Characteristic model-based power battery SOC estimation method and system

Abstract

The invention provides a power battery SOC estimation method and system based on a characteristic model, and relates to the technical field of SOC estimation, and the method specifically comprises the steps: obtaining three characteristic parameters of an open-circuit voltage, an internal resistance and a polarization voltage of a battery, introducing a temperature and aging correction mechanism, building a relation curve between the characteristic parameters and the SOC, and calculating the SOC of the battery according to the relation curve; mapping the characteristic parameters into SOC values through a fitting formula; constructing an SOC fusion model based on a relation formula of the calibrated characteristic parameters and the SOC, synthesizing SOC estimated values of the characteristics into a comprehensive estimated value by using a weighted linear combination method, dynamically adjusting weights of the characteristics through a particle swarm optimization algorithm, and determining an optimal weight coefficient; through the extended Kalman filtering, the comprehensive estimation value output by the model is corrected in real time, the SOC change is predicted based on the state transition equation in the correction process, the deviation between the estimation value and the true value is updated in combination with the measurement equation, and the real-time performance and reliability of SOC estimation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of SOC estimation, and in particular to a method and system for estimating the SOC of a power battery based on a characteristic model. Background Art

[0002] With the widespread application of new energy vehicles, the state of charge of power batteries is an important indicator for measuring the remaining power of batteries. Its accurate estimation plays a key role in achieving efficient utilization and safety management of batteries. However, the existing SOC estimation methods have certain technical limitations and are difficult to meet the requirements of high accuracy and high reliability in complex application scenarios. The traditional open circuit voltage method estimates the SOC by measuring the relationship between the static open circuit voltage of the battery and the SOC, but this method requires the battery to be static for a long time, and it is difficult to obtain accurate data in real time in practical applications. In addition, the open circuit voltage is significantly affected by temperature changes, and the estimation error is large without temperature compensation. The internal resistance method infers the SOC by measuring the change in the internal resistance of the battery, but the internal resistance is easily affected by the degree of battery aging and the working environment. Its accuracy and stability are poor, especially after the battery is aged, it is difficult to reflect the true state of the SOC. The polarization voltage law relies on the difference between the operating voltage and the open circuit voltage. Although it can reflect the dynamic characteristics of the SOC to a certain extent, it is greatly affected by the dynamic changes of the current and cannot provide stable estimation results under rapidly changing working conditions.

[0003] In addition, the existing technology generally adopts a single characteristic parameter for SOC estimation, lacking the comprehensive use of multiple characteristic parameters, resulting in poor robustness of the estimation results. Since the battery will be affected by multiple factors such as temperature, aging, and dynamic changes in current during actual operation, a single parameter is difficult to fully reflect the true SOC state of the battery. Even if some studies try to integrate multiple characteristic parameters, they usually use a fixed weight combination, which makes it difficult to dynamically adjust the estimation strategy according to changes in the battery state. This method does not have sufficient adaptability under complex working conditions. More importantly, the traditional SOC estimation method lacks a real-time correction mechanism for model errors, and cannot effectively reduce the accumulated errors in long-term use, thereby affecting the accuracy and reliability of SOC estimation. These deficiencies make it difficult for the existing technology to meet the requirements of new energy vehicles for high precision, dynamic adaptability and real-time performance of SOC estimation methods in practical applications.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The object of the present invention is to provide a method and system for estimating SOC of a power battery based on a characteristic model to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method and system for estimating SOC of a power battery based on a characteristic model, the specific steps comprising:

[0008] Step 1: Obtain the three characteristic parameters of the battery, namely, open circuit voltage, internal resistance and polarization voltage, and introduce temperature and aging correction mechanisms, including temperature compensation for open circuit voltage and aging correction for internal resistance, establish a relationship curve between characteristic parameters and SOC, and map the characteristic parameters to SOC values ​​through fitting formulas;

[0009] Step 2: Based on the relationship formula between the calibrated characteristic parameters and SOC, the SOC fusion model is constructed. The SOC estimation values ​​of each feature are synthesized into a comprehensive estimation value using the weighted linear combination method. The weight of each feature is dynamically adjusted through the particle swarm optimization algorithm to minimize the error objective function and determine the optimal weight coefficient.

[0010] Step 3: Through the extended Kalman filter, the comprehensive estimated value of the model output is corrected in real time. The correction process predicts the change of SOC based on the state transfer equation and updates the deviation between the estimated value and the true value in combination with the measurement equation.

[0011] Furthermore, after the battery is left to stand for 1 hour at different SOC points, the open circuit voltage of the battery is measured, the data is recorded, and the open circuit voltage is corrected for temperature:

[0012] V OC ′=V OC -γ*ΔT

[0013] Where V OC ′ is the corrected open circuit voltage, V OC is the open circuit voltage, measured by static placement, γ is the temperature sensitivity coefficient, and ΔT is the difference between the current temperature and the calibration temperature;

[0014] Fit the relationship between open circuit voltage and SOC after correction:

[0015]

[0016] Where V OC ′ is the corrected open circuit voltage, SOC is the battery state of charge, which is known from the experiment, a 1 、b 1 and a 2 is the fitting coefficient, obtained by regression of experimental data;

[0017] Using the current pulse method, apply a known current pulse to the battery, record the voltage drop, and calculate the internal resistance according to the following formula:

[0018]

[0019] In the formula, R int is the internal resistance of the battery, ΔV is the voltage change, and ΔI is the current pulse value;

[0020] Aging correction for internal resistance:

[0021] R int ′=R int *(1+λ*Aging)

[0022] In the formula, R int ′ is the corrected internal resistance, λ is the aging sensitivity coefficient, and Aging is the battery aging degree;

[0023] The calculation method of Aging is defined as:

[0024]

[0025] Where N current Indicates the current cumulative number of cycles of the battery, N rated Indicates the rated cycle life of the battery;

[0026] Record the battery internal resistance at different SOC points, and fit the relationship between the corrected internal resistance and SOC:

[0027]

[0028] In the formula, R int ′ is the corrected internal resistance, SOC is the battery state of charge, which is known from the experiment, c 1 、c 2 and c 3 is the fitting coefficient, obtained by regression of experimental data;

[0029] At different SOC points, the difference between the operating voltage and the open circuit voltage is measured to obtain the polarization voltage:

[0030] V pol =V OC -V work

[0031] Where V pol is the polarization voltage, V OC is the open circuit voltage, V work is the working voltage;

[0032] Fitting the relationship between polarization voltage and SOC:

[0033]

[0034] Where V polis the polarization voltage, SOC is the battery state of charge, which is known from the experiment, d 1 d 2 and d 3 is the fitting coefficient, obtained by regression of experimental data;

[0035] The relationship between the corrected open circuit voltage, corrected internal resistance, polarization voltage and SOC is fitted to the function model and saved as a lookup table.

[0036] Furthermore, based on the experimental relationship of characteristic parameters, the SOC fusion model is constructed:

[0037]

[0038] In the formula, SOC est is a comprehensive estimate of the battery’s current state of charge. Represents the SOC value calculated by the open circuit voltage fitting formula, Represents the SOC value calculated by the internal resistance fitting formula, represents the SOC value calculated by the polarization voltage fitting formula, ω 1 ,ω 2 and ω 3 is the feature weight coefficient, satisfying ω 1 +ω 2 +ω 3 =1;

[0039] The particle swarm optimization algorithm is used to dynamically adjust the weights of each feature to minimize the error objective function and determine the optimal weight coefficient. The specific logic is as follows:

[0040] The objective function is:

[0041]

[0042] Where, E is the mean square error, SOC est (i) is the comprehensive estimated value of the i-th sample, SOC true (i) is the experimental true value of the i-th sample, ω is the number of samples, and i is the index of the sample;

[0043] The specific steps of particle swarm optimization algorithm are:

[0044] Particle definition: Each particle represents a possible weight combination (ω 1 ,ω 2 ,ω 3 ), the search space dimension is 3, corresponding to ω 1 ,ω 2 ,ω 3 , initialize M particles, the initial position of each particle (ω 1 ,ω 2,ω 3 ) and speed are randomly generated;

[0045] When the particles are initialized, the weights satisfy the constraints:

[0046] ω 1 +ω 2 +ω 3 =1 and ω 1 ,ω 2 ,ω 3 >0

[0047] Objective function calculation: Get an error value based on the position of each particle, calculated according to the following formula:

[0048]

[0049] Initialize the speed of each particle to a random value, as follows:

[0050]

[0051] In PSO, the position and velocity of particles are updated according to the following rules:

[0052] The speed update formula is:

[0053] v j (t+1)=β*v j (t)+f 1 *rand 1 *(p best,j -x j (t))+f 2 *rand 2 *(g best,j -x j (t))

[0054] In the formula, v j (t+1) represents the velocity of the particle in the jth dimension at the next moment, which determines the direction and speed of the particle moving in this dimension. j (t) represents the velocity of the particle at the current moment, β is the inertia weight, which is used to balance the global and local nature of the search, and f 1 、f 2 is a learning factor used to control the speed at which particles approach the individual optimal position and the global optimal position. 1 and rand 2 is a random number in the interval [0,1], used to introduce randomness, p best,j represents the historical optimal position of the particle itself in the jth dimension, x j (t) is the current position of the particle in the jth dimension, g best,jrepresents the global optimal position of all particles in the search space in the jth dimension;

[0055] The position update formula is:

[0056] x j (t+1)=x j (t)+v j (t+1)

[0057] In the formula, x j (t+1) represents the position of the particle in the jth dimension at the next moment;

[0058] In each round of iteration, the error value E corresponding to the position of each particle is calculated, and the speed and position are updated at the same time, and p is updated. best and g best , repeat the process until the preset maximum number of iterations H is reached;

[0059] After the iteration, the global optimal solution of the particle swarm g best That is the optimal weight, and the fusion model is updated as follows:

[0060]

[0061] in, and is the optimal weight.

[0062] Furthermore, the SOC estimation value of the fusion model is corrected in real time by using the extended Kalman filter. The correction process predicts the change of SOC based on the state transfer equation and updates the deviation between the estimated value and the true value in combination with the measurement equation. Specifically:

[0063] According to the working state of the battery, the SOC state transfer equation is defined as:

[0064]

[0065] In the formula, SOC k+1 is the SOC value at the next moment, SOC k is the SOC value at the current moment, Q max is the rated capacity of the battery, ΔQ is the current charge and discharge capacity, calculated by the current integration method;

[0066] Combine the measured values ​​and define the measurement equation:

[0067] z k =h(SOC k )+s k

[0068] In the formula, z k is the actual measured SOC value, h(SOC k) represents the SOC value output by the fusion model, s k To measure noise;

[0069] Correction process: Predict the current SOC value according to the state transfer equation, calculate the deviation between the measured value and the predicted value in combination with the measurement equation, and correct the SOC value.

[0070] The present invention further provides a power battery SOC estimation system based on a characteristic model, wherein the power battery SOC estimation system based on a characteristic model is used to execute the above-mentioned power battery SOC estimation method based on a characteristic model, comprising:

[0071] The characteristic parameter extraction module is used to obtain the three characteristic parameters of the battery, namely, open circuit voltage, internal resistance and polarization voltage, and introduce temperature and aging correction mechanisms, including temperature compensation for open circuit voltage and aging correction for internal resistance, establish a relationship curve between characteristic parameters and SOC, and map the characteristic parameters to SOC values ​​through fitting formulas;

[0072] The fusion model building module builds the SOC fusion model based on the relationship formula between the calibrated feature parameters and SOC. The SOC estimation values ​​of each feature are synthesized into a comprehensive estimation value using the weighted linear combination method. The weight of each feature is dynamically adjusted through the particle swarm optimization algorithm to minimize the error objective function and determine the optimal weight coefficient.

[0073] The real-time correction module is used to perform real-time correction on the comprehensive estimated value output by the model through extended Kalman filtering. The correction process predicts the change of SOC based on the state transfer equation and updates the deviation between the estimated value and the true value in combination with the measurement equation.

[0074] Compared with the prior art, the present invention has the following beneficial effects:

[0075] This scheme proposes a power battery SOC estimation method based on three characteristic parameters: open circuit voltage, internal resistance and polarization voltage. By introducing temperature compensation and aging correction mechanisms, it effectively solves the problem that the estimation of a single characteristic parameter is greatly affected by the environment and aging. By constructing a fusion model and dynamically adjusting the feature weights using a particle swarm optimization algorithm, this method can adaptively optimize the SOC estimation results under different working conditions, significantly improving the accuracy of the estimation. In addition, the estimated value of the fusion model is corrected in real time using an extended Kalman filter, further enhancing the dynamic response capability and robustness of the model. This scheme can not only adapt to the multi-factor changes of the battery under complex working conditions, but also reduce the cumulative errors caused by aging or temperature fluctuations, improve the real-time and reliability of SOC estimation, and provide technical support for the efficient management of power batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1It is a schematic diagram of the overall method flow of the present invention;

[0077] Figure 2 It is a schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0078] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0079] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0080] Example:

[0081] See also Figure 1 , the present invention provides a technical solution:

[0082] A method for estimating SOC of a power battery based on a characteristic model, the specific steps comprising:

[0083] Step 1: Obtain the three characteristic parameters of the battery, namely, open circuit voltage, internal resistance and polarization voltage, and introduce temperature and aging correction mechanisms, including temperature compensation for open circuit voltage and aging correction for internal resistance, establish a relationship curve between characteristic parameters and SOC, and map the characteristic parameters to SOC values ​​through fitting formulas;

[0084] In this embodiment, after the battery is left to stand for 1 hour at different SOC points, the open circuit voltage of the battery is measured, the data is recorded, and the open circuit voltage is corrected for temperature:

[0085] V OC ′=V OC -γ*ΔT

[0086] Where V OC ′ is the corrected open circuit voltage, V OC is the open circuit voltage, measured by static placement, γ is the temperature sensitivity coefficient, γ=5mV / ℃, ΔT is the difference between the current temperature and the calibration temperature;

[0087] The different SOC points specifically include: 0, 25%, 50%, 75%, 100%;

[0088] Fit the relationship between open circuit voltage and SOC after correction:

[0089]

[0090] Where V OC ′ is the corrected open circuit voltage, SOC is the battery state of charge, which is known from the experiment, a 1 、b 1 and a 2 is the fitting coefficient, obtained by regression of experimental data;

[0091] Using the current pulse method, apply a known current pulse to the battery, record the voltage drop, and calculate the internal resistance according to the following formula:

[0092]

[0093] In the formula, R int is the internal resistance of the battery, ΔV is the voltage change, and ΔI is the current pulse value;

[0094] Aging correction for internal resistance:

[0095] R int ′=R int *(1+λ*Aging)

[0096] In the formula, R int ′ is the corrected internal resistance, λ is the aging sensitivity coefficient, the specific value is determined by experiment and combined with the actual working conditions, Aging is the degree of battery aging;

[0097] The calculation method of Aging is defined as:

[0098]

[0099] Where N current Indicates the current cumulative number of cycles of the battery, N rated Indicates the rated cycle life of the battery;

[0100] Record the battery internal resistance at different SOC points, and fit the relationship between the corrected internal resistance and SOC:

[0101]

[0102] In the formula, R int ′ is the corrected internal resistance, SOC is the battery state of charge, which is known from the experiment, c 1 、c 2and c 3 is the fitting coefficient, obtained by regression of experimental data;

[0103] At different SOC points, the difference between the operating voltage and the open circuit voltage is measured to obtain the polarization voltage:

[0104] V pol =V OC -V work

[0105] Where V pol is the polarization voltage, V OC is the open circuit voltage, V work is the working voltage;

[0106] Fitting the relationship between polarization voltage and SOC:

[0107]

[0108] Where V pol is the polarization voltage, SOC is the battery state of charge, which is known from the experiment, d 1 d 2 and d 3 is the fitting coefficient, obtained by regression of experimental data;

[0109] The relationship between the corrected open circuit voltage, corrected internal resistance, polarization voltage and SOC is fitted to the function model and saved as a lookup table.

[0110] Step 1 obtains three characteristic parameters, namely open circuit voltage, internal resistance and polarization voltage, and combines temperature compensation and aging correction mechanisms to accurately establish the relationship curve between characteristic parameters and SOC. This acquisition and correction method based on multiple characteristic parameters overcomes the limitations of traditional single parameter methods, such as the open circuit voltage method being greatly affected by temperature changes, the internal resistance method being significantly affected by the degree of aging, and the polarization voltage method being weak in dynamics. By performing temperature compensation on the open circuit voltage and aging correction on the internal resistance, this step effectively weakens the adverse effects of ambient temperature and battery aging on the accuracy of SOC estimation, laying a solid foundation for the accurate construction of subsequent models.

[0111] Compared with the prior art, this solution not only uses multiple characteristic parameters in step 1, but also introduces a correction mechanism for temperature sensitivity and aging sensitivity, which significantly improves the applicability and accuracy of SOC estimation. Traditional single parameter estimation methods such as open circuit voltage method or internal resistance method are difficult to provide stable and reliable SOC estimation results under complex operating conditions because they do not consider the dynamic changes of the external environment and battery state. In this step, the relationship between multiple characteristic parameters and SOC is accurately modeled through experimental fitting correction formulas, which realizes the comprehensive characterization and dynamic adaptability of the battery state, and provides an innovative solution to solve the problems of insufficient robustness and adaptability in the prior art. In this solution, the implementation of step 1 provides high-quality basic data for the construction of the subsequent SOC fusion model, ensuring the accuracy and diversity of the model input. Through the correction and fitting of the characteristic parameters, this step not only improves the estimation accuracy of the single parameter, but also provides a reliable basis for the multi-feature synthesis of the fusion model, and lays a good foundation for dynamic weight optimization and real-time correction. It can be said that this step is the core premise and starting point of the entire SOC estimation process, and its high-precision characteristic parameter acquisition and correction mechanism directly determines the reliability and stability of the final SOC estimation result.

[0112] Step 2: Based on the relationship formula between the calibrated characteristic parameters and SOC, the SOC fusion model is constructed. The SOC estimation values ​​of each feature are synthesized into a comprehensive estimation value using the weighted linear combination method. The weight of each feature is dynamically adjusted through the particle swarm optimization algorithm to minimize the error objective function and determine the optimal weight coefficient.

[0113] In this embodiment, a SOC fusion model is constructed based on the experimental relationship of characteristic parameters:

[0114]

[0115] In the formula, SOC est is a comprehensive estimate of the battery’s current state of charge. Represents the SOC value calculated by the open circuit voltage fitting formula, Represents the SOC value calculated by the internal resistance fitting formula, represents the SOC value calculated by the polarization voltage fitting formula, ω 1 ,ω 2 and ω 3 is the feature weight coefficient, satisfying ω 1 +ω 2 +ω 3 =1;

[0116] The particle swarm optimization algorithm is used to dynamically adjust the weights of each feature to minimize the error objective function and determine the optimal weight coefficient. The specific logic is as follows:

[0117] The objective function is:

[0118]

[0119] Where, E is the mean square error, SOC est (i) is the comprehensive estimated value of the i-th sample, SOC true (i) is the experimental true value of the i-th sample, N is the number of samples, and i is the index of the sample;

[0120] The specific steps of particle swarm optimization algorithm are:

[0121] Particle definition: Each particle represents a possible weight combination (ω 1 ,ω 2 ,ω 3 ), the search space dimension is 3, corresponding to ω 1 ,ω 2 ,ω 3 , initialize M particles, the initial position of each particle (ω 1 ,ω 2 ,ω 3 ) and speed are randomly generated;

[0122] When the particles are initialized, the weights satisfy the constraints:

[0123] ω 1 +ω 2 +ω 3 =1 and ω 1 ,ω 2 ,ω 3 >0

[0124] Objective function calculation: Get an error value based on the position of each particle, calculated according to the following formula:

[0125]

[0126] Initialize the speed of each particle to a random value, as follows:

[0127]

[0128] In PSO, the position and velocity of particles are updated according to the following rules:

[0129] The speed update formula is:

[0130] v j (t+1)=β*v j (t)+f 1 *rand 1 *(p best,j -x j (t))+f 2 *rand2 *(g best,j -x j (t))

[0131] In the formula, v j (t+1) represents the velocity of the particle in the jth dimension at the next moment, which determines the direction and speed of the particle moving in this dimension. j (t) represents the velocity of the particle at the current moment, β is the inertia weight, which is used to balance the global and local nature of the search, and f 1 、f 2 is a learning factor used to control the speed at which particles approach the individual optimal position and the global optimal position. 1 and rand 2 is a random number in the interval [0,1], used to introduce randomness, p best,j represents the historical optimal position of the particle itself in the jth dimension, x j (t) is the current position of the particle in the jth dimension, g best,j represents the global optimal position of all particles in the search space in the jth dimension;

[0132] The position update formula is:

[0133] x j (t+1)=x j (t)+v j (t+1)

[0134] In the formula, x j (t+1) represents the position of the particle in the jth dimension at the next moment;

[0135] In each round of iteration, the error value E corresponding to the position of each particle is calculated, and the speed and position are updated at the same time, and p is updated. best and g best , repeat the process until the preset maximum number of iterations H is reached;

[0136] After the iteration, the global optimal solution of the particle swarm g best That is the optimal weight, and the fusion model is updated as follows:

[0137]

[0138] in, and is the optimal weight.

[0139] Step 2 constructs an SOC fusion model and uses a weighted linear combination method to combine the SOC estimation values ​​corresponding to the three characteristic parameters into a unified estimation result, thereby effectively making up for the limitations of the single characteristic parameter method. In addition, this step introduces a particle swarm optimization algorithm to dynamically adjust the weights of the characteristic parameters, and adaptively determines the optimal weight combination according to the actual working conditions to minimize the estimation error of the fusion model. Compared with the traditional method of fixed weights, particle swarm optimization not only improves the flexibility of estimation, but also significantly enhances the model's adaptability to environmental changes under complex working conditions, ensuring that the final fusion result is more accurate and robust.

[0140] Compared with the prior art, the SOC fusion model in step 2 achieves complementary advantages through dynamic weight allocation, overcoming the problem of excessive dependence of a single parameter on a specific working condition in the traditional method. For example, the open circuit voltage method works well under static conditions, but performs poorly in dynamic changes, while the internal resistance method and polarization voltage method can make up for this shortcoming. By dynamically adjusting the weights through the particle swarm optimization algorithm, this step can flexibly allocate the contribution weight of each characteristic parameter according to different working conditions, reducing the error accumulation problem caused by the limitations of the characteristic parameters. Compared with the single parameter estimation method or the fixed weight fusion method, this step significantly improves the accuracy and adaptability of SOC estimation. In this solution, step 2 constructs a high-precision, dynamically adaptive SOC estimation model by fusion processing and weight optimization of multiple characteristic parameters, providing a reliable basis for subsequent real-time correction. The fusion model integrates a variety of characteristic information, enhances the stability and robustness of the SOC estimation results, and the particle swarm optimization algorithm further improves the dynamic response capability of the model. The implementation of this step solves the error problem caused by the insufficiency of a single parameter or the rigid allocation of fixed weights in the traditional SOC estimation method, and provides core support for the entire scheme to achieve high-precision SOC estimation in complex environments.

[0141] Step 3: The comprehensive estimated value output by the model is corrected in real time through the extended Kalman filter. The correction process predicts the change of SOC based on the state transfer equation and updates the deviation between the estimated value and the true value in combination with the measurement equation.

[0142] In this embodiment, the SOC estimation value of the fusion model is corrected in real time by using an extended Kalman filter. The correction process predicts the change of SOC based on the state transfer equation, and updates the deviation between the estimated value and the true value in combination with the measurement equation. Specifically,

[0143] According to the working state of the battery, the SOC state transfer equation is defined as:

[0144]

[0145] In the formula, SOC k+1is the SOC value at the next moment, SOC k is the SOC value at the current moment, Q max is the rated capacity of the battery, ΔQ is the current charge and discharge capacity, calculated by the current integration method;

[0146] Combine the measured values ​​and define the measurement equation:

[0147] z k =h(SOC k )+s k

[0148] In the formula, z k is the actual measured SOC value, h(SOC k ) represents the SOC value output by the fusion model, s k To measure noise;

[0149] Correction process: Predict the current SOC value according to the state transfer equation, calculate the deviation between the measured value and the predicted value in combination with the measurement equation, and correct the SOC value.

[0150] Step 3 introduces the extended Kalman filter to make real-time corrections to the SOC estimation value of the fusion model, which has the significant advantages of strong dynamics, high accuracy and good robustness. EKF can predict SOC based on the state transfer equation, and at the same time, combined with the actual measurement value, calculate the deviation between the estimated value and the true value through the measurement equation, and dynamically correct the predicted value. This filtering method can effectively deal with the noise and error accumulation problems in nonlinear systems, and significantly improve the real-time and accuracy of the estimation results. In addition, the dynamic correction characteristics of EKF enable it to cope with complex operating conditions during battery operation, which plays an important role in the long-term stability of SOC estimation.

[0151] Compared with the prior art, this step organically combines state prediction with error correction by extending the Kalman filter, thus overcoming the problem that the traditional SOC estimation method cannot self-correct errors under dynamic conditions. For example, a single SOC estimation method based on characteristic parameter fitting or fixed model is prone to increase in deviation due to cumulative errors, while this step significantly reduces the estimation error by correcting this defect in real time. In addition, EKF can comprehensively utilize the output of the fusion model and the measurement noise to optimize the real-time estimation effect of SOC, especially in the case of battery aging, temperature fluctuation or significant dynamic current characteristics. In this scheme, step 3 further improves the reliability and accuracy of the SOC estimation value output by the fusion model through the real-time correction of the extended Kalman filter. This step effectively compensates for the estimation deviation that may be caused by uncertainty or error accumulation in steps 1 and 2, and provides a dynamic correction mechanism for the final SOC estimation. Combined with the dual constraints of the state transfer equation and the measurement equation, the application of EKF ensures the timely correction and continuous optimization of the SOC estimation results under complex conditions, so that the overall scheme reaches a higher level in accuracy and adaptability, and meets the strict requirements of new energy vehicles for battery state management.

[0152] See also Figure 2 , a power battery SOC estimation system based on a characteristic model, comprising:

[0153] The characteristic parameter extraction module is used to obtain the three characteristic parameters of the battery, namely, open circuit voltage, internal resistance and polarization voltage, and introduce temperature and aging correction mechanisms, including temperature compensation for open circuit voltage and aging correction for internal resistance, establish a relationship curve between characteristic parameters and SOC, and map the characteristic parameters to SOC values ​​through fitting formulas;

[0154] The fusion model building module builds the SOC fusion model based on the relationship formula between the calibrated feature parameters and SOC. The SOC estimation values ​​of each feature are synthesized into a comprehensive estimation value using the weighted linear combination method. The weight of each feature is dynamically adjusted through the particle swarm optimization algorithm to minimize the error objective function and determine the optimal weight coefficient.

[0155] The real-time correction module is used to perform real-time correction on the comprehensive estimated value output by the model through extended Kalman filtering. The correction process predicts the change of SOC based on the state transfer equation and updates the deviation between the estimated value and the true value in combination with the measurement equation.

[0156] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0157] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0158] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0159] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A power battery SOC estimation method based on a characteristic model, characterized in that: The specific steps include: Step 1: Obtain the three characteristic parameters of the battery, namely, open circuit voltage, internal resistance and polarization voltage, and introduce temperature and aging correction mechanisms, including temperature compensation for open circuit voltage and aging correction for internal resistance, establish a relationship curve between characteristic parameters and SOC, and map the characteristic parameters to SOC values ​​through fitting formulas; Step 2: Based on the relationship formula between the calibrated characteristic parameters and SOC, the SOC fusion model is constructed. The SOC estimation values ​​of each feature are synthesized into a comprehensive estimation value using the weighted linear combination method. The weight of each feature is dynamically adjusted through the particle swarm optimization algorithm to minimize the error objective function and determine the optimal weight coefficient. Step 3: Through the extended Kalman filter, the comprehensive estimated value of the model output is corrected in real time. The correction process predicts the change of SOC based on the state transfer equation and updates the deviation between the estimated value and the true value in combination with the measurement equation.

2. The method for estimating SOC of a power battery based on a characteristic model according to claim 1, characterized in that: After the battery is left to stand for 1 hour at different SOC points, the open circuit voltage of the battery is measured, the data is recorded, and the open circuit voltage is corrected for temperature: V OC ′ =V OC -γ*ΔT Where V OC ′ is the corrected open circuit voltage, V OC is the open circuit voltage, measured by static placement, γ is the temperature sensitivity coefficient, and ΔT is the difference between the current temperature and the calibration temperature; Fit the relationship between open circuit voltage and SOC after correction: Where V OC ′ is the corrected open circuit voltage, SOC is the battery state of charge, which is known from the experiment, a1, b1 and a2 are fitting coefficients, which are obtained by regression of experimental data; Using the current pulse method, apply a known current pulse to the battery, record the voltage drop, and calculate the internal resistance according to the following formula: In the formula, R int is the internal resistance of the battery, ΔV is the voltage change, and ΔI is the current pulse value; Aging correction for internal resistance: R int ′ =R int *(1+λ*Aging) In the formula, R int ′ is the corrected internal resistance, λ is the aging sensitivity coefficient, and Aging is the battery aging degree; The calculation method of Aging is defined as: Where N current Indicates the current cumulative number of cycles of the battery, N rated Indicates the rated cycle life of the battery; Record the battery internal resistance at different SOC points, and fit the relationship between the corrected internal resistance and SOC: In the formula, R int ′ is the corrected internal resistance, SOC is the battery state of charge, which is known from the experiment, c1, c2 and c3 are fitting coefficients, which are obtained by regression of experimental data; At different SOC points, the difference between the operating voltage and the open circuit voltage is measured to obtain the polarization voltage: V pol =V OC -V work Where V pol is the polarization voltage, V OC is the open circuit voltage, V work is the working voltage; Fitting the relationship between polarization voltage and SOC: Where V pol is the polarization voltage, SOC is the battery state of charge, which is known from the experiment, d1, d2 and d3 are fitting coefficients, which are obtained by regression of experimental data; The relationship between the corrected open circuit voltage, corrected internal resistance, polarization voltage and SOC is fitted to the function model and saved as a lookup table.

3. The method for estimating SOC of a power battery based on a characteristic model according to claim 2, characterized in that: Based on the experimental relationship of characteristic parameters, the SOC fusion model is constructed: In the formula, SOC est is a comprehensive estimate of the battery’s current state of charge. Represents the SOC value calculated by the open circuit voltage fitting formula, Represents the SOC value calculated by the internal resistance fitting formula, represents the SOC value calculated by the polarization voltage fitting formula, ω1, ω2 and ω3 are characteristic weight coefficients, satisfying ω1+ω2+ω3=1; The particle swarm optimization algorithm is used to dynamically adjust the weights of each feature to minimize the error objective function and determine the optimal weight coefficient. The specific logic is as follows: The objective function is: Where, E is the mean square error, SOC est (i) is the comprehensive estimated value of the i-th sample, SOC true (i) is the experimental true value of the i-th sample, N is the number of samples, and i is the index of the sample; The specific steps of particle swarm optimization algorithm are: Particle definition: Each particle represents a possible weight combination (ω1, ω2, ω3). The search space dimension is 3, corresponding to ω1, ω2, ω3. M particles are initialized, and the initial position (ω1, ω2, ω3) and speed of each particle are randomly generated. When the particles are initialized, the weights satisfy the constraints: ω1+ω2+ω3=1 and ω1, ω2, ω3>0 Objective function calculation: Get an error value based on the position of each particle, calculated according to the following formula: Initialize the speed of each particle to a random value, the expression is as follows: In PSO, the position and velocity of particles are updated according to the following rules: The speed update formula is: v j (t+1)=β*v j (t)+f1*rand1*(p best,j -x j (t))+f2*rand2*(g best,j -x j (t)) In the formula, v j (t+1) represents the velocity of the particle in the jth dimension at the next moment, which determines the direction and speed of the particle moving in this dimension. j (t) represents the velocity of the particle at the current moment, β is the inertia weight, which is used to balance the global and local nature of the search, f1 and f2 are learning factors, which are used to control the speed at which the particle approaches the individual optimal position and the global optimal position, rand1 and rand2 are random numbers in the interval [0,1], which are used to introduce randomness, and p best,j represents the historical optimal position of the particle itself in the jth dimension, x j (t) is the current position of the particle in the jth dimension, g best,j represents the global optimal position of all particles in the search space in the jth dimension; The position update formula is: x j (t+1)=x j (t)+v j (t+1) In the formula, x j (t+1) represents the position of the particle in the jth dimension at the next moment; In each round of iteration, the error value E corresponding to the position of each particle is calculated, and the speed and position are updated at the same time, and p is updated. best and g best , repeat the process until the preset maximum number of iterations H is reached; After the iteration, the global optimal solution of the particle swarm g best That is the optimal weight, and the fusion model is updated as follows: in, and is the optimal weight.

4. The method for estimating SOC of a power battery based on a characteristic model according to claim 1, characterized in that: The SOC estimation value of the fusion model is corrected in real time through the extended Kalman filter. The correction process predicts the change of SOC based on the state transfer equation and updates the deviation between the estimated value and the true value in combination with the measurement equation. Specifically: According to the working state of the battery, the SOC state transfer equation is defined as: In the formula, SOC k+1 is the SOC value at the next moment, SOC k is the SOC value at the current moment, Q max is the rated capacity of the battery, ΔQ is the current charge and discharge capacity, calculated by the current integration method; Combined with the measured values, define the measurement equation: z k =h(SOC k )+s k In the formula, z k is the actual measured SOC value, h(SOC k ) represents the SOC value output by the fusion model, s k To measure noise; Correction process: Predict the current SOC value according to the state transfer equation, calculate the deviation between the measured value and the predicted value in combination with the measurement equation, and correct the SOC value.

5. A power battery SOC estimation system based on a characteristic model, characterized in that: The power battery SOC estimation system based on the characteristic model is used to execute the power battery SOC estimation method based on the characteristic model according to any one of claims 1 to 4, comprising: The characteristic parameter extraction module is used to obtain the three characteristic parameters of the battery, namely, open circuit voltage, internal resistance and polarization voltage, and introduce temperature and aging correction mechanisms, including temperature compensation for open circuit voltage and aging correction for internal resistance, establish a relationship curve between characteristic parameters and SOC, and map the characteristic parameters to SOC values ​​through fitting formulas; The fusion model building module builds the SOC fusion model based on the relationship formula between the calibrated feature parameters and SOC. The SOC estimation values ​​of each feature are synthesized into a comprehensive estimation value using the weighted linear combination method. The weight of each feature is dynamically adjusted through the particle swarm optimization algorithm to minimize the error objective function and determine the optimal weight coefficient. The real-time correction module is used to perform real-time correction on the comprehensive estimated value output by the model through extended Kalman filtering. The correction process predicts the change of SOC based on the state transfer equation and updates the deviation between the estimated value and the true value in combination with the measurement equation.

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