A method for estimating the state of charge of a battery based on a two-step approach

Abstract

The present invention provides a method for estimating the state of charge of a battery based on a two-step method. The two-step method is adopted in the present invention to estimate the battery SOC, taking into account the influence of ambient temperature and discharge rate on the battery capacity and characteristics. By analyzing and fitting, the relationship between the identification parameters and SOC is explored, and the relationship equation is determined. The relevant parameters are corrected in the second-order RC equivalent model, which can improve the accuracy of model parameter identification. Based on the identification and correction results, the battery SOC is estimated in real time through the extended Kalman filter. In addition, when the battery data acquisition is inaccurate or even fails due to reasons such as the failure of the sensor of the BMS system acquisition module, the parameter identification of the model and the evaluation of SOC can be completed according to the above correction results, ensuring the safety and stability of the BMS system function.

Description

Technical Field

[0001] The present invention relates to the technical field of batteries, and in particular to a method for estimating the state of charge of a battery based on a two-step method. Background Art

[0002] At present, most of the battery SOC estimation methods are based on experimental data modeling, using improved algorithms or deep learning to estimate the battery SOC, without considering the influence of different ambient temperatures and discharge rates on the internal characteristics of the battery. The accuracy of parameter identification determines the accuracy of the battery model, and the experimental data must be comprehensive.

[0003] During the common battery model building process, only the HPPC experimental data at one ambient temperature (usually 25°C) is input for identification. In low-temperature or high-temperature environments, the accuracy of the model parameter identification results will decrease. The present invention completes the HPPC experiment of the battery at different ambient temperatures and improves the model input to improve the accuracy of the identification results.

[0004] For the improved algorithm to estimate SOC, it takes a lot of time to adjust the parameters to obtain the measurement noise and process noise in the best estimation state. Otherwise, the filtering accuracy of the algorithm will decrease or even diverge; in addition, external factors such as sensor accuracy will also affect the measurement noise and process noise, resulting in a decrease in the estimation accuracy. The deep learning method for estimating SOC is often based on a neural network. Although the neural network has powerful computing capabilities, it has high requirements for the accuracy of training data and high computing costs, so it is not practical in actual applications. In the present invention, the improved Kalman filter algorithm can effectively eliminate noise and jitter, estimate the state in real time, and has a relatively fast response speed, so it is used as a method for SOC estimation. Summary of the Invention

[0005] The present invention mainly considers that the change of the battery capacity and its own temperature under different ambient temperatures and discharge rates of the battery will lead to inaccurate estimation of the state of charge of the battery, and proposes a method for estimating the state of charge of the battery based on a two-step method to improve the estimation accuracy of the state of charge of the battery.

[0006] The technical solution adopted by the present invention to solve its technical problems is as follows:

[0007] A method for estimating the state of charge of a battery based on a two-step method, comprising the following steps;

[0008] The first step of the "two-step method" - establishing a model:

[0009] S1. Collect the data of the HPPC experiment of the lithium battery at different ambient temperatures, including current and voltage, etc., and draw the lithium battery SOC-OCV relationship curve graph;

[0010] S2. Combine the characteristics of the battery equivalent circuit model to establish a second-order RC equivalent circuit model to simulate the characteristics of lithium batteries, and identify the parameters in the model, including the battery internal resistance R0, polarization internal resistances R1, R2, and polarization capacitances C1, C2, etc.;

[0011] S3. On the basis of step S2, use the extended Kalman filter algorithm to complete the estimation of the battery SOC of the model and complete the pre-training of the model;

[0012] The second step of the "two-step method" - correct the model parameters:

[0013] S4. Under the same ambient temperature, conduct charge and discharge cycle experiments on the battery at different discharge rates, record the data of the experimental battery, including current, voltage, temperature, and capacity, etc., and draw the SOC-X (identified parameter) change curve;

[0014] S5. Conduct charge and discharge cycle experiments on the battery at the same discharge rate and different ambient temperatures, record the data of the experimental battery, including current, voltage, temperature, and capacity, etc., draw the SOC-X (identified parameter) change curve, and determine the components that are sensitive to temperature changes and have a greater impact on the discharge rate according to the SOC-X (identified parameter) curves at different temperatures and different discharge rates;

[0015] S6. Use polynomial function fitting to obtain the relationship between the parameters of the components that are sensitive to temperature changes (mainly the ohmic resistance and polarization internal resistance, with an influence range of 0.5 - more than 2 times) and have a greater impact on the discharge rate (mainly the polarization internal resistance, with an influence range of 0.5 - more than 1 time) and the change of battery SOC, and obtain the relationship equations and weights between each parameter and SOC at different temperatures and discharge rates; use the change relationship equations to correct the identification of component parameters in different discharge rates and different temperature environments in the actual driving process to obtain the corrected parameter values;

[0016] S7. Use the extended Kalman filter algorithm to complete the estimation of the battery SOC of the corrected model.

[0017] As a further technical solution of the present invention, in step S2, combining Figure 2 the second-order RC equivalent circuit model and Kirchhoff's law gives:

[0018]

[0019] In the formula, U L represents the terminal voltage of the battery, U OC represents the open-circuit voltage of the battery, i represents the current passing through the battery, R0 represents the internal resistance of the battery, R1 and R2 respectively represent the polarization resistances of the battery, C1 and C2 respectively represent the polarization capacitances corresponding to R1 and R2, and U1 and U2 respectively represent the terminal voltages of R1 and R2;

[0020] Combined with the calculation formula of SOC In the formula: Q is the battery capacity, and SOC t0 is the initial value of the battery SOC, and the state space equation can be obtained as follows:

[0021]

[0022] In the formula, τ1 = R1C1, τ2 = R2C2, t is the sampling time, and w k is the system noise, and v k is the observation noise, and k is the discrete sampling point, representing the current moment;

[0023] Let: u k = I k , D k = [-R0], where R is the measurement noise covariance.

[0024] As a further technical solution of the present invention, step S3 includes:

[0025] 1), The state value of the non-linear discrete system at time k is:

[0026] Among them, f(x k , u k ) is the state equation, h(x k , u k ) is the observation equation, x k+1 is the state variable of the system at time k + 1, and y k is the output variable of the system at time k;

[0027] After linearizing the equation through the Taylor expansion, the system is estimated by the Kalman filter algorithm:

[0028] 2), Assume that the functions f(x k , u k ) and h(x k , u k ) are differentiable at each sampling moment. Using the Taylor expansion, we can obtain:

[0029]

[0030] 3), Let Substitute it into the state space equation of the non-linear discrete system to obtain:

[0031]

[0032] In the formula, is the estimated value of x k at time k;

[0033] Next, iterative estimation is performed through the Kalman filter algorithm.

[0034] As a further technical solution of the present invention, the iterative estimation through the Kalman filter algorithm includes the following steps:

[0035] State initialization: P 0 / 0 = var(x0), where E(·) is the expected value;

[0036] Estimate the predicted state variable:

[0037] Predict the error covariance matrix at time k:

[0038] Calculate the gain matrix K k :

[0039] According to the observed value, update the state estimate at time k to:

[0040] Update the error covariance matrix to: P k = (i - k k C k )P k / k-1 ;

[0041] In summary, the linearized state equation and observation equation are obtained:

[0042] According to the above expressions, combined with the principle of the extended Kalman algorithm, the estimation of the battery SOC can be completed.

[0043] The beneficial effects of the present invention are:

[0044] The "two-step method" of the present invention for improving the accuracy of battery SOC estimation further considers the influence of environmental temperature and discharge rate on the internal characteristics of the battery based on the basic working conditions. In the second-order RC equivalent model, the identification parameters are further corrected. On the premise of ensuring the stability and safety of the BMS function, the accuracy of the equivalent model parameter identification and the accuracy of battery SOC estimation are improved, and the calculation is more simple, fast and accurate. It can effectively improve the accuracy of battery SOC estimation during the operation of the whole vehicle, and at the same time improve the accuracy of battery SOH estimation, so as to extend the service life of the in-vehicle battery. Description of the Drawings

[0045] Figure 1 It is a flowchart of a method for estimating the state of charge of a battery based on a two-step method provided by an embodiment of the present invention.

[0046] Figure 2It is the second-order RC equivalent circuit model diagram provided by the embodiments of the present invention.

[0047] Figure 3 It is the iterative estimation process diagram of the Kalman filter algorithm provided by the embodiments of the present invention.

[0048] Figure 4 It is the specific flowchart of the second step in a battery state of charge estimation method based on a two-step method provided by the embodiments of the present invention. Detailed implementation manners

[0049] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0050] Please refer to Figure 1 , the embodiments of the present invention provide a battery state of charge estimation method based on a two-step method, including the following steps;

[0051] The first step of the "two-step method" - establishing a model:

[0052] S1. Collect data of HPPC experiments of lithium batteries at different ambient temperatures, including current, voltage, etc., and draw a lithium battery SOC-OCV relationship curve graph;

[0053] S2. Combining the characteristics of the battery equivalent circuit model, establish a second-order RC equivalent circuit model to simulate the characteristics of lithium batteries, and identify the parameters in the model, including battery internal resistance R0, polarization internal resistances R1, R2, and polarization capacitances C1, C2, etc.;

[0054] S3. On the basis of step S2, use the extended Kalman filter algorithm to complete the battery SOC estimation of the model and complete the pre-training of the model;

[0055] The second step of the "two-step method" - correcting model parameters:

[0056] S4. At the same ambient temperature, perform charge and discharge cycle experiments on the battery at different discharge rates, record the data of the experimental battery, including current, voltage, temperature, capacity, etc., and draw an SOC-X (identified parameter) change curve graph;

[0057] S5. Conduct charge and discharge cycle experiments on the battery at the same discharge rate but different ambient temperatures, record the data of the experimental battery, including current, voltage, temperature, capacity, etc., plot the SOC-X (identification parameter) change curve, and determine the components that are sensitive to temperature changes and have a greater impact on the discharge rate based on the SOC-X (identification parameter) curves at different temperatures and different discharge rates.

[0058] S6. Use polynomial function fitting to obtain the relationship between the parameters of the components that are sensitive to temperature changes (mainly ohmic resistance and polarization internal resistance, with an influence range of 0.5 - more than 2 times) and have a greater impact on the discharge rate (mainly polarization internal resistance, with an influence range of 0.5 - more than 1 time) and the change of battery SOC, and obtain the relationship equations and weights between each parameter and SOC at different temperatures and discharge rates; use the change relationship equations to correct the identification of component parameters in different discharge rates and different temperature environments in the actual driving process to obtain the corrected parameter values.

[0059] S7. Use the extended Kalman filter algorithm to complete the estimation of the battery SOC of the corrected model.

[0060] To facilitate better understanding of the technical solution of the present invention by those skilled in the art, the following are specific embodiments of the present invention:

[0061] The present invention selects the 40135 type lithium battery as an example to establish an equivalent model and identify parameters. The parameter standards of this type of battery are shown in Table 1:

[0062] Table 1 - Parameter Standards of 40135 Type Lithium Battery

[0063]

[0064] To explore the working characteristics of the 40135 type lithium battery and evaluate its pulse discharge characteristics, conduct an HPPC discharge experiment on this battery to obtain the SOC-OCV relationship curve of this battery.

[0065] This HPPC discharge experiment is carried out according to the standard of "Test Methods for Electrical Performance of Lithium-Ion Power Battery Packs and Systems for Electric Vehicles". To facilitate obtaining the experimental results, the set ambient temperatures are 0°C, 5°C, 10°C, 15°C, 20°C, 25°C, 30°C, 35°C, 40°C respectively, and the specific step settings are shown in Table 2:

[0066] Table 2 - HPPC Discharge Experiment Steps

[0067]

[0068] Record the battery voltage, current, temperature and other data during the HPPC experiment of the 40135-type lithium battery at different ambient temperatures. The data collected in this experiment will be used for the parameter identification of each component in the second-order RC equivalent circuit model.

[0069] The present invention uses a second-order RC equivalent model to simulate the working characteristics of the 40135-type lithium battery. The model is as Figure 2 shown. Parameters such as the internal resistance, polarization resistance, and polarization capacitance of the battery in the model can be identified by combining the HPPC discharge experiment data.

[0070] Combining Figure 2 the second-order RC equivalent circuit model and Kirchhoff's law, we can obtain:

[0071]

[0072] In the formula, U L represents the terminal voltage of the battery, U OC represents the open-circuit voltage of the battery, i represents the current passing through the battery, R0 represents the internal resistance of the battery, R1 and R2 respectively represent the polarization resistances of the battery, C1 and C2 respectively represent the polarization capacitances corresponding to R1 and R2, and U1 and U2 respectively represent the terminal voltages of R1 and R2.

[0073] Combining the calculation formula of SOC In the formula: Q is the battery capacity, SOC t0 is the initial value of the battery SOC, and the state-space equation can be obtained:

[0074]

[0075] In the formula, τ1 = R1C1, τ2 = R2C2, t is the sampling time, w k is the system noise, v k is the observation noise, and k is the discrete sampling point, representing the current moment.

[0076] Let: u k = I k , D k = [-R0], R is the measurement noise covariance;

[0077] So far, the establishment of the second-order RC equivalent model of the 40135-type lithium battery is completed, and the parameters such as the internal resistance, polarization resistance, and polarization capacitance of the battery are identified by combining the HPPC experiment data. Subsequently, the extended Kalman filter algorithm is used to complete the estimation of the battery SOC of the model.

[0078] 1), The state value of the nonlinear discrete system at time k is:

[0079] Among them, f(x k , u k ) is the state equation, h(x k , u k ) is the observation equation, x k+1 is the state variable of the system at time k + 1, and y k is the output variable of the system at time k.

[0080] After linearizing the equation through the Taylor expansion, the system is estimated by the Kalman filter algorithm:

[0081] 2), Let the functions f(x k , u k ) and h(x k , u k ) be differentiable at each sampling time. Using the Taylor expansion, we can obtain:

[0082]

[0083] 3), Let be substituted into the state - space equation of the nonlinear discrete system, and we can get:

[0084]

[0085] In the formula, is the estimated value of x k at time k.

[0086] Next, iterative estimation is carried out through the Kalman filter algorithm, as specifically Figure 3 shown;

[0087] S1. State initialization: P 0 / 0 = var(x0), where E(·) is the expected value;

[0088] S2. Predict the estimated value of the state variable:

[0089] S3. Predict the error covariance matrix at time k:

[0090] S4. Calculate the gain matrix K k :

[0091] S5. According to the observed value, update the state estimate value at time k as:

[0092] S6. Update the error covariance matrix as: P k = (I - K k C k )Pk / k-1 ;

[0093] In summary, the linearized state equation and the observation equation can be obtained:

[0094] According to the above expressions, combined with the principle of the extended Kalman algorithm, the estimation of the battery SOC can be completed.

[0095] So far, the first step of the "two-step method" has been completed, and the model training for the battery SOC estimation is completed.

[0096] Subsequently, the second step begins.

[0097] In the above second-order RC equivalent model, R0 represents the ohmic internal resistance, which describes the loss related to resistance; R1 and C1 respectively describe the loss caused by the reduction of the chemical reaction rate and the kinetics of the chemical reaction; R2 and C2 respectively describe the loss caused by the internal chemical diffusion of the lithium battery and the chemical diffusion kinetics.

[0098] For lithium-ion batteries, the ion concentration gradient and temperature inside the battery are the factors that have a greater impact on the ohmic internal resistance. The ohmic internal resistance R0 increases with the increase of the lithium-ion concentration gradient and decreases with the increase of temperature. At the beginning of high-rate discharge, the internal temperature of the battery is low, the chemical reaction is slow, and the battery R0 is high; during the discharge process, the battery temperature rises, and the ohmic resistance decreases rapidly; at the end of discharge, the lithium-ion concentration gradient increases, and R0 shows an upward trend. In the case of a larger discharge rate, the battery temperature is higher, and R0 also decreases.

[0099] For the polarization resistances R1 and R2: at the beginning of discharge, the polarization resistance changes with the increase of the ion concentration gradient; during the discharge process, the change of the ion concentration gradient tends to be stable, and the polarization internal resistance also tends to be stable; at the end of discharge, the ion concentration difference in the positive and negative active materials is large, resulting in an increase in the polarization internal resistance. At the same time, with the increase of the discharge rate, the ion concentration gradient in the battery will gradually increase, resulting in a decrease in the polarization internal resistance.

[0100] For the polarization capacitors C1 and C2: at different temperatures, the change of the polarization capacitor is relatively chaotic. However, when the SOC is lower than 20%, the polarization capacitors at different temperatures all become smaller, and with the decrease of temperature, the polarization capacitor has a small degree of decrease.

[0101] To explore the external characteristic data of the 40135-type lithium battery at different discharge rates and ambient temperatures, as well as the distribution law of the surface temperature during the charge and discharge process of the battery, a charge and discharge cycle experiment is carried out on this battery. To facilitate obtaining the experimental results, the set ambient temperatures are -10°C, 20°C, 0°C, 5°C, 10°C, 15°C, 20°C, 25°C, 30°C, 35°C, 40°C respectively.

[0102] Considering that the charge-discharge rate of the battery significantly accelerates the attenuation of its lifespan, but at high rates, lithium plating will inevitably occur on the negative electrode of the battery, and there is a risk of lithium dendrites piercing the separator, leading to battery short circuit and posing a great safety hazard. Therefore, three charge-discharge cycle rates of 1C, 2C, and 3C are selected to conduct charge-discharge cycle experiments on the battery, which can not only study the influence of the rate on the capacity change of the battery but also avoid inevitable safety accidents during high-rate charge-discharge cycles.

[0103] The set cycle rates are 1C, 2C, and 3C respectively, and the specific process steps are set as shown in Table 3: Among them, the average value of the discharge capacity in the first three cycles is recorded as the initial capacity of the battery.

[0104] Table 3 - Process Steps of Charge-Discharge Cycle Experiment

[0105]

[0106] Record the battery current, voltage, and temperature data of the lithium battery during experiments at different ambient temperatures and different discharge rates. This experimental data will be used to explore the influence of different ambient temperatures on battery characteristics and the influence of different discharge rates on battery temperature and characteristics.

[0107] Record the capacity of the battery discharged and the battery in 3 groups of experimental data with different ambient temperatures and the same discharge rate and 9 groups of experimental data with different discharge rates and the same ambient temperature, and draw the SOC-X (identification parameter) curve graph.

[0108] Based on the curve graph obtained from the above experiments and the parameter identification results in the first step. Taking 25°C as the benchmark, at the same discharge rate, both the increase and decrease of the ambient temperature will affect the discharge capacity and voltage change of the battery. When the ambient temperature is the same, as the discharge rate increases, the battery discharge ability gradually decays and the battery capacity also continuously decreases.

[0109] According to the SOC-X (identification parameter) curve graphs at different temperatures and different discharge rates obtained from the above experimental analysis, determine the components sensitive to temperature changes and the components greatly affected by the discharge rate. (Temperature range: -10 - 40°C)

[0110] According to the different change trends of the above parameters, select functions such as interpolation / quadratic polynomial / piecewise polynomial to fit the change relationship between R0, R1, R2 and SOC.

[0111] Obtain the relationship equations between each parameter and SOC at different temperatures and discharge rates, substitute the target SOC into the fitting relationship equation, and obtain the temperature-corrected identification parameter values to complete the correction of the parameters in the model affected by the ambient temperature and discharge rate.

[0112] On this basis, the extended Kalman filter algorithm is used to complete the estimation of the battery SOC of the corrected model. The specific steps are as Figure 4 shown.

[0113] So far, the second step of the two-step method has been completed, and the accuracy of the established second-order RC equivalent model with temperature and discharge rate correction has been greatly improved.

[0114] It should be noted that in this article, the terms "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such a process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, method, article or device including that element.

[0115] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the description and drawings of the present invention, or directly or indirectly applied in other related technical fields, are equally included in the patent protection scope of the present invention.

Claims

1. A method for estimating the state of charge of a battery based on a two-step method, characterized in that, Including the following steps; The first step of the "two-step method" - establishing the model: S1. Collect the data of HPPC experiments of lithium batteries at different ambient temperatures, including current and voltage, and draw the SOC-OCV relationship curve of the lithium battery; S2. Combine the characteristics of the battery equivalent circuit model, establish a second-order RC equivalent circuit model to simulate the characteristics of lithium batteries, and identify the parameters in the model, including the battery internal resistance , polarization internal resistance , and polarization capacitance , ; S3. On the basis of step S2, use the extended Kalman filter algorithm to complete the estimation of the battery SOC of the model and complete the pre-training of the model; The second step of the "two-step method" - correcting the model parameters: S4. Under the same ambient temperature, conduct charge and discharge cycle experiments on the battery at different discharge rates, record the data of the experimental battery, including current, voltage, temperature and capacity, and draw the SOC-X change curve; S5. Conduct charge and discharge cycle experiments on the battery at the same discharge rate and different ambient temperatures, record the data of the experimental battery, including current, voltage, temperature and capacity, draw the SOC-X change curve, and determine the components that are sensitive to temperature changes and have a greater impact on the discharge rate according to the SOC-X curves at different temperatures and different discharge rates; S6. Use polynomial function fitting to obtain the relationship between the parameters of the components that are sensitive to temperature changes and have a greater impact on the discharge rate and the change of battery SOC, and obtain the relationship equations and weights between each parameter and SOC at different temperatures and discharge rates; Use the change relationship equation to correct the identification of component parameters under different discharge rates and different temperature environments in the actual driving process, and obtain the corrected parameter values; S7. Use the extended Kalman filter algorithm to complete the estimation of the battery SOC of the corrected model; In step S2, combining the second-order RC equivalent circuit model and Kirchhoff's law, we get: ； Wherein, represents the terminal voltage of the battery, represents the open-circuit voltage of the battery, represents the current passing through the battery, represents the internal resistance of the battery, and respectively represent the polarization resistance of the battery, and respectively represent the polarization capacitance corresponding to and ; and respectively represent and the terminal voltages; Combined with the calculation formula of SOC , where: is the battery capacity, is the initial value of the battery SOC, and the state-space equation can be obtained: ； wherein, , , is the sampling time, is the system noise, is the observation noise, is the discrete sampling point, representing the current moment; Given: , , , , , , to measure the noise covariance.

2. The method for estimating the state of charge of a battery based on a two-step method according to claim 1, wherein Step S3 includes: 1). The state value of the non-linear discrete system at time k is: ; Among them, is the state equation, is the observation equation, is the system state variable at time is the system output variable at time; After linearizing the equation through Taylor expansion, then estimate the system through the Kalman filter algorithm: 2), Let the function and be differentiable at each sampling moment. By using the Taylor expansion, we can obtain: ； 3), Let , , substituting into the state - space equation of the non - linear discrete system, we get: ； In the formula, is the estimated value at time k; Next, perform iterative estimation through the Kalman filter algorithm.

3. A method for estimating the state of charge of a battery based on a two-step method according to claim 2, characterized in that, The iterative estimation through the Kalman filter algorithm includes the following steps: State initialization: , , where is the expected value; Estimated value of the predicted state variable: ; Prediction Error covariance matrix at a moment: ; Calculate the gain matrix : ; Update according to the observed value The state estimate value at the moment is as follows: ; The updated error covariance matrix is: ; In summary, the linearized state equation and observation equation are obtained: ; Combining the principle of the extended Kalman algorithm can complete the estimation of the battery SOC.

4. A method for estimating the state of charge of a battery based on a two-step method according to claim 1, characterized in that, In step S6, being sensitive to temperature changes means that the influence ranges of ohmic resistance and polarization internal resistance are more than 0.5-2 times, and having a greater impact on the discharge rate means that the influence range of polarization internal resistance is more than 0.5-1 times.

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