Lithium ion battery internal resistance heating power online estimation method, apparatus and device, and storage medium

Abstract

The invention discloses a lithium ion battery internal resistance heating power on-line estimation method, a lithium ion battery internal resistance heating power on-line estimation device, lithium ion battery internal resistance heating power on-line estimation equipment and a storage medium. Acquiring a coupling model of the lithium ion battery to be tested, a state equation of the internal resistance, an observation equation of the internal resistance, a state equation of the entropy coefficient and an observation equation of the entropy coefficient; calculating the internal resistance at the current moment and the estimated value of the entropy coefficient; calculating an error covariance matrix of the internal resistance and the entropy coefficient at the current moment; then calculating according to the coupling model to obtain a Kalman gain matrix of the internal resistance and the entropy coefficient at the current moment; calculating observation errors of the internal resistance and the entropy coefficient; calculating the corrected internal resistance and entropy coefficient at the current moment; and finally, calculating the heating power of the battery at the current moment according to the corrected entropy coefficient. According to the invention, the heating power of the lithium ion battery can be estimated online based on the corrected entropy coefficient.

Description

Technical Field

[0001] The invention relates to the field of heating power estimation, and in particular to an online heating power estimation method of the internal resistance of a lithium-ion battery. Background Art

[0002] Lithium-ion batteries have been widely used in electric vehicles, new power systems and other fields due to their high energy density, high power density, long cycle life and no memory effect. With the increase in the range of electric vehicles and the scale of grid energy storage, the energy and power density of lithium-ion batteries are increasingly required, and the risk of battery failure is also increasing. For battery fault diagnosis methods, most of them are currently implemented by combining battery characteristics and using optimization algorithms. The most important battery characteristics are the internal resistance that reflects the electrical characteristics and the heat generation power that reflects the thermal characteristics.

[0003] In the prior art, the battery heat generation model is used to describe the physical and chemical processes with thermal effects inside the battery, and the battery heat generation power is calculated online in real time. However, this method is difficult to accurately determine the entropy coefficient in the equation, and the testing process uses expensive and time-consuming equipment. Therefore, in actual application, the average value of the entropy coefficient is usually taken, resulting in a large deviation in the calculation result of the heat generation power.

[0004] Therefore, how to correct the entropy coefficient and reduce the calculation error of heating power caused by the uncorrected entropy coefficient is an urgent problem to be solved. Summary of the invention

[0005] The present invention provides a method, device, equipment and storage medium for online estimation of the heating power of the internal resistance of a lithium-ion battery, which can perform online estimation of the heating power of the lithium-ion battery based on a corrected entropy coefficient.

[0006] An embodiment of the present invention provides a method for online estimating the heating power of the internal resistance of a lithium-ion battery, comprising:

[0007] When the current moment is not the initial moment, obtaining a coupling model of the lithium-ion battery to be tested, a state equation of the internal resistance, an observation equation of the internal resistance, a state equation of the entropy coefficient, and an observation equation of the entropy coefficient;

[0008] Obtain the internal resistance and entropy coefficient corrected at the previous moment, and calculate the estimated values ​​of the internal resistance and entropy coefficient at the current moment based on the state equation of the internal resistance, the state equation of the entropy coefficient, the internal resistance and entropy coefficient corrected at the previous moment;

[0009] According to the error covariance matrix corrected at the previous moment and the process noise covariance matrix at the current moment, the error covariance matrix of the internal resistance and the entropy coefficient at the current moment is calculated; wherein, the current at the previous moment is obtained, and according to the current at the previous moment and the above coupling model, the error covariance matrix corrected at the previous moment is calculated;

[0010] According to the coupling model and the error covariance matrix of the internal resistance and entropy coefficient at the current moment, the Kalman gain matrix of the internal resistance and entropy coefficient at the current moment is calculated;

[0011] According to the observation equation of the internal resistance, the observation equation of the entropy coefficient, the observed value of the internal resistance at the current moment, and the observed value of the entropy coefficient at the current moment, the observation errors of the internal resistance and the entropy coefficient are calculated;

[0012] Calculate the corrected internal resistance and entropy coefficient at the current moment according to the estimated value of the internal resistance, the estimated value of the entropy coefficient, the Kalman gain matrix of the internal resistance, the Kalman gain matrix of the entropy coefficient, the observation error of the internal resistance, and the observation error of the entropy coefficient;

[0013] The heating power of the battery at the current moment is calculated according to the corrected entropy coefficient.

[0014] Furthermore, the construction of the coupling model includes:

[0015] Repeatedly apply a preset current excitation to the lithium-ion battery to be tested within the full charge state range, and obtain a number of voltage mutations corresponding to the moment of applying the preset current excitation, a shelf voltage value corresponding to the end of the shelf time, a number of voltage steady-state values ​​corresponding to the end of the constant current time, a number of transient voltages, a number of second internal resistances, and the battery temperature at each moment;

[0016] The coupling model is constructed according to the voltage mutation amounts, the shelf voltage value, the voltage steady-state values, the transient voltages, the second internal resistances and the battery temperature at each moment.

[0017] Furthermore, the coupling model is constructed based on the voltage mutation amounts, the shelf voltage values, the voltage steady-state values, the transient voltages, the second internal resistances and the battery temperature at each moment, including:

[0018] According to the above-mentioned shelf voltage value, the positive electrode initial lithium insertion concentration fraction, the negative electrode initial lithium insertion concentration fraction, the positive electrode capacity and the negative electrode capacity of the above-mentioned lithium-ion battery to be tested are calculated;

[0019] According to the above-mentioned voltage mutation amounts, several reaction polarization coefficients of the above-mentioned lithium-ion battery to be tested at each of the above-mentioned battery temperatures are calculated;

[0020] According to the above-mentioned several steady-state voltage values, several positive electrode solid phase diffusion time constants, several negative electrode solid phase diffusion time constants and several liquid phase diffusion proportional coefficients of the above-mentioned lithium-ion battery to be tested at each of the above-mentioned battery temperatures are calculated;

[0021] According to the above-mentioned several transient voltages and the above-mentioned liquid phase diffusion proportional coefficient, the liquid phase diffusion time constant of the above-mentioned lithium ion battery to be tested is calculated;

[0022] According to each of the battery temperatures, the reaction polarization coefficients, the positive electrode solid phase diffusion time constants, the negative electrode solid phase diffusion time constants and the liquid phase diffusion proportional coefficients, a corrected first internal resistance, reaction polarization coefficient, positive electrode solid phase diffusion time constant, negative electrode solid phase diffusion time constant and liquid phase diffusion proportional coefficient of the lithium ion battery to be tested are calculated;

[0023] Obtaining the surface area and mass of the lithium-ion battery to be tested, and calculating the heat transfer coefficient and the battery equivalent specific heat capacity of the lithium-ion battery to be tested according to the surface area, the temperature of each of the batteries and the mass;

[0024] A coupling model of the lithium-ion battery to be tested is constructed according to the battery temperature at each of the above-mentioned moments, the surface area, the mass, the initial lithium insertion concentration fraction of the positive electrode, the initial lithium insertion concentration fraction of the negative electrode, the positive electrode capacity, the negative electrode capacity, the liquid phase diffusion time constant, the corrected first internal resistance of the lithium-ion battery to be tested, the corrected reaction polarization coefficient, the corrected positive electrode solid phase diffusion time constant, the corrected negative electrode solid phase diffusion time constant, the corrected liquid phase diffusion proportional coefficient, the heat exchange coefficient and the battery equivalent specific heat capacity.

[0025] Further, the coupled model of the lithium-ion battery to be tested is constructed according to the battery temperature at each moment, the surface area, the mass, the initial lithium insertion concentration fraction of the positive electrode, the initial lithium insertion concentration fraction of the negative electrode, the positive electrode capacity, the negative electrode capacity, the liquid phase diffusion time constant, the corrected first internal resistance of the lithium-ion battery to be tested, the corrected reaction polarization coefficient, the corrected positive electrode solid phase diffusion time constant, the corrected negative electrode solid phase diffusion time constant, the corrected liquid phase diffusion proportional coefficient, the heat exchange coefficient and the battery equivalent specific heat capacity, including:

[0026] Constructing a battery operation process model for describing the battery operation process according to the positive electrode initial lithium insertion concentration fraction, the negative electrode initial lithium insertion concentration fraction, the positive electrode capacity, and the negative electrode capacity;

[0027] Constructing a solid phase diffusion model for describing the solid phase diffusion process according to the positive electrode initial lithium insertion concentration fraction, the negative electrode initial lithium insertion concentration fraction, the positive electrode capacity and the negative electrode capacity, the positive electrode solid phase diffusion time constant and the negative electrode solid phase diffusion time constant;

[0028] Obtaining the positive plate liquid phase lithium ion concentration, the negative plate liquid phase lithium ion concentration, and the change in liquid phase lithium ion concentration at the positive and negative electrode current collectors of the above-mentioned lithium-ion battery to be tested, and constructing a liquid phase diffusion model for describing the liquid phase diffusion process according to the above-mentioned positive plate liquid phase lithium ion concentration, the negative plate liquid phase lithium ion concentration, and the change in liquid phase lithium ion concentration at the positive and negative electrode current collectors, the above-mentioned corrected liquid phase diffusion proportional coefficient, and the above-mentioned liquid phase diffusion time constant;

[0029] According to the mass and the corrected reaction polarization coefficient, a reaction polarization model for describing the reaction polarization effect is constructed;

[0030] According to the corrected first internal resistance of the lithium-ion battery to be tested, constructing an ohmic polarization model for describing the ohmic polarization effect;

[0031] Constructing a heat generation model for describing the heat generation behavior inside the battery according to the corrected first internal resistance of the lithium-ion battery to be tested and the battery temperature at each time;

[0032] Constructing a temperature model for describing the battery temperature according to the battery temperature at each of the above moments and the battery equivalent specific heat capacity;

[0033] Based on the above surface area, an expression for the relationship between the convection heat transfer coefficient and the heat dissipation resistance of the battery to the environment is constructed;

[0034] Obtaining a reference temperature, and constructing a correction equation based on the reference temperature and the battery temperature at each of the above moments;

[0035] The coupling model is obtained according to the battery working process model, the solid phase diffusion model, the liquid phase diffusion model, the reaction polarization model, the ohmic polarization model, the heat generation model, the temperature model, the relationship expression and the correction equation.

[0036] Furthermore, the construction of the state equation of the internal resistance and the observation equation of the internal resistance includes:

[0037] According to the corrected internal resistance at the previous moment and the process noise at the current moment, a state equation of the internal resistance is constructed;

[0038] According to the corrected internal resistance at the previous moment and the observation noise at the current moment, the observation equation of the internal resistance is constructed.

[0039] Furthermore, the construction of the state equation of the entropy coefficient and the observation equation of the entropy coefficient includes:

[0040] According to the corrected entropy coefficient at the previous moment and the process noise at the current moment, the state equation of the internal resistance is constructed;

[0041] According to the corrected entropy coefficient at the previous moment and the observation noise at the current moment, the observation equation of the internal resistance is constructed.

[0042] Furthermore, when the current moment is the initial moment, the entropy coefficient corresponding to the initial moment is obtained according to the above coupling model;

[0043] The heating power at the current moment is calculated based on the first internal resistance and the entropy coefficient corresponding to the initial moment.

[0044] Based on the above method embodiment, the present invention provides a corresponding device embodiment;

[0045] The present invention provides a device for online estimating the heating power of the internal resistance of a lithium-ion battery, comprising:

[0046] Model and equation acquisition module, estimated value calculation module, error covariance matrix calculation module, Kalman gain matrix calculation module, observation error calculation module, internal resistance and entropy coefficient correction module and heating power calculation module;

[0047] The above-mentioned model and equation acquisition module is used to obtain the coupling model of the lithium-ion battery to be tested, the state equation of the internal resistance, the observation equation of the internal resistance, the state equation of the entropy coefficient, and the observation equation of the entropy coefficient when the current moment is not the initial moment;

[0048] The estimated value calculation module is used to obtain the internal resistance and entropy coefficient corrected at the previous moment, and calculate the estimated values ​​of the internal resistance and entropy coefficient at the current moment according to the state equation of the internal resistance, the state equation of the entropy coefficient, the internal resistance and entropy coefficient corrected at the previous moment;

[0049] The error covariance matrix calculation module is used to calculate the error covariance matrix of the internal resistance and the entropy coefficient at the current moment according to the error covariance matrix corrected at the previous moment and the process noise covariance matrix at the current moment; wherein, the current at the previous moment is obtained, and the error covariance matrix corrected at the previous moment is calculated according to the current at the previous moment and the coupling model;

[0050] The Kalman gain matrix calculation module is used to calculate the Kalman gain matrix of the internal resistance and the entropy coefficient at the current moment according to the coupling model and the error covariance matrix of the internal resistance and the entropy coefficient at the current moment;

[0051] The above-mentioned observation error calculation module is used to calculate the observation errors of the internal resistance and the entropy coefficient according to the observation equation of the internal resistance, the observation equation of the entropy coefficient, the observation value of the internal resistance at the current moment, and the observation value of the entropy coefficient at the current moment;

[0052] The internal resistance and entropy coefficient correction module is used to calculate the corrected internal resistance and entropy coefficient at the current moment according to the estimated value of the internal resistance, the estimated value of the entropy coefficient, the Kalman gain matrix of the internal resistance, the Kalman gain matrix of the entropy coefficient, the observation error of the internal resistance and the observation error of the entropy coefficient;

[0053] The heating power calculation module is used to calculate the heating power of the battery at the current moment according to the corrected entropy coefficient.

[0054] Based on the above method embodiment, the present invention provides a terminal device embodiment;

[0055] The present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for online estimation of the heating power of the internal resistance of a lithium-ion battery described in any embodiment of the present invention is implemented.

[0056] Based on the above method embodiment, the present invention provides a storage medium embodiment;

[0057] The present invention provides a storage medium, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for online estimation of the heating power of the internal resistance of a lithium-ion battery described in any embodiment of the present invention is implemented.

[0058] The embodiments of the present invention have the following beneficial effects:

[0059] The present invention provides a method, device, terminal device and storage medium for online estimation of internal resistance and heating power of lithium-ion batteries based on an electric-thermal-fluid coupling model. The above method first obtains the coupling model of the lithium-ion battery to be measured, the state equation of the internal resistance, the observation equation of the internal resistance, the state equation of the entropy coefficient and the observation equation of the entropy coefficient when the current moment is not the initial moment; then obtains the corrected internal resistance and entropy coefficient at the previous moment, and calculates the estimated values ​​of the internal resistance and entropy coefficient at the current moment based on the state equation of the internal resistance, the state equation of the entropy coefficient, and the corrected internal resistance and entropy coefficient at the previous moment; then calculates the error covariance matrix of the internal resistance and entropy coefficient at the current moment based on the corrected error covariance matrix at the previous moment and the process noise covariance matrix at the current moment; wherein, obtaining The current at the last moment, according to the current at the last moment and the above-mentioned coupling model, calculate the error covariance matrix corrected at the last moment; then according to the above-mentioned coupling model and the error covariance matrix of the internal resistance and the entropy coefficient at the current moment, calculate the Kalman gain matrix of the internal resistance and the entropy coefficient at the current moment; then according to the observation equation of the internal resistance, the observation equation of the entropy coefficient, the observed value of the internal resistance at the current moment and the observed value of the entropy coefficient at the current moment, calculate the observation error of the internal resistance and the entropy coefficient; then according to the estimated value of the internal resistance, the estimated value of the entropy coefficient, the Kalman gain matrix of the internal resistance, the Kalman gain matrix of the entropy coefficient, the observation error of the internal resistance and the observation error of the entropy coefficient, calculate the corrected internal resistance and entropy coefficient at the current moment; finally, calculate the heating power of the battery at the current moment according to the corrected entropy coefficient. Therefore, the present invention can estimate the heating power of the lithium-ion battery online based on the corrected entropy coefficient by constructing the state equation and observation equation of the entropy coefficient and based on the EKF iterative algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 The present invention is a flowchart of a method for online estimating the heating power of a lithium-ion battery internal resistance provided by an embodiment of the present invention.

[0061] Figure 2 This is a schematic diagram of current excitation provided by an embodiment of the present invention.

[0062] Figure 3 It is a voltage response schematic diagram provided by an embodiment of the present invention.

[0063] Figure 4 It is a structural principle diagram of the discharge process of a lithium-ion battery provided by an embodiment of the present invention.

[0064] Figure 5 It is a schematic diagram of the heat dissipation environment of the battery in the forced air system provided by one embodiment of the present invention.

[0065] Figure 6 It is a comparison curve diagram of the observed value and the simulated value of the terminal voltage provided by an embodiment of the present invention.

[0066] Figure 7 It is a comparison curve chart of the observed value and the simulated value of temperature provided by an embodiment of the present invention.

[0067] Figure 8 It is a simulation curve diagram of the internal resistance of a battery provided by an embodiment of the present invention.

[0068] Fig. 9 This is a battery heating power simulation curve diagram provided by an embodiment of the present invention.

[0069] Fig.10 It is a structural schematic diagram of a device for online estimating the heating power of the internal resistance of a lithium-ion battery provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings 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.

[0071] like Figure 1 As shown, an embodiment of the present invention provides an online estimation method for the heating power of a lithium-ion battery internal resistance, comprising:

[0072] Step S101: when the current moment is not the initial moment, obtaining a coupling model of the lithium-ion battery to be tested, a state equation of internal resistance, an observation equation of internal resistance, a state equation of entropy coefficient, and an observation equation of entropy coefficient;

[0073] In a preferred embodiment, the construction of the coupling model includes:

[0074] Repeatedly apply a preset current excitation to the lithium-ion battery to be tested within the full charge state range, and obtain a number of voltage mutations corresponding to the moment of applying the preset current excitation, a shelf voltage value corresponding to the end of the shelf time, a number of voltage steady-state values ​​corresponding to the end of the constant current time, a number of transient voltages, a number of second internal resistances, and the battery temperature at each moment;

[0075] The coupling model is constructed according to the voltage mutation amounts, the shelf voltage value, the voltage steady-state values, the transient voltages, the second internal resistances and the battery temperature at each moment.

[0076] Indicative, such as Figure 2As shown, the above current excitation is specifically: within the full charge state range, the constant current discharge is 20 minutes - stand for 20 minutes - constant current charge is 10 minutes - stand for 20 minutes, and the current charge and discharge rate is less than 0.4C. When performing cyclic charge and discharge, the discharge current is negative and the charging current is positive.

[0077] Indicative, such as Figure 3 As shown in the figure, the "voltage mutation amount" is the voltage mutation amount corresponding to the moment when the above-mentioned preset current excitation is applied, the "shelf end data point" is the shelf voltage value corresponding to the shelf end moment, and the "constant current end steady-state voltage" is the above-mentioned voltage steady-state value.

[0078] Specifically, the second internal resistance is measured by an ohm internal resistance meter.

[0079] Preferably, the use of the above-mentioned current excitation can ensure that the obtained parameters are applicable within the full state of charge range and reduce the impact of battery temperature rise on the parameters.

[0080] In this preferred embodiment, by repeatedly applying a preset current excitation to the lithium-ion battery to be tested, a number of voltage mutations corresponding to the moment of applying the above-mentioned preset current excitation, a shelf voltage value corresponding to the shelf end moment, a number of voltage steady-state values ​​corresponding to the constant current end moment, a number of transient voltages, a number of second internal resistances and the battery temperature at each moment are obtained, and the above-mentioned coupling model is obtained based on the number of voltage mutations, shelf voltage values, a number of voltage steady-state values, a number of transient voltages, a number of second internal resistances and the battery temperature at each moment.

[0081] In another preferred embodiment, the coupling model is constructed based on the voltage mutation amounts, the shelf voltage value, the voltage steady-state values, the transient voltages, the second internal resistances and the battery temperature at each moment, including:

[0082] According to the above-mentioned shelf voltage value, the positive electrode initial lithium insertion concentration fraction, the negative electrode initial lithium insertion concentration fraction, the positive electrode capacity and the negative electrode capacity of the above-mentioned lithium-ion battery to be tested are calculated;

[0083] Specifically, when the end of the suspension is reached, the solid phase diffusion has restored equilibrium, and the amount of lithium embedded on the particle surface is the same as the average amount of lithium embedded. It is considered that the battery terminal voltage is the open circuit voltage. Therefore, the initial lithium embedded concentration fraction of the positive electrode, the initial lithium embedded concentration fraction of the negative electrode, the positive electrode capacity, and the negative electrode capacity are calculated according to the following formula and the least squares method:

[0084] x avg =x0-It / Q n ;

[0085] y avg =y0-It / Q p ;

[0086] E(t)=U p (y surf )-U n (x surf );

[0087] In the formula, x avg represents the average amount of lithium embedded in the negative electrode, x0 represents the initial lithium embedded concentration fraction of the negative electrode, I represents the current, t represents the charge and discharge time, Q n represents the above negative electrode capacity, y avg represents the average lithium insertion amount of the positive electrode, y0 represents the initial lithium insertion concentration fraction of the positive electrode, Q p represents the positive electrode capacity, the unit is AS, E(t) represents the open circuit voltage during the charge and discharge time t, U p represents the positive electrode open circuit potential function, U n represents the negative electrode open circuit potential function, y surf represents the amount of lithium embedded on the positive electrode surface, x surf Indicates the amount of lithium embedded on the negative electrode surface.

[0088] According to the above-mentioned voltage mutation amounts, several reaction polarization coefficients of the above-mentioned lithium-ion battery to be tested at each of the above-mentioned battery temperatures are calculated;

[0089] Specifically, the voltage mutation generated at the moment of current excitation is determined by ohmic polarization and reaction polarization. Therefore, the reaction polarization coefficient is calculated by the following formula and least squares fitting method:

[0090]

[0091] In the formula, m p It represents the intermediate variable for calculating the reaction polarization overpotential and has no specific physical meaning. c0 represents the initial concentration of the electrolyte. P act Represents the polarization coefficient of the above reaction, in m -1.5 mol 0.5 s,m n Represents another intermediate variable for calculating the reaction polarization overpotential, with no specific physical meaning, η act represents the reaction polarization overpotential, F represents the Faraday constant, R represents the ideal gas constant, and T represents the battery temperature.

[0092] According to the above-mentioned several steady-state voltage values, several positive electrode solid phase diffusion time constants, several negative electrode solid phase diffusion time constants and several liquid phase diffusion proportional coefficients of the above-mentioned lithium-ion battery to be tested at each of the above-mentioned battery temperatures are calculated;

[0093] Specifically, at the end of short-time constant current charge and discharge, the solid-liquid phase diffusion process enters a stable state. Therefore, the positive electrode solid phase diffusion time constant, the negative electrode solid phase diffusion time constant and the liquid phase diffusion proportional coefficient are calculated according to the following formula and the least squares fitting method:

[0094] x surf (t k )=x avg (t k )-Δx(t k ),y surf (t k )=y avg (t k )-Δy(t k );

[0095]

[0096]

[0097] In the formula, x surf (t k ) indicates t k The amount of lithium embedded on the negative electrode surface at the moment, x avg (t k ) indicates t k The average amount of lithium inserted into the negative electrode at the time, Δx(t k ) indicates t k The difference between the average amount of lithium embedded in the solid phase of the negative electrode and the amount of lithium embedded on the surface at the moment, Δy(t k ) indicates t k The difference between the average amount of lithium embedded in the solid phase of the positive electrode and the amount of lithium embedded on the surface at the moment, τ n represents the above-mentioned anode solid phase diffusion time constant, in s, τ p represents the above-mentioned cathode solid phase diffusion time constant, η con It represents the polarization overpotential, which is caused by assuming that the change in the liquid lithium ion concentration at the positive and negative electrode collectors is the same, and there is a gradient distribution of the liquid lithium ion concentration of the positive and negative plates. + represents the migration coefficient, c0 represents the initial concentration of the electrolyte, Δc represents the change in the concentration of liquid lithium ions at the positive and negative electrode collectors, and P con represents the above liquid phase diffusion proportionality coefficient, in mol m -3 A -1 , τ e It represents the liquid phase diffusion time constant, in seconds, I(t k ) indicates t k Current at that moment, Δy1(t k ) represents the process state variable and has no actual physical meaning. k ) indicates t kThe change of electrolyte concentration at time, I(t k ) indicates t k Current at all times.

[0098] According to the above-mentioned several transient voltages and the above-mentioned liquid phase diffusion proportional coefficient, the liquid phase diffusion time constant of the above-mentioned lithium ion battery to be tested is calculated;

[0099] Specifically, the liquid phase diffusion time constant is calculated by the following formula:

[0100]

[0101] According to each of the battery temperatures, the reaction polarization coefficients, the positive electrode solid phase diffusion time constants, the negative electrode solid phase diffusion time constants and the liquid phase diffusion proportional coefficients, the corrected internal resistance, reaction polarization coefficient, positive electrode solid phase diffusion time constant, negative electrode solid phase diffusion time constant and liquid phase diffusion proportional coefficient of the lithium ion battery to be tested are calculated;

[0102] Specifically, since the positive electrode solid phase diffusion time constant, the negative electrode solid phase diffusion time constant, the reaction polarization coefficient, the liquid phase diffusion proportional coefficient and the internal resistance are affected by temperature, they are corrected by the following exponential Arrhenius formula:

[0103]

[0104] Where X represents the positive electrode solid phase diffusion time constant, negative electrode solid phase diffusion time constant, reaction polarization coefficient, liquid phase diffusion proportional coefficient and second internal resistance to be corrected, X ref represents the values ​​of the positive electrode solid phase diffusion time constant, the negative electrode solid phase diffusion time constant, the reaction polarization coefficient, the liquid phase diffusion proportional coefficient and the second internal resistance at the reference temperature, T ref represents the reference temperature, and λ represents the activation energy coefficient.

[0105] Specifically, since the open circuit potential is affected by temperature, the open circuit potential is corrected according to the following Nernst equation:

[0106]

[0107] Where, E represents the corrected open circuit potential, E ref The open circuit potential before correction, represents the entropy coefficient.

[0108] Obtaining the surface area and mass of the lithium-ion battery to be tested, and calculating the heat transfer coefficient and the battery equivalent specific heat capacity of the lithium-ion battery to be tested according to the surface area, the temperature of each of the batteries and the mass;

[0109] Specifically, since the total heat generation rate of the battery is zero when the external current is zero, the shell temperature and the internal temperature and their change rates can be considered to be approximately equal. Therefore, the heat transfer coefficient and the equivalent specific heat capacity of the battery are calculated according to the following formula:

[0110]

[0111] When k = 0, T 0 =T0; when k = ∞, T ∞ =T a , therefore, we can get the following formula:

[0112] T k =T a +(T0-T a )exp(-k / τ heat );

[0113] When T0 is the temperature of the battery shell at the initial moment, the time constant τ heat It can be calculated by the following formula:

[0114] τ heat =m p C p / (hA);

[0115] Where h represents the above heat transfer coefficient, the unit is W m -2 K -1 , A represents the above surface area, C p It represents the equivalent specific heat capacity of the above battery, in J kg -1 K -1 , τ heat represents the time constant, T k represents the battery temperature at the kth moment, T0 represents the initial battery temperature, T a Indicates the ambient temperature.

[0116] Preferably, the battery is left idle for 30 minutes after the battery terminal voltage reaches the discharge cut-off point, and the time constant is fitted using the shell temperature data during the idle period, and then the heat transfer coefficient is calculated.

[0117] A coupling model of the lithium-ion battery to be tested is constructed according to the battery temperature at each of the above-mentioned moments, the surface area, the mass, the initial lithium insertion concentration fraction of the positive electrode, the initial lithium insertion concentration fraction of the negative electrode, the positive electrode capacity, the negative electrode capacity, the liquid phase diffusion time constant, the corrected first internal resistance of the lithium-ion battery to be tested, the corrected reaction polarization coefficient, the corrected positive electrode solid phase diffusion time constant, the corrected negative electrode solid phase diffusion time constant, the corrected liquid phase diffusion proportional coefficient, the heat exchange coefficient and the battery equivalent specific heat capacity.

[0118] In this preferred embodiment, the above-mentioned voltage mutation amounts, shelf voltage values, voltage steady-state values, transient voltages, second internal resistances and battery temperatures at each moment are calculated to obtain a coupling model.

[0119] In another preferred embodiment, the battery temperature at each time, the surface area, the mass, the initial lithium insertion concentration fraction of the positive electrode, the initial lithium insertion concentration fraction of the negative electrode, the positive electrode capacity, the negative electrode capacity, the liquid phase diffusion time constant, the corrected first internal resistance of the lithium ion battery to be tested, the corrected reaction polarization coefficient, the corrected positive electrode solid phase diffusion time constant, the corrected negative electrode solid phase diffusion time constant, the corrected liquid phase diffusion proportional coefficient, the heat transfer coefficient and the battery equivalent specific heat capacity are used to construct a coupling model of the lithium ion battery to be tested, including:

[0120] Constructing a battery operation process model for describing the battery operation process according to the positive electrode initial lithium insertion concentration fraction, the negative electrode initial lithium insertion concentration fraction, the positive electrode capacity, and the negative electrode capacity;

[0121] Indicatively, Figure 4 As shown in the figure, the negative electrode active material in the battery can generally be regarded as composed of several spherical active particles. During the battery charging and discharging process, there are physical and chemical processes such as solid phase diffusion, electrochemical reaction on the particle surface, and liquid phase diffusion. The relevant parameters of the battery positive and negative electrode materials, electrolyte, diaphragm, interface, etc. are closely related to the physical and chemical processes during the charging and discharging process. For the model, the physical material parameters of the battery directly affect the model parameters, thereby affecting the external characteristics of the battery such as voltage, current, and temperature.

[0122] Specifically, assuming that the reaction distribution at each position inside the battery is uniform, the electrode plates are isotropic, and the lithium ion concentration at each position in the radius r direction is the same, all active particles in the positive and negative electrodes are represented by single particles respectively, and the basic working process of the battery is characterized by the average lithium insertion amount of the positive and negative electrodes.

[0123] Specifically, the battery working process model is as follows:

[0124] x avg =x0-It / Q n ;

[0125] y avg =y0-It / Q p ;

[0126] E(t)=U p (y surf )-U n (x surf );

[0127] Constructing a solid phase diffusion model for describing the solid phase diffusion process according to the positive electrode initial lithium insertion concentration fraction, the negative electrode initial lithium insertion concentration fraction, the positive electrode capacity and the negative electrode capacity, the positive electrode solid phase diffusion time constant and the negative electrode solid phase diffusion time constant;

[0128] Specifically, the influence of solid phase diffusion is considered under the basic working process, and the variation law of solid phase lithium ion concentration in the direction of radius r is established by using Fick's second law of diffusion. The descriptive parameters of the solid phase diffusion process are the time constants of the positive and negative electrode solid phase diffusion, which determine the length of time for the battery to transition to the steady state process and the difference between the average solid phase lithium insertion amount and the surface lithium insertion amount of the positive and negative electrodes.

[0129] Specifically, the solid phase diffusion model is as follows:

[0130] x surf (t k )=x avg (t k )-Δx(t k ),y surf (t k )=y avg (t k )-Δy(t k );

[0131]

[0132] Obtaining the positive plate liquid phase lithium ion concentration, the negative plate liquid phase lithium ion concentration, and the change in liquid phase lithium ion concentration at the positive and negative electrode current collectors of the above-mentioned lithium-ion battery to be tested, and constructing a liquid phase diffusion model for describing the liquid phase diffusion process according to the above-mentioned positive plate liquid phase lithium ion concentration, the negative plate liquid phase lithium ion concentration, and the change in liquid phase lithium ion concentration at the positive and negative electrode current collectors, the above-mentioned corrected liquid phase diffusion proportional coefficient, and the above-mentioned liquid phase diffusion time constant;

[0133] Specifically, it is assumed that the change in the liquid lithium ion concentration at the positive and negative current collectors is the same, and there is a gradient distribution of the liquid lithium ion concentration in the l direction of the positive and negative plates, which leads to the generation of polarization overpotential. The description of concentration polarization effect by liquid diffusion proportional coefficient and liquid diffusion time constant reflects the degree of polarization and the length of time to establish the concentration difference.

[0134] Specifically, the above liquid phase diffusion model is shown as follows:

[0135]

[0136] According to the mass and the corrected reaction polarization coefficient, a reaction polarization model for describing the reaction polarization effect is constructed;

[0137] Specifically, assuming that the reaction rate and the radius of the active particle are the same, the functional relationship of the reaction polarization overpotential can be obtained. The reaction polarization coefficient reflects the difficulty of the electrochemical reaction inside the battery.

[0138] Specifically, the above reaction polarization model is shown as follows:

[0139]

[0140] According to the corrected first internal resistance of the lithium-ion battery to be tested, constructing an ohmic polarization model for describing the ohmic polarization effect;

[0141] Specifically, the calculation of the ohmic polarization overpotential follows Ohm's law. Without considering the influence of temperature, the battery terminal voltage can be calculated from the open circuit voltage and the three-part polarization overpotential.

[0142] Specifically, the above ohmic polarization model is shown as follows:

[0143] η ohm =R ohm I;

[0144] U app =E ocv -η con -η ohm ;

[0145] Where η ohm represents the ohmic polarization overpotential, R ohm Indicates the first internal resistance, in Ω, U app Indicates the battery terminal voltage, E ocv represents the open circuit potential of the battery, η con Represents concentration polarization overpotential.

[0146] Constructing a heat generation model for describing the heat generation behavior inside the battery according to the corrected first internal resistance of the lithium-ion battery to be tested and the battery temperature at each time;

[0147] Specifically, assuming that the temperature distribution at each point inside the battery is uniform, the heat release caused by the internal resistance of the battery and the heat generation caused by the continuous internal reactions are taken as the main factors and other minor factors are ignored. The heat generation (heat generation rate) during the charge and discharge time based on the Bernardi heat generation model can be obtained.

[0148] Specifically, the row number heat generation model is shown as follows:

[0149]

[0150] Q=Q r +Q ir ;

[0151] In the formula, q represents the heat generation rate, Q represents the total heat generation of the battery, and Q r represents the reversible heat, Q ir represents irreversible heat, and t represents the charge and discharge time.

[0152] Constructing a temperature model for describing the battery temperature according to the battery temperature at each of the above moments and the battery equivalent specific heat capacity;

[0153] Specifically, the temperature model is as follows:

[0154]

[0155] In the formula, ρ represents the material density, C represents the specific heat capacity, λ represents the thermal conductivity, Q′ represents the net heat generation rate per unit volume, ρ p represents the battery density, q s represents the heat dissipation rate, V represents the battery volume, R amb It represents the thermal resistance of heat dissipation from the battery to the environment.

[0156] Based on the above surface area, an expression for the relationship between the convection heat transfer coefficient and the heat dissipation resistance of the battery to the environment is constructed;

[0157] Indicatively, Figure 5 As shown in the figure, heat is exchanged between the battery and the cooling medium through thermal convection. The convection heat transfer coefficient determines the amount of heat exchange, and its value is related to the flow rate of the cooling medium. The corresponding empirical formula is shown as follows:

[0158]

[0159] In the formula, μ represents air viscosity, v represents air velocity, and D H represents the characteristic length of the channel, k a represents the thermal conductivity of air, p r is the air Prandtl number, p a Indicates the air density.

[0160] Specifically, the above relationship expression is shown as follows:

[0161]

[0162] Obtaining a reference temperature, and constructing a correction equation based on the reference temperature and the battery temperature at each of the above moments;

[0163] Specifically, since changes in the internal temperature of the battery will have a certain impact on the internal processes of the battery, temperature is needed to correct the electrochemical behavior of the battery.

[0164] Specifically, since the positive electrode solid phase diffusion time constant, the negative electrode solid phase diffusion time constant, the reaction polarization coefficient, the liquid phase diffusion proportional coefficient and the second internal resistance are affected by temperature, and this influence obeys the exponential form of the Arrhenius formula, the correction equation for correcting them is as follows:

[0165]

[0166] Specifically, concentration polarization overpotential and reaction polarization overpotential will be affected by temperature, and they themselves are temperature-related functions.

[0167] Specifically, the open circuit potential is affected by temperature, and it is considered to be modified by the Nernst equation. Therefore, the modified equation of the open circuit potential is shown as follows:

[0168]

[0169] According to the battery working process model, the solid phase diffusion model, the liquid phase diffusion model, the reaction polarization model, the ohmic polarization model, the heat generation model, the temperature model, the relationship expression and the correction equation, the coupling model is obtained, and the input of the model is current, and the output is battery voltage and temperature.

[0170] In this preferred embodiment, a coupling model is obtained by constructing a battery working process model, a solid phase diffusion model, a liquid phase diffusion model, a reaction polarization model, an ohmic polarization model, a heat generation model, a temperature model, a relationship expression and a correction equation.

[0171] In another preferred embodiment, the construction of the state equation of the internal resistance and the observation equation of the internal resistance includes:

[0172] According to the corrected internal resistance at the previous moment and the process noise at the current moment, a state equation of the internal resistance is constructed;

[0173] According to the corrected internal resistance at the previous moment and the observation noise at the current moment, the observation equation of the internal resistance is constructed.

[0174] Specifically, the Kalman filter algorithm is a mathematical method for state estimation, which was originally proposed by Rudolf E. Kalman in 1960. The algorithm provides the best estimate of the system state by combining the dynamic model of the system and the measurement data. Its basic principle is to achieve state estimation through prediction and update steps. The prediction step uses the dynamic model of the system to predict the next state and estimate the covariance of the prediction error. The update step uses the latest measurement data to correct the predicted state estimate and fuses the prediction and measurement information through the Kalman gain. EKF (Extended Kalman Filter) is an extension of the Kalman filter algorithm and is suitable for nonlinear system state estimation problems. Unlike the standard Kalman filter for the dynamic model and measurement model of linear systems, EKF is mainly applicable to nonlinear system models.

[0175] Specifically, the above state equation is shown as follows:

[0176]

[0177] In the formula, Indicates the internal resistance after correction at the last moment, represents the internal resistance state equation after correction at the last moment, e k represents the process noise of the state equation at the current moment, Indicates the estimated value of the internal resistance at the current moment.

[0178] The above state equation is linearized based on the Taylor expansion method to obtain the following state equation:

[0179]

[0180] In the formula, Indicates the estimated value of internal resistance at the previous moment.

[0181] After sorting, the following state equation is obtained:

[0182]

[0183] In the formula, A k represents the state transition matrix.

[0184] Specifically, the above observation equation is shown as follows:

[0185]

[0186] In the formula, ε k represents the observation noise of the above observation equation, U app,k represents the observed value of the terminal voltage at the current moment, Represents the observation equation of the terminal voltage at the current moment.

[0187] The above observation equation is linearized based on the Taylor expansion method to obtain the following observation equation:

[0188]

[0189] After sorting, the following observation equation is obtained:

[0190]

[0191] In the formula, C k represents the observation matrix.

[0192] In this preferred embodiment, the state equation and observation equation of the internal resistance are constructed based on the corrected internal resistance at the previous moment, the process noise at the current moment, and the observation noise at the current moment.

[0193] In another preferred embodiment, the construction of the state equation of the entropy coefficient and the observation equation of the entropy coefficient includes:

[0194] According to the corrected entropy coefficient at the previous moment and the process noise at the current moment, the state equation of the internal resistance is constructed;

[0195] According to the corrected entropy coefficient at the previous moment and the observation noise at the current moment, the observation equation of the internal resistance is constructed.

[0196] Specifically, the above state equation is shown as follows:

[0197]

[0198] In the formula, represents the corrected entropy coefficient at the last moment, Represents the estimated value of the entropy coefficient at the current moment.

[0199] The above state equation is linearized based on the Taylor expansion method to obtain the following state equation:

[0200]

[0201] In the formula, The state equation representing the entropy coefficient.

[0202] After sorting, the following state equation is obtained:

[0203]

[0204] Specifically, the above observation equation is shown as follows:

[0205]

[0206] Where, T kIndicates the current battery temperature. Represents the observation equation of the battery temperature at the current moment.

[0207] The above observation equation is linearized based on the Taylor expansion method to obtain the following observation equation:

[0208]

[0209] After sorting, the following observation equation is obtained:

[0210]

[0211] In this preferred embodiment, the corrected entropy coefficient at the previous moment, the process noise at the current moment, and the observation noise at the current moment are used to construct the state equation and observation equation of the entropy coefficient.

[0212] Step S102: Obtain the internal resistance and entropy coefficient corrected at the last moment, and calculate the estimated values ​​of the internal resistance and entropy coefficient at the current moment according to the state equation of the internal resistance, the state equation of the entropy coefficient, the internal resistance and entropy coefficient corrected at the last moment;

[0213] Specifically, the estimated values ​​of the internal resistance and entropy coefficient at the current moment are calculated according to the following formula:

[0214]

[0215] In the formula, represents the estimated value of the internal resistance at the current moment, represents the estimated value of the entropy coefficient at the current moment, Indicates the corrected internal resistance at the last moment. represents the corrected entropy coefficient at the last moment, The state equation representing the internal resistance at the current moment is, The state equation representing the entropy coefficient at the current moment.

[0216] In this step, the estimated values ​​of the internal resistance and entropy coefficient at the current moment are calculated based on the state equation of the internal resistance, the state equation of the entropy coefficient, and the internal resistance and entropy coefficient corrected at the previous moment.

[0217] Step S103: Calculate the error covariance matrix of the internal resistance and the entropy coefficient at the current moment according to the error covariance matrix corrected at the previous moment and the process noise covariance matrix at the current moment; wherein, the current at the previous moment is obtained, and the error covariance matrix corrected at the previous moment is calculated according to the current at the previous moment and the above coupling model;

[0218] Specifically, the error covariance matrix of the internal resistance and entropy coefficient at the current moment is calculated according to the following formula:

[0219]

[0220] In the formula, represents the error covariance matrix at the current moment, Q k represents the process noise covariance matrix at the current moment, Represents the error covariance matrix after correction at the previous moment.

[0221] Specifically, the error covariance matrix corrected at the previous moment is calculated by the following formula:

[0222]

[0223] In the formula, K k-1 represents the Kalman gain matrix at the previous moment, Represents the prior value of the covariance matrix at the previous moment, C k-1 Represents the observation matrix at the previous moment.

[0224] In this step, the error covariance matrix of the internal resistance and entropy coefficient at the current moment is calculated through the corrected error covariance matrix at the previous moment and the process noise covariance matrix at the current moment.

[0225] Step S104: Calculate the Kalman gain matrix of the internal resistance and entropy coefficient at the current moment according to the coupling model and the error covariance matrix of the internal resistance and entropy coefficient at the current moment;

[0226] Specifically, the Kalman gain matrix of the internal resistance and entropy coefficient at the current moment is calculated according to the following formula:

[0227]

[0228] Where Uapp(·) is the equation set for calculating the terminal voltage in the coupling model, R k represents the observation noise covariance matrix at the current moment, K k Represents the Kalman gain matrix at the current moment.

[0229] Specifically, the equation group for calculating the terminal voltage in the above coupling model is:

[0230] x avg =x0-It / Q n

[0231] y avg =y0-It / Q p

[0232] E=U p (y surf )-U n (xsurf )

[0233] x surf (t k )=x avg (t k )-Δx(t k ),y surf (t k )=y avg (t k )-Δy(t k )

[0234]

[0235]

[0236] η ohm =R ohm I

[0237] U app =E ocv -η con -η ohm ;

[0238] In this step, the Kalman gain matrix of the internal resistance and entropy coefficient at the current moment is calculated by coupling the model and the error covariance matrix of the internal resistance and entropy coefficient at the current moment.

[0239] Step S105: Calculate the observation errors of the internal resistance and the entropy coefficient according to the observation equation of the internal resistance, the observation equation of the entropy coefficient, the observation value of the internal resistance at the current moment, and the observation value of the entropy coefficient at the current moment;

[0240] Specifically, the observation errors of the internal resistance and entropy coefficient are calculated according to the following formula:

[0241]

[0242] In the formula, z k represents the observation error of internal resistance, z' k represents the observation error of the entropy coefficient, U app,exp Represents the observed value of the terminal voltage, T exp represents the observed value of temperature, and T(·) is the set of equations for calculating temperature in the coupled model.

[0243] Specifically, the equation group for calculating temperature in the above coupling model is:

[0244]

[0245]

[0246] In this step, the observation errors of the internal resistance and the entropy coefficient are calculated through the observation equation of the internal resistance, the observation equation of the entropy coefficient, the observation value of the internal resistance at the current moment, and the observation value of the entropy coefficient at the current moment.

[0247] Step S106: Calculate the corrected internal resistance and entropy coefficient at the current moment according to the estimated value of the internal resistance, the estimated value of the entropy coefficient, the Kalman gain matrix of the internal resistance, the Kalman gain matrix of the entropy coefficient, the observation error of the internal resistance, and the observation error of the entropy coefficient;

[0248] Specifically, the corrected internal resistance and entropy coefficient at the current moment are calculated according to the following formula:

[0249]

[0250] In the formula, represents the corrected internal resistance, Represents the corrected entropy coefficient.

[0251] In this step, the corrected internal resistance and entropy coefficient at the current moment are calculated through the estimated value of the internal resistance, the estimated value of the entropy coefficient, the Kalman gain matrix of the internal resistance, the Kalman gain matrix of the entropy coefficient, the observation error of the internal resistance and the observation error of the entropy coefficient.

[0252] Step S107: Calculate the heating power of the battery at the current moment according to the corrected entropy coefficient.

[0253] Specifically, the heating power is calculated according to the following formula:

[0254]

[0255] Q=Q r +Q ir ;

[0256] In this step, the heating power of the battery at the current moment is calculated based on the corrected entropy coefficient.

[0257] In another preferred embodiment, when the current moment is the initial moment, the entropy coefficient corresponding to the initial moment is obtained according to the above coupling model;

[0258] The heating power at the current moment is calculated based on the first internal resistance and the entropy coefficient corresponding to the initial moment.

[0259] In this embodiment, the heating power at the initial moment is calculated by using the first internal resistance and the entropy coefficient corresponding to the initial moment.

[0260] In a preferred embodiment, in order to specifically illustrate the method used in the present invention, the internal resistance and heat generation power estimation process of a 50Ah lithium iron phosphate battery is used as a case, and the specific parameters of the battery are shown in the following table:

[0261] Battery parameter table

[0262]

[0263] This experiment uses an ambient temperature of 25°C and a battery discharge rate of 1C as the inspection condition.

[0264] The mean absolute error and simulation time of the battery are shown in the following table:

[0265] Battery mean absolute error and simulation schedule

[0266]

[0267] Schematically, the comparison curve between the observed value and the simulated value of the terminal voltage is shown in Figure 6 As shown in the figure, the comparison curve between the observed and simulated temperature values ​​is shown in Figure 7 As shown, the simulation curve of the battery internal resistance is as follows Figure 8 As shown, the battery heating power simulation curve is as follows Fig. 9 As shown. The simulation results show that for square lithium iron phosphate batteries, the electric-thermal-fluid coupling model of this embodiment can achieve battery terminal voltage and temperature simulation average absolute errors of no more than 0.5mV and 0.1℃ respectively, and the simulation time of each data does not exceed 9.152ms. Therefore, the present invention can achieve accurate and efficient simulation of the internal and external characteristics of the battery including internal resistance and heat generation power.

[0268] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0269] like Fig.10 As shown, an embodiment of the present invention provides an online estimation device for the heating power of the internal resistance of a lithium-ion battery, comprising: a model and equation acquisition module, an estimated value calculation module, an error covariance matrix calculation module, a Kalman gain matrix calculation module, an observation error calculation module, an internal resistance and entropy coefficient correction module, and a heating power calculation module;

[0270] The above-mentioned model and equation acquisition module is used to obtain the coupling model of the lithium-ion battery to be tested, the state equation of the internal resistance, the observation equation of the internal resistance, the state equation of the entropy coefficient, and the observation equation of the entropy coefficient when the current moment is not the initial moment;

[0271] The estimated value calculation module is used to obtain the internal resistance and entropy coefficient corrected at the previous moment, and calculate the estimated values ​​of the internal resistance and entropy coefficient at the current moment according to the state equation of the internal resistance, the state equation of the entropy coefficient, the internal resistance and entropy coefficient corrected at the previous moment;

[0272] The error covariance matrix calculation module is used to calculate the error covariance matrix of the internal resistance and the entropy coefficient at the current moment according to the error covariance matrix corrected at the previous moment and the process noise covariance matrix at the current moment; wherein, the current at the previous moment is obtained, and the error covariance matrix corrected at the previous moment is calculated according to the current at the previous moment and the coupling model;

[0273] The Kalman gain matrix calculation module is used to calculate the Kalman gain matrix of the internal resistance and the entropy coefficient at the current moment according to the coupling model and the error covariance matrix of the internal resistance and the entropy coefficient at the current moment;

[0274] The above-mentioned observation error calculation module is used to calculate the observation errors of the internal resistance and the entropy coefficient according to the observation equation of the internal resistance, the observation equation of the entropy coefficient, the observation value of the internal resistance at the current moment, and the observation value of the entropy coefficient at the current moment;

[0275] The internal resistance and entropy coefficient correction module is used to calculate the corrected internal resistance and entropy coefficient at the current moment according to the estimated value of the internal resistance, the estimated value of the entropy coefficient, the Kalman gain matrix of the internal resistance, the Kalman gain matrix of the entropy coefficient, the observation error of the internal resistance and the observation error of the entropy coefficient;

[0276] The heating power calculation module is used to calculate the heating power of the battery at the current moment according to the corrected entropy coefficient.

[0277] It should be noted that the device embodiments described above are merely schematic, wherein the modules described above as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without creative work. The above schematic diagram is only an example of an online estimation device for the heating power of the internal resistance of a lithium-ion battery, and does not constitute a limitation on an online estimation device for the heating power of the internal resistance of a lithium-ion battery, and may include more or fewer components than shown in the figure, or a combination of certain components, or different components.

[0278] Based on the above method item embodiments, the present invention provides corresponding terminal device item embodiments.

[0279] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for online estimation of the heating power of the internal resistance of a lithium-ion battery described in any embodiment of the present invention is implemented.

[0280] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more module elements may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the device;

[0281] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The device may include, but is not limited to, a processor, a memory;

[0282] The processor referred to herein may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The above-mentioned processor is the control center of the above-mentioned device, and uses various interfaces and lines to connect various parts of the entire device;

[0283] The above-mentioned memory can be used to store the above-mentioned computer programs and / or modules. The above-mentioned processor realizes various functions of the above-mentioned device by running or executing the computer programs and / or modules stored in the above-mentioned memory, and calling the data stored in the memory. The above-mentioned memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; in addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0284] Based on the above method item embodiments, the present invention provides a corresponding storage medium item embodiment.

[0285] Another embodiment of the present invention provides a storage medium, the storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the online estimation method for the heat generation power of the internal resistance of a lithium-ion battery described in any embodiment of the present invention.

[0286] In this embodiment, the storage medium is a computer-readable storage medium, the computer program includes computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0287] Compared with the prior art, by implementing the above-mentioned embodiments of the present invention, the heating power of the lithium-ion battery can be estimated online based on the corrected entropy coefficient.

[0288] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for online estimation of heating power of internal resistance of lithium-ion battery, characterized in that: include: When the current moment is not the initial moment, obtaining a coupling model of the lithium-ion battery to be tested, a state equation of the internal resistance, an observation equation of the internal resistance, a state equation of the entropy coefficient, and an observation equation of the entropy coefficient; Obtain the internal resistance and entropy coefficient corrected at the previous moment, and calculate the estimated values ​​of the internal resistance and entropy coefficient at the current moment based on the state equation of the internal resistance, the state equation of the entropy coefficient, the internal resistance and entropy coefficient corrected at the previous moment; According to the error covariance matrix corrected at the previous moment and the process noise covariance matrix at the current moment, the error covariance matrix of the internal resistance and the entropy coefficient at the current moment is calculated; wherein, the current at the previous moment is obtained, and the error covariance matrix corrected at the previous moment is calculated according to the current at the previous moment and the coupling model; Calculate the Kalman gain matrix of the internal resistance and the entropy coefficient at the current moment according to the coupling model and the error covariance matrix of the internal resistance and the entropy coefficient at the current moment; According to the observation equation of the internal resistance, the observation equation of the entropy coefficient, the observed value of the internal resistance at the current moment, and the observed value of the entropy coefficient at the current moment, the observation errors of the internal resistance and the entropy coefficient are calculated; Calculate the corrected internal resistance and entropy coefficient at the current moment according to the estimated value of the internal resistance, the estimated value of the entropy coefficient, the Kalman gain matrix of the internal resistance, the Kalman gain matrix of the entropy coefficient, the observation error of the internal resistance, and the observation error of the entropy coefficient; The heating power of the battery at the current moment is calculated according to the corrected entropy coefficient.

2. The method for online estimating the heating power of the internal resistance of a lithium-ion battery according to claim 1, characterized in that: The construction of the coupled model includes: Repeatedly applying a preset current excitation to the lithium-ion battery to be tested within a full state of charge range, and obtaining a number of voltage mutations corresponding to the moment of applying the preset current excitation, a shelf voltage value corresponding to the end of the shelf time, a number of voltage steady-state values ​​corresponding to the end of the constant current time, a number of transient voltages, a number of second internal resistances, and battery temperatures at each moment; The coupling model is constructed according to the plurality of voltage mutation amounts, the shelf voltage value, the plurality of voltage steady-state values, the plurality of transient voltages, the plurality of second internal resistances and the battery temperature at each moment.

3. The method for online estimating the heating power of the internal resistance of a lithium-ion battery according to claim 2, characterized in that: The step of constructing the coupling model according to the plurality of voltage mutation amounts, the shelf voltage value, the plurality of voltage steady-state values, the plurality of transient voltages, the plurality of second internal resistances and the battery temperature at each moment includes: According to the shelf voltage value, the positive electrode initial lithium insertion concentration fraction, the negative electrode initial lithium insertion concentration fraction, the positive electrode capacity and the negative electrode capacity of the lithium ion battery to be tested are calculated; According to the plurality of voltage mutation amounts, a plurality of reaction polarization coefficients of the lithium-ion battery to be tested at each of the battery temperatures are calculated; According to the plurality of steady-state voltage values, a plurality of positive electrode solid-phase diffusion time constants, a plurality of negative electrode solid-phase diffusion time constants and a plurality of liquid-phase diffusion proportional coefficients of the lithium-ion battery to be tested are calculated at each of the battery temperatures; Calculating the liquid phase diffusion time constant of the lithium ion battery to be tested according to the plurality of transient voltages and the liquid phase diffusion proportional coefficient; According to each of the battery temperatures, the reaction polarization coefficients, the positive electrode solid phase diffusion time constants, the negative electrode solid phase diffusion time constants and the liquid phase diffusion proportional coefficients, a corrected first internal resistance, reaction polarization coefficient, positive electrode solid phase diffusion time constant, negative electrode solid phase diffusion time constant and liquid phase diffusion proportional coefficient of the lithium ion battery to be tested are calculated; Obtaining the surface area and mass of the lithium-ion battery to be tested, and calculating the heat transfer coefficient and the battery equivalent specific heat capacity of the lithium-ion battery to be tested according to the surface area, the temperature of each battery and the mass; A coupling model of the lithium-ion battery to be tested is constructed according to the battery temperature at each moment, the surface area, the mass, the initial lithium insertion concentration fraction of the positive electrode, the initial lithium insertion concentration fraction of the negative electrode, the positive electrode capacity, the negative electrode capacity, the liquid phase diffusion time constant, the corrected first internal resistance of the lithium-ion battery to be tested, the corrected reaction polarization coefficient, the corrected positive electrode solid phase diffusion time constant, the corrected negative electrode solid phase diffusion time constant, the corrected liquid phase diffusion proportional coefficient, the heat exchange coefficient and the battery equivalent specific heat capacity.

4. The method for online estimating the heating power of the internal resistance of a lithium-ion battery according to claim 3, characterized in that: The method of constructing a coupling model of the lithium-ion battery to be tested according to the battery temperature at each moment, the surface area, the mass, the initial lithium insertion concentration fraction of the positive electrode, the initial lithium insertion concentration fraction of the negative electrode, the positive electrode capacity, the negative electrode capacity, the liquid phase diffusion time constant, the corrected first internal resistance of the lithium-ion battery to be tested, the corrected reaction polarization coefficient, the corrected positive electrode solid phase diffusion time constant, the corrected negative electrode solid phase diffusion time constant, the corrected liquid phase diffusion proportional coefficient, the heat transfer coefficient and the battery equivalent specific heat capacity comprises: Constructing a battery operation process model for describing a battery operation process according to the positive electrode initial lithium insertion concentration fraction, the negative electrode initial lithium insertion concentration fraction, the positive electrode capacity, and the negative electrode capacity; Constructing a solid phase diffusion model for describing the solid phase diffusion process according to the initial lithium insertion concentration fraction of the positive electrode, the initial lithium insertion concentration fraction of the negative electrode, the positive electrode capacity and the negative electrode capacity, the positive electrode solid phase diffusion time constant and the negative electrode solid phase diffusion time constant; Acquire the liquid phase lithium ion concentration of the positive electrode plate, the liquid phase lithium ion concentration of the negative electrode plate, and the change in the liquid phase lithium ion concentration at the positive and negative electrode current collectors of the lithium ion battery to be tested, and construct a liquid phase diffusion model for describing the liquid phase diffusion process according to the liquid phase lithium ion concentration of the positive electrode plate, the liquid phase lithium ion concentration of the negative electrode plate, and the change in the liquid phase lithium ion concentration at the positive and negative electrode current collectors, the corrected liquid phase diffusion proportional coefficient, and the liquid phase diffusion time constant; Constructing a reaction polarization model for describing reaction polarization according to the mass and the corrected reaction polarization coefficient; Constructing an ohmic polarization model for describing ohmic polarization according to the corrected first internal resistance of the lithium-ion battery to be tested; Constructing a heat generation model for describing the heat generation behavior inside the battery according to the corrected first internal resistance of the lithium-ion battery to be tested and the battery temperature at each moment; Constructing a temperature model for describing the battery temperature according to the battery temperature at each moment and the battery equivalent specific heat capacity; According to the surface area, construct an expression for the relationship between the convection heat transfer coefficient and the heat dissipation resistance of the battery to the environment; Acquiring a reference temperature, and constructing a correction equation according to the reference temperature and the battery temperature at each moment; The coupling model is obtained according to the battery working process model, the solid phase diffusion model, the liquid phase diffusion model, the reaction polarization model, the ohmic polarization model, the heat generation model, the temperature model, the relationship expression and the correction equation.

5. The method for online estimating the heating power of the internal resistance of a lithium-ion battery according to claim 4, characterized in that: The construction of the state equation of internal resistance and the observation equation of internal resistance includes: Constructing a state equation of the internal resistance according to the corrected internal resistance at the previous moment and the process noise at the current moment; An observation equation of the internal resistance is constructed according to the corrected internal resistance at the previous moment and the observation noise at the current moment.

6. The method for online estimating the heating power of the internal resistance of a lithium-ion battery according to claim 5, characterized in that: The state equation of the entropy coefficient and the construction of the observation equation of the entropy coefficient include: Constructing a state equation of the internal resistance according to the corrected entropy coefficient at the previous moment and the process noise at the current moment; The observation equation of the internal resistance is constructed according to the corrected entropy coefficient at the previous moment and the observation noise at the current moment.

7. The method for online estimating the heating power of the internal resistance of a lithium-ion battery according to claim 6, characterized in that: Also includes: When the current moment is the initial moment, obtaining an entropy coefficient corresponding to the initial moment according to the coupling model; The heating power at the current moment is calculated according to the first internal resistance and the entropy coefficient corresponding to the initial moment.

8. An online estimation device for the heating power of the internal resistance of a lithium-ion battery, characterized in that: include: Model and equation acquisition module, estimated value calculation module, error covariance matrix calculation module, Kalman gain matrix calculation module, observation error calculation module, internal resistance and entropy coefficient correction module and heating power calculation module; The model and equation acquisition module is used to acquire the coupling model of the lithium-ion battery to be tested, the state equation of the internal resistance, the observation equation of the internal resistance, the state equation of the entropy coefficient, and the observation equation of the entropy coefficient when the current moment is not the initial moment; The estimated value calculation module is used to obtain the internal resistance and entropy coefficient corrected at the previous moment, and calculate the estimated values ​​of the internal resistance and entropy coefficient at the current moment according to the state equation of the internal resistance, the state equation of the entropy coefficient, the internal resistance and entropy coefficient corrected at the previous moment; The error covariance matrix calculation module is used to calculate the error covariance matrix of the internal resistance and the entropy coefficient at the current moment according to the error covariance matrix corrected at the previous moment and the process noise covariance matrix at the current moment; wherein the current at the previous moment is obtained, and the error covariance matrix corrected at the previous moment is calculated according to the current at the previous moment and the coupling model; The Kalman gain matrix calculation module is used to calculate the Kalman gain matrix of the internal resistance and the entropy coefficient at the current moment according to the coupling model and the error covariance matrix of the internal resistance and the entropy coefficient at the current moment; The observation error calculation module is used to calculate the observation errors of the internal resistance and the entropy coefficient according to the observation equation of the internal resistance, the observation equation of the entropy coefficient, the observation value of the internal resistance at the current moment, and the observation value of the entropy coefficient at the current moment; The internal resistance and entropy coefficient correction module is used to calculate the corrected internal resistance and entropy coefficient at the current moment according to the estimated value of the internal resistance, the estimated value of the entropy coefficient, the Kalman gain matrix of the internal resistance, the Kalman gain matrix of the entropy coefficient, the observation error of the internal resistance and the observation error of the entropy coefficient; The heating power calculation module is used to calculate the heating power of the battery at the current moment according to the corrected entropy coefficient.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, an online estimation method for the heating power of the internal resistance of a lithium-ion battery as claimed in any one of claims 1 to 7 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the online estimation method for the heat generation power of the internal resistance of a lithium-ion battery as described in any one of claims 1 to 7.

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