Battery heating method and system based on low temperature heating and global temperature monitoring

Abstract

The application relates to a battery heating method and system based on low-temperature heating and global temperature monitoring, and belongs to the technical field of batteries. The method comprises the following steps: establishing an internal high-resolution thermal network of a battery monomer and a module thermal network structure which can be self-adaptively changed according to the structure of a battery and a battery module; establishing a battery system high-frequency working condition adaptive electric-thermal model and a heating current boundary condition limited by comprehensive factors based on the model; formulating a battery heating strategy and execution specification according to a heating boundary and a real-time temperature monitoring system; and embedding the above method and strategy into a core processor of a high-frequency heating device with partitions. The application can realize self-adaptive change according to the physical side structure, and does not need to independently and repeatedly model all specific battery systems; in the control device for heating, the power consumption of the processor is reduced by using partition storage and a battery thermal network structure mode.

Description

Technical Field

[0001] This invention belongs to the field of battery technology and relates to a battery heating method and system based on low-temperature heating and global temperature monitoring. Background Technology

[0002] Lithium-ion batteries, as a carrier of clean energy with high energy density, are now widely used in the field of new energy vehicles. At room temperature, lithium-ion batteries exhibit superior power and lifespan characteristics compared to traditional batteries. However, they still experience significant energy loss and reduced workability at low temperatures. Forcing the battery into operation at low temperatures may lead to the formation of large amounts of deposited lithium within the battery, causing an irreversible decrease in usable energy. When lithium dendrites grow to a certain extent, they can puncture the separator, causing a short circuit and potentially leading to thermal runaway and other accidents. Therefore, to achieve normal, non-aging use of lithium-ion batteries at low temperatures, the common practice is to preheat the battery at a low temperature and then allow it to reach a suitable temperature range before normal use.

[0003] Currently, several feasible low-temperature heating solutions have been developed for lithium-ion batteries, such as low-temperature high-frequency heating. However, in practical applications, the following problems still exist: 1) At the algorithm level, there is a lack of battery models adapted to low-temperature high-frequency operating conditions, which makes it difficult to obtain accurate temperature information when executing heating strategies on the hardware side; 2) At the algorithm level, battery models that are not adapted to low-temperature high-frequency operating conditions cannot provide reasonable low-temperature heating current boundaries; 3) At the hardware level, there is a lack of means to obtain the internal temperature of the battery in real time, which will ultimately lead to errors in the heating control process.

[0004] Therefore, this invention addresses the aforementioned application problems by developing a low-temperature high-frequency heating specification and device for battery systems with built-in low-temperature heating strategies and global temperature monitoring methods at both the algorithm and hardware levels, thereby achieving a healthy and efficient heating process for batteries under low-temperature conditions. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a battery heating method and system based on low-temperature heating and global temperature monitoring.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A battery heating method based on low-temperature heating and global temperature monitoring includes the following steps:

[0008] S1: Develop the battery heating process;

[0009] S2: Execute the battery heating process;

[0010] S1 includes the following steps:

[0011] S101: Establish a second-order RQ model of the battery and construct the battery model parameter basis;

[0012] The second-order RQ model of the battery includes the ohmic internal resistance of the battery, the polarization internal resistance of the SEI film, the polarization capacitance of the SEI film, the polarization internal resistance of charge transfer, and the polarization capacitance of charge transfer, which are tested by electrochemical impedance spectroscopy.

[0013] S102: Calculate the heating current boundary of the battery under different temperatures, different SOCs, and different current frequencies;

[0014] The heating current boundary of a battery generally includes a heating current boundary determined according to the lithium plating criterion, and a current boundary determined according to the voltage limit; wherein, the form of the lithium plating boundary needs to be matched with the corresponding specific form according to the second-order RQ model in this invention.

[0015] S103: Construct a method for calculating the heat generation of a battery under different temperatures, different SOCs, and different pulse current characteristic parameters;

[0016] Using the established second-order RQ model, an expression for the real part of the total battery impedance is given, and the effective heat generation rate of the battery under sinusoidal pulsed alternating current is calculated.

[0017] S104: Based on the battery's heating boundary, heat generation, and lumped parameter thermal model, obtain the battery's temperature rise curve and total heating time.

[0018] The heating time is calculated as follows: First, the initial conditions of the battery are selected, including the initial SOC and the initial temperature. Then, the initial conditions and the heat generation of the battery at t=0 are entered into the lumped parameter model. The temperature of the battery at t=1 is calculated, and so on. During the calculation process, the heating current parameters are continuously updated through the heating current boundary and the calculated temperature to obtain the total heating time of the battery under the determined initial conditions.

[0019] S105: Based on the heating time under different initial conditions, a heating time map is generated;

[0020] In S101, the second-order RQ model is expressed as follows:

[0021]

[0022] U ocv U represents the open-circuit voltage of the battery, L represents the inductive reactance of the battery, I represents the current, t represents time, and U represents the open-circuit voltage of the battery. t R represents the battery's terminal voltage. o U represents the ohmic internal resistance of the battery. ct This refers to the voltage applied across the polarization resistor, R. ct And Cct This represents the charge transfer internal resistance and charge transfer capacitance, C. sei,FOM n represents the constant-phase element coefficient associated with the SEI film. sei,FOM C represents the order of the constant-phase element associated with the SEI film. dl,FOM n represents the constant-phase element coefficient related to charge transfer in the electric double layer. dl,FOM Indicates the order of the constant-phase element associated with charge transfer in the electric double layer;

[0023] S2 includes the following steps:

[0024] S201: Calculate the total resistance of the battery during the heating process based on the input current and voltage of the battery;

[0025] S202: Calculate the SOC value of the battery during the heating process based on the battery input current and voltage;

[0026] S203: Establish the thermal network structure of the battery, and calculate the internal temperature of the battery by combining the internal resistance identified in S201 and the current parameters at the current moment during the heating process.

[0027] S204: Based on the battery temperature calculation results and the current boundary in S102, the heating current parameters are corrected in real time.

[0028] Optionally, in step S102, the heating current boundaries at different temperatures, different states of charge (SOC), and different current frequencies are obtained through the following steps:

[0029] S1021: Establish general lithium plating suppression conditions for batteries;

[0030]

[0031] in, Φ represents the lithium plating reaction potential. neg This represents the negative electrode potential; the equilibrium potential for the lithium plating reaction is assumed to be 0V within the battery system, and the lithium plating suppression condition is:

[0032] Φ neg ≥0V

[0033] S1022: Update the description of the new lithium plating suppression conditions under the second-order RQ model.

[0034] In the second-order RQ model, the negative electrode potential is represented as the difference between the negative electrode equilibrium potential and the charge transfer polarization partial voltage. Therefore, the lithium plating suppression condition is expressed as:

[0035] V ct ≤OCP neg

[0036] S1023: Corrected impedance expression under high-frequency pulses;

[0037] For the SEI membrane-related RQ circuit, there are:

[0038]

[0039] For RQ loops related to charge transfer, we have:

[0040]

[0041] For a second-order RQ circuit model, its total impedance is expressed as:

[0042] Z2 = R o +Z Re,ct +Z Re,SEI +j(Z Im,ct +Z Im,SEI +2πfL)

[0043] S1024: Based on the results of S1022 and S1023, the heating current boundary based on the lithium plating suppression condition is given;

[0044] V ct Expressions for converting to the magnitudes of current and impedance:

[0045] I ac |Z ct |≤OCP neg

[0046] S1025: The heating current boundary at different temperatures, different states of charge (SOC), and different current frequencies is as follows:

[0047]

[0048] The maximum current amplitude at any given time is:

[0049]

[0050] Optionally, in S103, the method for calculating the heat generation of the battery under different temperatures, different SOCs, and different pulse current characteristic parameters is as follows:

[0051] S1031: Establishing the expression for the real impedance of the battery:

[0052]

[0053] S1032: Considering the effective current of the AC pulse, establish the total heat generation of the battery:

[0054]

[0055] Optionally, in S104, the method for obtaining the battery temperature rise curve and total heating time based on the battery's heating boundary, heat generation, and lumped parameter thermal model is as follows:

[0056] Treating the battery as a lumped-parameter thermal model, using two temperature nodes to represent the battery temperature, then we have:

[0057]

[0058]

[0059] T in T represents the lumped temperature parameter of the battery body. surf The temperature T represents the surface temperature node of the battery. a R represents the ambient temperature. in-surf To characterize the thermal resistance between two nodes of a battery, R surf-a This represents the thermal resistance between the battery surface and the environment; m1 is the mass represented by the core node, and m2 is the mass represented by the surface node.

[0060] Given initial conditions, calculate the temperature rise of the battery;

[0061] In step S105, a heating time map is formed based on the heating time under different initial conditions.

[0062] Optionally, the specific steps of S201 are as follows:

[0063] Based on the established second-order RQ model of the battery, the system matrix and the parameter matrix to be identified of the battery are derived.

[0064] The apparent input-output relationship of the battery is as follows:

[0065] u k =b1u k-1 +b2u k-2 +b3I k +b4I k-1 +b5I k-1

[0066] Among them, u k I represents the system's output value at time k, i.e., the voltage. k Let be the input value of the system at time k, i.e., the current; b represents the coefficient between the input and output.

[0067] make

[0068] Φ(k)=[u k-1 u k-2 I k I k-1 I k-2 ]

[0069] θ(k) = [b1b2b3b4b5] T

[0070] The method for calculating the parameter to be measured is as follows:

[0071]

[0072] Where P(k) is the covariance of Φ(k), and V t P(k) represents the measured terminal voltage at time k; additionally, P(k) is obtained iteratively using the following formula by setting an initial value:

[0073]

[0074] In the formula, λ is the forgetting factor, which is taken as 0.9998; the final relationship between θ(k) identified in real time at each moment and the parameters of the second-order RQ is:

[0075]

[0076] The orders of the two fractional-order circuit components in the second-order RQ model were obtained through experimental testing.

[0077] Optionally, the specific steps of S202 are as follows:

[0078] In the second-order RQ model, the time-domain recursive form of the battery's polarization voltage is:

[0079]

[0080] The system equations and observation equations for the battery are as follows:

[0081]

[0082]

[0083] in:

[0084]

[0085]

[0086] After establishing the specific form of the parameter matrix for SOC estimation under the second-order RQ model, the Kalman filtering method is used to estimate the SOC.

[0087] Optionally, the specific steps in S203 are as follows:

[0088] S2031: Establish the basic heat exchange unit of the battery thermal network, which is represented as follows:

[0089]

[0090] in

[0091] F(A)| A =1-C(K(A) x1 )| A +K(A x2 )| A +K(A y1 )| A +K(A y2 )| A +K(A z1 )| A +K(A z2 )| A )

[0092] F(A x1 )| A =C·K(A x1 )| A

[0093] F(A x2 )| A =v·K(A x2 )| A

[0094] F(A y1 )| A =C·K(A y1 )| A

[0095] F(A y2 )| A =C·K(A y2 )| A

[0096] F(A z1 )| A =C·K(A z1 )| A

[0097] F(A z2 )| A =C·K(A z2 )| A

[0098] The above formula represents the heat transfer relationship between a node in a battery thermal network and its adjacent nodes, where K(·) represents the heat transfer coefficient of the corresponding heat transfer form in the corresponding direction, and is the thermal conductivity corresponding to heat conduction or the convective heat transfer coefficient corresponding to heat convection; A x1 The specific form of the direction K(·) is expressed as:

[0099]

[0100]

[0101] In the formula k x Indicates the relationship between point A and point A in the heat transfer unit. x1 The corresponding thermal conductivity between points A and A2, where x1 represents the thermal conductivity between points A and A3. x1 The distance between points A and A2, x2 represents the distance between points A and A2. x2 The distance between points A and A', h represents the distance between points A and A'. x1 The corresponding convective heat transfer coefficients between them; the heat transfer relationship between point A and the other points and the meaning of their parameters can be deduced similarly;

[0102] S2032; Establish the overall structure of the battery's thermal network, its expression is:

[0103]

[0104] Among them, T A，t+1 This is the battery temperature network at time t+1, where T... A，t It is the battery temperature network at time t. This refers to the set of temperature points on the outer side of the battery that exchange heat with the battery along the x1 direction. arrive The definitions are the same; T A，t and The expression is as follows:

[0105]

[0106]

[0107] Based on the definition of the node heat exchange matrix, the overall heat exchange matrix of the multi-node thermal network of the battery is:

[0108]

[0109]

[0110] The heat generated by the battery, Q A,t Calculations are performed based on the heat-generating formula;

[0111] S2033; Establish a thermal network for the battery inter-cell connection components in the module, and connect it with the thermal network of the adjacent battery cells in the manner described in S2032 to form the overall thermal network of the battery module.

[0112] S2034; Input the online impedance and input current, and calculate the temperature change of the battery during the heating process.

[0113] A battery heating system based on the method, the system comprising:

[0114] The input device 801 is used to receive current and voltage signals from the battery module of the sensor, as well as battery surface temperature signals from the thermocouple, and transmit the data to the storage device via the bus.

[0115] Storage device 802 is used to temporarily store the input parameters of the battery module. When the processor needs to perform calculations, the data is pushed from the bottom of the stack into the corresponding partition of the processor.

[0116] The processor 803 is used to calculate the current electro-thermal state parameters of the battery module, provide a reasonable heating strategy, and generate control signals;

[0117] The output device 804 transmits the control signal of the heating current generated during processing to the external hardware to control the change of the heating current.

[0118] Optionally, the processor 803 includes:

[0119] Thermal network node heat transfer matrix storage partition 8031 ​​stores the matrix of basic heat exchange units of the battery thermal network;

[0120] The individual cell thermal network forms partition 8032, establishing the thermal network of a specific cell in the battery module;

[0121] The inter-battery node thermal network forms partition 8033, establishing the thermal network of the inter-battery connection part in the module;

[0122] Heating strategy and heating current boundary storage partition 8034 stores heating strategies designed and tested offline and heating current boundaries under different conditions;

[0123] The module temperature distribution calculation and heating current dynamic calculation are divided into 8035. The dynamic temperature field within the battery module is calculated and the current reasonable heating current parameters are given.

[0124] Optionally, the processor 803 is used to invoke program instructions to perform the following operations:

[0125] The current, voltage, and surface temperature of the battery module, which are acquired in real time by the sensor system within the battery module and stored in the memory, are imported into the processor module. The processor adaptively generates the thermal network of the battery module, calculates the impedance and temperature distribution of the battery module in real time, and outputs the most suitable heating strategy for the battery at the current moment.

[0126] The beneficial effects of this invention are as follows:

[0127] (1) The battery system model can adapt to changes in the physical structure, without having to independently and repeatedly model all specific battery systems.

[0128] (2) The lithium plating limit and voltage limit of the battery system are considered at the same time, and combined with online temperature monitoring, the safety of the battery during the low temperature heating process can be significantly improved.

[0129] (3) In the heating control device, the processor's computing power consumption is reduced by using partitioned storage and forming a battery thermal network structure.

[0130] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0131] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0132] Figure 1 A flowchart illustrating a method for obtaining the low-temperature heating current boundary and heating time spectrum of a battery, provided in an embodiment of this application;

[0133] Figure 2 To Figure 1 A flowchart illustrating a further detailed description of step S102;

[0134] Figure 3 A schematic diagram of the heating current boundary provided in an embodiment of this application;

[0135] Figure 4 A schematic diagram of the heating time spectrum provided in the embodiments of this application;

[0136] Figure 5 A schematic diagram illustrating the execution process of the battery heating strategy provided in an embodiment of this application;

[0137] Figure 6 A schematic diagram of a basic heat exchange unit in a thermal network model provided in this application embodiment;

[0138] Figure 7 This is a schematic diagram of the structure of a single battery cell and the connection between batteries in the thermal network model of the battery module provided in the embodiments of this application.

[0139] Figure 8 This is a schematic diagram of a device for online control of a battery heating strategy provided in an embodiment of this application.

[0140] Reference numerals: Input device 801, Storage device 802, Processor 803, Output device 804, Thermal network node heat transfer matrix storage partition 8031, Individual thermal network composition partition 8032, Battery inter-node thermal network composition partition 8033, Heating strategy and heating current boundary storage partition 8034, Module temperature distribution calculation and heating current dynamic calculation partition 8035. Detailed Implementation

[0141] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0142] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0143] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0144] This application provides a scheme for formulating and executing a low-temperature heating strategy for a battery. In the strategy formulation stage, a battery low-temperature adaptation model is used to calculate the optimal heating current frequency and amplitude, forming the battery heating current boundary. Based on the battery model and the heating current boundary, the battery heating time under different initial conditions is calculated, forming a heating time map. In the strategy execution stage, based on the battery current and voltage information obtained at time t, the battery's internal impedance and capacitive reactance at that time are identified online. The battery's state of charge (SOC) at multiple times is obtained based on the current and voltage information and the online identification. The battery's internal temperature is calculated based on the online identified electrical parameters and the thermal network. No thermocouples are needed inside the battery; only real-time current and voltage, as well as the initial temperature conditions, are required to calculate the internal temperature change during the battery heating process.

[0145] like Figure 1 The diagram shown illustrates a process for developing a low-temperature heating strategy according to an embodiment of this application. Exemplarily, the method may include the following steps:

[0146] S101. Establish a second-order RQ model of the battery to build the battery model parameter foundation.

[0147] The second-order RQ model of a battery mainly includes the ohmic internal resistance of the battery, the polarization internal resistance of the SEI film, the polarization capacitance of the SEI film, the polarization internal resistance of charge transfer, and the polarization capacitance of charge transfer. In practice, these can be tested using electrochemical impedance spectroscopy.

[0148] S102. Calculate the heating current boundary of the battery at different temperatures, different SOCs, and different current frequencies.

[0149] The heating current boundary of a battery generally includes a heating current boundary determined according to the lithium plating criterion, and a current boundary determined according to the voltage limit. The form of the lithium plating boundary needs to be matched with the corresponding specific form according to the second-order RQ model of this invention.

[0150] S103. Construct a method for calculating the heat generation of a battery under different temperatures, different SOCs, and different pulse current characteristic parameters.

[0151] Using the established second-order RQ model, an expression for the real part of the battery's total impedance is given, which can be used to calculate the battery's effective heat generation rate under sinusoidal pulsed alternating current.

[0152] S104. Based on the battery's heating boundary, heat generation, and lumped parameter thermal model, obtain the battery's temperature rise curve and total heating time.

[0153] The calculation of heating time mainly consists of two steps. First, the initial conditions of the battery are selected, including the initial SOC and the initial temperature. Then, the initial conditions and the heat generation of the battery at t=0 are entered into the lumped parameter model to calculate the temperature of the battery at t=1. This process is repeated to continuously update the heating current parameters through the heating current boundary and the calculated temperature during the calculation process, and finally the total heating time of the battery under the determined initial conditions is obtained.

[0154] S105. Based on the heating time under different initial conditions, form a heating time map.

[0155] The steps described above will be explained in detail below:

[0156] For example, the second-order RQ model described in step S101 can be expressed as:

[0157]

[0158] U ocv U represents the open-circuit voltage of the battery, L represents the inductive reactance of the battery, I represents the current, t represents time, and U represents the open-circuit voltage of the battery. t R represents the battery's terminal voltage. o This represents the ohmic internal resistance of the battery. U ct This refers to the voltage applied across the polarization resistor, R. ct And C ct This represents the charge transfer internal resistance and charge transfer capacitance, C. sei,FOM n represents the constant-phase element coefficient associated with the SEI film. sei,FOM C represents the order of the constant-phase element associated with the SEI film. dl,FOM n represents the constant-phase element coefficient related to charge transfer in the electric double layer. dl,FOM This indicates the order of the constant-phase element associated with charge transfer in the electric double layer.

[0159] For example, the internal parameters of the battery described above can be determined by electrochemical impedance spectroscopy.

[0160] like Figure 2 As shown, this is for Figure 1 A flowchart illustrating a further detailed description of step S102 is provided. For example, the heating current boundaries at different temperatures, different states of charge (SOC), and different current frequencies described in step S102 can be obtained through the following steps:

[0161] S1021: Establish general lithium plating suppression conditions for batteries.

[0162]

[0163] in, Φ represents the lithium plating reaction potential.neg This represents the negative electrode potential. In practical applications of this invention, considering that the lithium plating reaction potential is not readily available, the expression can be simplified to some extent. Given that the equilibrium potential of the lithium plating reaction is assumed to be 0V within the battery system, the general lithium plating suppression condition is:

[0164] Φ neg ≥0V

[0165] S1022: Update the description of the new lithium plating suppression conditions under the second-order RQ model.

[0166] Based on S1021, the negative electrode potential in the second-order RQ model can be expressed as the difference between the negative electrode equilibrium potential and the charge transfer polarization partial voltage. Therefore, the lithium plating suppression condition can be expressed as:

[0167] V ct ≤OCP neg

[0168] S1023: Corrected impedance expression under high-frequency pulses.

[0169] In this example, the RQ loop related to the SEI membrane is as follows:

[0170]

[0171] For RQ loops related to charge transfer, we have:

[0172]

[0173] For a second-order RQ circuit model, its total impedance can be expressed as:

[0174] Z2 = R o +Z Re,ct +Z Re,SEI +j(Z Im,ct +Z Im,SEI +2πfL)

[0175] S1024: Based on the results of S1022 and S1023, the heating current boundary based on the lithium plating suppression condition is given.

[0176] In this example, V in S1022 ct This can be converted into expressions for the magnitudes of current and impedance:

[0177] I ac |Z ct |≤OCP neg

[0178] S1025: Further considering voltage limitations, the heating current boundaries are finally given for different temperatures, different SOCs, and different current frequencies.

[0179]

[0180] Based on the above conditions, the upper limit of the maximum rated current amplitude at any given time is:

[0181]

[0182] By substituting the specific battery model in S101, the current boundary spectrum of the battery can be given as an example, such as... Figure 3 As shown.

[0183] For example, the heat generation calculation method for the battery constructed in S103 under different temperatures, different SOCs, and different pulse current characteristic parameters is as follows:

[0184] S1031: Establishing the expression for the real impedance of the battery:

[0185]

[0186] S1032: Considering the effective current of the AC pulse, establish the total heat generation of the battery:

[0187]

[0188] For example, in S104, the method for obtaining the battery temperature rise curve and total heating time based on the battery's heating boundary, heat generation, and lumped parameter thermal model is as follows:

[0189] Treating the battery as a lumped-parameter thermal model, using two temperature nodes to represent the battery temperature, then we have:

[0190]

[0191]

[0192] T in T represents the lumped temperature parameter of the battery body. surf The temperature T represents the surface temperature node of the battery. a R represents the ambient temperature. in-surf To characterize the thermal resistance between two nodes of a battery, R surf-a This represents the thermal resistance between the battery surface and the environment. m1 is the mass represented by the core node, and m2 is the mass represented by the surface node.

[0193] Once the initial conditions are given, the temperature rise of the battery can be calculated.

[0194] For example, in S105, a heating time map is formed based on the heating time under different initial conditions. According to the specific battery model in the above example, an exemplary battery heating boundary map is as follows: Figure 4 As shown.

[0195] like Figure 5 The diagram shown is a flowchart illustrating the low-temperature heating strategy execution scheme provided in an embodiment of this application. Exemplarily, the method may include the following steps:

[0196] S201: Calculate the total resistance of the battery during the heating process based on the battery input current and voltage.

[0197] S202: Calculate the SOC value of the battery during the heating process based on the battery input current and voltage.

[0198] It needs to be explained that for a strategy that uses AC pulses as the heating current, the main role of the SOC is not to participate in the calculation of heat generation, but to monitor the overall internal state changes of the battery.

[0199] S203: Establish the thermal network structure of the battery, and calculate the internal temperature of the battery by combining the internal resistance identified in S201 and the current parameters at the current moment during the heating process.

[0200] S204: Based on the battery temperature calculation results and the current boundary in S102, the heating current parameters are corrected in real time.

[0201] For example, the specific steps of S201 are as follows:

[0202] Based on the second-order RQ model of the battery established in S101, the system matrix and the parameter matrix to be identified of the battery are derived.

[0203] The apparent input-output relationship of the battery is as follows:

[0204] u k =b1u k-1 +b2u k-2 +b3I k +b4I k-1 +b5I k-1

[0205] Among them, u k Let I be the system's output value at time k; generally, for a battery, this is the voltage. k Let be the system input value at time k, which is typically the current for a battery. Let b represent the coefficient between the input and output.

[0206] make

[0207] Φ(k)=[u k-1 u k-2 I k I k-1 I k-2 ]

[0208] θ(k) = [b1b2b3b4b5] T

[0209] The method for calculating the parameter to be measured is as follows:

[0210]

[0211] Where P(k) is the covariance of Φ(k), and V t P(k) represents the measured terminal voltage at time k. Additionally, P(k) can be obtained iteratively using the following formula by setting an initial value:

[0212]

[0213] In the formula, λ is the forgetting factor, which can be 0.9998 for example. The final relationship between θ(k) identified in real-time at each time step and the parameters of the second-order RQ is:

[0214]

[0215] In this embodiment, the orders of the two fractional-order circuit components in the second-order RQ model were obtained through experimental testing.

[0216] For example, the specific steps of S202 are as follows:

[0217] In the second-order RQ model of S101, the time-domain recursive form of the battery polarization voltage is as follows:

[0218]

[0219] The system equations and observation equations for the battery are as follows:

[0220]

[0221]

[0222] in:

[0223]

[0224]

[0225] For example, after establishing the specific form of the parameter matrix for SOC estimation under the second-order RQ model, the SOC can be estimated using the Kalman filtering method.

[0226] For example, the specific steps of S203 are as follows:

[0227] S2031: Establish the basic heat exchange unit of the battery thermal network, such as Figure 6 As shown, this heat exchange unit can be represented as:

[0228]

[0229] in

[0230] F(A)| A =1-C(K(A) x1 )| A +K(A x2 )| A +K(A y1 )| A +K(A y2 )| A +K(A z1 )| A +K(A z2 )| A )

[0231] F(A x1 )| A =C·K(A x1 )| A

[0232] F(A x2 )| A =C·K(A x2 )| A

[0233] F(A y1 )| A =C·K(A y1 )| A

[0234] F(A y2 )| A =C·K(A y2 )| A

[0235] F(A z1 )| A =C·K(A z1 )| A

[0236] F(A z2 )| A =C·K(A z2 )| A

[0237] The above equation represents the heat transfer relationship between a battery thermal network node and its adjacent nodes, where K(·) represents the heat transfer coefficient of the corresponding heat transfer form in the corresponding direction. Depending on the specific application environment, it can be the thermal conductivity corresponding to heat conduction, or the convective heat transfer coefficient corresponding to heat convection. For example, A x1The specific form of the direction K(·) can be expressed as:

[0238]

[0239]

[0240] In the formula k x Indicates the relationship between point A and point A in the heat transfer unit. x1 The corresponding thermal conductivity between points A and A2, where x1 represents the thermal conductivity between points A and A3. x1 The distance between points A and A2, x2 represents the distance between points A and A2. x2 The distance between points A and A', h represents the distance between points A and A'. x1 The corresponding convective heat transfer coefficients between them. The heat transfer relationship between point A and the other points, and the meaning of their parameters, can be deduced similarly.

[0241] S2032; For example, in most cases, the battery's thermal network contains more than one node. This step establishes the overall structure of the battery's thermal network, expressed as follows:

[0242]

[0243] Among them, T A，t+1 This is the battery temperature network at time t+1, where T... A，t It is the battery temperature network at time t. This refers to the set of temperature points on the outer side of the battery that exchange heat with the battery along the x1 direction. Similarly... arrive The definitions are similar. Specifically, T A，t and The expression is as follows:

[0244]

[0245]

[0246] Based on the definition of the node heat exchange matrix in S2031, the overall heat exchange matrix of the multi-node thermal network of the battery is as follows:

[0247]

[0248]

[0249] Additionally, for example, the heat generated by the battery, Q A，t The calculation can be performed based on the heat generation formula in S1032.

[0250] S2033; For example, using a method similar to S2032, a thermal network of the battery interconnection components in the module is established, and connected with the thermal network of the adjacent battery cells in the manner described in S2032 to form the overall thermal network of the battery module.

[0251] For example, the thermal network between the battery body and the battery connection components is as follows: Figure 7 As shown.

[0252] S2034; For example, by inputting the online identified impedance and the input current, the initial temperature can be used to calculate the temperature change of the battery during the heating process.

[0253] Based on the same concept as the above-described method for monitoring parameters during the low-temperature heating process of batteries, this application also provides a control device for the low-temperature high-frequency heating process of a battery system. Some or all of the above methods can be implemented through software or firmware. For example... Figure 8 The diagram shown is a structural schematic of a battery temperature acquisition device 800 provided in an embodiment of this application. This device is used to perform the aforementioned low-temperature high-frequency heating process control of the battery system. Specifically, the device 800 includes:

[0254] Input device 801 is used to receive current and voltage signals from the battery module from the sensor, and battery surface temperature signals from the thermocouple, and transmit the data to the memory via the bus; storage device 802 is mainly used to temporarily store the input parameters of the battery module, and push the data from the bottom of the stack into the corresponding partition of the processor when the processor needs to calculate; processor 803 is used to calculate the current electro-thermal state parameters of the battery module, provide a reasonable heating strategy, and generate control signals; and output device 804 transmits the control signal of the heating current generated in the processing to the external hardware to control the change of the heating current.

[0255] In one possible implementation, the processor 803 can be divided into: 8031, a thermal network node heat transfer matrix storage partition, which stores the matrix of the basic heat exchange units of the battery thermal network; 8032, a single-unit thermal network composition partition, which establishes the thermal network of a single unit in the battery module; 8033, a battery inter-node thermal network composition partition, which establishes the thermal network of the connection part between batteries in the module; 8034, a heating strategy and heating current boundary storage partition, which stores the heating strategies designed and tested offline and the heating current boundaries under different conditions; and 8035, a module temperature distribution calculation and heating current dynamic calculation partition, which calculates the dynamic temperature field within the battery module and provides the current reasonable heating current parameters.

[0256] Specifically, the processor 803 is used to invoke the program instructions to perform the following operations:

[0257] The current, voltage, and surface temperature of the battery module, which are acquired in real time by the sensor system within the battery module and stored in the memory, are imported into the processor module. The processor adaptively generates the thermal network of the battery module, calculates the impedance and temperature distribution of the battery module in real time, and outputs the most suitable heating strategy for the battery at the current moment.

[0258] In one possible implementation, the processor 803 further executes the following instructions: in offline mode, the specific parameters of the battery cell are input via the bus, such as the specific size of the battery and the number of nodes in the thermal network; in offline mode, the battery thermal network matrix in S203 is input into the thermal network node heat transfer matrix storage partition of 8031, and the aforementioned battery cell parameters are stored in the partition.

[0259] In another possible implementation, the processor 803 also executes the following instructions: after receiving the command to start the battery module, it calls the basic parameters and model from the thermal network node heat transfer matrix storage partition 8031, and constructs a temporary sub-module thermal network in the individual thermal network construction partition 8032 and the inter-battery node thermal network construction partition 8033.

[0260] In another possible implementation, the processor 803 also executes the following instructions: importing the battery model data temporarily stored in partitions 8032 and 8033, and the basic configuration data of the battery module in partition 8031 ​​into partition 8035 for module temperature distribution calculation and heating current dynamic calculation; synthesizing a thermal network model of the module that matches the current battery system in partition 8035, and temporarily storing it.

[0261] In another possible implementation, the processor 803 further executes the following instructions: importing the current and voltage at the current moment in the memory into partition 8035, and using the built-in algorithm as described above to solve the online impedance of the battery; importing the current and voltage at the current moment in the memory into partition 8035, and using the built-in algorithm as described above to solve the online SOC of the battery; importing the initial temperature at the current moment in the memory into partition 8035, and using the thermal network of the battery system matching module synthesized in the aforementioned steps to calculate the temperature field.

[0262] In another possible implementation, the processor 803 also executes the following instructions: importing the heating strategy of 8034 and the heating boundary stored in the heating current boundary storage partition into the 8035 partition; calculating the current reasonable pulse current parameters based on the battery module temperature obtained from the above steps; and outputting the calculated pulse current control parameters to the 804 output module.

[0263] In another possible implementation, the processor 803 also executes the following instructions: after completing the entire heating process, delete the thermal network matrix in partitions 8032 and 8033 to free up processor storage space; after completing the entire heating process, delete the module thermal network matrix in partition 8035 to free up more processor storage space.

[0264] The processor can be a central processing unit (CPU), a network processor (NP), or a WLAN device.

[0265] The processor may further include hardware chips. These hardware chips may be application-specific integrated circuits (ASICs), programmable logic devices (PLDs), or combinations thereof. The PLDs may be complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), generic array logic (GALs), or any combination thereof.

[0266] Memory may include volatile memory, such as random-access memory (RAM); memory may also include non-volatile memory, such as flash memory, hard disk drive (HDD), or solid-state drive (SSD); memory may also include combinations of the above types of memory.

[0267] The input device 901 / output device 902 may include a display screen and a keyboard, and optionally, may also include a standard wired interface and a wireless interface.

[0268] It should be noted that the term "multiple" in the embodiments of this application refers to two or more. Therefore, "multiple" can also be understood as "at least two" in the embodiments of this application. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / ", unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0269] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, units, and processes described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0270] In the embodiments provided in this application, it should be understood that the disclosed systems, methods, and approaches can be implemented in other ways. For example, the division of units is merely a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling, direct coupling, or communication connection between the shown or discussed units may be through some interfaces, and the indirect coupling or communication connection between the system or units may be electrical, mechanical, or other forms.

[0271] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0272] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be read-only memory (ROM), random access memory (RAM), or magnetic media, such as floppy disks, hard disks, magnetic tapes, magnetic disks, or optical media, such as digital versatile discs (DVDs), or semiconductor media, such as solid-state disks (SSDs).

[0273] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A battery heating method based on low-temperature heating and global temperature monitoring, characterized in that: The method includes the following steps: S1: Develop the battery heating process; S2: Execute the battery heating process; S1 includes the following steps: S101: Establish a second-order RQ model of the battery and build the battery model parameter foundation; The second-order RQ model of the battery includes the ohmic internal resistance of the battery, the polarization internal resistance of the SEI film, the polarization capacitance of the SEI film, the polarization internal resistance of charge transfer, and the polarization capacitance of charge transfer, which are tested by electrochemical impedance spectroscopy. S102: Calculate the heating current boundary of the battery under different temperatures, different SOCs, and different current frequencies; The heating current boundary of the battery includes the heating current boundary determined according to the lithium plating criterion, and the current boundary determined according to the voltage limit; wherein, the form of the lithium plating boundary needs to be matched with the corresponding specific form according to the second-order RQ model. The heating current boundaries at different temperatures, different states of charge (SOC), and different current frequencies are obtained through the following steps: S1021: Establish general lithium plating suppression conditions for batteries; in, Indicates the lithium plating reaction potential. This represents the negative electrode potential; the equilibrium potential for the lithium plating reaction is assumed to be 0V within the battery system, and the lithium plating suppression condition is: S1022: Update the description of the new lithium plating suppression conditions under the second-order RQ model. In the second-order RQ model, the negative electrode potential is represented as the difference between the negative electrode equilibrium potential and the charge transfer polarization partial voltage. Therefore, the lithium plating suppression condition is expressed as: S1023: Corrected impedance expression under high-frequency pulses; For the SEI membrane-related RQ circuit, there are: This represents the real part of the RQ loop impedance related to the SEI film. j Represents the imaginary unit. This represents the imaginary part of the SEI film-related RQ loop impedance. f Indicates the frequency of the heating pulse current; For RQ loops related to charge transfer, we have: This represents the complex impedance of the charge transfer-related RQ loop. This represents the real part of the RQ loop impedance related to charge transfer. This represents the imaginary part of the RQ loop impedance related to charge transfer; For a second-order RQ circuit model, its total impedance is expressed as: This represents the total complex impedance of a second-order RQ circuit model; S1024: Based on the results of S1022 and S1023, the heating current boundary based on the lithium plating suppression condition is given; Vct This represents the polarization voltage across the charge transfer polarization circuit, i.e., the polarization voltage across the RQ circuit related to charge transfer. V ct Expressions for converting to the magnitudes of current and impedance: This indicates the amplitude of the AC pulse heating current applied to the battery. This represents the equilibrium potential of the negative terminal of the battery. This represents the complex impedance magnitude of the charge transfer-related RQ circuit; S1025: The heating current boundary at different temperatures, different states of charge (SOC), and different current frequencies is as follows: This represents the total complex impedance of a second-order RQ circuit model. This indicates the upper limit of the battery's allowable terminal voltage. This indicates the lower limit of the battery's permissible terminal voltage. U This represents the battery voltage used to calculate the voltage margin under the current battery condition. This indicates that the battery is in the current-carrying phase during charging. This indicates the stage in which the battery is in the discharge direction and the current is applied. The maximum current amplitude at any given time is: This indicates the maximum allowable amplitude of the AC pulse heating current under the current temperature, SOC, and heating current frequency conditions. This indicates that the minimum value among the current limit values ​​within the parentheses is taken; S103: Construct a method for calculating the heat generation of a battery under different temperatures, different SOCs, and different pulse current characteristic parameters; Using the established second-order RQ model, an expression for the real part of the total battery impedance is given, and the effective heat generation rate of the battery under sinusoidal pulsed AC current is calculated. S104: Based on the battery's heating boundary, heat generation, and lumped parameter thermal model, obtain the battery's temperature rise curve and total heating time. The heating time is calculated as follows: First, the initial conditions of the battery are selected, including the initial SOC and the initial temperature. Then, the initial conditions and the heat generation of the battery at t=0 are entered into the lumped parameter model. The temperature of the battery at t=1 is calculated, and so on. During the calculation process, the heating current parameters are continuously updated through the heating current boundary and the calculated temperature to obtain the total heating time of the battery under the determined initial conditions. S105: Based on the heating time under different initial conditions, a heating time map is generated; In S101, the second-order RQ model is expressed as follows: ； This indicates the open-circuit voltage of the battery. L Indicates the inductive reactance of the battery. I Represents current. t Indicates time, U t Indicates the battery's terminal voltage. R o This indicates the ohmic internal resistance of the battery; U ct This refers to the voltage applied across the polarization resistor. R ct as well as C ct This represents the charge transfer internal resistance and charge transfer capacitance. This represents the constant-phase element coefficients associated with the SEI film. This indicates the order of the constant-phase element associated with the SEI film. This represents the constant-phase element coefficients related to charge transfer in the electric double layer. Indicates the order of the constant-phase element associated with charge transfer in the electric double layer; This represents the polarization voltage across the RQ branch associated with the SEI film. This indicates the polarization resistance corresponding to the SEI film. Indicates the order is Fractional differential operators; S2 includes the following steps: S201: Calculate the total resistance of the battery during the heating process based on the input current and voltage of the battery; S202: Calculate the SOC value of the battery during the heating process based on the battery input current and voltage; S203: Establish the thermal network structure of the battery, and calculate the internal temperature of the battery by combining the internal resistance identified in S201 and the current parameters at the current moment during the heating process. S204: Based on the battery temperature calculation results and the current boundary in S102, the heating current parameters are corrected in real time.

2. The battery heating method based on low-temperature heating and global temperature monitoring according to claim 1, characterized in that: In step S103, the method for calculating the heat generation of the battery under different temperatures, different SOCs, and different pulse current characteristic parameters is as follows: S1031: Establishing the expression for the real impedance of the battery: This represents the real part of the total impedance in the second-order RQ model of the battery; S1032: Considering the effective current of the AC pulse, establish the total heat generation of the battery: Q This indicates the total heat generation capacity of the battery.

3. The battery heating method based on low-temperature heating and global temperature monitoring according to claim 2, characterized in that: In step S104, the method for obtaining the battery's temperature rise curve and total heating time based on the battery's heating boundary, heat generation, and lumped parameter thermal model is as follows: Treating the battery as a lumped-parameter thermal model, using two temperature nodes to represent the battery temperature, then we have: This indicates the lumped temperature parameter of the battery itself. The temperature at which the battery surface temperature node is represented. Indicates ambient temperature. To characterize the thermal resistance between two nodes of a battery, Indicates the thermal resistance between the battery surface and the environment; The quality represented by the core nodes, The mass represented by the surface node; This represents the specific heat capacity parameter used in the battery thermal model. Indicates the time interval Internal battery body lumped temperature node temperature The change Indicates the time interval Internal battery surface temperature node temperature The change Indicates the time interval used in the temperature calculation process; Given initial conditions, calculate the temperature rise of the battery; In step S105, a heating time map is formed based on the heating time under different initial conditions.

4. The battery heating method based on low-temperature heating and global temperature monitoring according to claim 3, characterized in that: The specific steps of S201 are as follows: Based on the established second-order RQ model of the battery, the system matrix and the parameter matrix to be identified of the battery are derived. The apparent input-output relationship of the battery is as follows: in, For the system in k The output value at any given time, i.e., the voltage; For the system in k The input value at any given time, i.e., the current; b The coefficients between the input and output; ~ The input-output model coefficients represent the influence of historical output voltage on current output voltage. ~ The input-output model coefficients represent the influence of current and historical input current on the current output voltage. Indicates the system at the 1st k The output value at time -1 Indicates the system at the 1st k The output value at time -2 Indicates the system at the 1st k Input current at time -1; make Indicates the first k The regression vector at time step, Indicates the system at the 1st k The input current at time -2, Indicates the first k The vector of parameters to be identified at time t, where T represents the transpose operation identifier of the matrix; The method for calculating the parameter to be measured is as follows: in, for covariance, for k The terminal voltage at the time of the test; Indicates the first k The estimated value of the parameter vector to be identified obtained at time step [time]. Indicates the first k The estimated value of the parameter vector to be identified obtained at time -1. Represents the regression vector transpose, Indicates the first k The estimated value of the regression vector at time step; in addition By setting an initial value, the following formula is used for iteration: In the formula, The forgetting factor is set to 0.9998. Indicates the first k The covariance matrix in time-recursive parameter identification Indicates the first k Covariance matrix in recursive parameter identification at time -1; Ultimately, it is identified in real time at every moment. The relationship between the parameters and the second-order RQ is as follows: This represents the intermediate combined parameters established after parameter identification. This represents the intermediate combination parameter corresponding to the product of the time constants of the two RQ branches. This represents the intermediate combination parameter corresponding to the sum of the time constants of the two RQ branches. This represents the intermediate combination parameters corresponding to the sum of all resistance parameters in the second-order RQ model. This represents the intermediate parameter formed by the combination of the resistance parameter and the time constants of the two RQ branches. This represents the equivalent time constant corresponding to the RQ loop associated with the SEI membrane. This represents the equivalent time constant corresponding to the RQ loop associated with charge transfer. This indicates the fractional order used by the fractional-order circuit elements in the second-order RQ model in this parameter transformation relationship. This represents the charge transfer internal resistance of the RQ circuit related to charge transfer. The orders of the two fractional-order circuit components in the second-order RQ model were obtained through experimental testing.

5. The battery heating method based on low-temperature heating and global temperature monitoring according to claim 4, characterized in that: The specific steps of S202 are as follows: In the second-order RQ model, the time-domain recursive form of the battery's polarization voltage is: Indicates the first t Time of the first p Polarization voltage of each polarization branch Indicates the first t +1 moment p Polarization voltage of each polarization branch Indicates the first p The equivalent time constants corresponding to each polarization branch Indicates the first p The capacitance parameters corresponding to each polarization branch. Indicates the first t Constantly inputting current into the battery; The system equations and observation equations for the battery are as follows: Indicates the first t The state vector used for SOC estimation at time step 1. Indicates the first t The state vector used for SOC estimation at time -1 Indicates the first t The state transition matrix at time t, Indicates the first t The input matrix at time step, Indicates the first t Input current at time -1 Indicates the first t System noise in the state equation at time step Indicates the first t The observed voltage of the battery at any given time. Indicates the first t The observation matrix at time -1 Indicates the first t The coefficient of the direct influence of the input current on the observed voltage at time -1. Indicates the first t Measurement noise in the time-observation equation; in: This represents the equivalent time constant of the SEI film-related RQ polarization branch. This represents the equivalent time constant of the charge transfer-related RQ polarization branch. Indicates the maximum usable capacity of the battery. This represents the capacitance parameters of the SEI film-related RQ polarization branch. The capacitance parameters of the RQ polarization branch related to charge transfer are represented. After establishing the specific form of the parameter matrix for SOC estimation under the second-order RQ model, the Kalman filtering method is used to estimate the SOC.

6. The battery heating method based on low-temperature heating and global temperature monitoring according to claim 5, characterized in that: The specific steps in S203 are as follows: S2031: Establish the basic heat exchange unit of the battery thermal network, which is represented as follows: A This represents the central temperature node in the basic heat exchange unit of the battery thermal network. Indicates along Direction and center node A Adjacent temperature nodes, Indicates along Direction and center node A Adjacent temperature nodes, Indicates along Direction and center node A Adjacent temperature nodes, Indicates along Direction and center node A Adjacent temperature nodes, Indicates along Direction and center node A Adjacent temperature nodes, Indicates along Direction and center node A Adjacent temperature nodes, Indicates the central node A exist t The temperature at time +1 Indicates the central node A exist t The temperature at time +1 Indicates the central node A exist t Temperature at any moment Indicates adjacent nodes exist t Temperature at any moment Indicates adjacent nodes exist t Temperature at any moment Indicates adjacent nodes exist t Temperature at any moment Indicates adjacent nodes exist t Temperature at any moment Indicates adjacent nodes exist t Temperature at any moment Indicates adjacent nodes exist t Temperature at any moment Used to characterize the corresponding node to the central node A The heat exchange coefficient function contributed by temperature update. express t Moment acts on the node A Battery heat generation; , , , , , Each represents a pair of adjacent nodes. A The heat transfer recursion coefficient; The above equation represents the heat transfer relationship between a node in a battery thermal network and its adjacent nodes, where, C The heat transfer coefficient is expressed as... Convert to temperature recursive weights The coefficient; It represents the heat transfer coefficient of the corresponding heat transfer form in the corresponding direction, which is the thermal conductivity corresponding to heat conduction or the convective heat transfer coefficient corresponding to heat convection. Direction The specific form is expressed as follows: In the formula, Represents a node A Along Adjacent nodes in the direction The heat transfer coefficient between them; k x Indicates the midpoint of the heat transfer unit A With point A x1 The corresponding thermal conductivity between them x 1 represents a point A With point A x1 The spacing between them x 2 represents a point A With point A x2 The spacing between them h It represents a point. A With point A x1 The corresponding convective heat transfer coefficients between them; point A The heat transfer relationships and parameter meanings between the heat transfer points and the remaining points can be deduced similarly. S2032; Establish the overall structure of the battery's thermal network, its expression is: in, yes t Battery temperature network at time +1, yes t Real-time battery temperature network, It is the outer edge of the battery x 1. A set of temperature points that exchange heat with the battery in one direction. It is the outer edge of the battery x A set of temperature points that exchange heat with the battery in two directions; and The expression is as follows: Indicates the first n Internal nodes A n temperature, Indicates the first N Internal nodes A N temperature, Indicates the corresponding first n The temperature at an external temperature point Indicates the corresponding first M The temperature at an external temperature point; Based on the definition of the node heat exchange matrix, the overall heat exchange matrix of the multi-node thermal network of the battery is as follows: Indicates internal nodes A n For the target node A n The heat exchange recursion coefficient updated at temperature Indicates internal nodes A N For the target node A N The heat exchange recursion coefficient updated at temperature Indicates along The direction of the first n External heat exchange temperature node For the internal battery n Nodes A n The heat exchange recursion coefficient updated at temperature Indicates along The direction of the first M External heat exchange temperature node For the internal battery n Nodes A n Temperature-updated heat exchange recursion coefficient; heat generation of the battery Calculations are performed based on the heat-generating formula; S2033; Establish a thermal network for the battery inter-cell connection components in the module, and connect it with the thermal network of the adjacent battery cells in the manner described in S2032 to form the overall thermal network of the battery module. S2034; Input the online impedance and input current, and calculate the temperature change of the battery during the heating process.

7. A battery heating system based on the method of any one of claims 1 to 6, characterized in that: The system includes: The input device (801) is used to receive current and voltage signals from the battery module of the sensor and battery surface temperature signals from the thermocouple, and transmit the data to the storage device via the bus. The storage device (802) is used to temporarily store the input parameters of the battery module and push the data from the bottom of the stack into the corresponding partition of the processor when the processor needs to perform calculations. The processor (803) is used to calculate the current electro-thermal state parameters of the battery module, provide a reasonable heating strategy, and generate control signals; The output device (804) transmits the control signal of the heating current generated during processing to the external hardware to control the change of the heating current.

8. The battery heating system according to claim 7, characterized in that: The processor (803) includes: The thermal network node heat transfer matrix storage partition (8031) stores the matrix of basic heat exchange units of the battery thermal network; The individual cell thermal network forms a partition (8032), establishing the thermal network of a certain cell in the battery module; The inter-cell node thermal network forms a partition (8033), establishing a thermal network for the inter-cell connection part in the module; Heating strategy and heating current boundary storage partition (8034) stores heating strategies designed and tested offline and heating current boundaries under different conditions; The module temperature distribution calculation and heating current dynamic calculation partition (8035) calculates the dynamic temperature field within the battery module and provides the current reasonable heating current parameters.

9. The battery heating system according to claim 8, characterized in that: The processor (803) is used to invoke program instructions to perform the following operations: The battery module current, voltage, and surface temperature, which are acquired in real time by the sensor system within the battery module and stored in the memory, are imported into the processor module; the thermal network of the battery module is adaptively generated inside the processor. The system calculates the impedance and temperature distribution of the battery module in real time and outputs the most suitable heating strategy for the battery at the current moment.

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