A method for constructing an equivalent circuit model of solid-phase diffusion coefficient varying with temperature

Abstract

The present invention provides a method for constructing an equivalent circuit model in which the solid-phase diffusion coefficient changes with temperature, comprising the following steps: Step S1, constructing an equivalent circuit model of a battery and establishing a calculation formula, wherein the equivalent circuit model includes an open circuit voltage OCV and an internal resistance R0, and the equivalent circuit model calculates the terminal voltage U according to the input temperature T and current I; Step S2, determining the parameters to be calibrated of the equivalent circuit model according to the calculation formula of the equivalent circuit model, and testing the battery to obtain the parameters to be calibrated; Step S3, substituting the parameters to be calibrated into the equivalent circuit model, and inputting the temperature T and current I into the equivalent circuit model for simulation calculation. Among them, the parameters to be calibrated include the standard capacity Q max , the relationship between internal resistance R0 and temperature T and SOC R0(SOC,T), open circuit voltage OCV(SOC), solid phase diffusion coefficient k sd Relationship with temperature Tk sd (T), solid phase diffusion time constant τ sd Relationship with temperature Tτ sd (T). The present invention greatly improves the simulation accuracy in the low SOC range and at different magnifications at low temperatures by simulating the influence of solid-phase diffusion on the mechanism.

Description

Technical Field

[0001] The present invention belongs to the field of lithium-ion power battery equivalent circuit modeling, and specifically relates to a method for constructing an equivalent circuit model in which a solid-phase diffusion coefficient varies with temperature. Background Art

[0002] With global warming and the energy crisis, lithium batteries are gradually coming into our view. Lithium-ion batteries, with their high energy density and long lifespan, are used as the primary energy carrier in electric vehicles and electrochemical energy storage power stations. They play a crucial role in both electric vehicles and energy storage power stations. For electric vehicles, the performance of lithium batteries directly affects their range, power, and safety; for energy storage power stations, the performance of lithium batteries directly affects their capacity and output power. Whether in electric vehicles or energy storage power stations, an efficient and reliable battery management system (BMS) is fundamental to ensuring the stable operation of the battery system. Accurately estimating battery health (SOH) and state of charge (SOC), among other conditions, is a key function of a battery management system. Battery state estimation, in turn, relies on a reliable battery model. The quality of the battery model can be determined by the error between the output voltage and the actual voltage.

[0003] Lithium batteries exhibit significant differences in their characteristics at low temperatures and low SOC compared to room and high temperatures. Traditional ECM voltage estimation methods offer good simulation results at temperatures at or above 25°C. However, voltage simulation results can exhibit significant errors below 25°C, especially near 0°C. This is particularly true in the low SOC range, where lithium battery characteristics vary significantly as temperature decreases, causing voltage simulation results to deviate significantly from actual values. In practical applications, the ambient operating temperature of batteries is rarely stable above 25°C, making voltage simulation accuracy particularly important at low and medium temperatures. At low temperatures, the ohmic and polarization internal resistances of lithium batteries increase. The industry has established a mature approach to analyzing these changes in internal resistance: HPPC experiments measure the relationships between these two parameters and temperature and SOC. However, at low temperatures, the kinetics of lithium ion insertion and extraction in the electrode material slow, leading to a greater difference between the surface lithium ion concentration of the positive and negative electrode particles and the average lithium ion concentration. Furthermore, this difference varies with temperature due to temperature fluctuations during charge and discharge. This change can be simulated in a mechanistic model by combining Arrhenius and Fick's second law. However, this approach is inefficient in real-world applications such as vehicles and energy storage systems due to limitations in computing power and the number of parameters. Therefore, a method with fewer parameters and lower computing power requirements is needed to simulate this process and embed this local change into a simple overall model without conflicting with other components. Summary of the Invention

[0004] The present invention is made to solve the above-mentioned problem, and its purpose is to provide a method for constructing an equivalent circuit model in which the solid-phase diffusion coefficient varies with temperature.

[0005] The present invention provides a method for constructing an equivalent circuit model of a solid-phase diffusion coefficient that varies with temperature, which has the following characteristics and comprises the following steps:

[0006] Step S1, constructing an equivalent circuit model of the battery and establishing a calculation formula. The equivalent circuit model includes the open circuit voltage OCV and the internal resistance R0. The equivalent circuit model calculates the terminal voltage U based on the input temperature T and current I;

[0007] Step S2, determining the parameters to be calibrated of the equivalent circuit model according to the calculation formula of the equivalent circuit model, and testing the battery to obtain the parameters to be calibrated;

[0008] Step S3: Substitute the parameters to be calibrated into the equivalent circuit model, and input the temperature T and current I into the equivalent circuit model for simulation calculation.

[0009] In the equivalent circuit model, the open circuit voltage OCV is determined by the surface charge state SOC surf Determine, open circuit voltage OCV is OCV (SOC surf ), surface charge state SOC surf The calculation formula is as follows:

[0010]

[0011] Internal resistance R0 and surface charge state SOC surf Related to temperature T, internal resistance R0 is R0(SOC surf ,T),

[0012] The equivalent circuit model calculates the terminal voltage U based on the input temperature T and current I. The calculation formula of the terminal voltage U is as follows:

[0013] U=OCV(SOC surf )+I·R0(SOC surf ,T)

[0014] In the above formula, SOC mean is the average state of charge of the battery, SOC0 is the initial state of charge of the battery, Q max is the standard capacity of the battery, I(t) represents the current, charging is positive and discharging is negative,

[0015] ΔSOC k is the ΔSOC of the kth sampling point, k sd is the solid phase diffusion coefficient, τ sd is the solid phase diffusion time constant, I kis the current magnitude at the kth sampling point,

[0016] k sd (T) is the solid phase diffusion coefficient k sd Relationship with temperature T, τ sd (T) is the relationship between the solid phase diffusion time constant and temperature T, R s is the particle radius, A is the battery electrode area, a s is the particle specific surface area, δ is the solid phase conductivity, F is the Faraday constant, D0 is the solid phase diffusion coefficient at infinite temperature, E is the apparent activation energy, R is the ideal gas constant,

[0017] In step S2, according to the calculation formula of open circuit voltage OCV, internal resistance R0 and terminal voltage U, the parameters to be calibrated include standard capacity Q max , the relationship between internal resistance R0 and temperature T and SOC R0(SOC,T), open circuit voltage OCV(SOC), solid phase diffusion coefficient k sd Relationship with temperature Tk sd (T), solid phase diffusion time constant τ sd Relationship with temperature Tτ sd (T).

[0018] In the method for constructing an equivalent circuit model of the solid-phase diffusion coefficient varying with temperature provided by the present invention, the following features may also be provided: wherein, in step S2, the standard capacity Q is obtained by a 25°C capacity test. max .

[0019] The method for constructing an equivalent circuit model of the solid-phase diffusion coefficient changing with temperature provided by the present invention may also have the following characteristics: wherein, in step S2, HPPC tests are conducted at 0°C, 5°C, 10°C, 15°C, 20°C, 25°C, and 35°C, and the internal resistance obtained by the HPPC tests at different temperatures is used to obtain the relationship R0 (SOC, T) between the internal resistance R0 and the temperature T and the SOC.

[0020] The method for constructing an equivalent circuit model of the solid-phase diffusion coefficient changing with temperature provided by the present invention may also have the following characteristics: wherein, in step S2, the open circuit voltage OCV (SOC) is obtained by obtaining the relationship between the battery voltage and SOC through a constant current charge and discharge test at 25°C and 0.02C.

[0021] In the method for constructing an equivalent circuit model of the solid-phase diffusion coefficient varying with temperature provided by the present invention, the following features may also be provided: wherein, in step S2, the solid-phase diffusion coefficient k is obtained by performing a constant current charge-discharge test at 0°C, 5°C, 10°C, 15°C, 20°C, 25°C, 35°C and at a low rate of 0.1C or 0.2C.sd Relationship with temperature Tk sd (T) and solid phase diffusion time constant τ sd Relationship with temperature Tτ sd (T), as follows:

[0022] According to the 0.1C or 0.2C current, voltage data and Q at different temperatures max , R0(T,SOC), OCV(SOC) to identify the k corresponding to different temperatures Ti sd i and τ sd i, get the data point (k sd i,Ti),(τ sd i,Ti), and according to the formula k sd (T) = θ·e α / T For data points (k sd i,Ti) fitting, get k sd Relationship with Tk sd (T), and use linear interpolation to calculate the data points (τ sd i, Ti) interpolation processing to obtain the interpolation function τ sd (T) as τ sd Relationship with T.

[0023] Functions and effects of the invention

[0024] According to the method for constructing an equivalent circuit model of the solid-phase diffusion coefficient varying with temperature involved in the present invention, compared with the traditional equivalent circuit model, the present invention is based on the fact that the difference between the surface lithium ion concentration and the average lithium ion concentration of the positive and negative electrode particles of the lithium battery varies with temperature, and adds two parameters, namely the relationship between the solid-phase diffusion coefficient and temperature, and the relationship between the solid-phase diffusion time constant and temperature, to establish an equivalent circuit model. The difference between the surface SOC and the average SOC caused by solid-phase diffusion in the simulation mechanism is greatly improved, and the voltage simulation accuracy in the low SOC range and at different rates at low temperatures is greatly improved, solving the problem of large errors in traditional voltage simulation at low temperatures. In addition, all parameters in the equivalent circuit model of the present invention are identified offline, and the computing power requirements of the computing platform are very low. In addition, when the battery consistency is good, a batch of batteries can use the same set of parameters, which greatly reduces the difficulty of obtaining parameters for the battery pack and the computing power requirements for the BMS. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 1 is a flow chart of a method for constructing an equivalent circuit model of a solid-phase diffusion coefficient varying with temperature in an embodiment of the present invention;

[0026] Figure 2 is the relationship between the solid phase diffusion coefficient and temperature in the embodiment of the present invention;

[0027] Figure 3is the relationship between the solid phase diffusion time constant and temperature in the embodiment of the present invention;

[0028] Figure 4 This is a comparison of the 15°C 0.2C constant current discharge EECM simulation and Rint model simulation results in an embodiment of the present invention;

[0029] Figure 5 This is a comparison of the 15°C 0.8C constant current discharge EECM simulation and Rint model simulation results in an embodiment of the present invention;

[0030] Figure 6 This is a comparison of the 10°C 0.2C constant current discharge EECM simulation and Rint model simulation results in an embodiment of the present invention;

[0031] Figure 7 This is a comparison of the 10°C 0.8C constant current discharge EECM simulation and Rint model simulation results in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to make the technical means, creative features, objectives and effects of the present invention easier to understand, the following embodiments and accompanying drawings specifically illustrate the method for constructing an equivalent circuit model of the solid-phase diffusion coefficient varying with temperature of the present invention.

[0033] <Example>

[0034] Figure 1 It is a flow chart of a method for constructing an equivalent circuit model of solid-phase diffusion coefficient varying with temperature in an embodiment of the present invention.

[0035] like Figure 1 As shown, a method for constructing an equivalent circuit model (EECM) of a solid-phase diffusion coefficient varying with temperature in this embodiment includes the following steps:

[0036] Step S1, constructing an equivalent circuit model of the battery and establishing a calculation formula, the equivalent circuit model includes the open circuit voltage OCV and the internal resistance R0, and the equivalent circuit model calculates the terminal voltage U according to the input temperature T and current I.

[0037] In the equivalent circuit model, the open circuit voltage OCV is determined by the surface charge state SOC surf Determine, open circuit voltage OCV is OCV (SOC surf ), surface charge state SOC surf The calculation formula is as follows:

[0038]

[0039] In this embodiment, the surface charge state SOC surf The specific derivation process of the calculation formula is as follows:

[0040] The OCV of conventional ECMs only considers the relationship between the average lithium ion concentration within the lithium battery particles and the open circuit voltage, without factoring in the effects of solid-phase diffusion. The present invention's temperature-dependent equivalent circuit model (EECM) for solid-phase diffusion coefficients is based on the Arrhenius equation and a first-order inertia link. The OCV portion considers the effects of solid-phase diffusion, where lithium ions diffuse from the interior of solid particles to the surface.

[0041] At low and medium temperatures, due to solid-phase diffusion, the concentration of lithium ions on the particle surface is significantly different from that inside the particle, and only the lithium ion concentration on the particle surface is related to the OCV. This invention takes the influence of solid-phase diffusion into consideration. The specific derivation is as follows:

[0042] SOC represents the state of charge of the battery. The relationship between the macroscopic SOC and the microscopic lithium ion concentration is:

[0043]

[0044] Among them, SOC mean Indicates the average charge state of the lithium battery, c mean represents the average lithium ion concentration of solid particles, c max It represents the maximum lithium ion concentration of solid particles, that is, the lithium ion concentration when the battery is fully charged. The expression of SOC in ECM is:

[0045]

[0046] Among them, SOC0 represents the initial SOC, Q max It represents the maximum charge of the battery, that is, the battery capacity. I(t) represents the current, which is positive for charging and negative for discharging.

[0047] In ECM, the battery SOC is considered to be equal to SOC mean , and the surface SOC (SOC surf ), the surface SOC is mainly related to solid phase diffusion. When the battery is in use, that is, when lithium ions diffuse in the solid phase, the surface charge state SOC of the solid particles surf and average state of charge SOC mean There is a certain gap between them, which is ΔSOC:

[0048] ΔSOC=SOC surf -SOC mean

[0049] According to Fick's second law:

[0050]

[0051] The initial conditions are:

[0052] c s | t＝0 =c s,0

[0053] The boundary conditions are:

[0054]

[0055]

[0056] Where R s is the particle radius; c s is the lithium ion concentration of solid particles, which is a function of time t and polar coordinate r; D s is the diffusion coefficient of lithium ions inside the solid particles; j n is the pore flow rate on the particle surface.

[0057] Simplifying the above differential equation and using the first-order inertia link to simulate the solid phase diffusion process, it becomes:

[0058]

[0059]

[0060] Where k sd is the impact factor coefficient of the solid phase diffusion module, τ sd is the time constant of solid phase diffusion process, I k is the current size of the kth sampling point, ΔSOC k is the ΔSOC of the kth sampling point, and Δt is the sampling interval.

[0061] A represents the battery electrode area, a s is the particle specific surface area, δ is the solid phase conductivity, and F is the Faraday constant.

[0062] The solid-phase diffusion coefficient is closely related to temperature and can usually be expressed using the Arrhenius formula:

[0063] D s =D0e -E / RT

[0064] Where T is the temperature, D0 is the pre-exponential factor (solid-phase diffusion coefficient at infinite temperature), E is the apparent activation energy, and R is the ideal gas constant.

[0065] The solid phase diffusion coefficient based on the Arrhenius formula can be expressed as:

[0066]

[0067] Rewrite the above formula into

[0068] k sd (T) = θ·e α / T

[0069] in It can be seen that the solid phase diffusion coefficient k sd The relationship with temperature T is only related to the parameters θ and α.

[0070] Further according to k sd (T) = θ·e α / T The data points obtained from the solid phase diffusion parameter extraction experiment are fitted to obtain k sd Relationship with temperature Tk sd (T).

[0071] In summary, the surface SOC (SOC surf ) is calculated as:

[0072]

[0073] According to this formula, SOC can be easily calculated surf , and the voltage of lithium batteries is mainly determined by the surface charge state SOC surf Therefore, the open circuit voltage is OCV (SOC surf ).

[0074] Internal resistance R0 and surface charge state SOC surf Related to temperature T, internal resistance R0 is R0(SOC surf ,T).

[0075] The internal resistance of the traditional ECM only considers the influence of internal resistance and average SOC, ignoring the influence of solid phase diffusion of particles inside the lithium battery. The internal resistance of the EECM of the present invention partially considers the influence of solid phase diffusion of particles inside the lithium battery and temperature. Therefore, the internal resistance R0 of the present invention is R0(SOC surf ,T).

[0076] The equivalent circuit model calculates the terminal voltage U based on the input temperature T and current I. The calculation formula of the terminal voltage U is as follows:

[0077] U=OCV(SOC surf )+I·R0(SOC surf ,T)

[0078] Step S2: determining the parameters to be calibrated of the equivalent circuit model according to the calculation formula of the equivalent circuit model, and testing the battery to obtain the parameters to be calibrated.

[0079] According to the calculation formula of open circuit voltage OCV, internal resistance R0 and terminal voltage U, the parameters to be calibrated include standard capacity Qmax , the relationship between internal resistance R0 and temperature T and SOC R0(SOC,T), open circuit voltage OCV(SOC), solid phase diffusion coefficient k sd Relationship with temperature Tk sd (T), solid phase diffusion time constant τ sd Relationship with temperature Tτ sd (T).

[0080] In this embodiment, the process of testing the battery to obtain the parameters to be calibrated includes basic experiments and solid-phase diffusion parameter extraction experiments. The first experiment in the basic experiment is a 25°C capacity test, and the purpose of the experiment is to obtain the standard capacity of the battery. The second experiment of the basic experiment is an HPPC experiment at 0°C, 5°C, 10°C, 15°C, 20°C, 25°C, and 35°C. The purpose of the experiment is to obtain the internal resistance of the battery. The third experiment of the basic experiment is a 25°C 0.02C constant current charge and discharge experiment, and the purpose of the experiment is to obtain the open circuit voltage of the battery. The solid-phase diffusion parameter extraction experiment is a 0.1C or 0.2C low-rate constant current charge and discharge experiment at 0°C, 5°C, 10°C, 15°C, 20°C, 25°C, and 35°C. The purpose of the experiment is to obtain the solid-phase diffusion coefficient and solid-phase diffusion time constant corresponding to different temperatures.

[0081] In step S2, the specific process of obtaining the parameters to be calibrated is as follows:

[0082] Obtain standard capacity Q through 25℃ capacity test max .

[0083] Through HPPC tests at 0℃, 5℃, 10℃, 15℃, 20℃, 25℃, and 35℃, the relationship between internal resistance R0, temperature T, and SOC R0(SOC,T) was obtained based on the internal resistance obtained from HPPC tests at different temperatures, and R0(SOC surf ,T).

[0084] The relationship between battery voltage and SOC is obtained by constant current charge and discharge test at 25℃ 0.02C to obtain the open circuit voltage OCV (SOC), and the OCV (SOC surf ).

[0085] The solid phase diffusion coefficient k was obtained by constant current charge and discharge tests at 0°C, 5°C, 10°C, 15°C, 20°C, 25°C, and 35°C and at a low rate of 0.1C or 0.2C. sd Relationship with temperature Tk sd (T) and solid phase diffusion time constant τ sd Relationship with temperature Tτ sd (T), as follows:

[0086] According to the 0.1C or 0.2C current, voltage data and Q at different temperaturesmax , R0(T,SOC), OCV(SOC) to identify the k corresponding to different temperatures Ti sd i and τ sd i, get the data point (k sd i,Ti),(τ sd i,Ti), and according to the formula k sd (T) = θ·e α / T For data points (k sd i,Ti) fitting, get k sd Relationship with Tk sd (T), and use linear interpolation to calculate the data points (τ sd i, Ti) interpolation processing to obtain the interpolation function τ sd (T) as τ sd Relationship with T.

[0087] In this embodiment, the solid phase diffusion coefficient k sd Relationship with temperature Tk sd (T) Figure 2 As shown, the solid phase diffusion time constant τ sd Relationship with temperature Tτ sd (T) Figure 3 shown.

[0088] Step S3: Substitute the parameters to be calibrated into the equivalent circuit model, and input the temperature T and current I into the equivalent circuit model for simulation calculation to obtain the voltage U.

[0089] In this embodiment, the equivalent circuit model (EECM) of the solid-phase diffusion coefficient varying with temperature constructed by the present invention is simulated and compared with the Rint model, as follows:

[0090] Figure 4 This is a comparison of the 15°C 0.2C constant current discharge EECM simulation and Rint model simulation results in an embodiment of the present invention; Figure 5 This is a comparison of the 15°C 0.8C constant current discharge EECM simulation and Rint model simulation results in an embodiment of the present invention.

[0091] like Figure 4 and Figure 5 As shown in the figure, they are 0.2C and 0.8C constant current discharge experiments at 15℃, the solid line is the actual value of the terminal voltage, the dotted line is the simulation result of the Rint terminal voltage, and the horizontal dotted line is the simulation result of the EECM terminal voltage. It can be clearly seen that Figure 4 After the 16000s and Figure 5 After 4000s, the simulation accuracy of the EECM of the present invention is significantly improved.

[0092] Figure 6This is a comparison of the 10°C 0.2C constant current discharge EECM simulation and Rint model simulation results in an embodiment of the present invention; Figure 7 This is a comparison of the 10°C 0.8C constant current discharge EECM simulation and Rint model simulation results in an embodiment of the present invention.

[0093] like Figure 6 and Figure 7 As shown in the figure, they are 0.2C and 0.8C constant current discharge experiments at 10℃, the solid line is the actual value of the terminal voltage, the dotted line is the simulation result of the Rint terminal voltage, and the horizontal dotted line is the simulation result of the EECM terminal voltage. It can be clearly seen that Figure 6 After the 14,000s and Figure 7 After 3500s, the simulation accuracy of the EECM of the present invention is significantly improved.

[0094] Functions and Effects of the Embodiments

[0095] According to the method for constructing an equivalent circuit model of the solid-phase diffusion coefficient varying with temperature involved in this embodiment, compared with the traditional equivalent circuit model, this embodiment is based on the difference between the surface lithium ion concentration and the average lithium ion concentration of the positive and negative electrode particles of the lithium battery varying with temperature, and adds two parameters, the relationship between the solid-phase diffusion coefficient and the temperature, and the relationship between the solid-phase diffusion time constant and the temperature, to establish an equivalent circuit model. The difference between the surface SOC and the average SOC caused by solid-phase diffusion in the simulation mechanism is greatly improved, and the voltage simulation accuracy in the low SOC range and at different rates at low temperatures is greatly improved, solving the problem of large errors in traditional voltage simulation at low temperatures. In addition, all parameters in the equivalent circuit model of the present invention are identified offline, and the computing power requirements of the computing platform are very low. In addition, when the battery consistency is good, a batch of batteries can use the same set of parameters, which greatly reduces the difficulty of obtaining parameters for the battery pack and the computing power requirements for the BMS.

[0096] Furthermore, according to the simulation comparison results with the Rint model, it can be seen that the simulation accuracy of the equivalent circuit model constructed by the construction method of the equivalent circuit model of the solid-phase diffusion coefficient changing with temperature in this embodiment is significantly improved, which greatly improves the voltage simulation accuracy of the battery at a lower temperature.

[0097] The above embodiments are preferred examples of the present invention and are not intended to limit the scope of protection of the present invention.

Claims

1. A method for constructing an equivalent circuit model of solid-phase diffusion coefficient varying with temperature, characterized in that: The following steps are involved: Step S1, constructing an equivalent circuit model of the battery and establishing a calculation formula, wherein the equivalent circuit model includes the open circuit voltage OCV and the internal resistance R0, and the equivalent circuit model calculates the terminal voltage U based on the input temperature T and current I; Step S2, determining the parameters to be calibrated of the equivalent circuit model according to the calculation formula of the equivalent circuit model, and testing the battery to obtain the parameters to be calibrated; Step S3: Substitute the parameters to be calibrated into the equivalent circuit model, and input the temperature T and current I into the equivalent circuit model for simulation calculation. In the equivalent circuit model, the open circuit voltage OCV is determined by the surface charge state SOC surf Determine that the open circuit voltage OCV is OCV(SOC surf ), the surface charge state SOC surf The calculation formula is as follows: The internal resistance R0 and the surface charge state SOC surf Related to the temperature T, the internal resistance R0 is R0(SOC surf ,T), The equivalent circuit model calculates the terminal voltage U according to the input temperature T and current I. The calculation formula of the terminal voltage U is as follows: U=OCV(SOC surf )+I·R0(SOC surf ,T) In the above formula, SOC mean is the average state of charge of the battery, SOC0 is the initial state of charge of the battery, Q max is the standard capacity of the battery, I(t) represents the current, charging is positive and discharging is negative, ΔSOC k is the ΔSOC of the kth sampling point, k sd is the solid phase diffusion coefficient, τ sd is the solid phase diffusion time constant, I k is the current magnitude at the kth sampling point, k sd (T) is the solid phase diffusion coefficient k sd Relationship with temperature T, τ sd (T) is the relationship between the solid phase diffusion time constant and temperature T, R s is the particle radius, A is the battery electrode area, a s is the particle specific surface area, δ is the solid phase conductivity, F is the Faraday constant, D0 is the solid phase diffusion coefficient at infinite temperature, E is the apparent activation energy, R is the ideal gas constant, In step S2, according to the calculation formula of the open circuit voltage OCV, the internal resistance R0 and the terminal voltage U, the parameters to be calibrated include the standard capacity Q max , the relationship between internal resistance R0 and temperature T and SOC R0(SOC,T), open circuit voltage OCV(SOC), solid phase diffusion coefficient k sd Relationship with temperature Tk sd (T), solid phase diffusion time constant τ sd Relationship with temperature Tτ sd (T).

2. The method for constructing an equivalent circuit model of solid-phase diffusion coefficient varying with temperature according to claim 1, characterized in that: in, In step S2, the standard capacity Q is obtained by a 25°C capacity test. max .

3. The method for constructing an equivalent circuit model of solid-phase diffusion coefficient varying with temperature according to claim 1, characterized in that: in, In step S2, the relationship R0 (SOC, T) between the internal resistance R0 and the temperature T is obtained based on the internal resistance obtained from the HPPC test at different temperatures through HPPC test at 0°C, 5°C, 10°C, 15°C, 20°C, 25°C, and 35°C.

4. The method for constructing an equivalent circuit model of solid-phase diffusion coefficient varying with temperature according to claim 1, characterized in that: in, In step S2, the open circuit voltage OCV(SOC) is obtained by obtaining the relationship between the battery voltage and the SOC through a constant current charge and discharge test at 25°C and 0.02°C.

5. The method for constructing an equivalent circuit model of solid-phase diffusion coefficient varying with temperature according to claim 1, characterized in that: in, In step S2, the solid phase diffusion coefficient k is obtained by performing a constant current charge and discharge test at 0°C, 5°C, 10°C, 15°C, 20°C, 25°C, and 35°C at a low rate of 0.1C or 0.2C. sd Relationship with temperature Tk sd (T) and solid phase diffusion time constant τ sd Relationship with temperature Tτ sd (T), as follows: According to the 0.1C or 0.2C current, voltage data and Q at different temperatures max , R0(T,SOC), OCV(SOC) to identify the k corresponding to different temperatures Ti sd i and τ sd i, get the data point (k sd i,Ti),(τ sd i,Ti), and according to the formula k sd (T) = θ·e α / T For data points (k sd i,Ti) fitting, get k sd Relationship with Tk sd (T), and use linear interpolation to calculate the data points (τ sd i, Ti) interpolation processing to obtain the interpolation function τ sd (T) as τ sd Relationship with T.

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