A method for detecting the working performance of power batteries for new energy vehicles

Abstract

The present invention relates to the technical field of new energy battery, and particularly to a method for detecting the working performance of a power battery for a new energy vehicle. The method obtains a voltage residual sequence at the current sampling moment according to the terminal voltage acquisition values and terminal voltage prediction values at a preset number of historical sampling moments before the current sampling moment; obtains a first optimization factor and a second optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm according to the voltage residual sequence; obtains an optimized process noise covariance matrix by using the first optimization factor and the second optimization factor; and obtains an SOC estimation value of the power battery at the current sampling moment according to the terminal voltage acquisition value at the current sampling moment and the optimized process noise covariance matrix, so as to detect the working performance of the power battery. By online adjusting the process noise covariance matrix, the estimation accuracy of the SOC value of the power battery is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy batteries, and particularly to a method for detecting the working performance of a power storage battery for new energy vehicles. Background Art

[0002] With the increasingly severe global energy crisis and environmental pollution problems, new energy vehicles have become an inevitable trend in the development of the automotive industry due to their energy-saving and environmental protection characteristics. Lithium iron phosphate batteries have been widely used in the field of new energy vehicles due to their advantages such as high safety, long cycle life, and low cost. SOC (State of Charge, which refers to the ratio of the remaining capacity of the battery to its capacity when it is fully charged) is an important parameter reflecting the remaining battery power and is crucial for the safe and efficient operation of the battery. Accurately estimating the SOC value of the battery helps to optimize the battery management strategy, prevent overcharging and over-discharging, extend the battery life, and improve the estimation accuracy of the driving range of electric vehicles.

[0003] Currently, the methods for estimating the SOC value of the battery mainly include the ampere-hour integration method, the open-circuit voltage method, the neural network method, the Kalman filter method, etc. Among them, the ampere-hour integration method is simple and easy to implement, but there are problems of cumulative error and initial value dependence; the open-circuit voltage method requires long-term static state and is not suitable for online estimation; the neural network method requires a large amount of data for training and has limited model generalization ability; the Kalman filter (KF) and its extended algorithms (extended Kalman filter EKF, unscented Kalman filter UKF) can effectively process nonlinear systems and have a certain anti-noise ability, so they have been widely used in the field of battery SOC estimation.

[0004] The unscented Kalman filter (UKF) algorithm processes nonlinear systems through unscented transformation (UT), avoiding the process of linearizing the nonlinear function (calculating the Jacobian matrix) in the EKF algorithm, so it has higher estimation accuracy and calculation efficiency. The UKF algorithm has become one of the research hotspots in the field of battery SOC estimation. Although the UKF algorithm shows advantages in battery SOC estimation, its performance highly depends on the settings of the process noise covariance matrix Q and the measurement noise covariance matrix R.

[0005] In practical applications, parameters of the battery model (such as internal resistance and capacitance) change with factors such as battery aging, temperature variation, charge and discharge states, etc., showing obvious time-varying characteristics. In addition, the statistical characteristics of measurement noise are not constant but exhibit non-stationarity over time. However, traditional UKF algorithms usually set Q and R as fixed values and cannot adapt to the time-varying nature of battery model parameters and the non-stationarity of measurement noise. When the battery model parameters or noise characteristics do not match the preset values, the fixed Q value will cause the state estimation process of the UKF algorithm to fail to accurately reflect the prediction uncertainty of the battery model, thereby causing the SOC estimation result of the battery to deviate from the true value and generating non-negligible estimation biases.

[0006] Therefore, how to perform online adaptive adjustment on the process noise covariance matrix Q to improve the accuracy of SOC estimation of lithium iron phosphate batteries based on the UKF algorithm has become an urgent problem to be solved. Summary of the Invention

[0007] In view of this, embodiments of the present invention provide a method for detecting the working performance of power batteries for new energy vehicles to solve the problem of how to perform online adaptive adjustment on the process noise covariance matrix Q to improve the accuracy of SOC estimation of lithium iron phosphate batteries based on the UKF algorithm.

[0008] Embodiments of the present invention provide a method for detecting the working performance of power batteries for new energy vehicles, and the method includes the following steps:

[0009] Obtain the terminal voltage acquisition value of the power battery at the current sampling moment, and obtain the voltage residual sequence at the current sampling moment according to the terminal voltage acquisition values and terminal voltage prediction values at a preset number of historical sampling moments before the current sampling moment;

[0010] Obtain a first optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm according to the mean value and cumulative change of the voltage residual sequence;

[0011] Obtain a second optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm according to the residual fluctuation change of the voltage residual sequence;

[0012] Update and optimize the process noise covariance matrix at the previous sampling moment at the current sampling moment by using the first optimization factor and the second optimization factor to obtain the optimized process noise covariance matrix at the current sampling moment;

[0013] According to the terminal voltage acquisition value at the current sampling moment and the optimized process noise covariance matrix, use the unscented Kalman filter algorithm to obtain the SOC estimation value of the power battery at the current sampling moment for detecting the working performance of the power battery.

[0014] Preferably, obtaining the voltage residual sequence at the current sampling moment according to the terminal voltage acquisition values and terminal voltage prediction values at a preset number of historical sampling moments before the current sampling moment includes:

[0015] For any historical sampling moment, obtain the previous historical sampling moment of the any historical sampling moment, calculate the difference between the terminal voltage acquisition value at the any historical sampling moment and the terminal voltage prediction value at the previous historical sampling moment, and use the difference as the voltage residual at the any historical sampling moment; obtain the voltage residuals at each historical sampling moment to form the voltage residual sequence at the current sampling moment.

[0016] Preferably, obtaining the first optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm according to the mean value and change cumulative amount of the voltage residual sequence includes:

[0017] According to the mean value of the voltage residual sequence and the difference between adjacent residuals, obtain the voltage prediction deviation index; obtain the nominal voltage of the power battery, normalize the voltage prediction deviation index using the nominal voltage to obtain a normalized value, perform a non-linear mapping on the normalized value using the hyperbolic tangent function to obtain a corresponding first mapping value, and obtain the first optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm according to the sum of the constant 1 and the first mapping value.

[0018] Preferably, obtaining the voltage prediction deviation index according to the mean value of the voltage residual sequence and the difference between adjacent residuals includes:

[0019] Calculate the mean value of the voltage residual sequence, denoted as the voltage residual mean value, calculate the absolute value of the difference between every two adjacent voltage residuals in the voltage residual sequence to obtain the cumulative value of the absolute values of the differences, denoted as the voltage change cumulative amount, and obtain the voltage prediction deviation index according to the sum of the absolute value of the voltage residual mean value and the absolute value of the voltage change cumulative amount.

[0020] Preferably, obtaining the second optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm according to the residual fluctuation change of the voltage residual sequence includes:

[0021] Calculate the standard deviation of the voltage residual sequence to obtain the first ratio between the standard deviation and the nominal voltage; obtain the adaptive autocorrelation coefficient of the voltage residual sequence; obtain the second ratio between the absolute value of the voltage residual mean value and the nominal voltage;

[0022] Calculate the sum value between the calculation constant 1, the absolute value of the adaptive autocorrelation coefficient, and the second ratio, obtain the product between the sum value and the first ratio, perform a non-linear mapping on the product using the hyperbolic tangent function to obtain the corresponding second mapping value, and obtain a second optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm based on the sum of the constant 1 and the second mapping value.

[0023] Preferably, the obtaining of the adaptive autocorrelation coefficient of the voltage residual sequence includes:

[0024] According to the preset lag order, respectively obtain the lag voltage residuals corresponding to each voltage residual in the voltage residual sequence, accumulate the products between each voltage residual in the voltage residual sequence and its corresponding lag voltage residual to obtain a first accumulated value, accumulate the squares of each voltage residual in the voltage residual sequence to obtain a second accumulated value, and obtain the adaptive autocorrelation coefficient of the voltage residual sequence according to the ratio of the first accumulated value to the second accumulated value.

[0025] Preferably, the updating and optimizing of the process noise covariance matrix at the previous sampling moment of the current sampling moment by using the first optimization factor and the second optimization factor to obtain the optimized process noise covariance matrix at the current sampling moment includes:

[0026] Perform a weighted sum on the first optimization factor and the second optimization factor to obtain an adjustment coefficient, and use the product between the adjustment coefficient and the process noise covariance matrix at the previous sampling moment of the current sampling moment as the optimized process noise covariance matrix at the current sampling moment.

[0027] The beneficial effects of the embodiments of the present invention compared with the prior art are:

[0028] The present invention utilizes the mean value and the cumulative change amount of the voltage residual sequence to evaluate the average difference between the predicted value and the measured value of the battery model and the persistence of this difference, so as to obtain a first optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm according to the mean value and the cumulative change amount of the voltage residual sequence. At the same time, in order to reduce the random fluctuations existing in the battery model prediction, a second optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm is obtained according to the fluctuation amplitude, periodicity, and trend of the voltage residual sequence. Furthermore, by combining the first optimization factor and the second optimization factor, the process noise covariance matrix in the operation process of the unscented Kalman filter algorithm is optimized in real time, thereby effectively reducing the influence of the parameter time-variation of the battery model and the non-stationarity of the measurement noise on the SOC estimation accuracy by online adjusting the process noise covariance matrix (i.e., the optimized process noise covariance matrix), realizing the estimation accuracy of the SOC value of the power battery, and further reducing the error of the working performance detection of the power battery based on the SOC estimated value. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0030] Figure 1 FIG. is a flowchart of a method for detecting the working performance of a power battery for a new energy vehicle provided in Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] The following will describe in detail the embodiments of the present disclosure, and the examples of the embodiments are shown in the drawings. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present disclosure, but should not be construed as a limitation of the present disclosure.

[0032] It should be noted that the terms "first", "second", etc. in the specification of the present disclosure and the above drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. On the contrary, they are only examples of devices and methods consistent with some aspects of the present disclosure.

[0033] In order to illustrate the technical solutions of the present invention, the following will be described through specific embodiments.

[0034] See Figure 1 , which is a method flowchart of a method for detecting the working performance of a power battery for a new energy vehicle provided in the first embodiment of the present invention. As Figure 1 shown, the method may include:

[0035] Step S101, obtain the terminal voltage acquisition value of the power battery at the current sampling moment, and obtain the voltage residual sequence at the current sampling moment according to the terminal voltage acquisition values and terminal voltage prediction values at a preset number of historical sampling moments before the current sampling moment.

[0036] In the process of estimating the state of charge (SOC) of the battery based on the unscented Kalman filter algorithm (UKF algorithm), the operation of the UKF algorithm depends on an accurate battery model, which is used to describe the relationship between the voltage, current, SOC value and temperature of the battery. The parameters of this battery model include: the relationship between the open circuit voltage OCV and SOC (that is, the OCV-SOC curve), the ohmic internal resistance (R0), the polarization internal resistance (R P ), and the polarization capacitance (C P ), all of which are functions of SOC and temperature. Among them, the commonly used battery model is the Thevenin equivalent circuit model, which belongs to the prior art and will not be elaborated here in detail.

[0037] It should be noted that the process noise covariance matrix Q in the UKF algorithm directly affects the trust weight between the predicted value and the measured value of the battery model by the UKF algorithm. If Q is set smaller, the UKF algorithm is more inclined to trust the prediction result of the battery model; if Q is set larger, the UKF algorithm is more inclined to trust the actual measured value. The traditional UKF algorithm usually sets Q to a fixed value, and the premise of this approach is to assume that the parameters of the battery model are accurate enough and the measurement noise is stationary. However, in practical applications, these two assumptions are often difficult to meet. The parameters of the battery model will change with factors such as the aging degree of the battery, ambient temperature, charge and discharge rate, etc., showing obvious time-varying characteristics; the measurement noise (voltage sensor noise) may also be affected by various factors and show non-stationarity.

[0038] Therefore, when the parameters of the battery model change, if a fixed Q value is still used, the UKF algorithm will not be able to accurately evaluate the uncertainty predicted by the battery model. Specifically, if there is a deviation between the parameters of the battery model and the actual values, there will be a systematic deviation between the battery terminal voltage predicted by the battery model and the actual measured value. This systematic deviation will not only be reflected in the mean value of the residuals (i.e., the measured value minus the predicted value), but also in the trend of the residual sequence. If only relying on the mean value of the residuals to determine whether there is a systematic deviation, there will be cases of missed judgment or misjudgment. For example, when the parameters of the battery model change rapidly or oscillate, the residual sequence may quickly alternate between positive and negative, resulting in the mean value of the residuals approaching zero, masking the deviation predicted by the battery model. To capture the systematic deviation predicted by the battery model more comprehensively and accurately, the embodiments of the present invention provide a Q adaptive adjustment strategy based on the voltage residual sequence, making the estimation result of the battery SOC value based on the UKF algorithm more suitable for the actual usage scenario.

[0039] Specifically, in the embodiments of the present invention, during the operation of the power battery, a high-precision voltage sensor is used to collect the terminal voltage of the power battery at each sampling moment, denoted as the terminal voltage acquisition value. Among them, the sampling frequency of the voltage sensor is set to 10 Hz to meet the monitoring requirements for the changes in the dynamic characteristics of the battery. And the moment when the SOC value of the power battery needs to be analyzed in real time is used as the current sampling moment, denoted as moment t, and the terminal voltage acquisition value at the current sampling moment is obtained. At the same time, the terminal voltage acquisition values and terminal voltage prediction values at N historical sampling moments before the current sampling moment are obtained, so as to obtain the voltage residual sequence at the current sampling moment according to the preset number of terminal voltage acquisition values and terminal voltage prediction values at the historical sampling moments before the current sampling moment, and to obtain the adaptive Q value corresponding to the current sampling moment when using the UKF algorithm to estimate the SOC value of the power battery at the current sampling moment. Among them, considering that the voltage residual sequence is too long, with data redundancy, less effective information, and large computational complexity, while the voltage residual sequence is too short to observe the systematic deviation predicted by the battery model, therefore, N is set to 300, which is not limited here; the terminal voltage prediction value at each historical sampling moment is predicted by the battery model and belongs to the prior art, which will not be elaborated in detail here.

[0040] Among them, the method for obtaining the voltage residual sequence at the current sampling moment according to the preset number of terminal voltage acquisition values and terminal voltage prediction values at the historical sampling moments before the current sampling moment is as follows: for any historical sampling moment, obtain the previous historical sampling moment of the any historical sampling moment, and calculate the difference between the terminal voltage acquisition value at the any historical sampling moment and the terminal voltage prediction value at the previous historical sampling moment as the voltage residual at the any historical sampling moment; obtain the voltage residuals at each historical sampling moment to form the voltage residual sequence at the current sampling moment.

[0041] So far, according to the difference between the model prediction value and the measured value at the historical sampling moments before the current sampling moment, a voltage residual sequence within a period of time before the current sampling moment is obtained.

[0042] Step S102: Obtain a first optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm according to the mean value and the change cumulative amount of the voltage residual sequence.

[0043] The voltage residual is a key variable in the UKF algorithm, which reflects the difference between the predicted value and the measured value of the battery model. In an ideal situation, when the battery model is completely accurate and there is no measurement noise, the voltage residual should be 0. However, in practical applications, due to factors such as model error and measurement noise, the voltage residual is usually not 0. Therefore, the voltage residual sequence at the current sampling moment includes the prediction error information of the battery model and is also an important basis for evaluating the prediction accuracy of the battery model. Therefore, in the embodiments of the present invention, based on the voltage residual sequence at the current sampling moment, two indicators, namely the voltage residual mean value and the voltage change cumulative amount, are introduced to obtain a first optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm, which is used to adaptively adjust and optimize the process noise covariance matrix Q to cope with the systematic deviation caused by the parameter time-variation of the battery model.

[0044] Specifically, calculate the mean value of the voltage residual sequence, denoted as the voltage residual mean value , which is used to characterize the systematic deviation of the battery model prediction. If the voltage residual mean value is close to 0, it indicates that there is no obvious deviation in the battery model prediction on average, that is, the errors between the predicted value and the measured value are positive and negative, canceling each other out, showing the characteristics of random fluctuation. If the voltage residual mean value is significantly greater than 0, it indicates that the predicted value of the battery model is continuously low (the residual is continuously positive), and there is a negative systematic deviation. If the voltage residual mean value is significantly less than 0, it indicates that the predicted value of the battery model is continuously high (the residual is continuously negative), and there is a positive systematic deviation.

[0045] Calculate the absolute value of the difference between every two adjacent voltage residuals in the voltage residual sequence to obtain the cumulative value of the absolute value of the difference, denoted as the voltage change cumulative amount , which is used to reflect the severity and persistence of the change of the voltage residual sequence. Even if the voltage residual mean value is close to 0, there may still be large fluctuations in the voltage residual sequence. If the parameters of the battery model change rapidly or oscillate, the voltage residual sequence may alternate rapidly between positive and negative, resulting in the voltage residual mean value Close to 0, but the change in voltage residual will be relatively large, while the cumulative voltage change can capture this change trend and compensate for the average voltage residual deficiency.

[0046] If the average voltage residual is close to 0 and the cumulative voltage change is also small, it indicates that there is no obvious systematic deviation in the battery model prediction. At this time, Q can be kept unchanged; if the average voltage residual significantly deviates from zero, or the cumulative voltage change is large, it indicates that there is a systematic deviation in the battery model prediction. At this time, Q should be increased so that the UKF algorithm depends more on the measured values for correction, thereby reducing the SOC estimation deviation. Therefore, according to the sum between the absolute value of the average voltage residual and the absolute value of the cumulative voltage change, a voltage prediction deviation index is obtained, which is used to reflect the degree of systematic deviation existing in the battery model prediction. Preferably, the sum between the absolute value of the average voltage residual and the absolute value of the cumulative voltage change is used as the voltage prediction deviation index , which can ensure that for the prediction of the battery model, whether it is overestimated or underestimated, as long as there is a systematic deviation or the cumulative voltage change is large, Q needs to be increased.

[0047] Furthermore, after obtaining the voltage prediction deviation index, the nominal voltage of the power battery is obtained for normalizing the voltage prediction deviation index to make it a dimensionless value. Then, the voltage prediction deviation index is normalized using the nominal voltage to obtain a normalized value, which eliminates the influence brought by different battery types and different voltage levels. Then, the hyperbolic tangent function is used to perform a non-linear mapping on the normalized value to obtain a corresponding first mapping value. According to the sum of the constant 1 and the first mapping value, a first optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm is obtained.

[0048] In one embodiment, the calculation expression of the first optimization factor is:

[0049]

[0050] Wherein, represents the first optimization factor, 1 represents the constant, represents the hyperbolic tangent function, represents the voltage prediction deviation index, represents the nominal voltage.

[0051] It should be noted that, represents the normalized value after normalization by the nominal voltage, so that the first optimization factor has a wider applicability; and in order to further enhance the sensitivity of the first optimization factor to the voltage prediction deviation index and make it have nonlinear adjustment characteristics, the hyperbolic tangent function tanh() is introduced, and the domain of the tanh() function is all real numbers, and the value range is (-1, 1). The normalized value is used as the input of the hyperbolic tangent function, and the normalized value can be mapped to the (-1, 1) interval, and then through The operation will The value of is limited to the range of (1, 2).

[0052] At this point, the first optimization factor corresponding to the current sampling moment is obtained, which is used to optimize the process noise covariance matrix in the unscented Kalman filter algorithm. The first optimization factor realizes the sensitivity capture of the systematic deviation of the battery model prediction. When the battery model prediction has no obvious systematic deviation and the residual changes smoothly, Close to , Q remains basically unchanged; when the battery model prediction has obvious systematic deviations or the residuals change dramatically, Close to , Q increases. The nonlinear characteristics of the function make Ability to respond quickly to small deviations in battery model predictions and avoid over-adjustment when deviations are large.

[0053] Step S103: obtaining a second optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm according to the residual fluctuation change of the voltage residual sequence.

[0054] The first optimization factor The correction of the systematic deviation of the battery model prediction is realized. The first optimization factor focuses on the mean and cumulative change of the voltage residual sequence, which is used to evaluate the average difference between the predicted value and the measured value of the battery model and the persistence of this difference. However, even if the prediction of the battery model has no obvious systematic deviation, that is, the mean of the voltage residual is close to zero, the prediction results of the battery model may still have random fluctuations.

[0055] This random fluctuation stems from multiple aspects: Firstly, the measurement noise itself is random. Even if the measurement device (voltage sensor) has high precision, its measurement results will inevitably contain a certain degree of random error. Secondly, the battery model is usually a simplification and approximation of the actual battery system and cannot fully capture the complex electrochemical reactions and dynamic characteristics inside the battery. These factors not considered by the battery model, such as the microscopic structure changes inside the battery and the slight fluctuations in the electrolyte concentration, will also cause random fluctuations in the prediction results of the battery model. In addition, the working environment of the battery (such as temperature and load) also has random changes, which in turn affect the accuracy of the battery model prediction.

[0056] If the process noise covariance matrix Q is set too small, the UKF algorithm will overly trust the model prediction with random fluctuations, resulting in unnecessary oscillations in the SOC estimation result and reducing the smoothness and stability of the estimation result. To solve this problem, the embodiment of the present invention further proposes a second optimization factor , which is used to adjust Q according to the fluctuation pattern of the voltage residual sequence.

[0057] Specifically, first, calculate the standard deviation of the voltage residual sequence , to obtain a first ratio between the standard deviation and the nominal voltage , where the standard deviation characterizes the degree of residual dispersion within a period of time before the current sampling moment. The larger it is, the greater the fluctuation amplitude of the voltage residuals in the voltage residual sequence, and the greater the random error in the battery model prediction; The smaller it is, the smaller the fluctuation amplitude of the voltage residuals in the voltage residual sequence, and the smaller the random error in the battery model prediction.

[0058] Then, according to the main electrochemical time constant of the battery, the polarization time constant (R P and C P ) in the Thevenin equivalent circuit model is used in the embodiment of the present invention for setting. Considering that the sampling frequency of the common polarization time constant of lithium iron phosphate batteries is 10 Hz, the lag order p is set to 100. Then, according to the preset lag order, the lag voltage residuals corresponding to each voltage residual in the voltage residual sequence are obtained respectively. The products between each voltage residual in the voltage residual sequence and its corresponding lag voltage residual are accumulated to obtain a first accumulated value. The squares of each voltage residual in the voltage residual sequence are accumulated to obtain a second accumulated value. According to the ratio of the first accumulated value and the second accumulated value, the adaptive autocorrelation coefficient of the voltage residual sequence is obtained, which is used to measure the correlation between the voltage residuals separated by a specific time interval in the voltage residual sequence.

[0059] Preferably, the calculation expression of the adaptive autocorrelation coefficient of the voltage residual sequence is:

[0060]

[0061] in, represents the adaptive autocorrelation coefficient of the voltage residual sequence, N represents the length of the voltage residual sequence, represents the ith voltage residual in the voltage residual sequence, represents the lagged voltage residual of the ith voltage residual in the voltage residual sequence.

[0062] It should be noted that the traditional autocorrelation coefficient usually only considers the correlation between the voltage residual sequence and its lagged one-period sequence. In order to better identify the periodic fluctuations that may exist in the voltage residual sequence, the embodiment of the present invention introduces an adaptive autocorrelation coefficient to better measure the correlation between the residual values separated by a specific time interval p in the voltage residual sequence. Close to When , it means that there is no obvious correlation between the residual values of the voltage residual sequence separated by time interval p; when Significantly greater than When , it indicates that there is a positive correlation between the residual values of the voltage residual sequence at time intervals p, that is, the residual values tend to fluctuate in the same direction with a period of p; when Significantly less than When , it indicates that there is a negative correlation between the residual values of the voltage residual sequence separated by a time interval p, that is, the residual values tend to fluctuate in the opposite direction with a period of p.

[0063] Secondly, obtain a second ratio between the absolute value of the voltage residual mean and the nominal voltage , which is used to reflect the average variation of the voltage residual sequence. The larger the absolute value of , the more drastic the change of the voltage residual sequence is, and there may be mutations or rapid changes. and The difference is: The focus is on the overall fluctuation pattern (trend, periodicity) of the voltage residual series, while What we are concerned about is the local variation amplitude of the voltage residual sequence. Even if the voltage residual sequence has no obvious trend or periodicity, if its local variation amplitude is large (there are mutation points), It will also be larger.

[0064] Finally, calculate the sum value between the constant 1, the absolute value of the adaptive autocorrelation coefficient, and the second ratio, obtain the product between the sum value and the first ratio, perform a non-linear mapping on the product using the hyperbolic tangent function to obtain the corresponding second mapping value, and obtain the second optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm according to the sum of the constant 1 and the second mapping value.

[0065] In one embodiment, the calculation expression of the second optimization factor is:

[0066]

[0067] Where, represents the second optimization factor, 1 represents the constant, represents the hyperbolic tangent function, || represents the absolute value symbol, L1 represents the first ratio, and L2 represents the second ratio.

[0068] It should be noted that by introducing the standard deviation , it can directly reflect the fluctuation amplitude of the voltage residual sequence, thereby making corresponding adjustments to Q. When the residual fluctuation amplitude increases, increases, increases accordingly, and then Q increases, reducing the trust of the UKF algorithm in the battery model prediction and relying more on measurement values for state estimation; by introducing , it can identify the periodic fluctuation patterns in the voltage residual sequence and adjust Q accordingly. When obvious periodic fluctuations appear in the voltage residual sequence, increases, increases accordingly, and then Q increases.

[0069] So far, the second optimization factor corresponding to the current sampling moment is obtained for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm. The second optimization factor comprehensively evaluates the fluctuation amplitude, trend, periodicity, and mutation of the voltage residual sequence. When the voltage residual sequence fluctuates smoothly, is close to 1, and Q remains basically unchanged; when obvious fluctuations appear in the voltage residual sequence, will be significantly greater than 1, Q increases, reducing the trust of the UKF algorithm in the battery model prediction and relying more on measurement values for state estimation, thereby improving the stability of the battery SOC estimation.

[0070] Step S104, use the first optimization factor and the second optimization factor to update and optimize the process noise covariance matrix at the previous sampling moment of the current sampling moment to obtain the optimized process noise covariance matrix at the current sampling moment.

[0071] After obtaining the first optimization factor and the second optimization factor, the process noise covariance matrix at the previous sampling moment of the current sampling moment can be updated and optimized by using the first optimization factor and the second optimization factor, so as to obtain the process noise covariance matrix when evaluating the SOC value of the power battery at the current sampling moment by using the UKF algorithm, that is, the optimized process noise covariance matrix. The method of updating and optimizing is as follows: perform weighted summation on the first optimization factor and the second optimization factor to obtain an adjustment coefficient, and use the product between the adjustment coefficient and the process noise covariance matrix at the previous sampling moment of the current sampling moment as the optimized process noise covariance matrix at the current sampling moment.

[0072] In one embodiment, the calculation expression for updating and optimizing is:

[0073]

[0074] Wherein, represents the optimized process noise covariance matrix at the current sampling moment, represents the weight of the first optimization factor, represents the weight of the second optimization factor, represents the first optimization factor, represents the second optimization factor, represents the process noise covariance matrix at time t-1, that is, the process noise covariance matrix at the previous sampling moment of the current sampling moment.

[0075] It should be noted that considering that the first optimization factor and the second optimization factor are equally important for adjusting the process noise covariance matrix Q, then set , and there is no limitation here.

[0076] Step S105, according to the terminal voltage acquisition value at the current sampling moment and the optimized process noise covariance matrix, use the unscented Kalman filter algorithm to obtain the SOC estimated value of the power battery at the current sampling moment, so as to detect the working performance of the power battery.

[0077] After obtaining the optimized process noise covariance matrix at the current sampling moment , according to the calculation process of the standard unscented Kalman filter algorithm, estimate the SOC value of the power battery at the current sampling moment according to the terminal voltage acquisition value at the current sampling moment, and obtain the SOC estimated value of the power battery at the current sampling moment. It should be noted that the unscented Kalman filter algorithm includes three main steps: initialization, time update (prediction), and measurement update (correction). In the embodiment of the present invention, the It is used in the time update step, that is, the original process noise covariance matrix Q at the current sampling moment is replaced with the optimized process noise covariance matrix , while keeping other steps unchanged, using the unscented Kalman filter algorithm to estimate the SOC value of the battery belongs to the prior art and will not be elaborated here in detail.

[0078] After determining the SOC estimated value of the power battery at the current sampling moment, the working performance of the power battery can be detected according to the SOC estimated value, which can provide key technical support for the performance evaluation, safe operation and life management of the power battery of new energy vehicles, thereby improving the overall performance and user experience of new energy vehicles. Specifically, it includes but is not limited to the following battery performance evaluation and applications:

[0079] (1) Battery health state (SOH) evaluation: Long-term monitor the SOC estimated value of the power battery and combine it with the battery capacity attenuation model to evaluate the battery health state (SOH).

[0080] (2) Remaining driving range estimation: Based on the SOC estimated value and the vehicle's energy consumption model, the remaining driving range of the electric vehicle can be estimated more accurately.

[0081] (3) Charge and discharge control: Use the SOC estimated value as a feedback signal to achieve precise control of the battery charge and discharge process, avoid overcharging and over-discharging, extend the battery life, and improve the safety of the battery system.

[0082] It should be noted that the focus of the present invention is on how to perform online adaptive adjustment on the process noise covariance matrix Q to improve the accuracy of SOC estimation of lithium iron phosphate batteries based on the UKF algorithm. For detecting the working performance of the power battery based on the SOC estimated value of the battery, it belongs to the prior art and will not be elaborated here in detail.

[0083] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for detecting the working performance of a power battery for a new energy vehicle, characterized in that, The method includes: Obtaining the terminal voltage acquisition value of the power battery at the current sampling moment, and obtaining the voltage residual sequence at the current sampling moment according to the terminal voltage acquisition values and terminal voltage prediction values at a preset number of historical sampling moments before the current sampling moment; Obtaining a first optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm according to the mean value and change cumulative amount of the voltage residual sequence; Obtaining a second optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm according to the residual fluctuation change of the voltage residual sequence; Updating and optimizing the process noise covariance matrix at the previous sampling moment at the current sampling moment by using the first optimization factor and the second optimization factor to obtain the optimized process noise covariance matrix at the current sampling moment; According to the terminal voltage acquisition value at the current sampling moment and the optimized process noise covariance matrix, using the unscented Kalman filter algorithm to obtain the SOC estimation value of the power battery at the current sampling moment for detecting the working performance of the power battery; The obtaining a first optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm according to the mean value and change cumulative amount of the voltage residual sequence includes: Calculating the mean value of the voltage residual sequence, denoted as the voltage residual mean value, calculating the absolute value of the difference between every two adjacent voltage residuals in the voltage residual sequence to obtain the cumulative value of the absolute values of the differences, denoted as the voltage change cumulative amount, and obtaining the voltage prediction deviation index according to the sum of the absolute value of the voltage residual mean value and the absolute value of the voltage change cumulative amount; obtaining the nominal voltage of the power battery, normalizing the voltage prediction deviation index by using the nominal voltage to obtain the normalized value, and obtaining the first optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm by using the calculation expression of the first optimization factor, where the calculation expression of the first optimization factor is: Among them, represents the first optimization factor, and 1 represents a constant, represents the hyperbolic tangent function, represents the voltage prediction deviation index, represents the nominal voltage; The obtaining a second optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm according to the residual fluctuation change of the voltage residual sequence includes: Calculating the standard deviation of the voltage residual sequence to obtain the first ratio between the standard deviation and the nominal voltage; according to the preset lag order, respectively obtaining the lag voltage residuals corresponding to each voltage residual in the voltage residual sequence, accumulating the products of each voltage residual in the voltage residual sequence and its corresponding lag voltage residual to obtain the first accumulated value, accumulating the squares of each voltage residual in the voltage residual sequence to obtain the second accumulated value, and obtaining the adaptive autocorrelation coefficient of the voltage residual sequence according to the ratio of the first accumulated value and the second accumulated value; obtaining the second ratio between the absolute value of the voltage residual mean value and the nominal voltage; Obtaining the second optimization factor for optimizing the process noise covariance matrix in the unscented Kalman filter algorithm by using the calculation expression of the second optimization factor, where the calculation expression of the second optimization factor is: Among them, represents the second optimization factor, and 1 represents a constant. represents the hyperbolic tangent function, || represents the absolute value symbol, L1 represents the first ratio, and L2 represents the second ratio. represents the adaptive autocorrelation coefficient of the voltage residual sequence, and || represents the absolute value symbol; Updating and optimizing the process noise covariance matrix at the previous sampling moment of the current sampling moment by using the first optimization factor and the second optimization factor to obtain the optimized process noise covariance matrix at the current sampling moment, includes: The calculation expression for updating and optimizing is: Among them, represents the optimized process noise covariance matrix at the current sampling moment, represents the weight of the first optimization factor, represents the weight of the second optimization factor, represents the first optimization factor, represents the second optimization factor, represents the process noise covariance matrix at time t - 1, that is, the process noise covariance matrix at the previous sampling moment of the current sampling moment.

2. The power battery working performance detection method for new energy vehicles according to claim 1, wherein Obtaining the voltage residual sequence at the current sampling moment according to the terminal voltage acquisition values and terminal voltage prediction values at a preset number of historical sampling moments before the current sampling moment, includes: For any historical sampling moment, obtaining the previous historical sampling moment of the any historical sampling moment, calculating the difference between the terminal voltage acquisition value at the any historical sampling moment and the terminal voltage prediction value at the previous historical sampling moment as the voltage residual at the any historical sampling moment; obtaining the voltage residuals at each historical sampling moment to form the voltage residual sequence at the current sampling moment.

Images