A method and device for predicting battery power status

Abstract

The present invention discloses a prediction method and prediction device for battery power state. The prediction method for battery power state first corrects the average state of charge of the battery according to the difference in the state of charge of the battery to obtain the corrected state of charge of the battery surface; then corrects the original power prediction equation in the second-order RC equivalent circuit model according to the state of charge of the battery surface to obtain the corrected power prediction equation; finally, the prediction result of the battery power state is obtained according to the corrected power prediction equation. The average state of charge of the macroscopic battery is corrected by the difference in the state of charge of the microscopic battery, so that the corrected state of charge of the battery surface can accurately reflect the characteristics of the battery, so that the corrected power prediction equation corrected according to the state of charge of the battery surface can accurately predict the battery power state, so that the prediction result obtained according to the corrected power prediction equation can accurately guide the reasonable use of the battery and avoid potential safety risks.

Description

Technical Field

[0001] The present invention relates to the technical field of batteries, and particularly to a method and a device for predicting the power state of a battery. Background Art

[0002] Currently, the model prediction method is usually adopted to predict the power state of a battery, such as an equivalent circuit model. The equivalent circuit model usually predicts the power state of the battery according to the average state of charge of the battery. However, the average state of charge of the battery can only reflect the electrochemical mechanism inside the battery from a macroscopic level, resulting in the prediction result of the equivalent circuit model for the power state of the battery being prone to distortion, so that the equivalent circuit model cannot accurately guide the reasonable use of the battery and avoid potential safety risks. Summary of the Invention

[0003] The main object of the present invention is to provide a method and a device for predicting the power state of a battery, aiming to solve the technical problem that the prediction result of the equivalent circuit model in the prior art for the power state of the battery is prone to distortion, resulting in the equivalent circuit model being unable to accurately guide the reasonable use of the battery and avoid potential safety risks.

[0004] To solve the above technical problem, the technical solution provided by the present invention is as follows:

[0005] A method for predicting the power state of a battery, comprising:

[0006] Correcting the average state of charge of the battery according to the difference in the state of charge of the battery to obtain the corrected surface state of charge of the battery, wherein the difference in the state of charge of the battery is obtained according to the solid-phase diffusion coefficient and the solid-phase diffusion time constant of the battery;

[0007] Correcting the original power prediction equation in the second-order RC equivalent circuit model according to the surface state of charge of the battery to obtain a corrected corrected power prediction equation;

[0008] Obtaining a prediction result of the power state of the battery according to the corrected power prediction equation.

[0009] Preferably, the second-order RC equivalent circuit model includes a first resistor R0, a first RC parallel circuit, and a second RC parallel circuit connected in series, the first RC parallel circuit includes a second resistor R1 and a first capacitor C1 connected in parallel, and the second RC parallel circuit includes a third resistor R2 and a second capacitor C2 connected in parallel.

[0010] Preferably, the expression of the surface state of charge of the battery is as shown in Equation (1):

[0011] SOC_surf = SOC + ΔSOC; (1)

[0012] Among them, SOC_surf represents the surface state of charge of the battery, SOC represents the average state of charge of the battery, and ΔSOC represents the difference in the state of charge of the battery.

[0013] Preferably, the expression of the battery state of charge difference is as shown in Formula 2:

[0014] ΔSOC k =exp(-Δt / tau_sd)*ΔSOC k-1 +I k-1 *ksd(1-exp(-Δt / tau_sd)); (ii)

[0015] Among them, ΔSOC k represents the battery state of charge difference; k represents the current moment; k-1 represents the previous moment of the current moment; exp() represents the exponential function; Δt represents the sampling time interval; tau_sd represents the solid phase diffusion time constant of the battery; I represents the external current; ksd represents the solid phase diffusion coefficient of the battery.

[0016] Preferably, the expression of the modified power prediction equation is as shown in Formula 3:

[0017] I max =(ECM(SOC_surf) -U1*exp(-Δt / tau1)-U2*exp(-Δt / tau2)-U term ) / (η*Δt*dECM(SOC_surf) / dSOC_surf / Q+R1*(1-exp(-Δt / tau1))+R1*(1-exp(-Δt / tau2))+R0);

[0018] P max =((ECM(SOC_surf) -U1*exp(-Δt / tau1)-U2*exp(-Δt / tau2)-I max *(η*Δt*dECM(SOC_surf) / dSOC_surf / Q+R1*(1-exp(-Δt / tau1))+R1*(1-exp(-Δt / tau2))+R0))*I max ; (three)

[0019] Among them, I maxrepresents the maximum current of the battery; ECM() represents the functional relationship between the open-circuit voltage of the battery and the state of charge on the battery surface; U1 represents the voltage value across capacitor C1; tau1 represents the time constant of the first RC parallel circuit, tau1 = R1 * C1, where R1 represents the resistance value of the second resistor R1 and C1 represents the electrochemical polarization capacitance of the battery; U2 represents the voltage value across capacitor C2; tau2 is the time constant of the second RC parallel circuit, tau2 = R2 * C2, where R2 represents the resistance value of the third resistor R2 and C2 represents the concentration difference polarization capacitance of the battery; U term represents the upper and lower threshold values for battery charge and discharge; η represents the battery charge and discharge efficiency; d represents differentiation; Q represents the rated capacity of the battery; R0 represents the ohmic internal resistance of the battery; P max represents the maximum power of the battery.

[0020] Another technical solution provided by the present invention is:

[0021] A prediction device for the power state of a battery, comprising:

[0022] A first correction module: used to correct the average state of charge of the battery according to the difference in the state of charge of the battery to obtain the corrected state of charge on the battery surface, where the difference in the state of charge of the battery is obtained according to the solid-phase diffusion coefficient of the battery and the solid-phase diffusion time constant of the battery;

[0023] A second correction module: used to correct the original power prediction equation in the second-order RC equivalent circuit model according to the state of charge on the battery surface to obtain a corrected corrected power prediction equation;

[0024] A result acquisition module: used to obtain a prediction result of the power state of the battery according to the corrected power prediction equation.

[0025] Preferably, the second-order RC equivalent circuit model includes a first resistor R0, a first RC parallel circuit, and a second RC parallel circuit connected in series, the first RC parallel circuit includes a second resistor R1 and a first capacitor C1 connected in parallel, and the second RC parallel circuit includes a third resistor R2 and a second capacitor C2 connected in parallel.

[0026] Preferably, the expression of the state of charge on the battery surface is as shown in Equation (1):

[0027] SOC_surf = SOC + ΔSOC; (1)

[0028] Wherein, SOC_surf represents the state of charge on the battery surface, SOC represents the average state of charge of the battery, and ΔSOC represents the difference in the state of charge of the battery.

[0029] Preferably, the expression of the difference in the state of charge of the battery is as shown in Equation (2):

[0030] ΔSOC k =exp(-Δt / tau_sd)*ΔSOC k-1 +I k-1 *ksd(1-exp(-Δt / tau_sd)); (ii)

[0031] Among them, ΔSOC k represents the battery state of charge difference; k represents the current moment; k-1 represents the previous moment of the current moment; exp() represents the exponential function; Δt represents the sampling time interval; tau_sd represents the solid phase diffusion time constant of the battery; I represents the external current; ksd represents the solid phase diffusion coefficient of the battery.

[0032] Preferably, the expression of the modified voltage prediction equation is as shown in Formula 3:

[0033] I max =(ECM(SOC_surf) -U1*exp(-Δt / tau1)-U2*exp(-Δt / tau2)-U term ) / (η*Δt*dECM(SOC_surf) / dSOC_surf / Q+R1*(1-exp(-Δt / tau1))+R1*(1-exp(-Δt / tau2))+R0);

[0034] P max =((ECM(SOC_surf) -U1*exp(-Δt / tau1)-U2*exp(-Δt / tau2)-I max *(η*Δt*dECM(SOC_surf) / dSOC_surf / Q+R1*(1-exp(-Δt / tau1))+R1*(1-exp(-Δt / tau2))+R0))*I max ; (three)

[0035] Among them, I max represents the maximum current of the battery; ECM() represents the functional relationship between the open circuit voltage of the battery and the surface charge state of the battery; U1 represents the voltage value across the capacitor C1; tau1 represents the time constant of the first RC parallel circuit, tau1=R1*C1, R1 represents the resistance value of the second resistor R1, and C1 represents the electrochemical polarization capacitance of the battery; U2 represents the voltage value across the capacitor C2; tau2 is the time constant of the second RC parallel circuit, tau2=R2*C2, R2 represents the resistance value of the third resistor R2, and C2 represents the concentration difference polarization capacitance of the battery; U termrepresents the upper and lower limit thresholds of battery charge and discharge; η represents the battery charge and discharge efficiency; d represents the derivative; Q represents the rated capacity of the battery; R0 represents the ohmic internal resistance of the battery; P max Indicates the maximum power of the battery.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The above-mentioned battery power state prediction method and prediction device first correct the battery average state of charge according to the battery state of charge difference to obtain the corrected battery surface state of charge, wherein the battery state of charge difference is obtained according to the battery solid phase diffusion coefficient and the battery solid phase diffusion time constant; then correct the original power prediction equation in the second-order RC equivalent circuit model according to the battery surface state of charge to obtain the corrected power prediction equation; finally, the prediction result of the battery power state is obtained according to the corrected power prediction equation. Since the battery solid phase diffusion coefficient is low and the solid phase diffusion time constant is large, the ions inside the positive and negative electrode particles cannot be instantly transferred to the surface of the positive and negative electrode particles and output as high power instantaneously, that is, the ability of ions to diffuse from the inside of the positive and negative electrode particles to the surface of the positive and negative electrode particles is used as the premise for measuring the battery power. Therefore, by introducing the solid-phase diffusion coefficient and solid-phase diffusion time constant of the battery, the battery state of charge difference can reflect the electrochemical mechanism inside the battery from a microscopic level, and the macroscopic battery average state of charge can be corrected through the microscopic battery state of charge difference, so that the corrected battery surface state of charge can accurately reflect the characteristics of the battery, and the corrected power prediction equation corrected according to the battery surface state of charge can accurately predict the battery power state, so that the prediction results obtained according to the corrected power prediction equation can accurately guide the rational use of the battery and avoid potential safety risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0039] Figure 1 is a schematic diagram of a method for predicting a battery power state according to an embodiment of the present invention;

[0040] Figure 2 is a schematic diagram of a second-order RC equivalent circuit model of a battery according to an embodiment of the present invention;

[0041] Figure 3is a schematic diagram of the maximum discharge current of a battery at 10s before and after correction according to one embodiment of the present invention;

[0042] Figure 4 is a schematic diagram of the maximum discharge power of a battery at 10s before and after correction according to one embodiment of the present invention;

[0043] Figure 5 It is a schematic diagram of the average state of charge (SOC) of a battery before correction at 50%-0% SOC under 10C discharge conditions and the surface state of charge (SOC_surf) of a battery after correction at 50%-0% SOC under 10C discharge conditions according to one embodiment of the present invention;

[0044] Figure 6 It is a schematic diagram of a battery open circuit voltage (SOC) of a battery at 50%-0% SOC under a 10C discharge condition before correction and a battery open circuit voltage (SOC_surf) of a battery at 50%-0% SOC under a 10C discharge condition after correction according to an embodiment of the present invention;

[0045] Figure 7 4 is a structural block diagram of a device for predicting a battery power state according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0048] In addition, the descriptions of "first", "second", etc. in the present invention are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0049] like Figure 1 As shown, this embodiment provides a method for predicting a battery power state, and the prediction method includes the following steps:

[0050] S100, correcting the average state of charge of the battery according to the difference in state of charge of the battery to obtain a corrected surface state of charge of the battery, wherein the difference in state of charge of the battery is obtained according to the solid phase diffusion coefficient of the battery and the solid phase diffusion time constant of the battery.

[0051] In this embodiment, the battery is a lithium battery.

[0052] It can be understood that the solid-phase diffusion coefficient of the battery represents a physical concept, which is equivalent to dropping a drop of red ink into a container of water. After a period of time, the water in the container will be dyed red by the red ink. In a solid-state battery, the ion concentration will also tend to equilibrium after a period of time, and the important coefficient to measure this process is the solid-phase diffusion coefficient (a coefficient used to measure the diffusion capacity), which follows Fick's second law.

[0053] It can be understood that the solid phase diffusion time constant of the battery represents the time it takes for ions to diffuse from the solid particle diameter R direction to the stable stage.

[0054] S200, correcting the original power prediction equation in the second-order RC equivalent circuit model according to the surface charge state of the battery to obtain a corrected power prediction equation.

[0055] S300, obtaining a prediction result of a battery power state according to a modified power prediction equation.

[0056] The above-mentioned method and device for predicting the battery power state first correct the average state of charge (SOC) of the battery according to the difference in the state of charge of the battery, and obtain the corrected surface state of charge of the battery. Among them, the difference in the state of charge of the battery is obtained according to the solid-phase diffusion coefficient and the solid-phase diffusion time constant of the battery; then, the original power prediction equation in the second-order RC equivalent circuit model is corrected according to the surface state of charge of the battery, and the corrected power prediction equation is obtained; finally, the prediction result of the battery power state is obtained according to the corrected power prediction equation. Since the solid-phase diffusion coefficient of the battery is low and the solid-phase diffusion time constant is large, the ions inside the positive and negative electrode particles cannot be instantaneously transferred to the surface of the positive and negative electrode particles and output as high power instantaneously, that is, the ability of ions to diffuse from the inside of the positive and negative electrode particles to the surface of the positive and negative electrode particles is used as the premise for measuring the battery power. Therefore, by introducing the solid-phase diffusion coefficient and the solid-phase diffusion time constant of the battery, the difference in the state of charge of the battery can reflect the internal electrochemical mechanism of the battery from the microscopic level, and the macroscopic average state of charge of the battery is corrected by the microscopic difference in the state of charge of the battery, so that the corrected surface state of charge of the battery can accurately reflect the characteristics of the battery, and the corrected power prediction equation corrected according to the surface state of charge of the battery can accurately predict the battery power state, so that the prediction result obtained according to the corrected power prediction equation can accurately guide the reasonable use of the battery and avoid potential safety risks.

[0057] As Figure 2 shown, the second-order RC equivalent circuit model includes a first resistor R0, a first RC parallel circuit, and a second RC parallel circuit connected in series. The first RC parallel circuit includes a second resistor R1 and a first capacitor C1 connected in parallel, and the second RC parallel circuit includes a third resistor R2 and a second capacitor C2 connected in parallel. Among them, U oc represents the open-circuit voltage (OCV) of the battery; R0 represents the ohmic internal resistance of the battery; R1 represents the electrochemical polarization resistance during the charge and discharge process of the battery; R2 represents the concentration difference polarization resistance during the charge and discharge process of the battery; C1 represents the electrochemical polarization capacitance of the battery; C2 represents the concentration difference polarization capacitance of the battery; U1 represents the voltage value across the capacitor C1; U2 represents the voltage value across the capacitor C2; U0 represents the terminal voltage of the battery; I represents the terminal current of the battery.

[0058] The expression of the surface state of charge of the battery is shown in Equation (1):

[0059] SOC_surf = SOC + ΔSOC; (1)

[0060] Among them, SOC_surf represents the surface state of charge of the battery, SOC represents the average state of charge of the battery, and ΔSOC represents the difference in the state of charge of the battery.

[0061] The expression of the difference in the state of charge of the battery is shown in Equation (2):

[0062] ΔSOC k =exp(-Δt / tau_sd)*ΔSOC k-1 +I k-1 *ksd(1-exp(-Δt / tau_sd)); (ii)

[0063] Among them, ΔSOC k represents the battery state of charge difference; k represents the current moment; k-1 represents the previous moment of the current moment; exp() represents the exponential function; Δt represents the sampling time interval; tau_sd represents the solid phase diffusion time constant of the battery; I represents the external current; ksd represents the solid phase diffusion coefficient of the battery.

[0064] The expression of the modified power prediction equation is shown in Formula 3:

[0065] I max =(ECM(SOC_surf) -U1*exp(-Δt / tau1)-U2*exp(-Δt / tau2)-U term ) / (η*Δt*dECM(SOC_surf) / dSOC_surf / Q+R1*(1-exp(-Δt / tau1))+R1*(1-exp(-Δt / tau2))+R0);

[0066] P max =((ECM(SOC_surf) -U1*exp(-Δt / tau1)-U2*exp(-Δt / tau2)-I max *(η*Δt*dECM(SOC_surf) / dSOC_surf / Q+R1*(1-exp(-Δt / tau1))+R1*(1-exp(-Δt / tau2))+R0))*I max ; (three)

[0067] Among them, I max represents the maximum current of the battery; ECM() represents the functional relationship between the open circuit voltage of the battery and the surface charge state of the battery; U1 represents the voltage value across the capacitor C1; tau1 represents the time constant of the first RC parallel circuit, tau1=R1*C1, R1 represents the resistance value of the second resistor R1, and C1 represents the electrochemical polarization capacitance of the battery; U2 represents the voltage value across the capacitor C2; tau2 is the time constant of the second RC parallel circuit, tau2=R2*C2, R2 represents the resistance value of the third resistor R2, and C2 represents the concentration difference polarization capacitance of the battery; U term represents the upper and lower limit thresholds of battery charge and discharge; η represents the battery charge and discharge efficiency; d represents the derivative; Q represents the rated capacity of the battery; R0 represents the ohmic internal resistance of the battery; Pmax Indicates the maximum power of the battery.

[0068] like Figure 3 , 4 As shown in the figure, the peak discharge current and power of the battery after correction in this embodiment are both less than the peak discharge current and power of the battery before correction. As the battery discharge continues, the battery electrochemical polarization will inevitably accumulate, so that the difference between the peak discharge current and power of the battery after correction and the peak discharge current and power of the battery before correction continues to increase and is more obvious in the low SOC area at the end of the battery discharge. It can be seen that when the battery is running under high load, the traditional equivalent circuit model is used to evaluate the battery power state, which cannot reflect the electrochemical mechanism and dynamic changes inside the battery.

[0069] The original terminal voltage prediction equation in the second-order RC equivalent circuit model is corrected according to the surface charge state of the battery to obtain a corrected terminal voltage prediction equation.

[0070] The expression of the corrected terminal voltage prediction equation is shown in Formula 4:

[0071] U0=ECM(SOC_surf)+I*R0+U1+U2;

[0072] U 1,k =exp(-Δt / tau1)*U 1,k-1 +I k-1 *R1*(1-exp(-Δt / tau1));

[0073] U 2,k =exp(-Δt / tau2)*U 2,k-1 +I k-1 *R2*(1-exp(-Δt / tau2)); (IV)

[0074] Among them, U0 represents the terminal voltage of the second-order RC equivalent circuit model, ECM() represents the functional relationship between the open circuit voltage of the battery and the surface charge state of the battery, R0 represents the ohmic internal resistance of the battery, and U 1,k Represents the voltage value across capacitor C1, U 2,k Represents the voltage value passing through capacitor C2, tau1 represents the time constant of the first RC parallel circuit, tau1=R1*C1, R1 represents the resistance value of the second resistor R1, and C1 represents the electrochemical polarization capacitance of the battery; tau2=R2*C2, R2 represents the resistance value of the second resistor R2, and C2 represents the concentration difference polarization capacitance of the battery.

[0075] like Figure 5 , 6As shown, taking the 10C discharge condition of the HEV battery from 50% - 0% SOC as an example, in this embodiment, the corrected surface state of charge (SOC_surf) and open circuit voltage (SOC_surf) of the battery are significantly inconsistent with the original average state of charge (SOC) and open circuit voltage (SOC) of the battery. As the battery discharge continues, it will inevitably lead to the continuous accumulation of battery electrochemical polarization, resulting in an increasing difference between the corrected surface state of charge (SOC_surf) of the battery and the original average state of charge (SOC) of the battery, which is more obvious in the open circuit voltage OCV at the end of the battery discharge. It can be seen that when the battery operates at high load, evaluating the battery terminal voltage state with the traditional equivalent circuit model cannot reflect the electrochemical mechanism and dynamic changes inside the battery.

[0076] As Figure 7 shown, in one embodiment, a prediction device 10 for the battery power state is provided. The prediction device 10 for the battery power state specifically includes the following: a first correction module 11, a second correction module 12, and a result obtaining module 13.

[0077] The first correction module 11 is used to correct the average state of charge of the battery according to the difference in the state of charge of the battery to obtain the corrected surface state of charge of the battery, where the difference in the state of charge of the battery is obtained according to the solid-phase diffusion coefficient and the solid-phase diffusion time constant of the battery.

[0078] The first correction module 11 specifically includes the following: The expression of the surface state of charge of the battery is shown in Equation (1):

[0079] SOC_surf = SOC + ΔSOC; (1)

[0080] where SOC_surf represents the surface state of charge of the battery, SOC represents the average state of charge of the battery, and ΔSOC represents the difference in the state of charge of the battery.

[0081] The expression of the difference in the state of charge of the battery is shown in Equation (2):

[0082] ΔSOC k = exp(-Δt / tau_sd) * ΔSOC k-1 + I k-1 * ksd(1 - exp(-Δt / tau_sd)); (2)

[0083] where ΔSOC k represents the difference in the state of charge of the battery; k represents the current moment; k - 1 represents the previous moment of the current moment; exp() represents the exponential function; Δt represents the sampling time interval; tau_sd represents the solid-phase diffusion time constant of the battery; I represents the external current; ksd represents the solid-phase diffusion coefficient of the battery.

[0084] The second correction module 12 is used to correct the original power prediction equation in the second-order RC equivalent circuit model according to the battery surface charge state to obtain a corrected power prediction equation.

[0085] The second correction module 12 specifically includes the following contents: The expression of the corrected voltage prediction equation is shown in Formula 3:

[0086] I max =(ECM(SOC_surf) -U1*exp(-Δt / tau1)-U2*exp(-Δt / tau2)-U term ) / (η*Δt*dECM(SOC_surf) / dSOC_surf / Q+R1*(1-exp(-Δt / tau1))+R1*(1-exp(-Δt / tau2))+R0);

[0087] P max =((ECM(SOC_surf) -U1*exp(-Δt / tau1)-U2*exp(-Δt / tau2)-I max *(η*Δt*dECM(SOC_surf) / dSOC_surf / Q+R1*(1-exp(-Δt / tau1))+R1*(1-exp(-Δt / tau2))+R0))*I max ; (three)

[0088] Among them, I max represents the maximum current of the battery; ECM() represents the functional relationship between the open circuit voltage of the battery and the surface charge state of the battery; U1 represents the voltage value across the capacitor C1; tau1 represents the time constant of the first RC parallel circuit, tau1=R1*C1, R1 represents the resistance value of the second resistor R1, and C1 represents the electrochemical polarization capacitance of the battery; U2 represents the voltage value across the capacitor C2; tau2 is the time constant of the second RC parallel circuit, tau2=R2*C2, R2 represents the resistance value of the third resistor R2, and C2 represents the concentration difference polarization capacitance of the battery; U term represents the upper and lower limit thresholds of battery charge and discharge; η represents the battery charge and discharge efficiency; d represents the derivative; Q represents the rated capacity of the battery; R0 represents the ohmic internal resistance of the battery; P max Indicates the maximum power of the battery.

[0089] The result obtaining module 13 is used to obtain the prediction result of the battery power state according to the modified power prediction equation.

[0090] The above-mentioned battery power state prediction device first corrects the battery average state of charge according to the battery state of charge difference to obtain the corrected battery surface state of charge, wherein the battery state of charge difference is obtained according to the battery solid phase diffusion coefficient and the battery solid phase diffusion time constant; then corrects the original power prediction equation in the second-order RC equivalent circuit model according to the battery surface state of charge to obtain the corrected power prediction equation; finally, the prediction result of the battery power state is obtained according to the corrected power prediction equation. Since the battery solid phase diffusion coefficient is low and the solid phase diffusion time constant is large, the ions inside the positive and negative electrode particles cannot be instantly transferred to the surface of the positive and negative electrode particles and output as high power instantaneously, that is, the ability of ions to diffuse from the inside of the positive and negative electrode particles to the surface of the positive and negative electrode particles is used as the premise for measuring the battery power. Therefore, by introducing the solid-phase diffusion coefficient and solid-phase diffusion time constant of the battery, the battery state of charge difference can reflect the electrochemical mechanism inside the battery from a microscopic level, and the macroscopic battery average state of charge can be corrected through the microscopic battery state of charge difference, so that the corrected battery surface state of charge can accurately reflect the characteristics of the battery, and the corrected power prediction equation corrected according to the battery surface state of charge can accurately predict the battery power state, so that the prediction results obtained according to the corrected power prediction equation can accurately guide the rational use of the battery and avoid potential safety risks.

[0091] It should be noted that the specific implementation process of the battery power state prediction device in the embodiment of the present invention is the same as the battery power state prediction method. For details, please refer to the method part embodiment, which will not be repeated here.

[0092] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for predicting battery power status, characterized in that: Including: Correcting the average state of charge (SOC) of the battery according to the difference in the state of charge of the battery to obtain the corrected surface SOC of the battery, where the difference in the state of charge of the battery is obtained according to the solid-phase diffusion coefficient and the solid-phase diffusion time constant of the battery; Correcting the original power prediction equation in the second-order RC equivalent circuit model according to the surface SOC of the battery to obtain the corrected power prediction equation; Obtaining the prediction result of the battery power state according to the corrected power prediction equation.

2. The prediction method according to claim 1, characterized in that: The second-order RC equivalent circuit model includes a first resistor R0, a first RC parallel circuit, and a second RC parallel circuit connected in series. The first RC parallel circuit includes a second resistor R1 and a first capacitor C1 connected in parallel, and the second RC parallel circuit includes a third resistor R2 and a second capacitor C2 connected in parallel.

3. The prediction method according to claim 2, characterized in that: The expression of the surface SOC of the battery is as shown in Equation (1): SOC_surf = SOC + ΔSOC; (1) Wherein, SOC_surf represents the surface SOC of the battery, SOC represents the average SOC of the battery, and ΔSOC represents the difference in the state of charge of the battery.

4. The prediction method according to claim 3, characterized in that: The expression of the difference in the state of charge of the battery is as shown in Equation (2): ΔSOC k = exp(-Δt / tau_sd) * ΔSOC k-1 + I k-1 * ksd(1 - exp(-Δt / tau_sd)); (Two) Among them, ΔSOC k represents the battery state of charge difference; k represents the current moment; k-1 represents the previous moment of the current moment; exp() represents the exponential function; Δt represents the sampling time interval; tau_sd represents the solid phase diffusion time constant of the battery; I represents the external current; ksd represents the solid phase diffusion coefficient of the battery.

5. The prediction method according to claim 4, characterized in that: The expression of the corrected power prediction equation is as shown in Equation (3): I max =(ECM(SOC_surf)-U1*exp(-Δt / tau1)-U2*exp(-Δt / tau2)-U term ) / (η*Δt*dECM(SOC_surf) / dSOC_surf / Q+R1*(1-exp(-Δt / tau1))+R1*(1-exp(-Δt / tau2))+R0); P max =((ECM(SOC_surf) -U1*exp(-Δt / tau1)-U2*exp(-Δt / tau2)-I max *(η*Δt*dECM(SOC_surf) / dSOC_surf / Q+R1*(1-exp(-Δt / tau1))+R1*(1-exp(-Δt / tau2))+R0))*I max ; (iii) Among them, I max represents the maximum current of the battery; ECM() represents the functional relationship between the open circuit voltage of the battery and the surface charge state of the battery; U1 represents the voltage value across the capacitor C1; tau1 represents the time constant of the first RC parallel circuit, tau1=R1*C1, R1 represents the resistance value of the second resistor R1, and C1 represents the electrochemical polarization capacitance of the battery; U2 represents the voltage value across the capacitor C2; tau2 is the time constant of the second RC parallel circuit, tau2=R2*C2, R2 represents the resistance value of the third resistor R2, and C2 represents the concentration difference polarization capacitance of the battery; U term represents the upper and lower limit thresholds of battery charge and discharge; η represents the battery charge and discharge efficiency; d represents the derivative; Q represents the rated capacity of the battery; R0 represents the ohmic internal resistance of the battery; P max Indicates the maximum power of the battery.

6. A battery power state prediction device, characterized in that: Including: A first correction module: for correcting the average SOC of the battery according to the difference in the state of charge of the battery to obtain the corrected surface SOC of the battery, where the difference in the state of charge of the battery is obtained according to the solid-phase diffusion coefficient and the solid-phase diffusion time constant of the battery; A second correction module: for correcting the original power prediction equation in the second-order RC equivalent circuit model according to the surface SOC of the battery to obtain the corrected power prediction equation; A result obtaining module: for obtaining the prediction result of the battery power state according to the corrected power prediction equation.

7. The prediction device according to claim 6, characterized in that The second-order RC equivalent circuit model includes a first resistor R0, a first RC parallel circuit, and a second RC parallel circuit connected in series. The first RC parallel circuit includes a second resistor R1 and a first capacitor C1 connected in parallel, and the second RC parallel circuit includes a third resistor R2 and a second capacitor C2 connected in parallel.

8. The prediction device according to claim 7, characterized in that The expression of the surface SOC of the battery is as shown in Equation (1): SOC_surf = SOC + ΔSOC; (1) Wherein, SOC_surf represents the surface SOC of the battery, SOC represents the average SOC of the battery, and ΔSOC represents the difference in the state of charge of the battery.

9. The prediction device according to claim 8, characterized in that The expression of the difference in the state of charge of the battery is as shown in Equation (2): ΔSOC k = exp(-Δt / tau_sd) * ΔSOC k-1 + I k-1 * ksd(1 - exp(-Δt / tau_sd)); (Two) Among them, ΔSOC k represents the battery state of charge difference; k represents the current moment; k-1 represents the previous moment of the current moment; exp() represents the exponential function; Δt represents the sampling time interval; tau_sd represents the solid phase diffusion time constant of the battery; I represents the external current; ksd represents the solid phase diffusion coefficient of the battery.

10. The prediction device according to claim 9, characterized in that The expression of the corrected power prediction equation is as shown in Equation (3): I max =(ECM(SOC_surf)-U1*exp(-Δt / tau1)-U2*exp(-Δt / tau2)-U term ) / (η*Δt*dECM(SOC_surf) / dSOC_surf / Q+R1*(1-exp(-Δt / tau1))+R1*(1-exp(-Δt / tau2))+R0); P max = ((ECM(SOC_surf) - U1 * exp(-Δt / tau1) - U2 * exp(-Δt / tau2) - I max * (η * Δt * dECM(SOC_surf) / dSOC_surf / Q + R1 * (1 - exp(-Δt / tau1)) + R1 * (1 - exp(-Δt / tau2)) + R0)) * I max ; (III) Among them, I max represents the maximum current of the battery; ECM() represents the functional relationship between the open circuit voltage of the battery and the surface charge state of the battery; U1 represents the voltage value across the capacitor C1; tau1 represents the time constant of the first RC parallel circuit, tau1=R1*C1, R1 represents the resistance value of the second resistor R1, and C1 represents the electrochemical polarization capacitance of the battery; U2 represents the voltage value across the capacitor C2; tau2 is the time constant of the second RC parallel circuit, tau2=R2*C2, R2 represents the resistance value of the third resistor R2, and C2 represents the concentration difference polarization capacitance of the battery; U term represents the upper and lower limit thresholds of battery charge and discharge; η represents the battery charge and discharge efficiency; d represents the derivative; Q represents the rated capacity of the battery; R0 represents the ohmic internal resistance of the battery; P max Indicates the maximum power of the battery.

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