however, when solving the complex optimization problem, the algorithm has low convergence speed and is easy to fall into local optimization, so that the further improvement of the prediction precision of the battery state of charge is limited. Therefore, the invention provides a prediction method of unscented Kalman filtering (Crisscross Optimized Unscented Kalman Filter, CUKF) based on criss-cross algorithm optimization.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a CSO (continuous variable analysis) -optimization-based unscented Kalman filtering method for predicting the state of charge of a battery. By introducing the concept of crisscross algorithm (Crisscross Optimization Algorithm, CSO) to the full cross of inter-individual variables, the parameter setting mode of unscented Kalman filter UKF algorithm is optimized, the accurate prediction of the battery state of charge is realized, and the accurate tracking of the battery working state is realized.
In order to achieve the above purpose, the technical scheme provided by the invention is as follows:
the method for predicting the battery state of charge based on CSO optimization by unscented Kalman filtering is characterized by comprising the following steps:
s1: calculating relevant parameters affecting the prediction accuracy when predicting the state of charge (SOC) of the battery by adopting Unscented Kalman Filtering (UKF) algorithm;
s2: testing the predicted battery to obtain UUUDS data of the urban road circulation condition;
s3: adopting a criss-cross algorithm CSO to optimize an unscented Kalman filter UKF algorithm;
s4: the optimized unscented Kalman filter UKF algorithm is applied to the prediction of the battery state of charge (SOC) to obtain a more accurate battery working state, so that the service life and the service efficiency of the battery are improved;
in the step S1, the specific steps of calculating the relevant parameters affecting the prediction accuracy are as follows:
s1-1: in the unscented transformation UT parameter initialization stage of unscented Kalman filtering UKF algorithm, symmetry parameter lambda of Sigma point set is needed to be performed, and approximate mean Sigma point weight matrix W is approximated m And approximate covariance Sigma Point weight matrix W c The calculation of (1) is as follows:
(1) Wherein i is the dimension of unscented transformation, alpha is the sampling parameter of Sigma points, beta is one characteristic parameter of Gaussian distribution, L is the dimension of state vectors, and k is the characteristic parameter for controlling the distance from Sigma points to the mean value;
s1-2: in the prediction stage of unscented Kalman filtering UKF algorithm, the filter adopts the posterior estimation of the last state to calculate the prior estimation of the current state, and the prior estimation covariance needs to be calculated in the process
(2) In the method, in the process of the invention,sigma point sampling result for a priori probability distribution of dimension i at time t,>the prior estimated mean value at the moment T is represented by an inverse matrix symbol, and the covariance matrix of the process noise is represented by Q;
s1-3: in unscented KalmanIn the updating stage of the UKF algorithm, the filter optimizes the predicted value obtained in the predicting stage by using the observed value of the current state to obtain a more accurate new estimated value, and in the process, the observed value z at the moment t needs to be calculated t Covariance of (2)
(3) In the method, in the process of the invention,observation vector, mu, of dimension i at time t zt For the observation of the quantity z at time t t R is the covariance matrix of the measured noise;
s1-4: and (3) carrying out optimization parameter analysis of unscented Kalman filtering UKF algorithm:
according to unscented kalman filter UKF algorithm, there are the following parameters that need to be determined before the algorithm starts to run:
the covariance matrix Q of the process noise is used for estimating the error and uncertainty of the system model, and is usually a diagonal matrix, and in the process of predicting the battery state of charge SOC by using the unscented kalman filter UKF algorithm, the covariance matrix Q of the process noise is:
in equation (4), the parameters to be set are 3, the covariance Q of the process noise 1 ，Q 2 ，Q 3 ；
The covariance matrix R of the measurement noise is used for estimating the error and uncertainty of a measurement model, and is usually a diagonal matrix, and in the process of predicting the battery charge state SOC by using the unscented Kalman filter UKF algorithm, the covariance matrix R of the measurement noise is as follows:
R＝[R 1 ] (5)
then in equation (5), the parameters to be set are 1, which is the covariance R of the measurement noise 1 ；
The sampling parameter alpha is used for controlling the distribution density of Sigma points, and proper values are selected to ensure the full coverage of UT points, and the value range of the sampling parameter alpha is between 0 and 1;
step S2 tests the predicted battery to obtain UUDS data of the urban road circulation working condition, and the specific steps are as follows:
s2-1: determining rated capacity and rated voltage of a battery to be measured, and determining parameters of a testing device, including load resistance and load reactance;
s2-2: charging the battery to be measured to 100% of electric quantity, and standing for 1-2 hours to stabilize the battery state;
s2-3: removing the battery from the charger and allowing it to stand at room temperature for at least 30 minutes so that the internal temperature of the battery reaches room temperature;
s2-4: placing the battery on a UUDS testing device under the urban road circulation working condition, and ensuring that two ends of the battery are correctly contacted with electrodes of the testing device;
s2-5: setting testing conditions including a current range, a voltage range, a temperature and a time according to the UUUDS requirement of the urban road circulation working condition, and ensuring that the testing conditions accord with the service condition of the battery;
s2-6: starting a test, and recording test data including current, voltage and time until the electric quantity of the battery is reduced to a certain degree or the test time reaches a preset value;
s2-7: analyzing the test data, evaluating the validity of the data, and taking the current data and the voltage data in the test data for calculating in the step S3;
the step S3 adopts a criss-cross algorithm CSO to optimize an unscented Kalman filter UKF algorithm, and comprises the following specific steps:
s3-1: initializing upper and lower limits of five parameters in step S1, including covariance Q of process noise 1 ，Q 2 ，Q 3 Covariance R of measurement noise 1 And sampling parameter alpha, randomly generating N data pairs, called population, in the interval;
s3-2: and (3) performing first population fitness calculation by adopting the N data pairs generated in the step (S3-1), wherein the larger the population fitness is, the smaller the error of the group of parameters in the fitting process is represented, and the specific calculation steps of the population fitness are as follows:
s3-2-1: obtaining an observation matrix Z according to the test conditions determined in the step S2-1;
s3-2-2: initializing unscented transformation UT parameters according to the input parameter data pair to be set;
s3-2-3: adding certain normal noise to the current data of the UUDS of the urban road circulation working condition obtained in the step S2 to simulate the actual working environment;
s3-2-4: generating Sigma points according to the current data added with noise obtained in the step S3-2-3;
s3-2-5: carrying out state prediction of Kalman filtering KF according to the Sigma points obtained in the step S3-2-4;
s3-2-6: performing Sigma point reconstruction according to the state prediction result obtained in the step S3-2-5;
s3-2-7: according to the voltage data of the UUZDS of the urban road circulation working condition obtained in the step S2 and the point after the state prediction reconstruction obtained in the step S3-2-6, carrying out the observation updating of the Kalman filtering KF;
s3-2-8: repeating steps S3-2-4 to S3-2-7 until a predicted value of the battery state of charge is obtained for the whole test time period;
s3-2-9: calculating the state of charge of the battery by adopting an ampere-hour integration method, and taking the state of charge as a true reference value of the state of charge of the battery;
s3-2-10: calculating an error mean value by taking the prediction result of the S3-2-8 and the result of the ampere-hour integration method of the S3-2-9 as a standard;
s3-2-11: repeating the steps S3-2-4 to S3-2-10 for 5 times to eliminate accidental prediction too good or too bad caused by current noise, taking the average value of error means of 5 times, taking the reciprocal value as the population fitness of the population, and outputting the population fitness to the step S3-3 for use;
s3-3: the longitudinal crossover operation of the longitudinal crossover method CSO is carried out, the longitudinal crossover process is an arithmetic operation of crossing the preference parameters with different dimensions, and the expression is as follows:
M vc (m,d 1 )＝r×X(m,d 1 )+(1-r)×X(m,d 2 )+c(X(m,d 1 )-X(m,d 2 )) (6)
(6) Wherein r and c are random numbers ranging from 0 to 1, X (m, d 1 ) And X (m, d) 2 ) For parent preference parameters of different dimensions, M vc (m,d 1 ) The offspring trending parameters generated by longitudinally crossing parent different dimensions are represented by parameters m=1, 2, …, N and N being population sizes; d, d 1 ,d 2 =1, 2,..c, C is the number of dimensions of the population;
s3-4: calculating the population fitness of the offspring generated in the step S3-3, and comparing the population fitness with the population fitness of the father, and reserving the population with larger population fitness to enable the whole population to evolve towards a better direction;
s3-5: the transverse crossover operation of the crisscross CSO is carried out, the transverse crossover process is an arithmetic operation of crossing all the optimal parameters of different solutions, and the expression is as follows:
(7) Wherein, the parameter n=1, 2, …, N is population size;
s3-6: calculating the population fitness of the offspring generated in the step S3-5, and comparing the population fitness with the population fitness of the father, and reserving the population with larger population fitness to enable the whole population to evolve towards a better direction;
s3-7: the population is regenerated by the following steps:
s3-7-1: if the unscented Kalman filtering UKF algorithm prediction process is terminated, marking the population fitness of the population as 0;
s3-7-2: regenerating the population with the statistical population fitness of 0 before each round of iteration is finished;
s3-7-3: obtaining each dimension i, and respectively obtaining the maximum value max of the population with the population fitness not being 0 in the dimension i And minimum ofValue min i ；
S3-7-4: when generating new population, the parameters of each dimension i are respectively in (1+k) s )max i To (1-k) s )min i Is regenerated, k s For generating the diffusion coefficient, the population is not easy to converge to cause the population fitness to fall into local optimum;
s3-8: repeating the steps S3-3 to S3-7 until the iteration number reaches the maximum iteration number set by the system;
s3-9: acquiring a population with optimal population fitness at the moment as an optimal population, and taking the optimal population as a model application parameter of the step S4;
and step S4, applying the optimized unscented Kalman filtering UKF algorithm to the predicted battery state of charge SOC to obtain a more accurate battery working state, thereby improving the service life and the service efficiency of the battery.
From the above technical solution, the embodiment of the present invention has the following beneficial effects:
1. optimizing battery performance: accurate battery state of charge, SOC, predictions may help optimize battery use and maintenance, thereby extending battery life and performance;
2. improving the battery efficiency: the charging and discharging processes can be better managed by accurately predicting the state of charge (SOC) of the battery so as to improve the energy utilization rate and efficiency of the battery;
3. and (5) increasing the driving mileage: for electric vehicles, accurate battery state of charge, SOC, predictions may help determine remaining range, thereby increasing driving safety and reliability;
4. avoiding overcharging or underdischarging of the battery: accurately predicting the battery state of charge, SOC, may also help avoid over-charge or under-discharge conditions of the battery that may compromise the performance of the battery or lead to safety issues.
Drawings
FIG. 1 is a flow chart of a method of predicting battery state of charge based on CSO optimization with unscented Kalman filtering in accordance with the present invention;
FIG. 2 shows errors of three methods of artificial experience tuning, particle swarm optimization PSO tuning and crisscross algorithm CSO tuning at each predicted moment;
FIG. 3 is a graph of the predicted result of the state of charge SOC of the battery by substituting three methods, namely, artificial experience tuning, particle swarm optimization PSO tuning and criss-cross algorithm CSO tuning, into unscented Kalman filtering;
fig. 4 shows convergence rates of the particle swarm algorithm PSO tuning and the crisscross algorithm CSO tuning.
Detailed Description
The invention is further illustrated by the following examples:
referring to fig. 1, the method for predicting the battery state of charge based on CSO optimization unscented kalman filter according to the present embodiment includes the following steps:
s1: calculating relevant parameters affecting the prediction accuracy when predicting the state of charge (SOC) of the battery by adopting Unscented Kalman Filtering (UKF) algorithm;
s2: testing the predicted battery to obtain UUUDS data of the urban road circulation condition;
s3: adopting a criss-cross algorithm CSO to optimize an unscented Kalman filter UKF algorithm;
s4: the optimized unscented Kalman filter UKF algorithm is applied to the prediction of the battery state of charge (SOC) to obtain a more accurate battery working state, so that the service life and the service efficiency of the battery are improved;
in the step S1, the specific steps of calculating the relevant parameters affecting the prediction accuracy are as follows:
s1-1: in the unscented transformation UT parameter initialization stage of unscented Kalman filtering UKF algorithm, symmetry parameter lambda of Sigma point set is needed to be performed, and approximate mean Sigma point weight matrix W is approximated m And approximate covariance Sigma Point weight matrix W c The calculation of (1) is as follows:
(1) Wherein i is the dimension of unscented transformation, alpha is the sampling parameter of Sigma points, beta is one characteristic parameter of Gaussian distribution, L is the dimension of state vectors, and k is the characteristic parameter for controlling the distance from Sigma points to the mean value;
s1-2: in the prediction stage of unscented Kalman filtering UKF algorithm, the filter adopts the posterior estimation of the last state to calculate the prior estimation of the current state, and the prior estimation covariance needs to be calculated in the process
(2) In the method, in the process of the invention,sigma point sampling result for a priori probability distribution of dimension i at time t,>the prior estimated mean value at the moment T is represented by an inverse matrix symbol, and the covariance matrix of the process noise is represented by Q;
s1-3: in the updating stage of unscented Kalman filtering UKF algorithm, the filter optimizes the predicted value obtained in the predicting stage by using the observed value of the current state to obtain a more accurate new estimated value, and in the process, the observed value z at the moment t needs to be calculated t Covariance P of (2) zt ：
(3) In the method, in the process of the invention,for the observation vector of dimension i at time t, +.>For the observation of the quantity z at time t t R is the covariance matrix of the measured noise;
s1-4: and (3) carrying out optimization parameter analysis of unscented Kalman filtering UKF algorithm:
according to unscented kalman filter UKF algorithm, there are the following parameters that need to be determined before the algorithm starts to run:
the covariance matrix Q of the process noise is used for estimating the error and uncertainty of the system model, and is usually a diagonal matrix, and in the process of predicting the battery state of charge SOC by using the unscented kalman filter UKF algorithm, the covariance matrix Q of the process noise is:
in equation (4), the parameters to be set are 3, the covariance Q of the process noise 1 ，Q 2 ，Q 3 ；
The covariance matrix R of the measurement noise is used for estimating the error and uncertainty of a measurement model, and is usually a diagonal matrix, and in the process of predicting the battery charge state SOC by using the unscented Kalman filter UKF algorithm, the covariance matrix R of the measurement noise is as follows:
R＝[R 1 ] (5)
then in equation (5), the parameters to be set are 1, which is the covariance R of the measurement noise 1 ；
The sampling parameter alpha is used for controlling the distribution density of Sigma points, and proper values are selected to ensure the full coverage of UT points, and the value range of the sampling parameter alpha is between 0 and 1;
step S2 tests the predicted battery to obtain UUDS data of the urban road circulation working condition, and the specific steps are as follows:
s2-1: determining rated capacity and rated voltage of a battery to be measured, and determining parameters of a testing device, including load resistance and load reactance;
s2-2: charging the battery to be measured to 100% of electric quantity, and standing for 1-2 hours to stabilize the battery state;
s2-3: removing the battery from the charger and allowing it to stand at room temperature for at least 30 minutes so that the internal temperature of the battery reaches room temperature;
s2-4: placing the battery on a UUDS testing device under the urban road circulation working condition, and ensuring that two ends of the battery are correctly contacted with electrodes of the testing device;
s2-5: setting testing conditions including a current range, a voltage range, a temperature and a time according to the UUUDS requirement of the urban road circulation working condition, and ensuring that the testing conditions accord with the service condition of the battery;
s2-6: starting a test, and recording test data including current, voltage and time until the electric quantity of the battery is reduced to a certain degree or the test time reaches a preset value;
s2-7: analyzing the test data, evaluating the validity of the data, and taking the current data and the voltage data in the test data for calculating in the step S3;
the step S3 adopts a criss-cross algorithm CSO to optimize an unscented Kalman filter UKF algorithm, and comprises the following specific steps:
s3-1: initializing upper and lower limits of five parameters in step S1, including covariance Q of process noise 1 ，Q 2 ，Q 3 Covariance R of measurement noise 1 And sampling parameter alpha, randomly generating N data pairs, called population, in the interval;
s3-2: and (3) performing first population fitness calculation by adopting the N data pairs generated in the step (S3-1), wherein the larger the population fitness is, the smaller the error of the group of parameters in the fitting process is represented, and the specific calculation steps of the population fitness are as follows:
s3-2-1: obtaining an observation matrix Z according to the test conditions determined in the step S2-1;
s3-2-2: initializing unscented transformation UT parameters according to the input parameter data pair to be set;
s3-2-3: adding certain normal noise to the current data of the UUDS of the urban road circulation working condition obtained in the step S2 to simulate the actual working environment;
s3-2-4: generating Sigma points according to the current data added with noise obtained in the step S3-2-3;
s3-2-5: carrying out state prediction of Kalman filtering KF according to the Sigma points obtained in the step S3-2-4;
s3-2-6: performing Sigma point reconstruction according to the state prediction result obtained in the step S3-2-5;
s3-2-7: according to the voltage data of the UUZDS of the urban road circulation working condition obtained in the step S2 and the point after the state prediction reconstruction obtained in the step S3-2-6, carrying out the observation updating of the Kalman filtering KF;
s3-2-8: repeating steps S3-2-4 to S3-2-7 until a predicted value of the battery state of charge is obtained for the whole test time period;
s3-2-9: calculating the state of charge of the battery by adopting an ampere-hour integration method, and taking the state of charge as a true reference value of the state of charge of the battery;
s3-2-10: calculating an error mean value by taking the prediction result of the S3-2-8 and the result of the ampere-hour integration method of the S3-2-9 as a standard;
s3-2-11: repeating the steps S3-2-4 to S3-2-10 for 5 times to eliminate accidental prediction too good or too bad caused by current noise, taking the average value of error means of 5 times, taking the reciprocal value as the population fitness of the population, and outputting the population fitness to the step S3-3 for use;
s3-3: the longitudinal crossover operation of the longitudinal crossover method CSO is carried out, the longitudinal crossover process is an arithmetic operation of crossing the preference parameters with different dimensions, and the expression is as follows:
M vc (m,d 1 )＝r×X(m,d 1 )+(1-r)×X(m,d 2 )+c(X(m,d 1 )-X(m,d 2 )) (6)
(6) Wherein r and c are random numbers ranging from 0 to 1, X (m, d 1 ) And X (m, d) 2 ) For parent preference parameters of different dimensions, M vc (m,d 1 ) The offspring trending parameters generated by longitudinally crossing parent different dimensions are represented by parameters m=1, 2, …, N and N being population sizes; d, d 1 ,d 2 =1, 2,..c, C is the number of dimensions of the population;
s3-4: calculating the population fitness of the offspring generated in the step S3-3, and comparing the population fitness with the population fitness of the father, and reserving the population with larger population fitness to enable the whole population to evolve towards a better direction;
s3-5: the transverse crossover operation of the crisscross CSO is carried out, the transverse crossover process is an arithmetic operation of crossing all the optimal parameters of different solutions, and the expression is as follows:
(7) Wherein, the parameter n=1, 2, …, N is population size;
s3-6: calculating the population fitness of the offspring generated in the step S3-5, and comparing the population fitness with the population fitness of the father, and reserving the population with larger population fitness to enable the whole population to evolve towards a better direction;
s3-7: the population is regenerated by the following steps:
s3-7-1: if the unscented Kalman filtering UKF algorithm prediction process is terminated, marking the population fitness of the population as 0;
s3-7-2: regenerating the population with the statistical population fitness of 0 before each round of iteration is finished;
s3-7-3: obtaining each dimension i, and respectively obtaining the maximum value max of the population with the population fitness not being 0 in the dimension i And minimum value min i ；
S3-7-4: when generating new population, the parameters of each dimension i are respectively in (1+k) s )max i To (1-k) s )min i Is regenerated, k s For generating the diffusion coefficient, the population is not easy to converge to cause the population fitness to fall into local optimum;
s3-8: repeating the steps S3-3 to S3-7 until the iteration number reaches the maximum iteration number set by the system;
s3-9: acquiring a population with optimal population fitness at the moment as an optimal population, and taking the optimal population as a model application parameter of the step S4;
and step S4, applying the optimized unscented Kalman filtering UKF algorithm to the predicted battery state of charge SOC to obtain a more accurate battery working state, thereby improving the service life and the service efficiency of the battery.
In order to verify the effectiveness of a CSO optimization-based unscented Kalman filtering method for predicting the battery state of charge, unscented Kalman filtering is used in a Matlab environment to predict the battery state of charge, and parameters of the unscented Kalman filtering are set by using a manual experience setting, a particle swarm algorithm PSO setting and a criss-cross algorithm CSO setting respectively. And simultaneously, calculating the state of charge of the battery by using an ampere-hour integration method Ah, and taking the state of charge as a true reference value of the state of charge of the battery. The PSO and the CSO are iterated for 200 times, the evaluation method is to repeatedly predict 5 times by using the same parameter set, average the error mean value of the 5 prediction results and the true value, and the smaller the error mean value is, the better the error mean value is. The optimal parameter set obtained by the three methods of manual experience setting, particle swarm optimization PSO setting and crisscross algorithm CSO setting and the corresponding error mean value are shown in table 1. The errors of the three methods of manual experience tuning, particle swarm optimization PSO tuning and criss-cross algorithm CSO tuning at each predicted moment are shown in FIG. 2. The curves of the battery state of charge SOC prediction results obtained by substituting the three methods of manual experience setting, particle swarm optimization PSO setting and criss-cross algorithm CSO setting into unscented Kalman filtering are shown in FIG. 3. The convergence rates of the particle swarm algorithm PSO tuning and the crisscross algorithm CSO tuning are shown in fig. 4.
Table 1 optimal parameter set for three tuning methods and corresponding error mean value
As can be seen by combining table 1 and fig. 2 to 4, compared with the parameter sets set by artificial experience setting and particle swarm optimization PSO setting, when the parameter set by the crisscross algorithm CSO setting is used for predicting the battery state of charge by unscented kalman filtering, the prediction accuracy is respectively improved by 26520 times and 7.63 times, and the convergence rate of the CSO algorithm is improved by 91.7% compared with that of the PSO algorithm, which indicates that the global convergence rate of the CSO algorithm is faster, and unscented kalman filtering parameters can be effectively separated from local optimum, and the global optimizing capability is stronger.
The above embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, so variations in shape and principles of the present invention should be covered.
Claims (1)
1. The method for predicting the battery state of charge based on CSO optimization by unscented Kalman filtering is characterized by comprising the following steps:
step S1: calculating relevant parameters affecting the prediction accuracy when predicting the state of charge (SOC) of the battery by adopting Unscented Kalman Filtering (UKF) algorithm;
step S2: testing the predicted battery to obtain UUUDS data of the urban road circulation condition;
step S3: adopting a criss-cross algorithm CSO to optimize an unscented Kalman filter UKF algorithm;
step S4: the optimized unscented Kalman filter UKF algorithm is applied to the prediction of the battery state of charge (SOC) to obtain a more accurate battery working state, so that the service life and the service efficiency of the battery are improved;
in the step S1, the specific steps of calculating the relevant parameters affecting the prediction accuracy are as follows:
s1-1: in the unscented transformation UT parameter initialization stage of unscented Kalman filtering UKF algorithm, symmetry parameter lambda of Sigma point set is needed to be performed, and approximate mean Sigma point weight matrix W is approximated m And approximate covariance Sigma Point weight matrix W c The calculation of (1) is as follows:
(1) Wherein i is the dimension of unscented transformation, alpha is the sampling parameter of Sigma points, beta is one characteristic parameter of Gaussian distribution, L is the dimension of state vectors, and k is the characteristic parameter for controlling the distance from Sigma points to the mean value;
s1-2: in the prediction stage of unscented Kalman filtering UKF algorithm, the filter adopts the posterior estimation of the last state to calculate the prior estimation of the current state, and the prior estimation covariance needs to be calculated in the process
(2) In the method, in the process of the invention,sigma point sampling result for a priori probability distribution of dimension i at time t,>the prior estimated mean value at the moment T is represented by an inverse matrix symbol, and the covariance matrix of the process noise is represented by Q;
s1-3: in the updating stage of unscented Kalman filtering UKF algorithm, the filter optimizes the predicted value obtained in the predicting stage by using the observed value of the current state to obtain a more accurate new estimated value, and in the process, the observed value z at the moment t needs to be calculated t Covariance of (2)
(3) In the method, in the process of the invention,for the observation vector of dimension i at time t, +.>For the observation of the quantity z at time t t R is the covariance matrix of the measured noise;
s1-4: and (3) carrying out optimization parameter analysis of unscented Kalman filtering UKF algorithm:
according to unscented kalman filter UKF algorithm, there are the following parameters that need to be determined before the algorithm starts to run:
the covariance matrix Q of the process noise is used for estimating the error and uncertainty of the system model, and is usually a diagonal matrix, and in the process of predicting the battery state of charge SOC by using the unscented kalman filter UKF algorithm, the covariance matrix Q of the process noise is:
in equation (4), the parameters to be set are 3, the covariance Q of the process noise 1 ，Q 2 ，Q 3 ；
The covariance matrix R of the measurement noise is used for estimating the error and uncertainty of a measurement model, and is usually a diagonal matrix, and in the process of predicting the battery charge state SOC by using the unscented Kalman filter UKF algorithm, the covariance matrix R of the measurement noise is as follows:
R＝[R 1 ] (5)
then in equation (5), the parameters to be set are 1, which is the covariance R of the measurement noise 1 ；
The sampling parameter alpha is used for controlling the distribution density of Sigma points, and proper values are selected to ensure the full coverage of UT points, and the value range of the sampling parameter alpha is between 0 and 1;
step S2 tests the predicted battery to obtain UUDS data of the urban road circulation working condition, and the specific steps are as follows:
s2-1: determining rated capacity and rated voltage of a battery to be measured, and determining parameters of a testing device, including load resistance and load reactance;
s2-2: charging the battery to be measured to 100% of electric quantity, and standing for 1-2 hours to stabilize the battery state;
s2-3: removing the battery from the charger and allowing it to stand at room temperature for at least 30 minutes so that the internal temperature of the battery reaches room temperature;
s2-4: placing the battery on a UUDS testing device under the urban road circulation working condition, and ensuring that two ends of the battery are correctly contacted with electrodes of the testing device;
s2-5: setting testing conditions including a current range, a voltage range, a temperature and a time according to the UUUDS requirement of the urban road circulation working condition, and ensuring that the testing conditions accord with the service condition of the battery;
s2-6: starting a test, and recording test data including current, voltage and time until the electric quantity of the battery is reduced to a certain degree or the test time reaches a preset value;
s2-7: analyzing the test data, evaluating the validity of the data, and taking the current data and the voltage data in the test data for calculating in the step S3;
the step S3 adopts a criss-cross algorithm CSO to optimize an unscented Kalman filter UKF algorithm, and comprises the following specific steps:
s3-1: initializing upper and lower limits of five parameters in step S1, including covariance Q of process noise 1 ，Q 2 ，Q 3 Covariance R of measurement noise 1 And sampling parameter alpha, randomly generating N data pairs, called population, in the interval;
s3-2: and (3) carrying out first population fitness calculation by adopting the N data pairs generated in the step (S3-1), wherein the larger the population fitness is, the smaller the error of the group of parameters in the fitting process is represented, and the specific calculation steps of the population fitness are as follows:
s3-2-1: obtaining an observation matrix Z according to the test conditions determined in the step S2-1;
s3-2-2: initializing unscented transformation UT parameters according to the input parameter data pair to be set;
s3-2-3: adding certain normal noise to the current data of the UUDS of the urban road circulation working condition obtained in the step S2 to simulate the actual working environment;
s3-2-4: generating Sigma points according to the current data added with noise obtained in the step S3-2-3;
s3-2-5: carrying out state prediction of Kalman filtering KF according to the Sigma points obtained in the step S3-2-4;
s3-2-6: performing Sigma point reconstruction according to the state prediction result obtained in the step S3-2-5;
s3-2-7: according to the voltage data of the UUZDS of the urban road circulation working condition obtained in the step S2 and the point after the state prediction reconstruction obtained in the step S3-2-6, carrying out the observation updating of the Kalman filtering KF;
s3-2-8: repeating steps S3-2-4 to S3-2-7 until a predicted value of the battery state of charge is obtained for the whole test time period;
s3-2-9: calculating the state of charge of the battery by adopting an ampere-hour integration method, and taking the state of charge as a true reference value of the state of charge of the battery;
s3-2-10: calculating an error mean value by taking the prediction result of the S3-2-8 and the result of the ampere-hour integration method of the S3-2-9 as a standard;
s3-2-11: repeating the steps S3-2-4 to S3-2-10 for 5 times to eliminate accidental prediction too good or too bad caused by current noise, taking the average value of error means of 5 times, taking the reciprocal value as the population fitness of the population, and outputting the population fitness to the step S3-3 for use;
s3-3: the longitudinal crossover operation of the longitudinal crossover method CSO is carried out, the longitudinal crossover process is an arithmetic operation of crossing the preference parameters with different dimensions, and the expression is as follows:
M vc (m,d 1 )＝r×X(m,d 1 )+(1-r)×X(m,d 2 )+c(X(m,d 1 )-X(m,d 2 )) (6)
(6) Wherein r and c are random numbers ranging from 0 to 1, X (m, d 1 ) And X (m, d) 2 ) For parent preference parameters of different dimensions, M vc (m,d 1 ) The offspring trending parameters generated by longitudinally crossing parent different dimensions are represented by parameters m=1, 2, …, N and N being population sizes; d, d 1 ,d 2 =1, 2,..c, C is the number of dimensions of the population;
s3-4: calculating the population fitness of the offspring generated in the step S3-3, and comparing the population fitness with the population fitness of the father, and reserving the population with larger population fitness to enable the whole population to evolve towards a better direction;
s3-5: the transverse crossover operation of the crisscross CSO is carried out, the transverse crossover process is an arithmetic operation of crossing all the optimal parameters of different solutions, and the expression is as follows:
(7) Wherein, the parameter n=1, 2, …, N is population size;
s3-6: calculating the population fitness of the offspring generated in the step S3-5, and comparing the population fitness with the population fitness of the father, and reserving the population with larger population fitness to enable the whole population to evolve towards a better direction;
s3-7: the population is regenerated by the following steps:
s3-7-1: if the unscented Kalman filtering UKF algorithm prediction process is terminated, marking the population fitness of the population as 0;
s3-7-2: regenerating the population with the statistical population fitness of 0 before each round of iteration is finished;
s3-7-3: obtaining each dimension i, and respectively obtaining the maximum value max of the population with the population fitness not being 0 in the dimension i And minimum value min i ；
S3-7-4: when generating new population, the parameters of each dimension i are respectively in (1+k) s )max i To (1-k) s )min i Is regenerated, k s For generating the diffusion coefficient, the population is not easy to converge to cause the population fitness to fall into local optimum;
s3-8: repeating the steps S3-3 to S3-7 until the iteration number reaches the maximum iteration number set by the system;
s3-9: acquiring a population with optimal population fitness at the moment as an optimal population, and taking the optimal population as a model application parameter of the step S4;
and step S4, applying the optimized unscented Kalman filtering UKF algorithm to the predicted battery state of charge SOC to obtain a more accurate battery working state, thereby improving the service life and the service efficiency of the battery.
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