Joint estimation method of lithium battery state of charge and temperature for vehicle applications

Abstract

The present invention relates to a method for jointly estimating the state of charge and temperature of a lithium battery for vehicle-mounted applications, and belongs to the field of battery technology. The method comprises: based on the relevant parameters of the lithium-ion battery to be tested, establishing a suitable electrical model and thermal model of the battery, and using the dependency between the battery temperature and the electrical model parameters to establish an electrical-thermal coupling model, and at the same time determining the model parameters required for realizing the joint estimation of the battery state of charge and temperature; designing a static capacity test experiment and an HPPC experiment, based on the HPPC experimental data, using the recursive least squares method with a forgetting factor to identify the electrical model parameters, and establishing a quantitative relationship between the electrical model parameters and the battery SOC and temperature; at the same time, obtaining experimental data of a similar vehicle operating condition, and using a particle swarm optimization algorithm to identify the thermal model parameters; finally, based on the electrical-thermal coupling model, combined with the Kalman filter algorithm, realizing accurate joint estimation of the battery state of charge and temperature.

Description

Technical Field

[0001] The present invention belongs to the technical field of batteries and relates to a method for jointly estimating the state of charge and temperature of a lithium battery for vehicle-mounted applications. Background Art

[0002] Lithium-ion batteries are widely used in energy storage systems of new energy vehicles due to their excellent comprehensive performance. To ensure that the batteries can operate safely and efficiently, it is necessary to monitor and estimate the battery status and parameter changes in real time. As the basis of battery safety management, charge and discharge control, and thermal management, it is necessary to accurately estimate the battery state of charge and temperature in real time.

[0003] At present, there are mainly the following types of battery SOC estimation methods: 1) Estimating battery SOC based on characterization parameters. This method realizes battery SOC estimation by establishing a mapping relationship between characterization parameters and SOC. It has high requirements on the accuracy of characterization parameters and is only applicable to scenarios such as laboratories. 2) Estimating battery SOC based on the ampere-hour integration method. This method is prone to cumulative errors in SOC estimation due to current measurement errors and is highly dependent on the initial value of SOC. 3) Estimating battery SOC based on data-driven. This method has high requirements on the quality and quantity of data, poor generalization ability, and is difficult to apply to actual vehicles. 4) Estimating battery SOC based on models. This method is one of the estimation methods that is currently widely studied. It mainly uses estimation models and state estimation algorithms to complete the SOC estimation of power batteries, but its estimation accuracy is highly dependent on the accuracy of the model.

[0004] At present, there are mainly the following types of battery temperature estimation methods: 1) Estimating battery temperature based on data-driven. Although this method does not need to take into account the complex thermodynamic equations of the battery and the geometric characteristics of the battery, it has high requirements on the quality and quantity of experimental data and poor generalization ability. 2) Estimating battery temperature based on EIS measurement. This method has a simple model and does not require the installation of temperature sensors, etc., but the current on-board measurement equipment is difficult to meet the EIS measurement accuracy requirements, so it is difficult to apply to actual vehicles. 3) Estimating battery temperature based on numerical calculations. This method can reflect the actual temperature distribution of the battery and has high estimation accuracy, but its computational cost is large and its real-time performance is low. 4) Estimating battery temperature based on thermal models. This method has a simple model and algorithm structure and low computational cost, so the state estimation has good real-time performance and is currently the most practical estimation method for actual vehicle application scenarios.

[0005] Since the battery state of charge and temperature affect each other, if they are estimated separately, there will inevitably be large errors. Therefore, it is necessary to use a joint estimation strategy to achieve joint estimation of the battery state of charge and temperature. Summary of the invention

[0006] In view of this, an object of the present invention is to provide a method for jointly estimating the state of charge and temperature of a lithium battery for vehicle-mounted applications, so as to achieve accurate joint estimation of the state of charge and temperature of the battery.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] A method for jointly estimating the state of charge and temperature of a lithium battery for vehicle-mounted applications comprises the following steps:

[0009] S1: Based on the relevant parameters of the lithium-ion battery to be tested, establish a suitable battery electrical model and thermal model, and use the dependency between the battery temperature and the electrical model parameters to establish an electrical-thermal coupling model, and determine the model parameters required to achieve joint estimation of the battery state of charge and temperature;

[0010] S2: Static capacity test, HPPC test and real vehicle operating condition test at different temperatures;

[0011] S3: Based on the static capacity test experimental data and HPPC test experimental data at different ambient temperatures in step S2, the recursive least squares method with a forgetting factor is used to identify the electrical model parameters, and a quantitative relationship between the electrical model parameters and the battery SOC and temperature is established; based on the actual vehicle working condition test experimental data in step S2, the particle swarm optimization algorithm is used to identify the thermal model parameters; and based on the experimental data under other working conditions, the accuracy and effectiveness of the electrical model parameters and the thermal model parameters are verified;

[0012] S4: Based on the electric-thermal coupling model and combined with the Kalman filter algorithm, accurate joint estimation of the battery state of charge and temperature is achieved.

[0013] Further, in step S1, the electrical model of the battery is a second-order RC equivalent circuit model; the thermal model of the battery couples the tab thermal model and the cell thermal model, the tab thermal model adopts a lumped mass thermal model, and the cell thermal model adopts a two-dimensional thermal resistance network model.

[0014] Furthermore, in step S1, the thermal model of the battery cell is divided into a heat generation model and a temperature calculation model. The electrical model parameters are used as the input of the heat generation model to calculate the heat generation of the battery, and the heat generation is used as the input of the temperature calculation model to calculate the battery temperature. Then, the average temperature of the battery cell is used as the input of the electrical model to form an electrical-thermal coupling model.

[0015] Further, in step S1, the lumped mass thermal model mainly consists of three parts: a heat source, a heat transfer resistance between the battery cell and the environment, and a convection heat transfer resistance between the battery cell and the environment. The thermal balance equation can be expressed as:

[0016]

[0017]

[0018] R tp =1 / h tp A' t R tn =1 / h tn A' t

[0019] R cp =1 / h cp A c R cn =1 / h cn A c

[0020] Among them, C p and m p Respectively represent the specific heat capacity and mass of the positive electrode ear, C n and m n Respectively represent the specific heat capacity and mass of the negative electrode ear, T p and T n Indicates the temperature of the positive and negative lugs, T 1i and Respectively represent the temperature of the discrete unit near the positive and negative ears, T ∞ Indicates the ambient temperature, R p , R n Respectively represent the equivalent internal resistance of the positive and negative ears, h tp 、h tn Respectively represent the equivalent heat transfer coefficient between the positive and negative tabs and the battery cell, h cp 、h cn Respectively represent the equivalent convection heat transfer coefficient between the positive and negative lugs and the environment, A′ t and A c They are the heat transfer area between the tab and the discrete unit, and the convection heat transfer area between the tab and the environment.

[0021] Further, in step S1, the two-dimensional thermal resistance network model is to discretize the battery cell into N l ×N w discrete units, where N l is the number of discrete units along the length of the cell, N w is the number of discrete units along the width of the battery cell; each discrete unit is regarded as an independent heat source, and exchanges heat with the surrounding heat source through thermal resistance.

[0022] According to the location of the discrete units of the battery cells, the discrete units are divided into four types:

[0023] Type 1, discrete units located at the four corners of the battery cell, its thermal balance equation is:

[0024]

[0025] Among them, x=1, y=1.

[0026]

[0027] Where x = 1, y = N w .

[0028]

[0029] Where x = N l , y=1.

[0030]

[0031] Where x = N l , y=N w .

[0032] Type 2, the discrete units located on the left and right sides of the battery cell except the corners, its thermal balance equation is:

[0033]

[0034] Where x=1, y∈[2, N w -1].

[0035]

[0036] Where x = N l , y∈[2,N w -1].

[0037] Type 3, discrete units located on the upper and lower sides of the cell except the corners, the thermal balance equation is:

[0038]

[0039] Where x∈[2,N l -1], y=1.

[0040]

[0041] Where x∈[2,N l -1],y=N w .

[0042] Type 4, the discrete unit located in the center of the battery cell, its thermal balance equation is:

[0043]

[0044] Where x∈[2,N l -1],y∈[2,Nw -1].

[0045] In the above expressions, the subscripts j = 1, 2, 3, 4 represent different types of discrete units, C and m represent the specific heat capacity and mass of the discrete unit, respectively, and T x，y Indicates the temperature of the discrete cell, Q x，y Represents the heat generation of the discrete unit of the battery cell, satisfying the following expression:

[0046]

[0047] Where k = 1, ..., M, M is the data length, N is the number of discrete units, I x，y Represents the current size of the discrete unit, satisfying the following expression:

[0048] I x，y，k =I k / N

[0049] R 0，x，y , R 1，x，y and R 2，x，y Respectively represent the ohmic internal resistance and two polarization internal resistances of the discrete unit, obtained by interpolation method:

[0050] R 0，x，y，k =α(f1(SOC k , T k ) / N)

[0051] R 1，x，y，k =α(f2(SOC k , T k ) / N)

[0052] R 2，x，y，k =α(f3(SOC k , T k ) / N)

[0053] Among them, α is the internal resistance correction factor, f1, f2, f3 represent the quantitative relationship between the battery internal resistance and the battery SOC and temperature, T k Indicates the battery temperature, I k Indicates the battery current.

[0054] R con，1 and R con，2 Respectively represent the thermal resistance of the discrete unit of the battery cell along the length and width directions, satisfying the following expressions:

[0055] R con，1 =l / A1λ

[0056] R con，2 =w / A2λ

[0057] Among them, λ represents the thermal conductivity of the battery cell, l and w represent the heat conduction distance of the discrete unit along the length and width directions, respectively, and A1 and A2 represent the heat conduction area of ​​the discrete unit along the length and width directions, respectively.

[0058] R c，j Represents the convection heat transfer thermal resistance of the discrete unit of the battery cell, which satisfies the following expression:

[0059] R c，j =1 / h c A cj

[0060] Among them, h c represents the convective heat transfer coefficient between the discrete unit and the environment, A cj Represents the convective heat transfer area of ​​discrete units.

[0061] Further, step S2 specifically includes the following steps:

[0062] S21: Fully charge the lithium-ion battery to be tested in a constant temperature environment of 25°C using a standard charge and discharge protocol;

[0063] S22: Perform a static capacity test on the lithium-ion battery at a 1C charge and discharge rate (here the capacity is the rated capacity of the battery) at the experimental temperature to obtain the actual capacity of the battery at the current temperature;

[0064] S23: Fully charge the battery using a standard charge and discharge protocol in a constant temperature environment of 25°C, and then perform an HPPC test at the experimental temperature to obtain the current and voltage data of the battery at the current temperature;

[0065] S24: Repeat steps S21-S23 at ambient temperatures of -20°C, -15°C, -10°C, -5°C, 0°C, 5°C, 15°C, 25°C, and 40°C, and record experimental data at different temperatures;

[0066] S25: Carry out real vehicle operating condition experiments at -10℃, 5℃ and 25℃ respectively, and record the experimental data such as battery current, voltage and temperature at the current temperature.

[0067] Further, step S3 specifically includes the following steps:

[0068] S31: fitting the relationship between the open circuit voltage, SOC and temperature based on the experimental data obtained in step S2;

[0069] S32: Based on the experimental data obtained in step S2, the electrical model parameters are identified using a recursive least square method with a forgetting factor, and a quantitative relationship between the electrical model parameters and the battery SOC and temperature is established;

[0070] S33: Based on the experimental data obtained in step S2, using a particle swarm optimization algorithm to identify the tab thermal model parameters;

[0071] S34: Based on the experimental data obtained in step S2, the electrical model parameters identified in step S32, and the tab thermal model parameters identified in step S33, the cell thermal model parameters are identified using a particle swarm optimization algorithm.

[0072] S35: Verify the accuracy and validity of the electrical model parameters and thermal model parameters based on experimental data under other working conditions.

[0073] Further, step S4 specifically includes the following steps:

[0074] S41: Initialize the state estimator, determine the initial values ​​of the system error covariance P, process noise error covariance Q, measurement noise error covariance R and other system matrices in the state of charge estimator and the temperature estimator;

[0075] S42: Based on the quantitative relationship between the electric model parameters and the battery SOC and temperature, the electric model parameters are obtained by interpolation using the average value of the estimated temperature of the battery cells and the estimated SOC value;

[0076] S43: Based on the state space equation of the electric model, the battery SOC estimation is realized using the extended Kalman filter;

[0077] S44: Based on the state space equation of the thermal model, the battery temperature is estimated using the Kalman filter, and the center temperature of the battery cell is used as the feedback parameter of the estimator;

[0078] S45: Iterate steps S42 to S44.

[0079] The beneficial effects of the present invention are:

[0080] 1) The present invention takes into account the dependency between the electrical model parameters and the temperature, and can achieve accurate estimation of the state of charge and distributed temperature distribution of large-size batteries.

[0081] 2) The present invention establishes a temperature distribution estimation model based on a two-dimensional thermal resistance network, which can well simulate the temperature uneven distribution characteristics of the battery cell in the full operating temperature range.

[0082] 3) Based on the simplified electric-thermal coupling model, the present invention can achieve joint estimation of battery state of charge and temperature with consideration of both estimation accuracy and computational complexity.

[0083] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0085] Figure 1 The overall flow chart of the method for jointly estimating the state of charge and temperature of a lithium battery for vehicle-mounted applications of the present invention;

[0086] Figure 2 is a specific flow chart of step S1 in an embodiment of the present invention;

[0087] Figure 3 Schematic diagram of a second-order RC equivalent circuit model in an embodiment of the present invention;

[0088] Figure 4 The lug lumped mass thermal model in the embodiment of the present invention;

[0089] Figure 5 A two-dimensional thermal resistance network model of a battery cell in an embodiment of the present invention;

[0090] Figure 6 It is an electric-thermal coupling model in an embodiment of the present invention;

[0091] Figure 7 This is a specific flow chart of step S2 in an embodiment of the present invention;

[0092] Figure 8 This is a specific flow chart of step S3 in an embodiment of the present invention;

[0093] Fig. 9 This is a flowchart of joint estimation of battery state of charge and temperature in an embodiment of the present invention. DETAILED DESCRIPTION

[0094] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0095] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0096] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0097] See also Figures 1 to 9 , Figure 1 A method for jointly estimating the state of charge and temperature of a lithium battery for vehicle applications is shown, which specifically includes the following steps:

[0098] S1: Based on the relevant parameters of the lithium-ion battery to be tested, establish a suitable electrical model and thermal model, and use the dependency between the battery temperature and the electrical model parameters to establish an electrical-thermal coupling model, and determine the model parameters required to achieve joint estimation of the battery state of charge and temperature;

[0099] S2: Static capacity test and HPPC test are carried out at ambient temperatures of -20℃, -15℃, -10℃, -5℃, 0℃, 5℃, 15℃, 25℃, and 40℃, and real vehicle operating condition test is carried out at -10℃, 5℃, and 25℃;

[0100] S3: Based on static capacity test experimental data and HPPC test experimental data under different ambient temperatures, the recursive least squares method with forgetting factor is used to realize the identification of electrical model parameters, and the quantitative relationship between the electrical model parameters and battery SOC and temperature is established; based on real vehicle operating condition data, the particle swarm optimization algorithm is used to realize the identification of thermal model parameters; and based on experimental data under other operating conditions, the accuracy and effectiveness of the electrical model parameters and thermal model parameters are verified.

[0101] S4: Based on the electric-thermal coupling model, combined with the Kalman filter algorithm, the battery state of charge and temperature are jointly estimated

[0102] like Figure 2 As shown, step S1 specifically includes the following steps:

[0103] S11: Select a lithium-ion battery to be tested, establish a suitable electrical model and thermal model, and establish an electrical-thermal coupling model using the dependency between electrical model parameters and temperature.

[0104] like Figure 3 As shown, the equivalent circuit model is mainly composed of open circuit voltage, ohmic internal resistance and RC network pair, and its mathematical model can be expressed as:

[0105]

[0106]

[0107] U oc =U t -IR0-U1-U2

[0108] Among them, U oc and U t are the open circuit voltage and terminal voltage of the battery respectively, U1 and U2 are the polarization voltage of the battery respectively, R0 is the ohmic internal resistance of the battery, R1 and R2 are the polarization internal resistance of the battery, C1 and C2 are the polarization capacitance of the battery, I is the battery current, and the current direction is specified as positive for charging and negative for discharging.

[0109] The battery thermal model couples the tab thermal model and the cell thermal model. The tab thermal model uses a lumped mass thermal model, and the cell thermal model uses a two-dimensional thermal resistance network thermal model.

[0110] like Figure 4 As shown in the figure, the lumped mass thermal model mainly consists of three parts: the heat source, the heat transfer resistance between the battery cell and the convection heat transfer resistance between the battery cell and the environment. Its thermal balance equation can be expressed as:

[0111]

[0112]

[0113] R tp =1 / h tp A t ' R tn =1 / h tn A t '

[0114] R cp =1 / h cp A c R cn =1 / h cn A c

[0115] Among them, C p and mp Respectively represent the specific heat capacity and mass of the positive electrode ear, C n and m n Respectively represent the specific heat capacity and mass of the negative electrode ear, T p and T n Indicates the temperature of the positive and negative lugs, T 1i and Respectively represent the temperature of the discrete unit near the positive and negative ears, T ∞ Indicates the ambient temperature, R p , R n Respectively represent the equivalent internal resistance of the positive and negative ears, h tp 、h tn Respectively represent the equivalent heat transfer coefficient between the positive and negative tabs and the battery cell, h cp 、h cn Respectively represent the equivalent convection heat transfer coefficient between the positive and negative lugs and the environment, A′ t and A c They are the heat transfer area between the tab and the discrete unit, and the convection heat transfer area between the tab and the environment.

[0116] like Figure 5 As shown in the figure, the two-dimensional thermal resistance network model of the battery cell is to discretize the battery cell into N l ×N w discrete units, where N l is the number of discrete units along the length of the cell, N w is the number of discrete units along the width of the battery cell; each discrete unit is regarded as an independent heat source, and exchanges heat with the surrounding heat source through thermal resistance;

[0117] According to the location of the discrete units of the battery cells, the discrete units are divided into four types:

[0118] Type 1, discrete units located at the four corners of the battery cell, its thermal balance equation is:

[0119]

[0120] Among them, x=1, y=1.

[0121]

[0122] Where x = 1, y = N w .

[0123]

[0124] Where x = N l , y=1.

[0125]

[0126] Where x = N l , y=N w .

[0127] Type 2, the discrete units located on the left and right sides of the battery cell except the corners, its thermal balance equation is:

[0128]

[0129] Where x=1, y∈[2, N w -1].

[0130]

[0131] Where x = N l , y∈[2,N w -1].

[0132] Type 3, discrete units located on the upper and lower sides of the cell except the corners, the thermal balance equation is:

[0133]

[0134] Where x∈[2,N l -1], y=1.

[0135]

[0136] Where x∈[2,N l -1],y=N w .

[0137] Type 4, the discrete unit located in the center of the battery cell, its thermal balance equation is:

[0138]

[0139] Where x∈[2,N l -1],y∈[2,N w -1].

[0140] In the above expressions, the subscripts j = 1, 2, 3, 4 represent different types of discrete units, C and m represent the specific heat capacity and mass of the discrete unit, respectively, and T x，y Indicates the temperature of the discrete cell, Q x，y Represents the heat generation of the discrete unit of the battery cell, satisfying the following expression:

[0141]

[0142] Where k = 1, ..., M, M is the data length, N is the number of discrete units, I x，y Represents the current size of the discrete unit, satisfying the following expression:

[0143] I x，y，k =I k / N

[0144] R 0，x，y , R 1，x，y and R 2，x，y Respectively represent the ohmic internal resistance and two polarization internal resistances of the discrete unit, obtained by interpolation method:

[0145] R 0，x，y，k =α(f1(SOC k , T k ) / N)

[0146] R 1，x，y，k =α(f2(SOC k , T k ) / N)

[0147] R 2，x，y，k =α(f3(SOC k , T k ) / N)

[0148] Among them, α is the internal resistance correction factor, f1, f2, f3 represent the quantitative relationship between the battery internal resistance and the battery SOC and temperature, T k Indicates the battery temperature, I k Indicates the battery current.

[0149] R con，1 and R con，2 Respectively represent the thermal resistance of the discrete unit of the battery cell along the length and width directions, satisfying the following expressions:

[0150] R con，1 =l / A1λ

[0151] R con，2 =w / A2λ

[0152] Among them, λ represents the thermal conductivity of the battery cell, l and w represent the heat conduction distance of the discrete unit along the length and width directions, respectively, and A1 and A2 represent the heat conduction area of ​​the discrete unit along the length and width directions, respectively.

[0153] R c，j Represents the convection heat transfer thermal resistance of the discrete unit of the battery cell, which satisfies the following expression:

[0154] R c，j =1 / h c A cj

[0155] Among them, h c represents the convective heat transfer coefficient between the discrete unit and the environment, A cj Represents the convective heat transfer area of ​​discrete units.

[0156] like Figure 6 As shown, the thermal model of the battery cell is divided into a heat generation model and a temperature calculation model. The electrical model parameters are used as the input of the heat generation model to calculate the heat generation of the battery, and the heat generation is used as the input of the temperature calculation model to calculate the battery temperature. The average temperature of the battery cell is then used as the input of the electrical model to form an electrical-thermal coupling model.

[0157] S12: Discretize the electrical model and thermal model established in step S11, and convert them into a control-oriented state space expression.

[0158] Discretize the second-order RC equivalent circuit model, and its state space expression is as follows:

[0159] Equation of state:

[0160] Output equation:

[0161] Where Δt represents the sampling time, k = 1, ..., M, M is the data length, w k and v k represent process noise and measurement noise respectively.

[0162] Discretize the ear thermal model, and its state space expression is as follows:

[0163]

[0164]

[0165]

[0166]

[0167]

[0168] The battery thermal model is discretized, and its state space expression is as follows:

[0169] Equation of state: x k =Ax k-1 +Bu k-1 +w k-1

[0170] Output equation: y k =Cx k +v k

[0171] Among them, the state matrix

[0172] The input matrix is

[0173] The output matrix is ​​y k =x k , so the matrix C is the identity matrix,

[0174] The system matrices A and B are shown below:

[0175]

[0176]

[0177] Among them, σ and φ are determined by the thermal balance equation of the tab and the discrete unit of the battery cell, which can be expressed by the following expression:

[0178] When x=1, y=1

[0179] σ xy =-(h tp A t +A1λ / l+A2λ / w+h c A c1 ) / Cm

[0180] φ xy =(Q xy +h tp A t T p +A1λT (x+1)y / l+A2λT x(y+1) / w+h c A c1 T ∞ ) / Cm

[0181] When x=1,y=N w hour

[0182] σ xy =-(h tp A t +A1λ / l+A2λ / w+h c A c1 ) / Cm

[0183] φ xy =(Q xy +h tp A t T p +A1λT (x+1)y / l+A2λT x(y-1) / w+h c A c1 T ∞ ) / Cm

[0184] When x = N l , when y=1

[0185] σxy = -(h tn A t + A1λ / l + A2λ / w + h c A c1 ) / Cm

[0186] φ xy = (Q xy + h tn A t T n + A1λT (x-1)y / l + A2λT x(y+1) / w + h c A c1 T ∞ ) / Cm

[0187] When x = N l , y = N w at that time

[0188] σ xy = -(h tn A t + A1λ / l + A2λ / w + h c A c1 ) / Cm

[0189] φ xy = (Q xy + h tn A t T n + A1λT (x-1)y / l + A2λT x*(y-1) / w + h c A c1 T ∞ ) / Cm

[0190] When x = 1, y ∈ [2, N w - 1] at that time

[0191] σ xy = -(h tp A t + A1λ / l + 2A2λ / w + h c A c2 ) / Cm

[0192] φ xy = (Q xy + h tp A t T p + A1λT (x+1)y / l + A2λT x(y+1) / w + A2λT x(y-1) / w + h c A c2 T∞ ) / Cm

[0193] When x = N l , y ∈ [2, N w -1]

[0194] σ xy = -(h tn A t + A1λ / l + 2A2λ / w + h c A c2 ) / Cm

[0195] φ xy = (Q xy + h tp A t T p + A1λT (x-1)y / l + A2λT x(y+1) / w + A2λT x(y-1) / w + h c A c2 T ∞ ) / Cm

[0196] When x ∈ [2, N l -1], y = 1

[0197] σ xy = -(2A1λ / l + A2λ / w + h c A c3 ) / Cm

[0198] φ xy = (Q xy + A1λT (x-1)y / l + A1λT (x+1)y / l + A2λT x(y+1) / w + h c A c3 T ∞ ) / Cm

[0199] When x ∈ [2, N l -1], y = N w

[0200] σ xy = -(2A1λ / l + A2λ / w + h c A c3 ) / Cm

[0201] φ xy = (Q xy + A1λT (x-1)y / l + A1λT (x+1)y / l + A2λT x(y-1) / w + h c A​c3 T ∞ ) / Cm

[0202] When x∈[2, N l -1],y∈[2,N w -1]

[0203] σ xy =-(2A1λ / l+2A2λ / w+h c A c4 ) / Cm

[0204] φ xy =(Q xy +A1λT (x-1)y / l+A1λT (x+1) y / l+A2λT x(y-1) / w+A2λT x(y+1) / w+h c A c4 T ∞ ) / Cm

[0205] See also Figure 7 Step S2 specifically includes

[0206] S21: Fully charge the lithium-ion battery to be tested in a constant temperature environment of 25°C using a standard charge and discharge protocol;

[0207] S22: Perform a static capacity test on the lithium-ion battery at a 1C charge and discharge rate (here the capacity is the rated capacity of the battery) at the experimental temperature to obtain the actual capacity of the battery at the current temperature.

[0208] S23: Fully charge the battery using a standard charge and discharge protocol in a constant temperature environment of 25°C, and then perform an HPPC test at the experimental temperature to obtain the current and voltage data of the battery at the current temperature.

[0209] S24: Repeat steps S21-S23 at ambient temperatures of -20°C, -15°C, -10°C, -5°C, 0°C, 5°C, 15°C, 25°C, and 40°C, and record the experimental data at different temperatures.

[0210] S25: Carry out real vehicle operating condition experiments at -10℃, 5℃ and 25℃ respectively, and record the experimental data such as battery current, voltage and temperature at the current temperature.

[0211] See also Figure 8 Step S3 specifically includes

[0212] S31: Based on the HPPC experimental data obtained in step S2, determine the quantitative relationship between the battery open circuit voltage and the battery SOC and temperature.

[0213] S32: Based on the HPPC experimental data obtained in step S2, the electrical model parameters are identified using the recursive least squares method with a forgetting factor. Step S32 includes Specifically,

[0214] S321: The identification process of R0, R1, R2, C1 and C2 in the second-order RC equivalent circuit model is:

[0215] According to the least squares theory, the electrical model can be defined as:

[0216] y k =Φ k θ+e k

[0217] Among them, e k represents white noise with a mean of 0, Φ k and θ k are data vector and parameter vector respectively, which can be expressed as:

[0218]

[0219] The performance indicators are:

[0220]

[0221] Among them, λ represents the forgetting factor, represents the estimated value of the parameter matrix θ, and L represents the data length.

[0222] After initializing the parameter vector and the system error covariance matrix, the parameter vector is iteratively optimized using the following formula.

[0223]

[0224] Among them, K k represents the gain matrix, p k represents the system error covariance matrix, and E represents the identity matrix.

[0225] S322: Based on the electrical model parameters identified in step S321, a quantitative relationship between the electrical model parameters and the temperature and the battery SOC is obtained through data fitting.

[0226] S33: Step S33 specifically includes S331 to S332.

[0227] S331: Based on the real vehicle operating condition data obtained in step S2, the thermal model parameters of the tab are identified using the particle swarm optimization algorithm. The parameters of the tab thermal model to be identified are the equivalent ohmic internal resistance R, the equivalent heat transfer coefficient h t and the equivalent convection heat transfer coefficient h c, the specific identification process is:

[0228] Based on the state space equation of the anode thermal model obtained in step S12, after determining parameters such as the number of particles, the maximum number of iterations, and the parameter range, the PSO toolbox in MATLAB is used to identify the anode thermal model parameters.

[0229] S332: Based on the real vehicle operating condition data obtained in step S2, the electric model parameters identified in step S32, and the tab thermal model parameters identified in step S331, the cell thermal model parameters are identified using a particle swarm optimization algorithm. The cell thermal model parameters to be identified are equivalent specific heat capacity C, equivalent thermal conductivity λ, equivalent convection heat transfer coefficient h c And the correction factor α, the specific identification process is:

[0230] Based on the battery thermal model state space equation obtained in step S12, after determining parameters such as the number of particles, the maximum number of iterations, and the parameter range, the PSO toolbox in MATLAB is used to identify the battery cell thermal model parameters.

[0231] S34: Based on the experimental data obtained in step S2, verify the accuracy and validity of the electrical model parameters and the thermal model parameters.

[0232] See also Fig. 9 Based on the electric-thermal coupling model, the Kalman filter algorithm is combined to realize the joint estimation of battery state of charge and temperature. Step S4 specifically includes

[0233] S41: Initialize the state estimator to determine the initial values ​​of the system error covariance P, process noise error covariance Q, measurement noise error covariance R and other system matrices in the state of charge estimator and temperature estimator.

[0234] S42: Based on the quantitative relationship between the electric model parameters and the battery SOC and temperature, the electric model parameters are obtained by interpolation using the average value of the estimated temperature of the battery cells and the estimated SOC value.

[0235] S43: Based on the state space equation of the electric model obtained in step S12, the battery SOC estimation is realized by using the extended Kalman filter. The specific estimation process is as follows:

[0236] Status estimated time update:

[0237] System error covariance time update:

[0238] Calculate the Kalman gain:

[0239] State estimation measurement update:

[0240] System error covariance measure update:

[0241] S44: Based on the thermal model state space equation obtained in step S12, the battery temperature is estimated using a Kalman filter, and the center temperature of the battery cell is used as a feedback parameter of the estimator. The specific estimation process is as follows:

[0242] The temperature estimation process is the same except that the state estimation measurement update is different from the state of charge estimation process. The state estimation measurement update in the temperature estimation process is:

[0243]

[0244] Among them, T c，mea and T c，mod Respectively represent the measured value and model value of the center temperature of the battery cell.

[0245] S45: Iteration step

[0246] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A method for jointly estimating the state of charge and temperature of a lithium battery for vehicle-mounted applications, characterized in that: The method comprises the following steps: S1: Based on the relevant parameters of the lithium-ion battery to be tested, the electrical model and thermal model of the battery are established, and the electrical-thermal coupling model is established using the dependency between the battery temperature and the electrical model parameters, and the model parameters required to realize the joint estimation of the battery state of charge and temperature are determined; The electrical model of the battery is a second-order RC equivalent circuit model; the thermal model of the battery couples the tab thermal model and the cell thermal model, the tab thermal model adopts a lumped mass thermal model, and the cell thermal model adopts a two-dimensional thermal resistance network model; S2: Static capacity test, HPPC test and real vehicle operating condition test at different temperatures; S3: Based on the static capacity test experimental data and HPPC test experimental data at different ambient temperatures in step S2, the recursive least squares method with a forgetting factor is used to identify the electrical model parameters, and a quantitative relationship between the electrical model parameters and the battery SOC and temperature is established; based on the actual vehicle working condition test experimental data in step S2, the thermal model parameters are identified using a particle swarm optimization algorithm; S4: Based on the electric-thermal coupling model and combined with the Kalman filter algorithm, accurate joint estimation of the battery state of charge and temperature is achieved.

2. The method for jointly estimating the state of charge and temperature of a lithium battery for vehicle-mounted applications according to claim 1, characterized in that: In step S1, the thermal model of the battery cell is divided into a heat generation model and a temperature calculation model. The electrical model parameters are used as the input of the heat generation model to calculate the heat generation of the battery, and the heat generation is used as the input of the temperature calculation model to calculate the battery temperature. The average temperature of the battery cell is then used as the input of the electrical model to form an electrical-thermal coupling model.

3. The method for jointly estimating the state of charge and temperature of a lithium battery for vehicle-mounted applications according to claim 1, characterized in that: In step S1, the lumped mass thermal model includes three parts: a heat source, a heat transfer resistance between the battery cell and the environment, and a convection heat transfer resistance between the battery cell and the environment. The heat balance equation is expressed as: R tp =1 / h tp A’ t R tn =1 / h tn A’ t R cp =1 / h cp A c R cn =1 / h cn A c Among them, C p and m p Respectively represent the specific heat capacity and mass of the positive electrode ear, C n and m n Respectively represent the specific heat capacity and mass of the negative electrode ear, T p and T n Indicates the positive and negative lug temperatures, T 1i and Respectively represent the temperature of the discrete unit near the positive and negative ears, T ∞ Indicates the ambient temperature, R p , R n Respectively represent the equivalent internal resistance of the positive and negative ears, h tp 、h tn Respectively represent the equivalent heat transfer coefficient between the positive and negative tabs and the battery cell, h cp 、h cn Respectively represent the equivalent convection heat transfer coefficient between the positive and negative lugs and the environment, A′ t and A c They are the heat transfer area between the tab and the discrete unit, and the convection heat transfer area between the tab and the environment.

4. The method for jointly estimating the state of charge and temperature of a lithium battery for vehicle-mounted applications according to claim 1, characterized in that: In step S1, the two-dimensional thermal resistance network model is to discretize the battery cell into N l ×N w discrete units, where N l is the number of discrete units along the length of the cell, N w is the number of discrete units along the width of the battery cell; each discrete unit is regarded as an independent heat source, and exchanges heat with the surrounding heat source through thermal resistance; According to the location of the discrete units of the battery cells, the discrete units are divided into four types: Type 1, discrete units located at the four corners of the battery cell, its thermal balance equation is: Where x=1, y=1; Where x = 1, y = N w ; Where x = N l , y=1; Where x = N l , y=N w ; Type 2, the discrete units located on the left and right sides of the battery cell except the corners, its thermal balance equation is: Where x=1, y∈[2, N w -1]; Where x = N l , y∈[2,N w -1]; Type 3, discrete units located on the upper and lower sides of the cell except the corners, the thermal balance equation is: Where x∈[2,N l -1], y = 1; Where x∈[2,N l -1],y=N w ; Type 4, the discrete unit located in the center of the battery cell, its thermal balance equation is: Where x∈[2,N l -1],y∈[2,N w -1]; In the above expressions, the subscripts j = 1, 2, 3, 4 represent different types of discrete units, C and m represent the specific heat capacity and mass of the discrete unit, respectively, and T x，y Indicates the temperature of the discrete cell, Q x，y Represents the heat generation of the discrete unit of the battery cell, satisfying the following expression: Where k = 1, ..., M, M is the data length, N is the number of discrete units, I x，y Represents the current size of the discrete unit, satisfying the following expression: I x，y，k =I k / N R 0，x，y , R 1，x，y and R 2，x，y Respectively represent the ohmic internal resistance and two polarization internal resistances of the discrete unit, obtained by interpolation method: R 0，x，y，k =α(f1(SOC k ,T k ) / N) R 1，x，y，k =α(f2(SOC k ,T k ) / N) R 2，x，y，k =α(f3(SOC k ,T k ) / N) Among them, α is the internal resistance correction factor, f1, f2, f3 represent the quantitative relationship between the battery internal resistance and the battery SOC and temperature, T k Indicates the battery temperature, I k Indicates the battery current; R con，1 and R con，2 Respectively represent the thermal resistance of the discrete unit of the battery cell along the length and width directions, satisfying the following expressions: R con，1 =l / A1λ R con，2 =w / A2λ Wherein, λ represents the thermal conductivity of the battery cell, l and w represent the heat conduction distance of the discrete unit along the length and width directions, respectively, and A1 and A2 represent the heat conduction area of ​​the discrete unit along the length and width directions, respectively; R c，j Represents the convection heat transfer thermal resistance of the discrete unit of the battery cell, which satisfies the following expression: R c，j =1 / h c A cj Among them, h c represents the convective heat transfer coefficient between the discrete unit and the environment, A cj Represents the convective heat transfer area of ​​discrete units.

5. The method for jointly estimating the state of charge and temperature of a lithium battery for vehicle-mounted applications according to claim 1, characterized in that: Step S2 specifically includes the following steps: S21: Fully charge the lithium-ion battery to be tested in a constant temperature environment of 25°C using a standard charge and discharge protocol; S22: Perform a static capacity test on the lithium-ion battery at a 1C charge and discharge rate at the experimental temperature to obtain the actual capacity of the battery at the current temperature; S23: Fully charge the battery using a standard charge and discharge protocol in a constant temperature environment of 25°C, and then perform an HPPC test at the experimental temperature to obtain the current and voltage data of the battery at the current temperature; S24: Repeat steps S21-S23 at ambient temperatures of -20°C, -15°C, -10°C, -5°C, 0°C, 5°C, 15°C, 25°C, and 40°C, and record experimental data at different temperatures; S25: Carry out real vehicle operating condition experiments at -10℃, 5℃ and 25℃ respectively, and record the current, voltage and temperature experimental data of the battery at the current temperature.

6. The method for jointly estimating the state of charge and temperature of a lithium battery for vehicle-mounted applications according to claim 1, characterized in that: Step S3 specifically includes the following steps: S31: fitting the relationship between the open circuit voltage, SOC and temperature based on the experimental data obtained in step S2; S32: Based on the experimental data obtained in step S2, the electrical model parameters are identified using a recursive least square method with a forgetting factor, and a quantitative relationship between the electrical model parameters and the battery SOC and temperature is established; S33: Based on the experimental data obtained in step S2, using a particle swarm optimization algorithm to identify the tab thermal model parameters; S34: Based on the experimental data obtained in step S2, the electrical model parameters identified in step S32, and the tab thermal model parameters identified in step S33, the cell thermal model parameters are identified using a particle swarm optimization algorithm.

7. The method for jointly estimating the state of charge and temperature of a lithium battery for vehicle-mounted applications according to claim 1, characterized in that: Step S4 specifically includes the following steps: S41: Initialize the state estimator, determine the initial values ​​of the system error covariance P, process noise error covariance Q, measurement noise error covariance R and other system matrices in the state of charge estimator and the temperature estimator; S42: Based on the quantitative relationship between the electric model parameters and the battery SOC and temperature, the electric model parameters are obtained by interpolation using the average value of the estimated temperature of the battery cells and the estimated SOC value; S43: Based on the state space equation of the electric model, the battery SOC estimation is realized using the extended Kalman filter; S44: Based on the state space equation of the thermal model, the battery temperature is estimated using the Kalman filter, and the center temperature of the battery cell is used as the feedback parameter of the estimator; S45: Iterate steps S42 to S44.

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