A method, device, equipment and storage medium for online estimation of heating power of internal resistance of lithium-ion battery

Abstract

The present invention discloses a method, apparatus, device, and storage medium for online estimation of the heat generation power of the internal resistance of a lithium-ion battery. The method comprises: first, when the current moment is not the initial moment, obtaining a coupling model of the lithium-ion battery to be measured, 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; then calculating an estimate of the internal resistance and entropy coefficient at the current moment; then calculating an error covariance matrix of the internal resistance and entropy coefficient at the current moment; then calculating a Kalman gain matrix of the internal resistance and entropy coefficient at the current moment based on the coupling model; then calculating the observation error of the internal resistance and entropy coefficient; then calculating a corrected internal resistance and entropy coefficient at the current moment; and finally calculating the heat generation power of the battery at the current moment based on the corrected entropy coefficient. By implementing the present invention, the heat generation power of a lithium-ion battery can be estimated online based on the corrected entropy coefficient.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of heat generation power estimation, in particular to a heat generation power online estimation method of lithium ion battery internal resistance. BACKGROUND

[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 increasing demand for electric vehicle range and the increasing scale of power grid energy storage, the demand for energy and power density of lithium ion batteries is increasing, and the risk of battery failure is also increasing. For battery fault diagnosis methods, most of them are combined with battery characteristics and implemented by optimization algorithms, in which the most important battery characteristics are internal resistance representing electrical characteristics and heat generation power representing thermal characteristics.

[0003] In the prior art, a 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 has a large deviation in the calculation result of the heat generation power because it is difficult to accurately determine the entropy coefficient in the equation, the test process uses expensive and time-consuming equipment, and the average value of the entropy coefficient is usually taken in actual application, 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 heat generation power calculation error caused by the uncorrected entropy coefficient is a problem that needs to be solved. SUMMARY

[0005] The present application provides a lithium ion battery internal resistance heat generation power online estimation method, device, equipment and storage medium, which can estimate the heat generation power of lithium ion battery based on the corrected entropy coefficient.

[0006] An embodiment of the present application provides a lithium ion battery internal resistance heat generation power online estimation method, comprising:

[0007] When the current time is not the initial time, 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 are obtained;

[0008] The corrected internal resistance and entropy coefficient of the previous time are obtained, and the estimated values of the internal resistance and entropy coefficient at the current time are calculated according to the state equation of the internal resistance, the state equation of the entropy coefficient, the corrected internal resistance and entropy coefficient of the previous time;

[0009] According to the error covariance matrix corrected at the last 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 last moment is obtained, and the error covariance matrix corrected at the last moment is calculated according to the current at the last moment and the coupling model.

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

[0012] 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 corrected internal resistance and entropy coefficient at the current moment are calculated.

[0013] According to the corrected entropy coefficient, the heat generation power of the battery at the current moment is calculated.

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

[0015] The preset current excitation is repeatedly applied to the above-mentioned lithium ion battery in the full state of charge range, and a plurality of voltage jump variables corresponding to the time when the above-mentioned preset current excitation is applied, a standing voltage value corresponding to the end of standing, a plurality of voltage steady-state values corresponding to the end of constant current, a plurality of transient voltages, a plurality of second internal resistances and the battery temperature at each moment are obtained.

[0016] According to the above-mentioned plurality of voltage jump variables, the standing 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, the above-mentioned coupling model is constructed.

[0017] Further, the above-mentioned construction of the coupling model according to the above-mentioned plurality of voltage jump variables, the standing 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:

[0018] According to the above-mentioned standing voltage value, the initial lithium intercalation concentration fraction of the positive electrode, the initial lithium intercalation concentration fraction of the negative electrode, 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 plurality of voltage jump variables, a plurality of 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 several voltage steady-state values, the positive electrode solid-phase diffusion time constants, the negative electrode solid-phase diffusion time constants and the liquid-phase diffusion proportionality coefficients of the lithium ion battery to be measured at the several battery temperatures are calculated;

[0021] According to the several transient voltages and the liquid-phase diffusion proportionality coefficients, the liquid-phase diffusion time constant of the lithium ion battery to be measured is calculated;

[0022] According to the several battery temperatures, the several reaction polarization coefficients, the several positive electrode solid-phase diffusion time constants, the several negative electrode solid-phase diffusion time constants and the several liquid-phase diffusion proportionality coefficients, the first internal resistance, the reaction polarization coefficient, the positive electrode solid-phase diffusion time constant, the negative electrode solid-phase diffusion time constant and the liquid-phase diffusion proportionality coefficient of the lithium ion battery to be measured after correction are calculated;

[0023] The surface area and the mass of the lithium ion battery to be measured are obtained, and according to the surface area, the several battery temperatures and the mass, the heat exchange coefficient and the equivalent specific heat capacity of the lithium ion battery to be measured are calculated;

[0024] According to the battery temperature at each time, the surface area, the mass, the positive electrode initial lithium intercalation concentration fraction, the negative electrode initial lithium intercalation concentration fraction, the positive electrode capacity, the negative electrode capacity, the liquid-phase diffusion time constant, the first internal resistance of the lithium ion battery to be measured after correction, the reaction polarization coefficient after correction, the positive electrode solid-phase diffusion time constant after correction, the negative electrode solid-phase diffusion time constant after correction, the liquid-phase diffusion proportionality coefficient after correction, the heat exchange coefficient and the equivalent specific heat capacity of the battery, a coupling model of the lithium ion battery to be measured is constructed.

[0025] Further, according to the battery temperature at each time, the surface area, the mass, the positive electrode initial lithium intercalation concentration fraction, the negative electrode initial lithium intercalation concentration fraction, the positive electrode capacity, the negative electrode capacity, the liquid-phase diffusion time constant, the first internal resistance of the lithium ion battery to be measured after correction, the reaction polarization coefficient after correction, the positive electrode solid-phase diffusion time constant after correction, the negative electrode solid-phase diffusion time constant after correction, the liquid-phase diffusion proportionality coefficient after correction, the heat exchange coefficient and the equivalent specific heat capacity of the battery, a coupling model of the lithium ion battery to be measured is constructed, including:

[0026] According to the positive electrode initial lithium intercalation concentration fraction, the negative electrode initial lithium intercalation concentration fraction, the positive electrode capacity and the negative electrode capacity, a battery working process model for describing the battery working process is constructed;

[0027] According to the above positive electrode initial lithium intercalation concentration fraction, the above negative electrode initial lithium intercalation concentration fraction, the above positive electrode capacity and the above negative electrode capacity, the above positive electrode solid phase diffusion time constant and the above negative electrode solid phase diffusion time constant, a solid phase diffusion model for describing the solid phase diffusion process is constructed;

[0028] The positive plate liquid phase lithium ion concentration, the negative plate liquid phase lithium ion concentration and the liquid phase lithium ion concentration change at the positive and negative current collectors of the lithium ion battery to be measured are obtained, and a liquid phase diffusion model for describing the liquid phase diffusion process is constructed according to the positive plate liquid phase lithium ion concentration, the negative plate liquid phase lithium ion concentration and the liquid phase lithium ion concentration change at the positive and negative current collectors, the corrected liquid phase diffusion proportionality coefficient and the liquid phase diffusion time constant;

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

[0030] According to the above corrected first internal resistance of the lithium ion battery to be measured, an ohmic polarization model for describing the ohmic polarization is constructed;

[0031] According to the above corrected first internal resistance of the lithium ion battery to be measured and the battery temperature at each time, a heat generation model for describing the heat generation behavior inside the battery is constructed;

[0032] According to the battery temperature at each time and the equivalent specific heat capacity of the battery, a temperature model for describing the battery temperature is constructed;

[0033] According to the above surface area, a relationship expression between the convective heat transfer coefficient and the heat dissipation resistance of the battery to the environment is constructed;

[0034] A reference temperature is obtained, and a correction equation is constructed according to the reference temperature and the battery temperature at each time;

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

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

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

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

[0039] Further, the state equation of the entropy coefficient and the observation equation of the entropy coefficient are constructed, including:

[0040] According to the entropy coefficient corrected at the last moment and the process noise at the current moment, the state equation of the above-mentioned internal resistance is constructed.

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

[0042] Further, when the current moment is the initial moment, the entropy coefficient corresponding to the initial moment is obtained according to the coupling model.

[0043] According to the first internal resistance and the entropy coefficient corresponding to the initial moment, the heat power at the current moment is calculated.

[0044] On the basis of the above-mentioned method embodiment, the present application correspondingly provides a device embodiment;

[0045] The present application provides a kind of lithium ion battery internal resistance heat power online estimation device, including:

[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 heat power calculation module;

[0047] The above-mentioned model and equation acquisition module are used to obtain 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;

[0048] The above-mentioned estimated value calculation module is used to obtain the internal resistance and the entropy coefficient corrected at the last moment, and according to the state equation of the internal resistance, the state equation of the entropy coefficient, the internal resistance and the entropy coefficient corrected at the last moment, the estimated value of the internal resistance and the entropy coefficient at the current moment is calculated;

[0049] The above-mentioned 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 last moment and the process noise covariance matrix at the current moment;Wherein, the current at the last moment is obtained, and the error covariance matrix corrected at the last moment is calculated according to the current at the last moment and the above-mentioned coupling model;

[0050] The above-mentioned 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 above-mentioned coupling model and the error covariance matrix of the internal resistance and the entropy coefficient at the current moment;

[0051] The observation error calculation module is configured 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 time, and the observation value of the entropy coefficient at the current time.

[0052] The internal resistance and entropy coefficient correction module is configured to calculate the corrected internal resistance and entropy coefficient at the current time 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 heat generation power calculation module is configured to calculate the heat generation power of the battery at the current time according to the corrected entropy coefficient.

[0054] On the basis of the method embodiment, the application provides a terminal device embodiment.

[0055] The application provides a terminal device, which comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor realizes the online heat generation power estimation method of the internal resistance of the lithium ion battery according to any one of the embodiments of the application when executing the computer program.

[0056] On the basis of the method embodiment, the application provides a storage medium embodiment.

[0057] The application provides a storage medium, which comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor realizes the online heat generation power estimation method of the internal resistance of the lithium ion battery according to any one of the embodiments of the application when executing the computer program.

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

[0059] The application provides an online estimation method and device for internal resistance and heat generation power of a lithium ion battery based on an electro-thermal-fluid coupling model, a terminal device and a storage medium. The method comprises the following steps: obtaining a coupling model of a lithium ion battery to be measured, a state equation of internal resistance, an observation equation of internal resistance, a state equation of an entropy coefficient and an observation equation of the entropy coefficient at a current time point which is not an initial time point; obtaining the internal resistance and the entropy coefficient which are corrected at a previous time point; calculating the estimation values of the internal resistance and the entropy coefficient at the current time point according to the state equation of the internal resistance, the state equation of the entropy coefficient, the internal resistance and the entropy coefficient which are corrected at the previous time point; calculating the error covariance matrix of the internal resistance and the entropy coefficient at the current time point according to the error covariance matrix which is corrected at the previous time point and a process noise covariance matrix at the current time point; calculating the Kalman gain matrix of the internal resistance and the entropy coefficient at the current time point according to the coupling model and the error covariance matrix of the internal resistance and the entropy coefficient at the current time point; calculating 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, an observation value of the internal resistance at the current time point and an observation value of the entropy coefficient at the current time point; calculating the internal resistance and the entropy coefficient which are corrected at the current time point according to the estimation value of the internal resistance, the estimation 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; and calculating the heat generation power of the battery at the current time point according to the corrected entropy coefficient. Therefore, the state equation and the observation equation of the entropy coefficient are constructed, and the heat generation power of the lithium ion battery is estimated online based on the corrected entropy coefficient and the EKF iterative algorithm. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 FIG. 1 is a flowchart of an online estimation method for internal resistance and heat generation power of a lithium ion battery according to an embodiment of the application.

[0061] Figure 2 FIG. 2 is a current excitation diagram according to an embodiment of the application.

[0062] Figure 3 FIG. 3 is a voltage response diagram according to an embodiment of the application.

[0063] Figure 4 FIG. 4 is a structure principle diagram of a discharge process of a lithium ion battery according to an embodiment of the application.

[0064] Figure 5 FIG. 5 is a battery heat dissipation environment diagram in a forced air system according to an embodiment of the application.

[0065] Figure 6 FIG. 6 is a comparison curve diagram of an observation value and a simulation value of an end voltage according to an embodiment of the application.

[0066] Figure 7 is a temperature observation value and simulation value comparison curve provided by an embodiment of the present application.

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

[0068] Figure 9 is a battery heating power simulation curve provided by an embodiment of the present application.

[0069] Figure 10 is a structure schematic diagram of a lithium ion battery internal resistance heating power online estimation device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0070] The technical solutions in the present application will be described clearly and completely below in combination with the drawings in the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0071] As shown in the figure, Figure 1 An online estimation method of lithium ion battery internal resistance heating power provided by an embodiment of the present application comprises:

[0072] Step S101: When the current time is not the initial time, the coupling model, the internal resistance state equation, the internal resistance observation equation, the entropy coefficient state equation and the entropy coefficient observation equation of the lithium ion battery to be measured are obtained.

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

[0074] The above lithium ion battery to be measured is repeatedly applied with preset current excitation in a full state of charge range, and a plurality of voltage jump variables corresponding to the time when the above preset current excitation is applied, a rest voltage value corresponding to the rest end time, a plurality of voltage steady-state values corresponding to the constant current end time, a plurality of transient voltages, a plurality of second internal resistances and the battery temperature at each time are obtained.

[0075] According to the above plurality of voltage jump variables, the rest 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 time, the above coupling model is constructed.

[0076] Schematically, as shown in the figure, Figure 2As shown, the current excitation is specifically: discharging at a constant current for 20 min-resting for 20 min-charging at a constant current for 10 min-resting for 20 min in a cycle within the full state of charge range, and the current charge and discharge rate is less than 0.4C. When the cycle charging and discharging is performed, the discharging current is negative and the charging current is positive.

[0077] As shown, the "voltage jump" in the figure is the voltage jump corresponding to the moment when the preset current excitation is applied, the "rest end data point" is the rest voltage value corresponding to the moment when the rest is ended, and the "constant current end steady-state voltage" is the voltage steady-state value. Figure 3

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

[0079] Preferably, using the current excitation can ensure that the obtained parameters are applicable within the full state of charge range, and reduce the influence 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 measured, a plurality of voltage jumps corresponding to the moment when the preset current excitation is applied, a rest voltage value corresponding to the moment when the rest is ended, a plurality of voltage steady-state values corresponding to the moment when the constant current is ended, a plurality of transient voltages, a plurality of second internal resistances, and the battery temperature at each moment are obtained, and the coupling model is obtained according to the plurality of voltage jumps, the rest 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.

[0081] In another preferred embodiment, the coupling model is constructed according to the plurality of voltage jumps, the rest 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, including:

[0082] According to the rest voltage value, the positive electrode initial lithium intercalation concentration fraction, the negative electrode initial lithium intercalation concentration fraction, the positive electrode capacity, and the negative electrode capacity of the lithium ion battery to be measured are calculated;

[0083] Specifically, at the end of the rest, the solid-phase diffusion has recovered equilibrium, the lithium intercalation amount on the particle surface is the same as the average lithium intercalation amount, and the battery terminal voltage is considered to be the open circuit voltage at this time. Therefore, the positive electrode initial lithium intercalation concentration fraction, the negative electrode initial lithium intercalation concentration fraction, the positive electrode capacity, and the negative electrode capacity are calculated according to the following formula and the least square method:

[0084] ;

[0085] ;

[0086] ; ​

[0087] wherein, represents the average lithium intercalation amount of the negative electrode, represents the initial lithium intercalation concentration fraction of the negative electrode, represents the current, represents the charge-discharge time, represents the capacity of the negative electrode, represents the average lithium intercalation amount of the positive electrode, represents the initial lithium intercalation concentration fraction of the positive electrode, represents the capacity of the positive electrode, in AS, represents the open circuit voltage in the charge-discharge time t, represents the open circuit potential function of the positive electrode, represents the open circuit potential function of the negative electrode, represents the surface lithium intercalation amount of the positive electrode, represents the surface lithium intercalation amount of the negative electrode.

[0088] According to the above several voltage jump values, the reaction polarization coefficients of the lithium ion battery to be measured at each battery temperature are calculated;

[0089] Specifically, the voltage jump generated at the moment of applying the current excitation is determined by the ohmic polarization and the reaction polarization, and therefore, the reaction polarization coefficient is calculated by the following formula and the least square fitting method:

[0090]

[0091]

[0092]

[0093] wherein, represents an intermediate variable for reaction polarization overpotential calculation, without specific physical meaning, represents the initial concentration of the electrolyte, represents the reaction polarization coefficient, in m -1.5 mol 0.5 s, represents another intermediate variable for reaction polarization overpotential calculation, without specific physical meaning, represents the reaction polarization overpotential, represents the Faraday constant, represents the ideal gas constant, represents the battery temperature.

[0094] According to the above several voltage steady-state values, the positive electrode solid-phase diffusion time constant, the negative electrode solid-phase diffusion time constant and the liquid-phase diffusion proportion coefficient of the lithium ion battery to be measured at each battery temperature are calculated;

[0095] 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:

[0096] ;

[0097] ;

[0098]

[0099] ;

[0100]

[0101] ;

[0102] Where, express The amount of lithium embedded on the negative electrode surface at a given moment, express Average lithium insertion amount of negative electrode at each moment, express 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, express The difference between the average solid-phase lithium insertion amount and the surface lithium insertion amount of the positive electrode at the moment, represents the above-mentioned negative electrode solid phase diffusion time constant, in s, represents the above-mentioned cathode solid phase diffusion time constant, It represents the polarization overpotential, which is caused by assuming that the change in the liquid phase lithium ion concentration at the positive and negative electrode collectors is the same, and there is a gradient distribution of the liquid phase lithium ion concentration of the positive and negative plates. represents the migration coefficient, represents the initial concentration of the electrolyte, represents the change in the liquid phase lithium ion concentration at the positive and negative electrode current collectors, represents the above liquid phase diffusion proportional coefficient, in mol m -3 A -1 , represents the liquid phase diffusion time constant, in seconds, express Current at each moment, Indicates process state variables, which have no actual physical meaning. express The change of electrolyte concentration at each moment, express Current at all times.

[0103] According to the above several transient voltages and the above liquid-phase diffusion proportionality coefficients, the liquid-phase diffusion time constant of the lithium ion battery to be measured is calculated.

[0104] Specifically, the liquid-phase diffusion time constant is calculated by the following formula:

[0105]

[0106] According to the above battery temperatures, the above several reaction polarization coefficients, the above several positive electrode solid-phase diffusion time constants, several negative electrode solid-phase diffusion time constants, and several liquid-phase diffusion proportionality 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 proportionality coefficient of the lithium ion battery to be measured are calculated.

[0107] Specifically, since the positive electrode solid-phase diffusion time constant, negative electrode solid-phase diffusion time constant, reaction polarization coefficient, liquid-phase diffusion proportionality coefficient, and internal resistance are affected by temperature, they are corrected by the following Arrhenius formula in exponential form:

[0108]

[0109] In the formula, X represents the positive electrode solid-phase diffusion time constant, negative electrode solid-phase diffusion time constant, reaction polarization coefficient, liquid-phase diffusion proportionality coefficient, and second internal resistance to be corrected, represents the value of the positive electrode solid-phase diffusion time constant, negative electrode solid-phase diffusion time constant, reaction polarization coefficient, liquid-phase diffusion proportionality coefficient, and second internal resistance at the reference temperature, represents the reference temperature, represents the activation energy coefficient.

[0110] Specifically, since the open-circuit potential is affected by temperature, the open-circuit potential is corrected according to the following Nernst equation:

[0111]

[0112] In the formula, E represents the corrected open-circuit potential, the open-circuit potential before correction, represents the entropy coefficient.

[0113] The surface area and mass of the lithium ion battery to be measured are obtained, and the heat transfer coefficient and battery equivalent specific heat capacity of the lithium ion battery to be measured are calculated according to the above surface area, the above battery temperatures, and the above mass.

[0114] Specifically, when the external current is zero, the total heat generation rate of the battery is zero, and the shell temperature and the internal temperature, as well as their change rates, can be considered approximately equal. Therefore, the heat transfer coefficient and the battery equivalent specific heat capacity are calculated according to the following formula:​

[0115]

[0116] When k = 0, When k = ∞, Therefore, the following equation can be obtained:

[0117]

[0118] When is the initial time battery shell temperature, the time constant can be calculated by the following equation:

[0119]

[0120] In the equation, h represents the above-mentioned heat transfer coefficient, with the unit of W m -2 K -1 , A represents the above-mentioned surface area, represents the above-mentioned battery equivalent specific heat capacity, with the unit of J kg -1 K -1 , represents the time constant, represents the battery temperature at the kth time, represents the initial temperature of the battery, represents the ambient temperature.

[0121] Preferably, after the battery terminal voltage reaches the discharge cutoff point, a 30-minute rest is performed, the shell temperature data during the rest period is used to fit the time constant, and then the heat transfer coefficient is calculated.

[0122] According to the above-mentioned battery temperature at each time, the above-mentioned surface area, the above-mentioned mass, the above-mentioned initial lithium intercalation concentration fraction of the positive electrode, the above-mentioned initial lithium intercalation concentration fraction of the negative electrode, the above-mentioned positive electrode capacity, the above-mentioned negative electrode capacity, the above-mentioned liquid phase diffusion time constant, the above-mentioned first internal resistance of the lithium ion battery to be measured after correction, the above-mentioned reaction polarization coefficient after correction, the above-mentioned positive electrode solid phase diffusion time constant after correction, the above-mentioned negative electrode solid phase diffusion time constant after correction, the above-mentioned liquid phase diffusion proportion coefficient after correction, the above-mentioned heat transfer coefficient, and the above-mentioned battery equivalent specific heat capacity, a coupling model of the lithium ion battery to be measured is constructed.

[0123] In this preferred embodiment, the above-mentioned several voltage mutations, the rest voltage value, the several voltage steady-state values, the several transient voltages, the several second internal resistances, and the battery temperature at each time are calculated to obtain the coupling model.

[0124] ​​​In another preferred embodiment, the above-mentioned battery temperature, the above-mentioned surface area, the above-mentioned mass, the above-mentioned initial lithium intercalation concentration fraction of the positive electrode, the above-mentioned initial lithium intercalation concentration fraction of the negative electrode, the above-mentioned positive electrode capacity, the above-mentioned negative electrode capacity, the above-mentioned liquid-phase diffusion time constant, the above-mentioned first internal resistance of the lithium ion battery to be measured after correction, the above-mentioned reaction polarization coefficient after correction, the above-mentioned positive electrode solid-phase diffusion time constant after correction, the above-mentioned negative electrode solid-phase diffusion time constant after correction, the above-mentioned liquid-phase diffusion proportionality coefficient after correction, the above-mentioned heat exchange coefficient, and the above-mentioned battery equivalent specific heat capacity at each time point are used to construct a coupling model of the lithium ion battery to be measured, which includes:

[0125] According to the above-mentioned initial lithium intercalation concentration fraction of the positive electrode, the above-mentioned initial lithium intercalation concentration fraction of the negative electrode, the above-mentioned positive electrode capacity, and the above-mentioned negative electrode capacity, a battery working process model for describing the working process of the battery is constructed.

[0126] As shown in the schematic diagram, Figure 4 In the battery, the negative active material can generally be regarded as composed of a plurality of spherical active particles. During the charging and discharging process of the battery, there are physical and chemical processes such as solid-phase diffusion, electrochemical reaction on the surface of the particles, and liquid-phase diffusion. The related parameters of the positive and negative electrode materials, electrolyte, separator, and interface 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 such as the voltage, current, and temperature of the battery.

[0127] Specifically, assuming that the reaction distribution is uniform at each position inside the battery, the electrode plate is isotropic, and the lithium ion concentration is the same at each position in the radius r direction, a single particle is used to represent all active particles of the positive and negative electrodes, and the average lithium intercalation amount of the positive and negative electrodes is used to represent the basic working process of the battery.

[0128] Specifically, the above-mentioned battery working process model is as follows:

[0129] ;

[0130] ;

[0131] ;

[0132] According to the above-mentioned initial lithium intercalation concentration fraction of the positive electrode, the above-mentioned initial lithium intercalation concentration fraction of the negative electrode, the above-mentioned positive electrode capacity, and the above-mentioned negative electrode capacity, the above-mentioned positive electrode solid-phase diffusion time constant, and the above-mentioned negative electrode solid-phase diffusion time constant, a solid-phase diffusion model for describing the solid-phase diffusion process is constructed.

[0133] Specifically, the influence of solid phase diffusion is considered under the basic working process, and the variation law of the solid phase lithium ion concentration in the radius r direction is established according to the Fick diffusion second law. The description 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 lithium intercalation amount and the surface lithium intercalation amount of the positive and negative electrodes.

[0134] Specifically, the above-mentioned solid phase diffusion model is as follows:

[0135] ;

[0136] ;

[0137]

[0138]

[0139] The liquid phase lithium ion concentration of the positive plate, the liquid phase lithium ion concentration of the negative plate, and the liquid phase lithium ion concentration change amount at the positive and negative electrode current collectors of the above-mentioned lithium ion battery to be tested are obtained, so as to construct a liquid phase diffusion model for describing the liquid phase diffusion process according to the liquid phase lithium ion concentration of the positive plate, the liquid phase lithium ion concentration of the negative plate, and the liquid phase lithium ion concentration change amount at the positive and negative electrode current collectors, the above-mentioned corrected liquid phase diffusion proportionality coefficient, and the above-mentioned liquid phase diffusion time constant.

[0140] Specifically, it is assumed that the liquid phase lithium ion concentration change amount at the positive and negative electrode current collectors is the same, and there is a gradient distribution of the liquid phase lithium ion concentration in the l direction of the positive and negative plates, which leads to the generation of polarization overpotential. The liquid phase diffusion proportionality coefficient and the liquid phase diffusion time constant are used to describe the concentration polarization effect, which reflects the polarization degree and the length of time for establishing the concentration difference.

[0141] Specifically, the above-mentioned liquid phase diffusion model is as follows:

[0142]

[0143] ;

[0144] According to the above-mentioned quality and the above-mentioned corrected reaction polarization coefficient, a reaction polarization model for describing the reaction polarization effect is constructed.

[0145] Specifically, it is assumed that the reaction rate and the active particle radius are the same, and the functional relationship of the reaction polarization overpotential and the reaction polarization coefficient can be obtained, which reflects the difficulty of the electrochemical reaction inside the battery.

[0146] Specifically, the above-mentioned reaction polarization model is as follows:

[0147]

[0148]

[0149]

[0150] According to the first internal resistance of the to-be-tested lithium ion battery after the above correction, an ohmic polarization model for describing ohmic polarization is constructed.

[0151] Specifically, the calculation of the ohmic polarization overpotential follows Ohm's law. Without considering the temperature influence, the battery terminal voltage can be calculated by the open-circuit voltage and the three-part polarization overpotential.

[0152] Specifically, the ohmic polarization model is as follows:

[0153]

[0154]

[0155] In the formula, represents the ohmic polarization overpotential, represents the first internal resistance, and the unit is Ω, represents the battery terminal voltage, represents the battery open-circuit potential, represents the concentration polarization overpotential.

[0156] According to the first internal resistance of the to-be-tested lithium ion battery after the above correction and the battery temperature at each time, a heat generation model for describing the internal heat generation behavior of the battery is constructed.

[0157] Specifically, assuming that the temperature distribution of each point in the battery is uniform, the heat release due to the internal resistance of the battery and the heat generation accompanying the continuous reactions in the internal are regarded as the main factors, and other secondary factors are ignored. The heat generation amount (heat generation rate) based on the Bernardi heat generation model during charging and discharging time can be obtained.

[0158] Specifically, the heat generation model is as follows:

[0159]

[0160]

[0161] In the formula, q represents the heat generation rate, Q represents the total heat generation amount of the battery, represents the reversible heat, represents the irreversible heat, and t represents the charging and discharging time.

[0162] According to the battery temperature at each time and the equivalent specific heat capacity of the battery, a temperature model for describing the battery temperature is constructed. ​​

[0163] Specifically, the temperature model is shown in the following formula:

[0164] ;

[0165]

[0166]

[0167] In the formula, represents the material density, C represents the specific heat capacity, λ represents the thermal conductivity, represents the net heat generation rate per unit volume, represents the battery density, represents the heat dissipation rate, V represents the battery volume, represents the heat dissipation thermal resistance of the battery to the environment.

[0168] According to the above surface area, the relationship expression of the convective heat transfer coefficient and the heat dissipation thermal resistance of the battery to the environment is constructed;

[0169] As shown in the schematic diagram Figure 5 , the heat exchange between the battery and the cooling medium is carried out by heat convection, and the convective heat transfer coefficient determines the heat exchange amount, which is related to the flow rate of the cooling medium. The corresponding empirical formula is shown in the following formula:

[0170] ;

[0171] In the formula, μ represents the air viscosity, v represents the air speed, represents the characteristic length of the channel, represents the air thermal conductivity, represents the air Prandtl number, represents the air density.

[0172] Specifically, the relationship expression is shown in the following formula:

[0173] ;

[0174] The reference temperature is obtained, and the correction equation is constructed according to the reference temperature and the battery temperature at each time point.

[0175] Specifically, since the change of the battery internal temperature will have a certain influence on the battery internal process, the temperature needs to be used to correct the electrochemical behavior of the battery.

[0176] 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 proportion coefficient and the second internal resistance will be affected by the temperature, and this influence obeys the Arrhenius formula in exponential form, therefore, the correction equation for correcting them is shown in the following formula:

[0177]

[0178] Specifically, the concentration polarization overpotential and the reaction polarization overpotential are affected by temperature, and are temperature-dependent functions.

[0179] Specifically, the open-circuit potential is affected by temperature, and is considered to be modified by the Nernst equation, so the modified equation of the open-circuit potential is as follows:

[0180]

[0181] According to the above battery operation process model, the above solid-phase diffusion model, the above liquid-phase diffusion model, the above reaction polarization model, the above ohmic polarization model, the above heat generation model, the above temperature model, the above relationship expression and the above modified equation, the above coupling model is obtained, and the input of the model is the current, and the output is the battery voltage and the temperature.

[0182] In this preferred embodiment, by constructing the battery operation 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 modified equation, the coupling model is obtained.

[0183] In another preferred embodiment, the construction of the state equation of the internal resistance and the observation equation of the internal resistance comprises:

[0184] According to the modified internal resistance at the last moment and the process noise at the current moment, the above state equation of the internal resistance is constructed;

[0185] According to the modified internal resistance at the last moment and the observation noise at the current moment, the above observation equation of the internal resistance is constructed.

[0186] Specifically, the Kalman filter algorithm is a mathematical method for state estimation, which was first proposed by Rudolf E. Kalman in 1960. This 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 realize 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, which is suitable for nonlinear system state estimation problems. Unlike the standard Kalman filter, which is based on linear system dynamic models and measurement models, EKF is mainly suitable for nonlinear system models.

[0187] Specifically, the above state equation is as follows: ​

[0188]

[0189] wherein, represents the internal resistance corrected at the last time, represents the internal resistance state equation corrected at the last time, represents the process noise of the state equation at the current time, represents the estimated value of the internal resistance at the current time.

[0190] The above state equation is linearized based on the Taylor expansion method to obtain the following state equation:

[0191]

[0192] wherein, represents the internal resistance estimated value at the last time.

[0193] After arrangement, the following state equation is obtained:

[0194]

[0195] wherein, represents the state transition matrix.

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

[0197]

[0198] wherein, represents the observation noise of the above observation equation, represents the observation value of the terminal voltage at the current time, represents the observation equation of the terminal voltage at the current time.

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

[0200]

[0201] After arrangement, the following observation equation is obtained:

[0202]

[0203] wherein, represents the observation matrix.

[0204] In this preferred embodiment, the state equation and the observation equation of the internal resistance are constructed according to the internal resistance corrected at the last time, the process noise at the current time, and the observation noise at the current time.

[0205] ​​​​In another preferred embodiment, the state equation of the entropy coefficient and the construction of the observation equation of the entropy coefficient include:

[0206] According to the entropy coefficient corrected at the last moment and the process noise at the current moment, the state equation of the above-mentioned internal resistance is constructed.

[0207] According to the entropy coefficient corrected at the last moment and the observation noise at the current moment, the observation equation of the above-mentioned internal resistance is constructed.

[0208] Specifically, the above-mentioned state equation is as follows:

[0209]

[0210] In the formula, indicates the entropy coefficient corrected at the last moment, indicates the estimated value of the entropy coefficient at the current moment.

[0211] The above-mentioned state equation is linearized based on the Taylor expansion method to obtain the following state equation formula:

[0212]

[0213] In the formula, indicates the state equation of the entropy coefficient.

[0214] After arrangement, the following state equation formula is obtained:

[0215] ;

[0216] Specifically, the above-mentioned observation equation is as follows:

[0217]

[0218] In the formula, indicates the battery temperature at the current moment, indicates the observation equation of the battery temperature at the current moment.

[0219] The above-mentioned observation equation is linearized based on the Taylor expansion method to obtain the following observation equation formula:

[0220] ;

[0221] After arrangement, the following observation equation formula is obtained:

[0222] .

[0223] In this preferred embodiment, the entropy coefficient corrected at the last moment, the process noise at the current moment and the observation noise at the current moment are constructed to obtain the state equation and the observation equation of the entropy coefficient.

[0224] Step S102: obtaining the internal resistance and the entropy coefficient corrected at the last time, and calculating the estimated value of the internal resistance and the entropy coefficient at the current time according to the state equation of the internal resistance, the state equation of the entropy coefficient, the internal resistance and the entropy coefficient corrected at the last time.

[0225] Specifically, the estimated value of the internal resistance and the entropy coefficient at the current time is calculated according to the following formula:

[0226] ;

[0227] ;

[0228] In the formula, represents the estimated value of the internal resistance at the current time, represents the estimated value of the entropy coefficient at the current time, represents the internal resistance corrected at the last time, represents the entropy coefficient corrected at the last time, represents the state equation of the internal resistance at the current time, represents the state equation of the entropy coefficient at the current time.

[0229] In this step, the estimated value of the internal resistance and the entropy coefficient at the current time is calculated according to the state equation of the internal resistance, the state equation of the entropy coefficient, the internal resistance and the entropy coefficient corrected at the last time.

[0230] Step S103: calculating the error covariance matrix of the internal resistance and the entropy coefficient at the current time according to the error covariance matrix corrected at the last time and the process noise covariance matrix at the current time; wherein the current at the last time is obtained, and the error covariance matrix corrected at the last time is calculated according to the current at the last time and the coupling model.

[0231] Specifically, the error covariance matrix of the internal resistance and the entropy coefficient at the current time is calculated according to the following formula:

[0232]

[0233] In the formula, represents the error covariance matrix at the current time, represents the process noise covariance matrix at the current time, represents the error covariance matrix corrected at the last time.

[0234] Specifically, the error covariance matrix corrected at the last time is calculated by the following formula:

[0235] ;

[0236] ;

[0237] ;

[0238] wherein, denotes the Kalman gain matrix of the previous time, denotes the covariance matrix prior value of the previous time, denotes the observation matrix of the previous time.

[0239] In this step, the error covariance matrix of the internal resistance and the entropy coefficient at the current time is calculated by the error covariance matrix of the previous time after correction and the process noise covariance matrix at the current time.

[0240] Step S104: the Kalman gain matrix of the internal resistance and the entropy coefficient at the current time is calculated according to the coupling model and the error covariance matrix of the internal resistance and the entropy coefficient at the current time.

[0241] Specifically, the Kalman gain matrix of the internal resistance and the entropy coefficient at the current time is calculated according to the following formula:

[0242] ;

[0243]

[0244] ;

[0245] wherein, Uapp(·) is the equation group for calculating the terminal voltage in the coupling model, denotes the observation noise covariance matrix at the current time, denotes the Kalman gain matrix at the current time.

[0246] Specifically, the equation group for calculating the terminal voltage in the coupling model is:

[0247]

[0248]

[0249]

[0250]

[0251]

[0252]

[0253]

[0254]

[0255]

[0256]

[0257]

[0258]

[0259]

[0260] ;

[0261] In this step, the Kalman gain matrix of the internal resistance and the entropy coefficient at the current time is calculated by coupling the model and the error covariance matrix of the internal resistance and the entropy coefficient at the current time.

[0262] Step S105: 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 time and the observation value of the entropy coefficient at the current time, the observation error of the internal resistance and the entropy coefficient is calculated.

[0263] Specifically, the observation error of the internal resistance and the entropy coefficient is calculated according to the following formula:

[0264]

[0265]

[0266] In the formula, The observation error of the internal resistance is represented by r, The observation error of the entropy coefficient is represented by s, The observation value of the terminal voltage is represented by V, The observation value of the temperature is represented by T, and T(·) is the equation group for calculating the temperature in the coupling model.

[0267] Specifically, the equation group for calculating the temperature in the coupling model is:

[0268]

[0269]

[0270]

[0271] ;

[0272] In this step, the observation error of the internal resistance and the entropy coefficient is calculated by the observation equation of the internal resistance, the observation equation of the entropy coefficient, the observation value of the internal resistance at the current time and the observation value of the entropy coefficient at the current time.

[0273] Step S106: 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 corrected internal resistance and the entropy coefficient at the current time are calculated.

[0274] Specifically, the corrected internal resistance and the entropy coefficient at the current time are calculated according to the following formula:

[0275]

[0276]

[0277] In the formula, the corrected internal resistance is represented by The corrected entropy coefficient is represented by

[0278] In this step, the corrected internal resistance and the entropy coefficient at the current time are calculated by 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.

[0279] Step S107: the heat generation power of the battery at the current time is calculated according to the corrected entropy coefficient.

[0280] Specifically, the heat generation power is calculated according to the following formula:

[0281]

[0282]

[0283] In this step, the heat generation power of the battery at the current time is calculated according to the corrected entropy coefficient.

[0284] In another preferred embodiment, when the current time is the initial time, the entropy coefficient corresponding to the initial time is obtained according to the coupling model;

[0285] According to the first internal resistance and the entropy coefficient corresponding to the initial time, the heat generation power at the current time is calculated.

[0286] In this embodiment, the heat generation power at the initial time is calculated by the first internal resistance and the entropy coefficient corresponding to the initial time.

[0287] In a preferred embodiment, in order to specifically illustrate the method used in the present application, the internal resistance and heat generation power estimation process of a 50Ah type lithium iron phosphate battery is used as a case, and the specific parameters of the battery are shown in the following table:

[0288] Battery parameter table

[0289] ​

[0290] The experiment takes 25 DEG C ambient temperature and 1C rate discharging of the battery as the working condition.

[0291] The average absolute error and simulation time of the battery are shown in the following table:

[0292] The average absolute error and simulation time of the battery

[0293]

[0294] The comparison curve of the observed value and the simulation value of the terminal voltage is shown in Figure 6 The comparison curve of the observed value and the simulation value of the temperature is shown in Figure 7 The simulation curve of the internal resistance of the battery is shown in Figure 8 The simulation curve of the heat generation power of the battery is shown in Figure 9 The simulation results show that: for the square lithium iron phosphate battery, the electro-thermal-fluid coupling model of the embodiment can realize that the average absolute error of the terminal voltage and temperature simulation is not more than 0.5 mV and 0.1 DEG C respectively, and the simulation time of each data is not more than 9.152 ms. Therefore, the application can realize accurate and efficient simulation of the internal and external characteristics of the battery including the internal resistance and the heat generation power.

[0295] On the basis of the above-mentioned method embodiment, the application provides a device embodiment.

[0296] As shown in Figure 10 An embodiment of the application provides an online heat generation power estimation device for the internal resistance of a lithium ion battery, which comprises a model and equation acquisition module, an estimation 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 heat generation power calculation module.

[0297] The model and equation acquisition module is used to acquire 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 at the current time which is not the initial time.

[0298] The estimation value calculation module is used to acquire the corrected internal resistance and entropy coefficient at the last time, and calculate the estimation value of the internal resistance and the entropy coefficient at the current time according to the state equation of the internal resistance, the state equation of the entropy coefficient, the corrected internal resistance and entropy coefficient at the last time.

[0299] The error covariance matrix calculation module is configured to calculate the error covariance matrix of the internal resistance and the entropy coefficient at the current moment according to the error covariance matrix after the correction 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 after the correction at the previous moment is calculated according to the current at the previous moment and the coupling model.

[0300] The Kalman gain matrix calculation module is configured 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.

[0301] The observation error calculation module is configured to calculate the observation error 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.

[0302] The internal resistance and entropy coefficient correction module is configured to calculate the internal resistance and the entropy coefficient after the correction 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.

[0303] The heat power calculation module is configured to calculate the heat power of the battery at the current moment according to the corrected entropy coefficient.

[0304] It should be noted that the apparatus embodiments described above are merely illustrative, wherein the modules described above as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, i.e., they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the apparatus embodiment provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor. The above schematic diagram is only an example of the lithium ion battery internal resistance heat power online estimation device, and does not constitute a limitation on the lithium ion battery internal resistance heat power online estimation device, which can include more or fewer components than the diagram, or combine certain components, or different components.

[0305] On the basis of the above-mentioned method embodiment, the present application correspondingly provides terminal device embodiments.

[0306] Another embodiment of the present application provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the method for estimating the heat power of the internal resistance of a lithium ion battery in any one of the embodiments of the present application when executing the computer program.

[0307] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present application. The one or more modules can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the device.

[0308] The terminal device can be a desktop computer, a notebook computer, a palm computer, a cloud server, or other computing devices. The device can include, but is not limited to, a processor and a memory.

[0309] The processor can be a central processing module (CPU), and can also be 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 can be a microprocessor or any conventional processor, etc. The processor is the control center of the device, which connects various parts of the device through various interfaces and lines.

[0310] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the device by running or executing the computer program and / or modules stored in the memory, and calling the data stored in the memory. The 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 by 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 media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0311] On the basis of the method embodiment, the application provides a storage medium embodiment.

[0312] Another embodiment of the application provides a storage medium, which comprises a stored computer program, wherein the computer program controls a device where the storage medium is located to perform the online estimation method of the heat generation power of the internal resistance of the lithium ion battery according to any one of the embodiments of the application when the computer program is running.

[0313] In this embodiment, the storage medium is a computer readable storage medium, the computer program comprises computer program codes, and the computer program codes can be in the form of source codes, object codes, executable files or some intermediate forms. The computer readable medium can include any entity or device capable of carrying the computer program codes, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier wave signal, telecommunication signal and software distribution medium.

[0314] Compared with the prior art, the heat generation power of the lithium ion battery can be online estimated based on the corrected entropy coefficient by implementing the embodiments of the application.

[0315] The above is the preferred embodiment of the application, and it should be pointed out that, for those skilled in the art, some improvements and refinements can be made without departing from the principles of the application, and these improvements and refinements are also regarded as the protection scope of the application.

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; The error covariance matrix of the internal resistance and the entropy coefficient at the current moment is calculated based on 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 based on 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; Calculate the observation errors of the internal resistance and the entropy coefficient based on 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 corrected internal resistance and entropy coefficient at the current moment are calculated based on 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 corrected internal resistance and entropy coefficient at the current moment are calculated according to the following formula: Where, Indicates the corrected internal resistance, represents the corrected entropy coefficient, Represents the estimated value of the internal resistance at the current moment, represents the estimated value of the entropy coefficient at the current moment, represents the Kalman gain matrix of the internal resistance at the current moment, The Kalman gain matrix representing the entropy coefficient at the current moment, represents the observation error of the internal resistance, represents the observation error of the entropy coefficient, T represents the battery temperature; The heat generation power of the battery at the current moment is calculated based on the corrected entropy coefficient. The heat generation power of the battery is calculated based on the following formula: In the formula, q represents the heat generation rate, Q represents the total heat generation of the battery, t represents the charge and discharge time, I represents the current, represents the open circuit potential of the battery, E represents the corrected open circuit potential, Indicates the battery terminal voltage, represents the reversible heat, Represents irreversible heat.

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 the battery temperature at each moment; The coupling model is constructed according to the plurality of voltage mutation amounts, the shelf voltage values, 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 constructing of the coupling model according to the plurality of voltage mutation amounts, the shelf voltage values, 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: Calculating 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 according to the shelf voltage value; Calculating, based on 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; Calculating, based on 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 at each of the battery temperatures; Calculating a 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; Calculate the 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 based on the battery temperatures, the reaction polarization coefficients, the positive electrode solid-phase diffusion time constant, the negative electrode solid-phase diffusion time constant, and the liquid-phase diffusion proportional coefficient; 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 based on the surface area, the temperature of each battery and the mass; A coupled model of the lithium-ion battery to be tested is constructed based on the battery temperature at each moment, the surface area, the mass, the positive electrode initial lithium insertion concentration fraction, the negative electrode initial lithium insertion concentration fraction, 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.

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 coupled model of the lithium-ion battery to be tested based on the battery temperature at each moment, the surface area, the mass, the positive electrode initial lithium insertion concentration fraction, the negative electrode initial lithium insertion concentration fraction, 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 includes: Constructing a battery operating process model for describing a battery operating 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 a 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, the negative electrode capacity, the positive electrode solid-phase diffusion time constant, and the negative electrode solid-phase diffusion time constant; Obtaining the positive electrode plate liquid phase lithium ion concentration, the negative electrode 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 lithium ion battery to be tested, and constructing a liquid phase diffusion model for describing the liquid phase diffusion process based on the positive electrode plate liquid phase lithium ion concentration, the negative electrode plate liquid phase lithium ion concentration, and the change in 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 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; Based on 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; Obtaining a reference temperature, and constructing a correction equation based on 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 relational 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 based on the internal resistance corrected at the previous moment and the process noise at the current moment; An observation equation of the internal resistance is constructed based on 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 construction of the state equation of the entropy coefficient and the observation equation of the entropy coefficient includes: Constructing a state equation of the internal resistance based on the corrected entropy coefficient at the previous moment and the process noise at the current moment; An observation equation of the internal resistance is constructed based on 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 based on the first internal resistance and the entropy coefficient corresponding to the initial moment.

8. A device for online estimation of heating power of internal resistance of lithium-ion battery, characterized in that: A method for online estimating the heating power of the internal resistance of a lithium-ion battery according to any one of claims 1 to 7, comprising: 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 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; 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 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; 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 based on 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 based on 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 entropy coefficient at the current moment based on the coupling model and the error covariance matrix of the internal resistance and 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 based on 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 based on 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; wherein the corrected internal resistance and entropy coefficient at the current moment are calculated according to the following formula: Where, Indicates the corrected internal resistance, represents the corrected entropy coefficient, Represents the estimated value of the internal resistance at the current moment, represents the estimated value of the entropy coefficient at the current moment, represents the Kalman gain matrix of the internal resistance at the current moment, The Kalman gain matrix representing the entropy coefficient at the current moment, represents the observation error of the internal resistance, represents the observation error of the entropy coefficient, T represents the battery temperature; 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; wherein the heating power of the battery is calculated according to the following formula: In the formula, q represents the heat generation rate, Q represents the total heat generation of the battery, t represents the charge and discharge time, I represents the current, represents the open circuit potential of the battery, E represents the corrected open circuit potential, Indicates the battery terminal voltage, represents the reversible heat, Represents irreversible heat.

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, the method for online estimation of the heating power of the internal resistance of a lithium-ion battery according to 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 heating power of the internal resistance of a lithium-ion battery according to any one of claims 1 to 7.

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