Battery SOC estimation method and system and vehicle

Abstract

The invention discloses a battery SOC estimation method and system and a vehicle, and the method comprises the steps: calculating a current SOC value based on a predicted SOC value and a measured SOC value, taking the current SOC value as a battery SOC estimation value, calculating a charging / discharging target SOC value when a charging / discharging boundary condition is triggered, and then correcting the battery SOC estimation value based on the current SOC value and the charging / discharging target SOC value. Therefore, the accuracy of SOC estimation is greatly improved, and the problems of accumulative errors, model errors and SOC estimation which is not suitable for real-time dynamic working conditions of a traditional SOC estimation method are effectively reduced. Besides, during correction, a correction rate factor is designed for gradual correction, so that the stability and reliability of the correction process of the SOC estimated value of the battery can be ensured, SOC estimation error accumulation caused by voltage fluctuation is avoided, and the stability of a battery management system is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery management, and in particular relates to a battery SOC estimation method, system and vehicle. Background Art

[0002] SOC, or State of Charge, refers to the ratio between the current remaining power of the battery and the maximum available power. This parameter plays a vital role in the Battery Management System (BMS), and it directly reflects the actual available capacity of the battery. Accurate SOC estimation is crucial to optimizing the battery's charging and discharging process. It can prevent the battery from being overcharged or over-discharged, thereby extending the battery's service life. For new energy vehicles, accurate SOC estimation has a direct impact on the vehicle's cruising range and driving safety, and can effectively avoid driving interruptions caused by insufficient power. Highly accurate SOC estimation helps monitor the battery status in real time and prevent potential safety risks such as overheating and overcharging. At the same time, it can also promote efficient management of battery energy, improve energy utilization, and optimize the vehicle's energy distribution and usage strategy.

[0003] At present, the mainstream methods of SOC estimation include open circuit voltage method, coulomb counting method, model prediction method, Kalman filter method and neural network method. The open circuit voltage method is favored because of its simple principle and easy implementation, but this method requires the battery to be in a static state, so it is not suitable for real-time dynamic conditions. The coulomb counting method calculates SOC by current integration, which can achieve real-time estimation, but it has high requirements on the accuracy of the current sensor, and is prone to cumulative errors, requiring regular calibration. However, methods such as model prediction method and neural network method are more complicated in model construction, require a large amount of training data, and have a large amount of calculation. Summary of the invention

[0004] The object of the present invention is to provide a battery SOC estimation method, system and vehicle to improve the accuracy of SOC estimation.

[0005] In a first aspect, the present invention provides a battery SOC estimation method, comprising:

[0006] Obtain the real-time power P, real-time voltage U, real-time current I, real-time temperature T and real-time internal resistance R of the battery during vehicle driving.

[0007] The real-time current I and the previous predicted SOC value Substitute the preset battery prediction model to calculate the current predicted SOC value

[0008] Substitute the real-time voltage U, real-time current I and real-time internal resistance R into the preset battery measurement model to calculate the measured SOC value SOC ocv .

[0009] Using the formula: Calculate the current SOC value SOC corrected ; Among them, K k represents the Kalman gain.

[0010] The current SOC value SOC corrected As the battery SOC estimation value SOC est , even if SOC est =SOC corrected .

[0011] Determine whether the discharge boundary condition or the charge boundary condition is triggered; if so, calculate the discharge target SOC value SOC target_f Or the charging target SOC value SOC target_c , and based on the current SOC value SOC corrected And the discharge target SOC value SOC target_f Or the charging target SOC value SOC target_c , correct the battery SOC estimate SOC est , then update the Kalman gain K k .

[0012] Preferably, the preset battery prediction model is: Among them, Δt represents the preset time step, Q represents the preset battery capacity, and the initial predicted SOC value (i.e., the initial value of the predicted SOC value) is obtained by querying the preset voltage and SOC correspondence table based on the initial battery voltage (i.e., the real-time voltage of the battery obtained when the vehicle starts driving).

[0013] Preferably, the preset battery measurement model is: SOC ocv =a3*(UI*R) 3 +a2*(UI*R) 2 +a1*(UI*R)+a0; wherein a0, a1, a2, and a3 represent preset model coefficients.

[0014] Preferably, if the real-time voltage of the battery (i.e., the current voltage of the battery) decreases and decreases to a value less than or equal to U min +U th1 , then determine the trigger discharge boundary conditions; where U min Indicates the preset lower cut-off voltage, U th1 Indicates the first preset voltage threshold. If the real-time voltage of the battery (i.e. the current voltage of the battery) rises and rises to a value greater than or equal to U max -Uth2 , then the charging boundary condition is determined to be triggered; where U max Indicates the preset upper cut-off voltage, U th2 represents the second preset voltage threshold.

[0015] Preferably, the discharge target SOC value SOC is calculated target_f The method is: substitute the real-time power P, real-time temperature T and real-time internal resistance R into the preset battery discharge expectation model to calculate the discharge target SOC value SOC target_f ; Calculate the charging target SOC value SOC target_c The method is as follows: Substitute the real-time power P, real-time temperature T and real-time internal resistance R into the preset battery charging expectation model to calculate the charging target SOC value SOC target_c .

[0016] Preferably, the preset battery discharge expectation model is obtained by:

[0017] First, under m different current conditions, the battery is continuously discharged until the voltage is less than or equal to U min +U th1 , and record the discharge power, discharge internal resistance, discharge temperature and discharge SOC of the battery at this time to obtain m sets of discharge experimental data. Under one current condition, a set of discharge experimental data (i.e., a set of discharge power, discharge internal resistance, discharge temperature and discharge SOC) is obtained. Under m different current conditions, m sets of discharge experimental data will be obtained.

[0018] Then, the multiple regression analysis tool is used to fit the m groups of discharge experimental data to establish a regression model, and the regression coefficients c0, c1, c2, c3, c4, c5, c6, c7, c8, c9 are calculated by the least squares method, and then the preset battery discharge expectation model is obtained: SOC targe_f =c0+c1*P+c2*R+c3*T+c4*P 2 +c5*R 2 +c6*T 2 +c7*P*R+c8*P*T+c9*R*T.

[0019] Preferably, the preset battery charging expectation model is obtained by:

[0020] First, under m different current conditions, the battery is continuously charged until the voltage is greater than or equal to U max -U th2, and record the battery's charging power, charging internal resistance, charging temperature and charging SOC at this time to obtain m sets of charging experimental data. Under one current condition, a set of charging experimental data (i.e., a set of charging power, charging internal resistance, charging temperature and charging SOC) is obtained. Under m different current conditions, m sets of charging experimental data will be obtained.

[0021] Then, the multivariate regression analysis tool is used to fit the m groups of charging experimental data to establish a regression model, and the regression coefficients d0, d1, d2, d3, d4, d5, d6, d7, d8, and d9 are obtained by the least squares method, thereby obtaining the preset battery charging expectation model: SOC targe_c =d0+d1*P+d2*R+d3*T+d4*P 2 +d5*R 2 +d6*T 2 +d7*P*R+d8*P*T+d9*R*T.

[0022] Preferably, based on the current SOC value SOC corrected And the discharge target SOC value SOC target_f , correct the battery SOC estimate SOC est The method is: if SOC target_f <SOC corrected , then SOC est By SOC corrected According to the first gradient ΔSOC f Gradually correct to SOC target_f ; Among them, ΔSOC f =α*(SOC target_f -SOC corrected ), α represents the correction rate factor, α0 represents the preset correction rate threshold, V max Indicates the preset upper limit of the voltage change rate, V min represents the preset lower limit of voltage change rate, t0 represents the preset time, U t Indicates the battery voltage at the current moment, U t；t0 It represents the voltage of the battery before t0 seconds, and min() represents the minimum function.

[0023] Preferably, based on the current SOC value SOC corrected And the charging target SOC value SOC target_c , correct the battery SOC estimate SOC est The method is: if SOC target_c >SOC corrected , then SOC est By SOC corrected According to the second gradient ΔSOC cGradually correct to SOC target_c ; Among them, ΔSOC c =α*(SOC target_c -SOC corrected ).

[0024] Preferably, the Kalman gain K k The initial value is equal to Among them, H T represents a preset measurement matrix, N represents a preset process noise covariance matrix, and G represents a preset measurement noise covariance matrix.

[0025] Preferably, update the Kalman gain K k The method is:

[0026] Using the formula: Calculate the updated Kalman gain K k .

[0027] In a second aspect, the present invention provides a battery SOC estimation system, comprising a controller, wherein the controller is programmed to execute the above-mentioned battery SOC estimation method.

[0028] In a third aspect, the vehicle provided by the present invention includes the above-mentioned battery SOC estimation system.

[0029] The present invention calculates the current SOC value based on the predicted SOC value and the measured SOC value, takes the current SOC value as the battery SOC estimation value, and calculates the charge / discharge target SOC value when the charge / discharge boundary condition is triggered, and then corrects the battery SOC estimation value (i.e., dynamically adjusts the battery SOC estimation value) based on the current SOC value and the charge / discharge target SOC value, thereby greatly improving the accuracy of SOC estimation, effectively reducing the cumulative error, model error, and SOC estimation problem of the traditional SOC estimation method that cannot be applied to real-time dynamic working conditions. In addition, during the correction, the correction rate factor is designed to be corrected step by step, ensuring the stability and reliability of the battery SOC estimation correction process, avoiding the accumulation of SOC estimation errors caused by voltage fluctuations, and improving the stability of the battery management system. In the field of battery management, it has the value of promotion and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 Flow chart of the battery SOC estimation method in an embodiment of the present invention.

[0031] Figure 2 This is a flow chart for obtaining a preset battery discharge expectation model in an embodiment of the present invention.

[0032] Figure 3 This is a flow chart for obtaining a preset battery charging expectation model in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to enable a more detailed understanding of the features and technical contents of the embodiments of the present invention, the implementation of the embodiments of the present invention is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not intended to limit the embodiments of the present invention.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0035] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0036] like Figures 1 to 3 As shown, the battery SOC estimation method provided in the embodiment of the present invention is executed by a controller (such as a BMS controller), and specifically includes:

[0037] Step 1: Obtain the real-time power P, real-time voltage U, real-time current I, real-time temperature T and real-time internal resistance R of the battery during vehicle driving, and then execute step 2.

[0038] As an example, real-time power P, real-time voltage U, real-time current I, real-time temperature T and real-time internal resistance R can be obtained directly from the corresponding sensors, or from the CAN bus (collected, calculated and uploaded to the CAN bus by other controllers).

[0039] Step 2: Combine the real-time current I and the previous predicted SOC value Substitute the preset battery prediction model to calculate the current predicted SOC value Then proceed to step three.

[0040] In some embodiments, the preset battery prediction model is: Among them, Δt represents the preset time step, Q represents the preset battery capacity, the initial predicted SOC value (i.e., the initial value of the predicted SOC value) is obtained by querying the preset voltage and SOC correspondence table based on the initial battery voltage (i.e., the real-time voltage of the battery obtained when the vehicle starts driving), and the preset voltage and SOC correspondence table is obtained through offline calibration.

[0041] Step 3: Substitute the real-time voltage U, real-time current I and real-time internal resistance R into the preset battery measurement model to calculate the measured SOC value SOC ocv , and then proceed to step 4.

[0042] In some embodiments, the preset battery measurement model is: SOC ocv =a3*(UI*R) 3 +a2*(UI*R) 2 +a1*(UI*R)+a0; where a0, a1, a2, and a3 represent preset model coefficients, which are obtained through experimental calibration. Different types of batteries have different model coefficients.

[0043] Taking a 57Ah lithium iron phosphate battery as an example, according to the experimental calibration, a0 = 2.3457, a1 = -1.2025, a2 = 0.217, a3 = -0.0091, then SOC ocv =-0.0091*(UI*R) 3 +0.217*(UI*R) 2 -1.2025*(UI*R)+2.3457.

[0044] Step 4: Use the formula: Calculate the current SOC value SOC corrected Among them, K k represents the Kalman gain, and then execute step 5.

[0045] In some embodiments, the Kalman gain K k The initial value is equal to Among them, H T represents the preset measurement matrix, N represents the preset process noise covariance matrix, and G represents the preset measurement noise covariance matrix. T It is usually 1. N and G can be tuned and designed offline according to experimental data (which belongs to the existing technology). That is, by observing the battery performance under different SOC, temperature, discharge rate and other conditions, the variance of the process noise is statistically analyzed, and the changes in SOC estimation under different conditions are recorded to quantify the process noise. At the same time, its variance under static and dynamic conditions is calculated as the initial value of the measurement noise. Subsequently, the residual between the SOC estimate and the actual SOC is recorded, and real-time feedback adjustment is performed for iterative optimization.

[0046] Step 5: Set the current SOC value SOC corrected As the battery SOC estimation value SOC est , and then proceed to step 6.

[0047] Step 6: Determine whether the discharge boundary condition is triggered, if yes, execute step 7, otherwise execute step 9.

[0048] In some embodiments, if the real-time voltage of the battery (i.e., the current voltage of the battery) decreases and decreases to less than or equal to U min +U th1, then determine the trigger discharge boundary conditions. Among them, U min Indicates the preset lower cut-off voltage, U th1 represents the first preset voltage threshold. As an example, U th1 =300mV.

[0049] Step 7: Calculate the discharge target SOC value SOC target_f , and then proceed to step eight.

[0050] In some embodiments, the target SOC value SOC is calculated. target_f The method is: substitute the real-time power P, real-time temperature T and real-time internal resistance R into the preset battery discharge expectation model to calculate the discharge target SOC value SOC target_f .

[0051] like Figure 2 As shown, the preset battery discharge expectation model is obtained by:

[0052] First, under m different current conditions, the battery is continuously discharged until the voltage is less than or equal to U min +U th1 , and record the discharge power, discharge internal resistance, discharge temperature and discharge SOC of the battery at this time to obtain m sets of discharge experimental data. Under one current condition, a set of discharge experimental data (i.e., a set of discharge power, discharge internal resistance, discharge temperature and discharge SOC) is obtained. Under m different current conditions, m sets of discharge experimental data will be obtained.

[0053] Then, the multivariate regression analysis tool is used to fit the m groups of discharge experimental data, establish a regression model, and use the least squares method to find the regression coefficients c0, c1, c2, c3, c4, c5, c6, c7, c8, c9, and then obtain the preset battery discharge expectation model: SOC targe_f =c0+c1*P+c2*R+c3*T+c4*P 2 +c5*R 2 +c6*T 2 +c7*P*R+c8*P*T+c9*R*T.

[0054] Taking a 57Ah lithium iron phosphate battery as an example, its lower limit cut-off voltage U min =2.0V,U min +U th1 =2.3V, according to the experimental calibration, c0 = 20.5, c1 = -0.03, c2 = -8, c3 = -0.5, c4 = 0.00002, c5 = 0.05, c6 = 0.002, c7 = -0.01, c8 = 0.005, c9 = -0.03, then SOC targe_f=20.5-0.03*P-8*R-0.5*T+

[0055] 0.00002*P 2 +0.05*R 2 +0.002*T 2 -0.01*P*R+0.005*P*T-0.03*R*T.

[0056] Step 8: Based on the current SOC value and the discharge target SOC value, correct the battery SOC estimate SOC est , and then proceed to step 12.

[0057] In some embodiments, based on the current SOC value SOC corrected And the discharge target SOC value SOC target_f , correct the battery SOC estimate SOC est ways, including:

[0058] Determine SOC target_f With SOC corrected Size relationship:

[0059] If SOC target_f ≥SOC corrected , no correction is made, SOC est Still equal to SOC corrected .

[0060] If SOC target_f <SOC corrected , then SOC est By SOC corrected According to the first gradient ΔSOC f Gradually correct to SOC target_f . Among them, ΔSOC f =α*(SOC target_f -SOC corrected ), α represents the correction rate factor, α0 represents the preset correction rate threshold, V max Indicates the preset upper limit of the voltage change rate, V min represents the preset lower limit of voltage change rate, t0 represents the preset time, U t Indicates the battery voltage at the current moment, U t；t0 represents the battery voltage before t0 seconds, and min() represents the minimum function, that is, Indicates taking and the minimum value in α0. As an example, α0=0.002, V max =0.5V / s, V min=0, t0 = 0.1s, t0 seconds ago means 0.1s ago,

[0061] Step 9: Determine whether the charging boundary condition is triggered. If yes, execute step 10; otherwise, return to execute step 1.

[0062] In some embodiments, if the real-time voltage of the battery (i.e., the current voltage of the battery) rises and rises to a value greater than or equal to U max -U th2 , then the charging boundary condition is determined to be triggered. Among them, U max Indicates the preset upper cut-off voltage, U th2 represents the second preset voltage threshold. As an example, U th2 =70mV.

[0063] Step 10: Calculate the charging target SOC value SOC target_c , and then proceed to step eleven.

[0064] In some embodiments, the charging target SOC value SOC is calculated. target_c The method is as follows: Substitute the real-time power P, real-time temperature T and real-time internal resistance R into the preset battery charging expectation model to calculate the charging target SOC value SOC target_c .

[0065] like Figure 3 As shown, the preset battery charging expectation model is obtained by:

[0066] First, under m different current conditions, the battery is continuously charged until the voltage is greater than or equal to U max -U th2 , and record the battery's charging power, charging internal resistance, charging temperature and charging SOC at this time to obtain m sets of charging experimental data. Under one current condition, a set of charging experimental data (i.e., a set of charging power, charging internal resistance, charging temperature and charging SOC) is obtained. Under m different current conditions, m sets of charging experimental data will be obtained.

[0067] Then, the multivariate regression analysis tool is used to fit the m groups of charging experimental data, establish a regression model, and use the least squares method to find the regression coefficients d0, d1, d2, d3, d4, d5, d6, d7, d8, and d9, and then the preset battery charging expectation model is obtained: SOC targe_c =d0+d1*P+d2*R+d3*T+d4*P 2 +d5*R 2 +d6*T 2 +d7*P*R+d8*P*T+d9*R*T.

[0068] Taking a 57Ah lithium iron phosphate battery as an example, its upper cut-off voltage U max =3.65V, U max -U th2 =3.58V, according to the experimental calibration, d0 = 10.5, d1 = -0.02, d2 = -5, d3 = -0.4, d4 = 0.00001, d5 = 0.03, d6 = 0.001, d7 = -0.008, d8 = 0.004, d9 = -0.02, then SOC targe_c =10.5-0.02*P-5*R-0.4*T+0.00001*P 2 +0.03*R 2 +0.001*T 2 -0.008*P*R+0.004*P*T-0.02*R*T.

[0069] Step 11: Based on the current SOC value and the charging target SOC value, correct the battery SOC estimation value SOC est , and then proceed to step 12.

[0070] In some embodiments, based on the current SOC value SOC corrected And the charging target SOC value SOC target_c , correct the battery SOC estimate SOC est ways, including:

[0071] Determine SOC target_c With SOC corrected Size relationship:

[0072] If SOC target_c ≤SOC corrected , no correction is made, SOC est Still equal to SOC corrected .

[0073] If SOC target_c >SOC corrected , then SOC est By SOC corrected According to the second gradient ΔSOC c Gradually correct to SOC target_c . Among them, ΔSOC c =α*(SOC target_c -SOC corrected ).

[0074] Step 12: Update Kalman gain K k , then return to step 1.

[0075] In some embodiments, the Kalman gain K is updatedk The method is:

[0076] Using the formula: Calculate the updated Kalman gain K k .

[0077] An embodiment of the present invention further provides a battery SOC estimation system, which includes a controller programmed to execute the above-mentioned battery SOC estimation method.

[0078] An embodiment of the present invention further provides a vehicle, which includes the above-mentioned battery SOC estimation system.

[0079] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A battery SOC estimation method, characterized in that: include: Obtain the real-time power P, real-time voltage U, real-time current I, real-time temperature T and real-time internal resistance R of the battery during vehicle driving; The real-time current I and the previous predicted SOC value Substitute the preset battery prediction model to calculate the current predicted SOC value Substitute the real-time voltage U, real-time current I and real-time internal resistance R into the preset battery measurement model to calculate the measured SOC value SOC ocv ; Using the formula: Calculate the current SOC value SOC corrected ; Among them, K k represents the Kalman gain; The current SOC value SOC corrected As the battery SOC estimation value SOC est ; Determine whether the discharge boundary condition or the charge boundary condition is triggered; if so, calculate the discharge target SOC value SOC target_f Or the charging target SOC value SOC target_c , and based on the current SOC value and the discharge target SOC value or the charging target SOC value, correct the battery SOC estimation value SOC est , then update the Kalman gain K k .

2. The battery SOC estimation method according to claim 1, characterized in that: The preset battery prediction model is: Wherein, Δt represents a preset time step, Q represents a preset battery capacity, and the initial predicted SOC value is obtained by querying a preset voltage-SOC correspondence table according to the initial battery voltage.

3. The battery SOC estimation method according to claim 1, characterized in that: The preset battery measurement model is: SOC ocv =a3*(UI*R) 3 +a2*(UI*R) 2 +a1*(UI*R)+a0; Among them, a0, a1, a2, and a3 represent preset model coefficients.

4. The battery SOC estimation method according to any one of claims 1 to 3, characterized in that: If the real-time battery voltage drops and drops to less than or equal to U min +U th1 , then determine the trigger discharge boundary conditions; where U min Indicates the preset lower cut-off voltage, U th1 represents a first preset voltage threshold; If the real-time battery voltage rises and rises to a value greater than or equal to U max -U th2 , then the charging boundary condition is determined to be triggered; where U max Indicates the preset upper cut-off voltage, U th2 represents the second preset voltage threshold.

5. The battery SOC estimation method according to claim 4, characterized in that: Calculate the discharge target SOC value SOC target_f The method is: substitute the real-time power P, real-time temperature T and real-time internal resistance R into the preset battery discharge expectation model to calculate the discharge target SOC value SOC target_f ; Calculate the charging target SOC value SOC target_c The method is as follows: Substitute the real-time power P, real-time temperature T and real-time internal resistance R into the preset battery charging expectation model to calculate the charging target SOC value SOC target_c .

6. The battery SOC estimation method according to claim 5, characterized in that: The preset battery discharge expectation model is obtained in the following manner: First, under m different current conditions, the battery is continuously discharged until the voltage is less than or equal to U min +U th1 , and record the discharge power, discharge internal resistance, discharge temperature and discharge SOC of the battery at this time, and obtain m groups of discharge experimental data; Then, the multiple regression analysis tool is used to fit the m groups of discharge experimental data to establish a regression model, and the regression coefficients c0, c1, c2, c3, c4, c5, c6, c7, c8, c9 are calculated by the least squares method, and then the preset battery discharge expectation model is obtained: SOC targe_f =c0+c1*P+c2*R+c3*T+c4*P 2 +c5*R 2 +c6*T 2 +c7*P*R+c8*P*T+c9*R*T; The preset battery charging expectation model is obtained in the following manner: First, under m different current conditions, the battery is continuously charged until the voltage is greater than or equal to U max -U th2 , and record the battery's charging power, charging internal resistance, charging temperature and charging SOC at this time, and obtain m groups of charging experimental data; Then, the multivariate regression analysis tool is used to fit the m groups of charging experimental data to establish a regression model, and the regression coefficients d0, d1, d2, d3, d4, d5, d6, d7, d8, and d9 are obtained by the least squares method, thereby obtaining the preset battery charging expectation model: SOC targe_c =d0+d1*P+d2*R+d3*T+d4*P 2 +d5*R 2 +d6*T 2 +d7*P*R+d8*P*T+d9*R*T.

7. The battery SOC estimation method according to any one of claims 1 to 3, characterized in that: Based on the current SOC value SOC corrected And the discharge target SOC value SOC target_f , correct the battery SOC estimate SOC est The method is: if SOC target_f <SOC corrected , then SOC est By SOC corrected According to the first gradient ΔSOC f Gradually correct to SOC target_f ; Among them, ΔSOC f =α*(SOC target_f -SOC corrected ), α represents the correction rate factor, α0 represents the preset correction rate threshold, V max Indicates the preset upper limit of the voltage change rate, V min represents the preset lower limit of voltage change rate, t0 represents the preset time, U t Indicates the battery voltage at the current moment, U t；t0 represents the battery voltage before t0 seconds, and min() represents the minimum function; Based on the current SOC value SOC corrected And the charging target SOC value SOC target_c , correct the battery SOC estimate SOC est The method is: if SOC target_c >SOC corrected , then SOC est By SOC corrected According to the second gradient ΔSOC c Gradually correct to SOC target_c ; Among them, ΔSOC c =α*(SOC target_c -SOC corrected ).

8. The battery SOC estimation method according to any one of claims 1 to 3, characterized in that: Kalman gain K k The initial value is equal to Among them, H T represents a preset measurement matrix, N represents a preset process noise covariance matrix, and G represents a preset measurement noise covariance matrix; Update Kalman gain K k The method is: Using the formula: Calculate the updated Kalman gain K k .

9. A battery SOC estimation system, comprising a controller, characterized in that: The controller is programmed to execute the battery SOC estimation method according to any one of claims 1 to 8.

10. A vehicle, characterized in that: Comprising the battery SOC estimation system as claimed in claim 9.

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