A battery SOC estimation method, system, and vehicle

Abstract

This invention discloses a battery SOC estimation method, system, and vehicle. It calculates the current SOC value based on the predicted and measured SOC values, uses this current SOC value as the estimated battery SOC value, and calculates the target SOC value for charging / discharging when charge / discharge boundary conditions are triggered. Then, based on the current SOC value and the target SOC value, the estimated battery SOC value is corrected, thereby significantly improving the accuracy of SOC estimation and effectively reducing the accumulated errors, model errors, and inapplicability to real-time dynamic operating conditions inherent in traditional SOC estimation methods. Furthermore, during correction, a correction rate factor is designed for gradual correction, ensuring the smoothness 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.

Description

Technical Field

[0001] This invention belongs to the field of battery management technology, specifically relating to a battery SOC estimation method, system, and vehicle. Background Technology

[0002] SOC, or State of Charge, refers to the ratio between a battery's current remaining charge and its maximum usable charge. This parameter plays a crucial role in the Battery Management System (BMS), directly reflecting the battery's actual usable capacity. Accurate SOC estimation is essential for optimizing the battery's charging and discharging process, preventing overcharging or over-discharging, and thus extending battery life. For new energy vehicles, accurate SOC estimation directly impacts driving range and safety, effectively preventing driving interruptions due to insufficient charge. Highly accurate SOC estimation helps monitor battery status in real time, preventing potential safety risks such as overheating and overcharging. Simultaneously, it promotes efficient battery energy management, improves energy utilization, and optimizes the vehicle's energy allocation and usage strategies.

[0003] Currently, mainstream methods for SOC estimation include the open-circuit voltage method, coulomb counting method, model prediction method, Kalman filtering method, and neural network method. The open-circuit voltage method is favored due to its simple principle and ease of implementation, but it requires the battery to be in a static state, making it unsuitable for real-time dynamic conditions. The coulomb counting method calculates SOC through current integration, enabling real-time estimation, but it requires high accuracy from the current sensor and is prone to cumulative errors, necessitating periodic calibration. Model prediction and neural network methods, on the other hand, are more complex in model construction, requiring large amounts of training data and significant computational costs. Summary of the Invention

[0004] The purpose of this 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 battery SOC estimation method provided by the present invention includes:

[0006] The system acquires 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 operation.

[0007] The real-time current I is compared with the previous predicted SOC value. Substitute the values ​​into 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. ocv .

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

[0010] Set the current SOC value to SOC corrected As an estimated value of battery SOC est Even SOC est =SOC corrected .

[0011] Determine whether the discharge boundary condition or the charging boundary condition has been triggered; if so, calculate the target discharge SOC value. target_f Or the target SOC value for charging target_c And based on the current SOC value SOC corrected and the target SOC value of discharge target_f Or the target SOC value for charging target_c Correcting the battery SOC estimate est Then update the Kalman gain K. k .

[0012] Preferably, the preset battery prediction model is: Where 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 looking up the preset voltage-SOC correspondence table based on the initial battery voltage (i.e. the real-time battery voltage 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; where a0, a1, a2, and a3 represent the preset model coefficients.

[0014] Preferably, if the real-time voltage of the battery (i.e., the current battery voltage) drops and falls to less than or equal to U... min +U th1 Then, the discharge boundary condition is determined; where, U min U represents the preset lower cutoff voltage. th1 This indicates the first preset voltage threshold. If the battery's real-time voltage (i.e., the current battery voltage) rises and reaches a value greater than or equal to U... max -Uth2 If U is found to be in a state of charging boundary condition, then the charging boundary condition is triggered; where U max U represents the preset upper limit cutoff voltage. th2 This indicates the second preset voltage threshold.

[0015] Preferably, the SOC value of the discharge target is calculated. target_f 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 discharge expectation model to calculate the target discharge SOC value. target_f ; Calculate the target SOC value for charging. 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 target SOC value. target_c .

[0016] Preferably, the preset battery discharge expectation model is obtained in the following way:

[0017] First, under m different current conditions, continuously discharge the battery until the voltage is less than or equal to U. min +U th1 The discharge power, internal resistance, temperature, and state of charge (SOC) of the battery are recorded, resulting in m sets of discharge experimental data. Under one current condition, one set of discharge experimental data (i.e., one set of discharge power, internal resistance, temperature, and SOC) is obtained. Under m different current conditions, m sets of discharge experimental data will be obtained.

[0018] Then, a regression model was established by fitting the m sets of discharge experimental data using a multiple regression analysis tool. The regression coefficients c0, c1, c2, c3, c4, c5, c6, c7, c8, and c9 were calculated using the least squares method, thereby obtaining 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.

[0019] Preferably, the preset battery charging expectation model is obtained in the following way:

[0020] First, under m different current conditions, continuously charge the battery until the voltage is greater than or equal to U. max -U th2The charging power, internal resistance, temperature, and state of charge (SOC) of the battery are recorded, resulting in m sets of charging experimental data. Under one current condition, one set of charging experimental data (i.e., one set of charging power, internal resistance, temperature, and SOC) is obtained. Under m different current conditions, m sets of charging experimental data will be obtained.

[0021] Then, a regression model is established by fitting the m sets of charging experimental data using a multiple regression analysis tool. The regression coefficients d0, d1, d2, d3, d4, d5, d6, d7, d8, and d9 are then calculated using 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. corrected and the target SOC value of discharge target_f Correcting the battery SOC estimate est The method is: if SOC target_f <SOC corrected This makes SOC est By SOC corrected According to the first gradient ΔSOC f Gradually correct to SOC target_f ; where ΔSOC f =α*(SOC) target_f -SOC corrected ), where α represents the correction rate factor. α0 represents the preset correction rate threshold, V max This represents the preset upper limit of the voltage change rate, V. min This represents the preset lower limit of the voltage change rate, t0 represents the preset time, and U... t U represents the current voltage of the battery. t；t0 This represents the battery voltage before time t0, and min() represents the minimum value function.

[0023] Preferably, based on the current SOC value. corrected and the target SOC value for charging target_c Correcting the battery SOC estimate est The method is: if SOC target_c >SOC corrected This makes SOC est By SOC corrected According to the second gradient ΔSOC cGradually correct to SOC target_c ; where 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 the preset measurement matrix, N represents the preset process noise covariance matrix, and G represents the preset measurement noise covariance matrix.

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

[0026] Using the formula: The updated Kalman gain K is calculated. k .

[0027] Secondly, the battery SOC estimation system provided by the present invention includes a controller, which is programmed to execute the battery SOC estimation method described above.

[0028] Thirdly, the vehicle provided by the present invention includes the aforementioned battery SOC estimation system.

[0029] This invention calculates the current SOC value based on the predicted and measured SOC values, using this current SOC value as the battery SOC estimate. When charge / discharge boundary conditions are triggered, the target SOC value for charge / discharge is calculated. Then, based on the current SOC value and the target SOC value, the battery SOC estimate is corrected (i.e., the battery SOC estimate is dynamically adjusted), thereby significantly improving the accuracy of SOC estimation and effectively reducing the accumulated errors, model errors, and inapplicability to real-time dynamic operating conditions inherent in traditional SOC estimation methods. Furthermore, a correction rate factor is designed for gradual correction, ensuring the smoothness and reliability of the battery SOC estimate correction process. This avoids the accumulation of SOC estimation errors caused by voltage fluctuations, improves the stability of the battery management system, and has significant application value in the field of battery management. Attached Figure Description

[0030] Figure 1 This is a flowchart of the battery SOC estimation method in an embodiment of the present invention.

[0031] Figure 2 This is a flowchart illustrating the process of obtaining the preset battery discharge expectation model in an embodiment of the present invention.

[0032] Figure 3 This is a flowchart illustrating the process of obtaining the preset battery charging expectation model in an embodiment of the present invention. Detailed Implementation

[0033] To gain a more detailed understanding of the features and technical content of the embodiments of the present invention, the implementation of the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for reference and illustration 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 commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0035] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is 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 this embodiment of the 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 operation, and then proceed to 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 (which is collected, calculated, and uploaded to the CAN bus by other controllers).

[0039] Step 2: Compare the real-time current I with the previously predicted SOC value. Substitute the values ​​into 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: Where 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 looking up the preset voltage-SOC correspondence table based on the initial battery voltage (i.e. the real-time battery voltage obtained when the vehicle starts driving). The preset voltage-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. ocv Then proceed to step four.

[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 experimental calibration, a0 = 2.3457, a1 = -1.2025, a2 = 0.217, and a3 = -0.0091. Therefore, the State of Charge (SOC) is... ocv = -0.0091 * (UI * R) 3 +0.217*(UI*R) 2 -1.2025*(UI*R)+2.3457.

[0044] Step 4: Using the formula: Calculate the current SOC value. corrected Among them, K k Indicate the Kalman gain, and then proceed to step five.

[0045] In some embodiments, the Kalman gain K k The initial value is equal to Among them, H T H represents the preset measurement matrix, N represents the preset process noise covariance matrix, and G represents the preset measurement noise covariance matrix. T Typically, 1, N, and G can be optimized offline based on experimental data (this is an existing technology). This involves observing battery performance under different SOC, temperature, and discharge rate conditions, statistically analyzing the variance of process noise, recording the changes in SOC estimates under different conditions to quantify process noise, and simultaneously calculating its variance under static and dynamic conditions as the initial value for measuring noise. Subsequently, by recording the residual between the estimated SOC and the actual SOC, iterative optimization is performed in real time based on feedback.

[0046] Step 5: Set the current SOC value to SOC. corrected As an estimated value of battery SOC est Then proceed to step six.

[0047] Step 6: Determine if the discharge boundary condition has been triggered. If yes, proceed to Step 7; otherwise, proceed to Step 9.

[0048] In some embodiments, if the real-time voltage of the battery (i.e., the current battery voltage) drops and falls to less than or equal to U... min +U th1If this condition is met, then the discharge boundary condition is determined. Where, U min U represents the preset lower cutoff voltage. th1 This represents the first preset voltage threshold. As an example, U... th1 =300mV.

[0049] Step 7: Calculate the SOC value of the discharge target. target_f Then proceed to step eight.

[0050] In some embodiments, the discharge target SOC value is calculated. target_f 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 discharge expectation model to calculate the target discharge SOC value. target_f .

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

[0052] First, under m different current conditions, continuously discharge the battery until the voltage is less than or equal to U. min +U th1 The discharge power, internal resistance, temperature, and state of charge (SOC) of the battery are recorded, resulting in m sets of discharge experimental data. Under one current condition, one set of discharge experimental data (i.e., one set of discharge power, internal resistance, temperature, and SOC) is obtained. Under m different current conditions, m sets of discharge experimental data will be obtained.

[0053] Then, multiple regression analysis was used to fit the discharge experimental data of m groups, a regression model was established, and the regression coefficients c0, c1, c2, c3, c4, c5, c6, c7, c8, and c9 were obtained using the least squares method, thus obtaining 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 cutoff voltage U min =2.0V, U min +U th1 =2.3V. Based on experimental calibration, the values ​​are: 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. Therefore, the 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 target discharge SOC value, correct the estimated battery SOC value. est Then proceed to step twelve.

[0057] In some embodiments, based on the current SOC value SOC corrected and the target SOC value of discharge target_f Correcting the battery SOC estimate est The methods include:

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

[0059] If SOC target_f ≥SOC corrected If not, no correction will be made; SOC est Still equal to SOC corrected .

[0060] If SOC target_f <SOC corrected This makes 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 ), where α represents the correction rate factor. α0 represents the preset correction rate threshold, V max This represents the preset upper limit of the voltage change rate, V. min This represents the preset lower limit of the voltage change rate, t0 represents the preset time, and U... t U represents the current voltage of the battery. t；t0 This represents the battery voltage before time t0, and min() represents the minimum value function, i.e. Indicates taking The minimum value in α0. For 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 if the charging boundary condition has been triggered. If yes, proceed to Step 10; otherwise, return to Step 1.

[0062] In some embodiments, if the real-time voltage of the battery (i.e., the current battery voltage) rises and rises to a value greater than or equal to U... max -U th2 If this condition is met, then the charging boundary condition is triggered. Where U max U represents the preset upper limit cutoff voltage. th2 This indicates the second preset voltage threshold. As an example, U... th2 =70mV.

[0063] Step 10: Calculate the target SOC value. target_c Then proceed to step eleven.

[0064] In some embodiments, the target SOC value 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 target SOC value. target_c .

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

[0066] First, under m different current conditions, continuously charge the battery until the voltage is greater than or equal to U. max -U th2 The charging power, internal resistance, temperature, and state of charge (SOC) of the battery are recorded, resulting in m sets of charging experimental data. Under one current condition, one set of charging experimental data (i.e., one set of charging power, internal resistance, temperature, and SOC) is obtained. Under m different current conditions, m sets of charging experimental data will be obtained.

[0067] Then, a regression model was established by fitting the m sets of charging experimental data using a multiple regression analysis tool. The regression coefficients d0, d1, d2, d3, d4, d5, d6, d7, d8, and d9 were calculated using the least squares method, thus 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.

[0068] Taking a 57Ah lithium iron phosphate battery as an example, its upper limit cutoff voltage U max =3.65V, U max -U th2 =3.58V. Based on 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. Therefore, the 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 target SOC value for charging, correct the estimated SOC value of the battery. est Then proceed to step twelve.

[0070] In some embodiments, based on the current SOC value SOC corrected and the target SOC value for charging target_c Correcting the battery SOC estimate est The methods include:

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

[0072] If SOC target_c ≤SOC corrected If not, no correction will be made; SOC est Still equal to SOC corrected .

[0073] If SOC target_c >SOC corrected This makes 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 one.

[0075] In some embodiments, update Kalman gain Kk The method is as follows:

[0076] Using the formula: The updated Kalman gain K is calculated. k .

[0077] This invention also provides a battery SOC estimation system, which includes a controller programmed to perform the battery SOC estimation method described above.

[0078] This invention also provides a vehicle that includes the aforementioned 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 this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for estimating battery SOC, characterized in that, include: Acquire 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 operation; The real-time current I is compared with the previous predicted SOC value. Substitute the values ​​into 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. ocv ; Using the formula: Calculate the current SOC value. corrected K k Indicates Kalman gain; Set the current SOC value to SOC corrected As an estimated value of battery SOC est ; Determine whether the discharge boundary condition or the charging boundary condition has been triggered; if so, 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 target discharge SOC value. target_f Alternatively, the real-time power P, real-time temperature T, and real-time internal resistance R can be substituted into a preset battery charging expectation model to calculate the target SOC value. target_c Based on the current SOC value and the target SOC value for discharge or charge, the estimated SOC value of the battery is corrected. est Then update the Kalman gain K. k ; Among them, if the real-time voltage of the battery drops and falls to less than or equal to U min +U th1 Then determine the trigger discharge boundary condition, U min U represents the preset lower cutoff voltage. th1 This indicates the first preset voltage threshold; if the real-time voltage of the battery rises and reaches a value greater than or equal to U... max -U th2 Then it is determined that the charging boundary condition has been triggered, U max U represents the preset upper limit cutoff voltage. th2 This indicates the second preset voltage threshold. Among them, based on the current SOC value SOC corrected and the target SOC value of discharge target_f Correcting the battery SOC estimate est The method is: if SOC target_f <SOC corrected This makes SOC est By SOC corrected According to the first gradient ΔSOC f Gradually correct to SOC target_f ΔSOC f =α*(SOC) target_f -SOC corrected ), where α represents the correction rate factor. α0 represents the preset correction rate threshold, V max This represents the preset upper limit of the voltage change rate, V. min This represents the preset lower limit of the voltage change rate, t0 represents the preset time, and U... t U represents the current voltage of the battery. t-t0 This represents the battery voltage before time t0, and min() represents the minimum value function; based on the current SOC value. corrected and the target SOC value for charging target_c Correcting the battery SOC estimate est The method is: if SOC target_c >SOC corrected This makes SOC est By SOC corrected According to the second gradient ΔSOC c Gradually correct to SOC target_c ΔSOC c =α*(SOC) target_c -SOC corrected ); Wherein, Kalman gain K k The initial value is equal to H T Let N represent the preset measurement matrix, N represent the preset process noise covariance matrix, and G represent the preset measurement noise covariance matrix; update the Kalman gain K. k The method is as follows: using the formula: The updated Kalman gain K is calculated. k .

2. The battery SOC estimation method according to claim 1, characterized in that: The preset battery prediction model is: Where Δt represents the preset time step, Q represents the preset battery capacity, and the initial predicted SOC value is obtained by looking up the preset voltage-SOC correspondence table based on 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; Where a0, a1, a2, and a3 represent the preset model coefficients.

4. The battery SOC estimation method according to any one of claims 1 to 3, characterized in that: The preset battery discharge expectation model is obtained in the following way: First, under m different current conditions, continuously discharge the battery until the voltage is less than or equal to U. min +U th1 The discharge power, internal resistance, temperature and SOC of the battery were recorded at this time, and m sets of discharge experimental data were obtained. Then, a regression model was established by fitting the m sets of discharge experimental data using a multiple regression analysis tool. The regression coefficients c0, c1, c2, c3, c4, c5, c6, c7, c8, and c9 were calculated using the least squares method, thereby obtaining 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; The preset battery charging expectation model is obtained in the following way: First, under m different current conditions, continuously charge the battery until the voltage is greater than or equal to U. max -U th2 The charging power, internal resistance, temperature and SOC of the battery were recorded at this time, and m sets of charging experimental data were obtained. Then, a regression model is established by fitting the m sets of charging experimental data using a multiple regression analysis tool. The regression coefficients d0, d1, d2, d3, d4, d5, d6, d7, d8, and d9 are then calculated using 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.

5. A battery SOC estimation system, comprising a controller, characterized in that: The controller is programmed to perform the battery SOC estimation method as described in any one of claims 1 to 4.

6. A vehicle, characterized in that: Includes the battery SOC estimation system as described in claim 5.

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