A method and system for preheating a lithium-ion battery in a low-temperature environment

Abstract

The present invention discloses a method and system for preheating a lithium-ion battery in a low-temperature environment. The method includes: performing experimental data analysis on the lithium-ion battery to construct an RC equivalent circuit model and a thermal model of the lithium-ion battery; simulating the RC equivalent circuit model and the thermal model of the lithium-ion battery to obtain a simulation data set of the lithium-ion battery; optimizing the simulation data set of the lithium-ion battery through a neural network regression prediction model and a multi-objective optimization algorithm to obtain the optimal preheating parameters of the lithium-ion battery; and performing optimal preheating treatment on the lithium-ion battery based on the optimal preheating parameters of the lithium-ion battery to obtain a preheated lithium-ion battery. The present invention can find the optimal preheating parameters that enable the battery to reach the maximum available energy under different low-temperature conditions, reduce the preheating energy consumption, and improve the overall energy utilization rate. As a method and system for preheating a lithium-ion battery in a low-temperature environment, the present invention can be widely applied to the technical field of lithium-ion battery processing.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion battery processing, and particularly to a method and system for preheating lithium-ion batteries in a low-temperature environment. Background Art

[0002] The performance of lithium-ion batteries significantly degrades in a low-temperature environment. This phenomenon is mainly reflected in aspects such as reduced capacity, increased internal resistance, decreased charge-discharge efficiency, and shortened cycle life. To overcome these problems, low-temperature preheating has been widely studied and applied to improve the working performance of lithium-ion batteries in low-temperature conditions. The core objective of low-temperature preheating is to enable the battery to quickly warm up to an appropriate working temperature in a low-temperature environment by reasonably controlling the battery heating process, thereby enhancing its electrochemical reaction activity and ion migration speed, and further improving the available energy.

[0003] Currently, low-temperature preheating of lithium-ion batteries mainly includes two methods: internal heating and external heating. Internal heating directly heats the battery by integrating heating elements, such as heating films, resistance wires, or thermoelectric materials, inside the battery. This method has a fast preheating speed, but it may accelerate battery aging and pose safety hazards. At the same time, it has high requirements for the precision of the control circuit. External heating, on the other hand, heats the entire battery by installing heating devices, such as electric heating pads, heating boxes, or heating cabins, outside the battery. By using external heat sources to conduct heat and increase the battery temperature, this method has high safety and reliability, but its preheating efficiency is low under extremely low-temperature conditions, resulting in extended preheating time and energy waste. Summary of the Invention

[0004] In order to solve the above technical problems, the objective of the present invention is to provide a method and system for preheating lithium-ion batteries in a low-temperature environment, which can find the optimal preheating parameters that enable the battery to achieve the maximum available energy under different low-temperature conditions, reduce preheating energy consumption, and improve the overall energy utilization rate.

[0005] The first technical solution adopted by the present invention is: A method for preheating lithium-ion batteries in a low-temperature environment, comprising the following steps:

[0006] Conduct experimental data analysis on the lithium-ion battery, and construct an RC equivalent circuit model and a thermal model of the lithium-ion battery;

[0007] Conduct experimental simulations on the RC equivalent circuit model and the thermal model of the lithium-ion battery to obtain a simulation dataset of the lithium-ion battery;

[0008] Optimize the simulation dataset of the lithium-ion battery through a neural network regression prediction model and a multi-objective optimization algorithm to obtain the optimal preheating parameters of the lithium-ion battery;

[0009] Optimal preheating treatment is performed on a lithium-ion battery based on the optimal preheating parameters of the lithium-ion battery to obtain the preheated lithium-ion battery.

[0010] Furthermore, the step of performing experimental data analysis on the lithium-ion battery and constructing an RC equivalent circuit model and a thermal model of the lithium-ion battery specifically includes:

[0011] Obtain the internal temperature data of the lithium-ion battery through a thermocouple and the external temperature data of the lithium-ion battery through a PI heating film;

[0012] Based on the internal temperature data and the external temperature data of the lithium-ion battery, adjust the preheating power and preheating time of the PI heating film, perform external preheating on the lithium-ion battery, measure the internal and external temperature values of the lithium-ion battery under different ambient temperatures, preheating powers, and preheating times, and construct a polynomial regression equation for the preheating of the PI heating film;

[0013] Perform discharge experiments on the fully charged lithium-ion battery under different ambient temperatures and different discharge currents to obtain the values of the discharge capacity of the lithium-ion battery varying with the ambient temperature and the discharge current;

[0014] Perform hybrid power pulse characteristic experiments on the fully charged lithium-ion battery under different ambient temperatures, different states of charge, and different discharge currents to obtain the terminal voltage and current data of the lithium-ion battery;

[0015] Fit the values of the discharge capacity of the lithium-ion battery varying with the ambient temperature and the discharge current and the terminal voltage and current data of the lithium-ion battery to construct an RC equivalent circuit model and a thermal model of the lithium-ion battery.

[0016] Furthermore, the step of fitting the values of the discharge capacity of the lithium-ion battery varying with the ambient temperature and the discharge current and the terminal voltage and current data of the lithium-ion battery to construct an RC equivalent circuit model and a thermal model of the lithium-ion battery specifically includes:

[0017] Use the cftool toolbox in Matlab to perform fitting processing on the terminal voltage and current data of the lithium-ion battery to construct a polynomial regression equation for the terminal voltage and current data of the lithium-ion battery;

[0018] Determine the polarization internal resistance value, the polarization capacitance value, and the ohmic internal resistance value of the lithium-ion battery according to the values of the discharge capacity of the lithium-ion battery varying with the ambient temperature and the discharge current;

[0019] Combine the polarization internal resistance value, the polarization capacitance value, and the ohmic internal resistance value of the lithium-ion battery to construct a polynomial regression equation for the values of the discharge capacity of the lithium-ion battery varying with the ambient temperature and the discharge current;

[0020] Based on the polynomial regression equation for the terminal voltage and current data of the lithium-ion battery and the polynomial regression equation for the value of the discharge capacity of the lithium-ion battery varying with the ambient temperature and discharge current, combined with Kirchhoff's voltage and current laws, a lithium-ion battery RC equivalent circuit model is constructed;

[0021] Based on the polynomial regression equation for the terminal voltage and current data of the lithium-ion battery and the polynomial regression equation for the value of the discharge capacity of the lithium-ion battery varying with the ambient temperature and discharge current, combined with Joule's law, a lithium-ion battery thermal model is constructed.

[0022] Furthermore, the step of experimentally simulating the lithium-ion battery RC equivalent circuit model and the lithium-ion battery thermal model to obtain a lithium-ion battery simulation data set specifically includes:

[0023] Construct a percentage model for the capacity decay of the lithium-ion battery according to the battery semi-empirical aging model formula;

[0024] Couple the lithium-ion battery RC equivalent circuit model, the lithium-ion battery thermal model, the percentage model for the capacity decay of the lithium-ion battery, and the PI heating film preheating polynomial regression equation and input them into the COMSOL simulation software for experimental simulation to obtain a lithium-ion battery simulation data set.

[0025] Furthermore, the step of optimizing the lithium-ion battery simulation data set through the neural network regression prediction model and the multi-objective optimization algorithm to obtain the optimal preheating parameters of the lithium-ion battery specifically includes:

[0026] Train the neural network regression prediction model based on the lithium-ion battery simulation data set to obtain a trained neural network regression prediction model;

[0027] Obtain the input data of the lithium-ion battery and perform predictions through the trained neural network regression prediction model to obtain the discharge capacity of the lithium-ion battery and the percentage value of the capacity decay of the lithium-ion battery;

[0028] Calculate the preheating energy consumption and available energy of the ion battery, define the available energy weight factor, the aging loss rate weight factor, and the preheating energy consumption weight factor, and construct a weighted objective function;

[0029] Perform multi-objective optimization on the discharge capacity of the lithium-ion battery, the percentage value of the capacity decay of the lithium-ion battery, and the weighted objective function through the multi-objective optimization algorithm to obtain the optimal preheating parameters of the lithium-ion battery.

[0030] Furthermore, the step of training the neural network regression prediction model based on the lithium-ion battery simulation data set to obtain a trained neural network regression prediction model specifically includes:

[0031] Input the lithium-ion battery simulation data set into the neural network regression prediction model, where the neural network regression prediction model includes a sparrow search algorithm optimization model, a convolutional neural network, and a long short-term memory network;

[0032] Based on the sparrow search algorithm optimization model, optimize the parameters of the neural network regression prediction model to obtain an optimized neural network regression prediction model;

[0033] Based on the convolutional neural network of the optimized neural network regression prediction model, perform local feature extraction on the lithium-ion battery simulation data set to obtain the characteristics of the lithium-ion battery simulation data set;

[0034] Based on the long short-term memory network of the optimized neural network regression prediction model, perform time series feature extraction on the characteristics of the lithium-ion battery simulation data set to obtain the time series characteristics of the lithium-ion battery simulation data set;

[0035] Use the time series characteristics of the lithium-ion battery simulation data set as the verification data set index to verify the optimized neural network regression prediction model and construct a trained neural network regression prediction model.

[0036] Further, the step of obtaining the input data of the lithium-ion battery and predicting through the trained neural network regression prediction model to obtain the discharge capacity of the lithium-ion battery and the percentage value of the lithium-ion battery capacity attenuation specifically includes:

[0037] Based on the PI heating film preheating polynomial regression equation, obtain the external temperature and internal temperature values of the lithium-ion battery after preheating at different preheating powers, different preheating times, and different ambient temperatures;

[0038] Combine the external temperature and internal temperature values, ambient temperature, state of charge, and lithium-ion battery discharge current value of the lithium-ion battery after preheating at different preheating powers, different preheating times, and different ambient temperatures to construct the input data of the lithium-ion battery;

[0039] Predict the input data of the lithium-ion battery through the trained neural network regression prediction model to obtain the discharge capacity of the lithium-ion battery and the percentage value of the lithium-ion battery capacity attenuation.

[0040] Further, the step of performing multi-objective optimization on the discharge capacity of the lithium-ion battery, the percentage value of the lithium-ion battery capacity attenuation, and the weighted objective function through a multi-objective optimization algorithm to obtain the optimal preheating parameters of the lithium-ion battery specifically includes:

[0041] Solve the weighted objective function through a multi-objective optimization algorithm to obtain the Pareto optimal solution;

[0042] Determine the target value based on the Pareto optimal solution, where the target value includes the available energy of the lithium-ion battery, the capacity loss rate of the lithium-ion battery, and the preheating energy consumption of the lithium-ion battery;

[0043] Perform a maximum available energy index search process on the target value to obtain the optimal preheating power and preheating time;

[0044] Calculate the discharge capacity of the lithium-ion battery, the capacity loss rate of the lithium-ion battery, the preheating energy consumption of the lithium-ion battery, and the available energy of the lithium-ion battery under the optimal preheating power and preheating time through an auxiliary function. Combine the discharge capacity of the lithium-ion battery and the percentage value of the lithium-ion battery capacity attenuation to draw a Pareto front diagram to obtain the optimal preheating parameters of the lithium-ion battery.

[0045] The second technical solution adopted by the present invention is: a preheating system for a lithium-ion battery in a low-temperature environment, including:

[0046] The first module is used to analyze the experimental data of the lithium-ion battery and construct an RC equivalent circuit model and a thermal model of the lithium-ion battery;

[0047] The second module is used to perform experimental simulations on the RC equivalent circuit model and the thermal model of the lithium-ion battery to obtain a simulation data set of the lithium-ion battery;

[0048] The third module is used to optimize the simulation data set of the lithium-ion battery through a neural network regression prediction model and a multi-objective optimization algorithm to obtain the optimal preheating parameters of the lithium-ion battery;

[0049] The fourth module is used to perform optimal preheating on the lithium-ion battery based on the optimal preheating parameters of the lithium-ion battery to obtain a preheated lithium-ion battery.

[0050] The beneficial effects of the method and system of the present invention are: by analyzing the experimental data of the lithium-ion battery, constructing an RC equivalent circuit model and a thermal model of the lithium-ion battery, performing experimental simulations on the RC equivalent circuit model and the thermal model of the lithium-ion battery, obtaining a simulation data set of the lithium-ion battery, further optimizing the simulation data set of the lithium-ion battery through a neural network regression prediction model and a multi-objective optimization algorithm, finding the optimal preheating parameters that enable the battery to reach the maximum available energy under different low-temperature conditions by optimizing the heating power and heating time, and finally performing optimal preheating on the lithium-ion battery based on the optimal preheating parameters of the lithium-ion battery, it can cope with complex and changeable usage scenarios; the optimized preheating strategy can minimize the preheating energy consumption and improve the overall energy utilization rate while ensuring the battery performance, enabling the battery to provide more effective electric energy in a low-temperature environment. Description of the Drawings

[0051] Figure 1It is the flowchart of the steps of a method for preheating a lithium-ion battery in a low-temperature environment according to the present invention;

[0052] Figure 2 It is the structural block diagram of a preheating system for a lithium-ion battery in a low-temperature environment according to the present invention;

[0053] Figure 3 It is the schematic flowchart of the optimal preheating strategy for a lithium-ion battery adapted to a low-temperature environment provided by a specific embodiment of the present invention;

[0054] Figure 4 It is the schematic comparison diagram of HPPC experimental curves when the discharge current is 0.9 A and 1.5 A respectively at SOC = 1 provided by a specific embodiment of the present invention;

[0055] Figure 5 It is the schematic comparison diagram of the temperature rise curves fitted at different preheating powers when PI heating is at 248.15 K provided by a specific embodiment of the present invention;

[0056] Figure 6 It is the coupling diagram of the RC equivalent circuit model, thermal model, aging model, and external heating model provided by a specific embodiment of the present invention;

[0057] Figure 7 It is the schematic comparison curve diagram of the battery simulation voltages of COMSOL at different temperatures and different discharge currents provided by a specific embodiment of the present invention;

[0058] Figure 8 It is the schematic comparison curve diagram of the battery simulation temperatures of COMSOL at different temperatures and different discharge currents provided by a specific embodiment of the present invention;

[0059] Figure 9 It is the schematic comparison curve diagram of the external temperature and internal temperature of the battery at discharge currents of 1.5 A and 3.0 A when COMSOL has a preheating power of 9 W and a preheating time of 3 minutes provided by a specific embodiment of the present invention;

[0060] Figure 10 It is the schematic comparison curve diagram of the voltages of the battery at discharge rates of 0.5C and 1.0C when COMSOL has a preheating power of 9 W and different preheating times provided by a specific embodiment of the present invention;

[0061] Figure 11 It is the schematic diagram of the training results of the discharge capacity of a lithium-ion battery by the SSA-CNN-LSTM algorithm provided by a specific embodiment of the present invention;

[0062] Figure 12 It is the schematic diagram of the training results of the percentage of capacity loss of a lithium-ion battery by the SSA-CNN-LSTM algorithm provided by a specific embodiment of the present invention;

[0063] Figure 13 It is a Pareto front graph composed of available capacity and capacity loss rate solutions found by using NSGA-II multi-objective optimization provided by a specific embodiment of the present invention;

[0064] Figure 14 It is a Pareto front graph composed of available capacity and preheating energy consumption solutions found by using NSGA-II multi-objective optimization provided by a specific embodiment of the present invention;

[0065] Figure 15 It is a Pareto front graph composed of capacity loss rate and preheating energy consumption solutions found by using NSGA-II multi-objective optimization provided by a specific embodiment of the present invention. Specific Embodiment

[0066] The following further elaborates the present invention in detail in conjunction with the accompanying drawings and specific embodiments. For the step numbers in the following embodiments, they are only set for the convenience of elaboration and explanation, and no limitation is imposed on the order between steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0067] First of all, it should be noted that the design of the preheating strategy for lithium-ion batteries needs to consider the following key factors: preheating power, preheating time. In the design of the preheating strategy, the maximum available energy is an important evaluation index. The available energy refers to the effective electric energy that the battery can provide after completing the preheating process. It not only depends on the discharge capacity of the battery but is also affected by the preheating energy consumption. Therefore, the preheating strategy needs to find a balance between increasing the battery discharge capacity and reducing the preheating energy consumption to achieve the maximum available energy. The methods for optimizing the preheating strategy include multi-objective optimization algorithms, which find the optimal preheating parameters that enable the battery to reach the maximum available energy under different low-temperature conditions by optimizing the heating power and heating time. The optimization objectives include maximizing the available energy, minimizing the preheating energy consumption, and minimizing the aging loss rate. By comprehensively considering these objectives and using multi-objective optimization algorithms such as NSGA-II, the Pareto optimal solution can be found, so as to select the preheating parameters suitable for specific application requirements and apply them to the actual battery preheating process to achieve the best performance.

[0068] Based on this, the embodiments of the present invention utilize the safety and reliability of the external heating method, adopt a multi-objective optimization algorithm (NSGA-II), and find the optimal preheating parameters that enable the battery to reach the maximum available energy under different low-temperature conditions by optimizing the heating power and heating time. By balancing the increase in battery discharge capacity and the reduction of preheating energy consumption, and comprehensively considering objectives such as maximizing the available energy, minimizing the preheating energy consumption, and minimizing the aging loss rate, the Pareto optimal solution is found, and finally the efficiency and reliability in the battery preheating process are achieved.

[0069] Refer to Figure 1, the present invention provides a preheating method for lithium-ion batteries in a low-temperature environment, and the method includes the following steps:

[0070] S100. Conduct experimental data analysis on the lithium-ion battery, and construct an RC equivalent circuit model and a thermal model of the lithium-ion battery;

[0071] Specifically, obtain the internal temperature data of the lithium-ion battery through a thermocouple and obtain the external temperature data of the lithium-ion battery through a PI heating film; based on the internal temperature data and the external temperature data of the lithium-ion battery, adjust the preheating power and preheating time of the PI heating film, perform external preheating on the lithium-ion battery, and measure the internal and external temperature values of the lithium-ion battery at different ambient temperatures, preheating powers, and preheating times, and construct a polynomial regression equation for the preheating of the PI heating film;

[0072] In this embodiment, select a lithium-ion battery, open holes in the negative electrode, insert a thermocouple into the negative electrode to measure the internal temperature; wrap the PI (polyimide film) heating film around the outer surface of the lithium-ion battery and attach a thermocouple to measure the external temperature of the battery; at different ambient temperatures, change the power and time applied to the PI heating film to achieve external preheating of the battery, and measure the internal and external temperature values of the lithium-ion battery at different ambient temperatures, preheating powers, and preheating times, and construct a polynomial regression equation between the external temperature, internal temperature, preheating power, preheating time, and ambient temperature.

[0073] It should be noted that the PI heating film preheats the lithium-ion battery at different preheating powers, preheating times, and ambient temperatures, obtains the external and internal temperatures of the lithium-ion battery, obtains the temperature data after preheating by the PI heating film, and respectively establishes polynomial regression equations for the external temperature and internal temperature with power, time, and ambient temperature.

[0074] Perform discharge experiments on the fully charged lithium-ion battery at different ambient temperatures and different discharge currents to obtain the values of the discharge capacity of the lithium-ion battery changing with the ambient temperature and discharge current; perform hybrid power pulse characteristic experiments on the fully charged lithium-ion battery at different ambient temperatures, different state of charge, and different discharge currents to obtain the terminal voltage and current data of the lithium-ion battery;

[0075] In this embodiment, select another complete lithium-ion battery, charge it fully at 25°C, perform discharge experiments at different ambient temperatures and different discharge currents to obtain the values of the battery discharge capacity changing with the ambient temperature and discharge current; then perform HPPC (hybrid power pulse characteristic) experiments at different ambient temperatures, SOC (state of charge), and discharge currents to measure the terminal voltage and current data of the lithium-ion battery.

[0076] Furthermore, it is illustrated in combination with experimental data. The NCR18650BD lithium battery is selected as the experimental object. The negative electrode of the battery is perforated, and a thermocouple is inserted to measure the internal temperature value. The outer surface of the battery is wrapped with a PI heating film, and a thermocouple is pasted to measure the external temperature value. The power of the PI heating film is changed, and the lithium-ion battery is preheated at preheating powers of 7W, 9W, 13W, preheating times of 1, 2, 3, 4, 5 minutes, and ambient temperatures of 248.15K, 263.15K, 273.15K to obtain the external and internal temperatures of the lithium-ion battery.

[0077] Furthermore, temperature data of the PI heating film at different powers, times, and ambient temperatures are obtained, and the cftool tool in Matlab is used for fitting to separately obtain the undetermined coefficients a 1 -a 7 values, and a polynomial regression equation of the external temperature T out and the internal temperature T in with respect to the preheating power P, preheating time t, and ambient temperature T er is established. As Figure 4 shown, the maximum error between the fitted polynomial regression equation and the experimental data is controlled within 2%, and R 2 = 0.996, indicating a good fitting effect;

[0078] T out = a 1 + a 2 Pt + a 3 PT er + a 4 tT er + a 5 P 2 + a 6 t 2 + a 7 T er 2

[0079] T in = a 1 + a 2 Pt + a 3 PT er + a 4 tT er + a 5 P 2 + a 6 t 2 + a 7 T er 2

[0080] Select a new NCR18650BD type battery and fully charge it at room temperature of 298.15K. Then, at temperatures of 248.15K, 263.15K, 273.15K, 283.15K, and 298.15K, discharge the battery with currents of 0.9A, 1.5A, 2.1A, and 3.0A to obtain data such as the battery discharge capacity and terminal voltage.

[0081] At temperatures of 248.15K, 263.15K, 273.15K, 283.15K, and 298.15K, with SOC values of 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%, and currents of 0.9A, 1.5A, 2.1A, and 3.0A, conduct HPPC experiments. The process is shown in Table 1. Figure 5 Shows the HPPC experiments of the battery at an ambient temperature of 248.15K, SOC = 100%, with currents of 0.9A and 1.5A respectively.

[0082] Table 1 HPPC Discharge Test Process

[0083] Step Operation content 1 After keeping the battery at 298.15K for 1h, set the SOC to 100% 2 Adjust the temperature of the thermostat to 248.15K and let it stand for 2h 3 Discharge at a current of 0.9A for 30s, let it stand for 60s, and then charge at a current of 0.9A for 30s 4 Adjust the temperature of the thermostat to 298.15K and let it stand for 2h 5 Discharge at a current of 1.5A for 0.3Ah and let it stand for 1h 6 Repeat steps 2 to 5 for 9 cycles and end

[0084] Fit the values of the lithium-ion battery discharge capacity varying with the ambient temperature and discharge current, as well as the lithium-ion battery terminal voltage and current data, and construct an RC equivalent circuit model and a thermal model of the lithium-ion battery.

[0085] Among them, the process of fitting the values of the lithium-ion battery discharge capacity varying with the ambient temperature and discharge current, as well as the lithium-ion battery terminal voltage and current data, is as follows:

[0086] Use the cftool toolbox in Matlab to fit the lithium-ion battery terminal voltage and current data, and construct a polynomial regression equation for the lithium-ion battery terminal voltage and current data; according to the values of the lithium-ion battery discharge capacity varying with the ambient temperature and discharge current, determine the polarization internal resistance value, polarization capacitance value, and ohmic internal resistance value of the lithium-ion battery; combine the polarization internal resistance value, polarization capacitance value, and ohmic internal resistance value of the lithium-ion battery to construct a polynomial regression equation for the values of the lithium-ion battery discharge capacity varying with the ambient temperature and discharge current; according to the polynomial regression equation for the lithium-ion battery terminal voltage and current data and the polynomial regression equation for the values of the lithium-ion battery discharge capacity varying with the ambient temperature and discharge current, combined with Kirchhoff's voltage and current laws, construct an RC equivalent circuit model of the lithium-ion battery; according to the polynomial regression equation for the lithium-ion battery terminal voltage and current data and the polynomial regression equation for the values of the lithium-ion battery discharge capacity varying with the ambient temperature and discharge current, combined with Joule's law, construct a thermal model of the lithium-ion battery.

[0087] In this embodiment, the data in the HPPC experiments under different ambient temperatures, SOCs, and discharge currents are identified, and the coefficients k 0 , k 1 , and b 1 can be obtained by fitting through the cftool toolbox in Matlab. The expression is as follows:

[0088]

[0089] Furthermore, the polarization internal resistance R 1 and the polarization capacitance C 1 of the battery under different ambient temperatures, SOCs, and discharge currents are calculated. The expression is as follows:

[0090]

[0091]

[0092] The ohmic internal resistance R 0 of the battery under different ambient temperatures, SOCs, and discharge currents is calculated. The expression is as follows:

[0093]

[0094] Among them, the terminal voltage V A of the power battery at the end of discharge. Due to the internal electrochemical reaction not stopping immediately, the external manifestation is that the terminal voltage rises rapidly and then gradually tends to a stable value V B , that is, the "rebound characteristic" of the power battery. At this time, it is a zero-input response. The "rebound characteristic" of the terminal voltage is mainly affected by the ohmic internal resistance R 0 . Therefore, according to Ohm's law, the ohmic internal resistance R 0 can be obtained;

[0095] According to the above, the R 0 , R 1 , and C 1 parameters of the battery under different ambient temperatures, SOCs, and discharge currents are obtained, and a polynomial regression equation regarding the ambient temperature, SOC, and discharge current is established; combined with the RC equivalent circuit model, Kirchhoff's voltage and current laws, the state equation of the RC equivalent circuit model is obtained; combined with Joule's law, the thermal model of the battery is obtained.

[0096] Specifically, the numerical values of the polarization internal resistance R 1 , the polarization capacitance C 1 , and the ohmic internal resistance R 0 under different ambient temperatures, SOCs, and discharge currents are obtained, and the ohmic internal resistance R 0 and the polarization internal resistance R 1, polarization capacitor C 1 Regarding the ambient temperature T er , the polynomial regression equation under SOC and discharge current I is expressed as follows:

[0097] y = a 1 + a 2 I + a 3 SOC + a 4 T er + a 5 I 2 + a 6 SOC 2 + a 7 T er 2 + a 8 I 3 + a 9 SOC 3 + a 10 T er 3

[0098] Combined with the first-order RC equivalent circuit model and based on Kirchhoff's voltage and current laws, the equation of the first-order RC equivalent circuit model can be obtained as follows:

[0099]

[0100] In the above formula, U L is the battery terminal voltage, U OC is the battery open-circuit voltage, I 1 is the polarization current flowing through the polarization internal resistance.

[0101] Combined with Joule's law, the thermal model of the battery can be obtained, and its expression is as follows:

[0102]

[0103] In the above formula, Q z is the total heat of the battery, t p is the discharge time, Q 1 represents the heat loss during the heat generation process, h is the equivalent convective heat transfer coefficient, A is the battery heat transfer area, and T er is the external ambient temperature.

[0104] S200. Conduct experimental simulations on the RC equivalent circuit model of the lithium-ion battery and the thermal model of the lithium-ion battery to obtain the lithium-ion battery simulation dataset;

[0105] Specifically, a percentage model of the capacity decay of a lithium-ion battery is constructed according to the battery semi-empirical aging model formula; the RC equivalent circuit model of the lithium-ion battery, the thermal model of the lithium-ion battery, the percentage model of the capacity decay of the lithium-ion battery, and the PI heating film preheating polynomial regression equation are coupled and input into the COMSOL simulation software for experimental simulation to obtain a lithium-ion battery simulation dataset.

[0106] In this embodiment, the constructed equivalent circuit model, thermal model, combined with the battery aging loss model and the PI heating film preheating polynomial regression equation are input into the COMSOL simulation software. By changing the preheating time, preheating power of the PI heating film, and the ambient temperature, the simulation of the battery under different ambient temperatures, SOCs, and discharge currents is realized, and the simulation results are recorded.

[0107] The percentage model of the battery capacity decay is obtained by using the battery semi-empirical aging model formula. The expression of the battery semi-empirical aging model formula is:

[0108]

[0109] In the above formula, C is the battery discharge capacity, and R is the gas constant;

[0110] Furthermore, the equivalent circuit model, thermal model, battery aging capacity decay model, and external heating model of the battery are coupled, as Figure 6 shown, and input into the COMSOL software for simulation; the simulation data is compared and evaluated with the real data; if the RMSE of the voltage < 0.05V and the RMSE of the temperature < 2K, it is considered that the simulation model is good, as Figure 7 、 Figure 8 and Figure 9 shown; if not satisfied, the model is further adjusted.

[0111] Further input into the COMSOL simulation software, and by changing the preheating power and preheating time of the PI heating film, and changing the ambient temperature, the SOC of the battery, and the discharge current for simulation, the battery voltage, discharge capacity, external temperature of the battery, internal temperature of the battery, and percentage value of the battery capacity decay after simulation are obtained, and a dataset is constructed, as Figure 10 shown, the voltage change conditions of the battery when discharging at 1.5A and 3A respectively after preheating for 1 minute, 3 minutes, and 5 minutes at a preheating power of 9W.

[0112] S300. The lithium-ion battery simulation dataset is optimized by the neural network regression prediction model and the multi-objective optimization algorithm to obtain the optimal preheating parameters of the lithium-ion battery;

[0113] Specifically, a neural network regression prediction model is trained based on a lithium-ion battery simulation dataset to obtain a trained neural network regression prediction model;

[0114] Among them, the lithium-ion battery simulation dataset is input into the neural network regression prediction model, and the neural network regression prediction model includes a sparrow search algorithm optimization model, a convolutional neural network, and a long short-term memory network; based on the sparrow search algorithm optimization model, the parameters of the neural network regression prediction model are optimized to obtain an optimized neural network regression prediction model; based on the convolutional neural network of the optimized neural network regression prediction model, local feature extraction processing is performed on the lithium-ion battery simulation dataset to obtain lithium-ion battery simulation dataset features; based on the long short-term memory network of the optimized neural network regression prediction model, temporal feature extraction processing is performed on the lithium-ion battery simulation dataset features to obtain lithium-ion battery simulation dataset temporal features; the lithium-ion battery simulation dataset temporal features are used as verification dataset indicators to verify the optimized neural network regression prediction model, and a trained neural network regression prediction model is constructed.

[0115] In this embodiment, the simulation results are constructed into a dataset, and the SSA-CNN-LSTM model is trained through this dataset. Specifically, the external temperature and internal temperature values under different environmental temperatures, SOCs, discharge currents, and different preheating strategies are sorted into input data, and the discharge capacity and the percentage value of battery capacity attenuation are sorted into output data. Then, these data are used to train the SSA-CNN-LSTM model to accurately estimate the discharge capacity of the battery and the percentage value of battery capacity attenuation under the above different conditions.

[0116] Furthermore, it should be noted that first, 70% of the dataset is used as the training dataset, and 30% is used as the test dataset; the training dataset is used to train the SSA-CNN-LSTM model, and the test dataset is used to verify the accuracy of the model.

[0117] Furthermore, the data in the training set includes environmental temperature, SOC, discharge current, and external temperature and internal temperature values after battery preheating; the output data includes the battery discharge capacity and the percentage value of battery capacity attenuation; the training process of the SSA-CNN-LSTM model includes three main parts: parameter optimization, local feature extraction, and temporal feature processing, which are specifically as follows:

[0118] 1) Parameter Optimization: The sparrow search algorithm (SSA) is used to optimize the key parameters of the model, including the number of neurons in the (NumOfUnits) LSTM layer, the initial learning rate (InitialLearnRate) during the optimization process, and the L2 regularization coefficient (L2Regularization); The SSA algorithm sets the population size to 30 and the number of iterations to 8; Through steps such as initializing the population, calculating fitness values, and updating the population, the SSA finally finds the optimal parameter combination to ensure the model has optimal training performance;

[0119] 2) Local Feature Extraction: A convolutional neural network (CNN) is adopted to extract local features of the input data; In this model, the size of the input sequence is set to [5, 1, 1], indicating that each input sample is a time series of size 5; Two convolutional layers are set. The first convolutional layer uses (convolution2dLayer), the size of the first convolutional kernel is 2x1, the number of output channels is 10, and the stride is [1, 1]; The second convolutional layer also uses (convolution2dLayer), the size of the second convolutional kernel is 1x1, the number of output channels is 10, and the stride is [1, 1]; The pooling layer uses (maxPooling2dLayer) and sets the pooling window size to 1x3 and the stride to 1; (flattenLayer) flattens the two-dimensional feature map output by the pooling layer into a one-dimensional vector, and (fullyConnectedLayer) sets the output size to 2 to adapt to the dimensionality requirements of the output; Finally, there is (regressionLayer), the output layer for the regression task, to ensure a direct numerical comparison between the network output and the target value;

[0120] 3) Temporal feature processing: Use a long short-term memory network (LSTM) to process the temporal features of the data; in this model, three LSTM layers are set. The first LSTM (lstmLayer) is set to have (NumOfUnits) neurons, the output mode is sequence (OutputMode, sequence), and a dropout layer (dropoutLayer) is added after it with a dropout rate of 0.3; the second LSTM layer is also set to have (NumOfUnits) neurons, the output mode is sequence, and then a dropout layer is also set with a dropout rate of 0.3; the third LSTM layer has (NumOfUnits) neurons, the output mode is the last element (OutputMode, last), and a dropout layer is added after it with a dropout rate of 0.3. Among them, NumOfUnits is a parameter optimized by the SSA algorithm. Finally, the output of the LSTM layer is converted to the required output size through a fully connected layer (fullyConnectedLayer), then a non-linear transformation is performed through a tanh layer (tanhLayer), and finally the output of the regression task is achieved through a regression layer (regressionLayer) to ensure a direct numerical comparison between the network output and the target value;

[0121] Input the validation set into the SSA-CNN-LSTM regression prediction model to predict the battery discharge capacity and the percentage of battery capacity attenuation; as Figure 11 shown in the training effect of the battery discharge capacity; as Figure 12 shown in the training effect of the percentage of battery capacity attenuation; where the RMSE of the battery capacity < 50 mAh, and the RMSE of the percentage of battery capacity attenuation < 0.008‰, showing a high training effect.

[0122] Obtain the input data of the lithium-ion battery and perform prediction through the trained neural network regression prediction model to obtain the discharge capacity of the lithium-ion battery and the percentage value of the lithium-ion battery capacity attenuation;

[0123] Specifically, based on the PI heating film preheating polynomial regression equation, obtain the external temperature and internal temperature values of the lithium-ion battery after preheating at different preheating powers, different preheating times, and different ambient temperatures; combine the external temperature and internal temperature values, ambient temperature, state of charge, and lithium-ion battery discharge current value of the lithium-ion battery after preheating at different preheating powers, different preheating times, and different ambient temperatures to construct the lithium-ion battery input data; perform prediction on the lithium-ion battery input data through the trained neural network regression prediction model to obtain the discharge capacity of the lithium-ion battery and the percentage value of the lithium-ion battery capacity attenuation.

[0124] Calculate the preheating energy consumption and available energy of the lithium-ion battery, define the available energy weight factor, aging loss rate weight factor, and preheating energy consumption weight factor, and construct a weighted objective function; perform multi-objective optimization on the discharge capacity of the lithium-ion battery, the percentage value of lithium-ion battery capacity attenuation, and the weighted objective function through a multi-objective optimization algorithm to obtain the optimal preheating parameters of the lithium-ion battery.

[0125] Among them, the predicted discharge capacity of the battery is converted into the discharge energy of the battery, and its expression is;

[0126] W sum = CV * 3600

[0127] Among them, the available energy is defined as the discharge energy minus the energy consumed by preheating, and its expression is:

[0128] W act = W sum - W con

[0129] Among them, the weighted objective function is solved through a multi-objective optimization algorithm to obtain the Pareto optimal solution; the target values are determined based on the Pareto optimal solution, and the target values include the available energy of the lithium-ion battery, the lithium-ion battery capacity loss rate, and the lithium-ion battery preheating energy consumption; perform a maximum available energy index search process on the target values to obtain the optimal preheating power and preheating time; calculate the discharge capacity of the lithium-ion battery, the lithium-ion battery capacity loss rate, the lithium-ion battery preheating energy consumption, and the lithium-ion battery available energy under the optimal preheating power and preheating time through an auxiliary function, and combine the discharge capacity of the lithium-ion battery and the percentage value of lithium-ion battery capacity attenuation to draw a Pareto front graph to obtain the optimal preheating parameters of the lithium-ion battery.

[0130] In this embodiment, the polynomial regression equation of the PI heating film is loaded to calculate the external temperature and internal temperature values of the battery under different preheating strategies; the trained SSA-CNN-LSTM neural network model is loaded to predict the battery discharge capacity and the percentage value of battery capacity attenuation; creating the input network specifically includes SOC, discharge current, external temperature, internal temperature, and ambient temperature, and performing normalization processing. The normalized input data is input into the SSA-CNN-LSTM neural network model to predict the battery discharge capacity and the percentage value of battery capacity attenuation; calculating the preheating energy consumption and available energy, and defining weight factors w1, w2, and w3 corresponding to the available energy, aging loss rate, and preheating energy consumption respectively. Specifically, w1, w2, and w3 are 0.4, 0.3, and 0.3 respectively, to construct a weighted objective function; using the NSGA-II algorithm for multi-objective optimization, with the goal of maximizing the available energy while minimizing the percentage of battery capacity attenuation and preheating energy consumption, and extracting the optimal solution obtained by optimization, including the preheating power, preheating time, and corresponding battery discharge capacity, maximum available energy, and performance indicators of the percentage of battery capacity attenuation.

[0131] Further, it should be noted that first, the polynomial regression equation of the PI heating film is loaded to calculate the external temperature and internal temperature values of the battery after preheating under different preheating powers, preheating times, and ambient temperatures;

[0132] Further, the trained SSA-CNN-LSTM neural network model is loaded. According to the input data including the external temperature, internal temperature, ambient temperature, SOC, and discharge current value of the battery, it is used to predict the battery discharge capacity and the percentage value of battery capacity attenuation;

[0133] Construct the optimization objective function (optimizationObjectiveFunction), and the input parameters include preheating power, preheating time, ambient temperature, SOC, discharge current, model, LSTM network, and normalization parameters. Set the constraint conditions as the upper and lower limits of the preheating power and the upper and lower limits of the preheating time.

[0134] Define weight factors w1, w2, and w3 corresponding to the available energy, aging loss rate, and preheating energy consumption respectively; use the weighting factors in the optimization objective function to perform weighted calculations on different objective values.

[0135] Use NSGA-II for multi-objective optimization, set the population size to 100, the maximum number of iterations to 200, and the optimization variables to preheating power and preheating time. Solve through the multi-objective optimization function (gamultiobj) to obtain the Pareto optimal solution (paretoSolutions) and the corresponding objective values (paretoObjectives);

[0136] Extract the target values ​​from the Pareto optimal solution, including available energy, capacity loss rate, and preheating energy consumption. Find the index (optimalIndex) corresponding to the maximum available energy and obtain the corresponding optimal preheating power and preheating time;

[0137] Call the auxiliary function (calculateMetrics) to calculate the discharge capacity, capacity loss rate, preheating energy consumption and available energy under the optimal strategy. Display the corresponding preheating power, preheating time, discharge capacity, available energy, aging capacity loss rate, and draw the Pareto frontier diagram of available energy and preheating energy consumption, available energy and aging loss rate, and preheating energy consumption and aging loss rate, as shown in the following figure. Figure 13 , Figure 14 and Figure 15 As shown, including available energy vs. preheating energy consumption, available energy vs. aging loss rate, preheating energy consumption vs. aging loss rate.

[0138] S400, performing optimal preheating treatment on the lithium-ion battery based on optimal preheating parameters of the lithium-ion battery to obtain a preheated lithium-ion battery.

[0139] Specifically, the obtained optimal preheating parameters are applied to lithium-ion batteries, and the effectiveness and stability of the optimal preheating strategy are verified through battery low-temperature experiments; the specific process includes using the best preheating strategy to test battery performance in an actual low-temperature environment, measuring key parameters such as discharge capacity, percentage of battery capacity decay and preheating energy consumption, and making a detailed comparison with the battery performance under the ordinary preheating strategy; the verification experiment results are used to evaluate the feasibility and advantages of the optimization strategy in practical applications; after the experiment is completed, the actual effect of the optimization strategy is analyzed according to the experimental data, and the optimal preheating strategy is applied to larger-scale battery packs. By adjusting the preheating strategy in the battery management system, the performance of the battery pack in a low-temperature environment can be improved, the service life can be extended, and its reliability and safety under different working conditions can be ensured, so as to achieve efficient and safe operation of lithium-ion batteries under various low-temperature conditions.

[0140] The optimal preheating strategy was input into the simulation model for verification and compared with the original data, as shown in Table 2 below, which shows the comparative data at an ambient temperature of 248.15 K, 100% SOC, and 3.0 A discharge current.

[0141] Table 2 Comparison between the best preheating strategy and the common preheating strategy

[0142] Comparative analysis Optimal preheating strategy Ordinary preheating strategy Ordinary preheating strategy Ordinary preheating strategy Preheating power (W) 12.4 7 9 13 Preheating time (s) 299 300 300 300 Discharge capacity (Ah) 2.181 2.3032 2.3228 2.048 Aging loss rate (%) 0.00578 0.01163 0.01067 0.00624 Preheating energy consumption (J) 3707.6 2100 2700 3900 Available energy (J) 25316.2 28845.0 28239.7 23379.4

[0143] As can be seen from Table 2, when the optimal preheating strategy is adopted compared with a preheating power of 13 W and a preheating time of 300 s, the available energy increases by 8.28%, the preheating energy consumption decreases by 4.93%, and the aging loss rate decreases by 7.37%; compared with a preheating power of 9 W and a preheating time of 300 s, although the available energy decreases by 10.35% and the preheating energy consumption increases by 37.32%, the aging loss rate decreases by 45.829%; this proves that the proposed optimal preheating strategy has good practicability.

[0144] In addition, it should be noted that the optimal preheating strategy under different optimization objectives can be obtained by changing the ratios of the weight factors w1, w2, and w3.

[0145] In summary, the embodiment of the present invention uses the method of external preheating of the PI heating film to obtain the external temperature and internal temperature values of the lithium-ion battery under different preheating powers, preheating times, and ambient temperatures, and establishes a regression equation related thereto; and conducts HPPC experiments on the lithium-ion battery under different ambient temperatures, SOCs, and discharge currents, and performs parameter identification on the experimental data to establish a polynomial regression equation of the electrical parameters with respect to the ambient temperature, SOC, and discharge current; couples the RC equivalent circuit model, thermal model, aging capacity loss model, and external preheating model, and inputs them into the COMSOL software, changes multiple variables such as the preheating strategy, ambient temperature, SOC, and discharge current to perform simulations, and obtains simulation data; establishes a data set from the simulation data, and proposes a regression prediction of the discharge capacity and the percentage of battery aging capacity loss based on the SSA-CNN-LSTM neural network model; uses the multi-objective optimization genetic algorithm NSGA-II to automatically find the optimal preheating strategy for the input data of the ambient temperature, internal and external battery temperatures, SOC, and discharge current, so that the discharge capacity of the battery is maximized, the percentage of battery capacity attenuation is minimized, and the preheating energy consumption is minimized. As a method for obtaining the optimal preheating strategy of lithium-ion batteries in a low-temperature environment, the present invention has strong adaptability and can cope with complex and changeable usage scenarios; the optimized preheating strategy can minimize the preheating energy consumption and improve the overall energy utilization rate on the premise of ensuring the battery performance, enabling the battery to provide more effective electric energy in a low-temperature environment.

[0146] Therefore, the embodiment of the present invention has the following beneficial effects:

[0147] 1) The embodiment of the present invention uses the advantages of reliability and safety of external heating to heat the battery; couples the equivalent circuit model, thermal model, external heating model of the lithium battery with the battery capacity attenuation model; considers the influence of the temperature difference between the internal temperature and the external temperature of the battery on battery aging; the method of using a physical model plus a data-driven model can make full use of physical laws and data characteristics to improve the accuracy and reliability of the model.

[0148] 2) By reasonably designing the preheating strategy, the embodiments of the present invention can significantly improve the working performance of lithium-ion batteries in low-temperature environments. During the process of optimizing the preheating power and preheating time, the preheating energy consumption and discharge capacity are comprehensively considered to achieve the maximum available energy; reduce the capacity loss rate and slow down battery aging, which helps to extend the service life of lithium-ion batteries, reduce the replacement frequency and maintenance costs;

[0149] 3) The preheating strategy of the embodiments of the present invention can also be adjusted according to the specific ambient temperature, SOC and discharge current, and is applicable to different low-temperature conditions and application scenarios, with strong flexibility and adaptability.

[0150] Referring to Figure 2 , a lithium-ion battery preheating system in a low-temperature environment includes:

[0151] The first module 201 is used to analyze the experimental data of the lithium-ion battery and construct an RC equivalent circuit model and a thermal model of the lithium-ion battery;

[0152] The second module 202 is used to perform experimental simulations on the RC equivalent circuit model and the thermal model of the lithium-ion battery to obtain a simulation data set of the lithium-ion battery;

[0153] The third module 203 is used to optimize the simulation data set of the lithium-ion battery through a neural network regression prediction model and a multi-objective optimization algorithm to obtain the optimal preheating parameters of the lithium-ion battery;

[0154] The fourth module 204 is used to perform optimal preheating on the lithium-ion battery based on the optimal preheating parameters of the lithium-ion battery to obtain a preheated lithium-ion battery.

[0155] The content in the above method embodiments is applicable to the system embodiments. The functions specifically implemented by the system embodiments are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0156] The above is a specific description of the preferred embodiments of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art can make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for preheating a lithium-ion battery in a low temperature environment, characterized in that: The following steps are involved: The internal temperature data of the lithium-ion battery is obtained through the thermocouple and the external temperature data of the lithium-ion battery is obtained through the PI heating film; Based on the internal temperature data and external temperature data of the lithium-ion battery, the preheating power and preheating time of the PI heating film are adjusted, the lithium-ion battery is externally preheated, and the internal and external temperature values ​​of the lithium-ion battery under different ambient temperatures, preheating powers and preheating times are measured to construct a PI heating film preheating polynomial regression equation; Conduct discharge experiments on fully charged lithium-ion batteries at different ambient temperatures and different discharge currents to obtain the value of lithium-ion battery discharge capacity changing with ambient temperature and discharge current; Conduct mixed power pulse characteristic experiments on fully charged lithium-ion batteries at different ambient temperatures, different states of charge, and different discharge currents to obtain lithium-ion battery terminal voltage and current data; Fit the values ​​of lithium-ion battery discharge capacity changing with ambient temperature and discharge current, as well as the lithium-ion battery terminal voltage and current data, to construct the lithium-ion battery RC equivalent circuit model and lithium-ion battery thermal model; Conduct experimental simulation on the lithium-ion battery RC equivalent circuit model and the lithium-ion battery thermal model to obtain a lithium-ion battery simulation data set; The neural network regression prediction model is trained based on the lithium-ion battery simulation data set to obtain a trained neural network regression prediction model; Obtaining lithium-ion battery input data and predicting through the trained neural network regression prediction model to obtain the percentage value of the discharge capacity of the lithium-ion battery and the capacity attenuation of the lithium-ion battery; Calculate the preheating energy consumption and available energy of the ion battery, define the available energy weight factor, aging loss rate weight factor and preheating energy consumption weight factor, and construct a weighted objective function; The discharge capacity of the lithium-ion battery, the percentage value of the capacity attenuation of the lithium-ion battery and the weighted objective function are optimized by a multi-objective optimization algorithm to obtain the optimal preheating parameters of the lithium-ion battery; The lithium-ion battery is optimally preheated based on the optimal preheating parameters of the lithium-ion battery to obtain a preheated lithium-ion battery.

2. The method for preheating a lithium-ion battery in a low temperature environment according to claim 1, characterized in that: The step of fitting the values ​​of the lithium-ion battery discharge capacity changing with the ambient temperature and the discharge current and the lithium-ion battery terminal voltage and current data to construct the lithium-ion battery RC equivalent circuit model and the lithium-ion battery thermal model specifically includes: The terminal voltage and current data of lithium-ion batteries are fitted by Matlab's cftool toolbox to construct a polynomial regression equation for the terminal voltage and current data of lithium-ion batteries. According to the change of the discharge capacity of the lithium-ion battery with the ambient temperature and the discharge current, the polarization internal resistance value, the polarization capacitance value and the ohmic internal resistance value of the lithium-ion battery are determined; Combining the polarization internal resistance value, polarization capacitance value and ohmic internal resistance value of the lithium-ion battery, a polynomial regression equation for the change of the discharge capacity of the lithium-ion battery with the ambient temperature and the discharge current is constructed; According to the polynomial regression equation about the terminal voltage and current data of the lithium-ion battery and the polynomial regression equation about the value of the discharge capacity of the lithium-ion battery changing with the ambient temperature and the discharge current, combined with Kirchhoff's voltage and current law, the RC equivalent circuit model of the lithium-ion battery is constructed; According to the polynomial regression equation about the terminal voltage and current data of the lithium-ion battery and the polynomial regression equation about the value of the discharge capacity of the lithium-ion battery changing with the ambient temperature and the discharge current, combined with Joule's law, a thermal model of the lithium-ion battery is constructed.

3. The method for preheating a lithium-ion battery in a low temperature environment according to claim 2, characterized in that: The step of performing experimental simulation on the lithium-ion battery RC equivalent circuit model and the lithium-ion battery thermal model to obtain a lithium-ion battery simulation data set specifically includes: Construct a percentage model of capacity attenuation of lithium-ion batteries based on the battery semi-empirical aging model formula; The lithium-ion battery RC equivalent circuit model, lithium-ion battery thermal model, and lithium-ion battery capacity attenuation percentage model are coupled with the PI heating film preheating polynomial regression equation and input into the COMSOL simulation software for experimental simulation to obtain a lithium-ion battery simulation data set.

4. The method for preheating a lithium-ion battery in a low temperature environment according to claim 3, characterized in that: The step of training the neural network regression prediction model based on the lithium-ion battery simulation data set to obtain the trained neural network regression prediction model specifically includes: Inputting a lithium-ion battery simulation data set into a neural network regression prediction model, wherein the neural network regression prediction model includes a sparrow search algorithm optimization model, a convolutional neural network, and a long short-term memory network; Based on the sparrow search algorithm optimization model, the parameters of the neural network regression prediction model are optimized to obtain the optimized neural network regression prediction model; Based on the convolutional neural network of the optimized neural network regression prediction model, local feature extraction processing is performed on the lithium-ion battery simulation data set to obtain the features of the lithium-ion battery simulation data set; Based on the long short-term memory network of the optimized neural network regression prediction model, the time series feature extraction processing is performed on the characteristics of the lithium-ion battery simulation data set to obtain the time series features of the lithium-ion battery simulation data set; The time series characteristics of the lithium-ion battery simulation dataset are used as verification dataset indicators to verify the optimized neural network regression prediction model and construct the trained neural network regression prediction model.

5. A lithium-ion battery preheating method in a low temperature environment according to claim 4, characterized in that: The step of obtaining the lithium-ion battery input data and predicting through the trained neural network regression prediction model to obtain the percentage value of the lithium-ion battery discharge capacity and the lithium-ion battery capacity decay specifically includes: Based on the PI heating film preheating polynomial regression equation, the external temperature and internal temperature values ​​of the lithium-ion battery after preheating at different preheating powers, different preheating times, and different ambient temperatures are obtained; Construct lithium-ion battery input data by combining the external temperature and internal temperature values ​​of lithium-ion batteries after preheating at different preheating powers, different preheating times, and different ambient temperatures, ambient temperature, state of charge, and lithium-ion battery discharge current values; The input data of the lithium-ion battery is predicted by the trained neural network regression prediction model to obtain the percentage value of the discharge capacity of the lithium-ion battery and the capacity attenuation of the lithium-ion battery.

6. A lithium-ion battery preheating method in a low temperature environment according to claim 5, characterized in that: The step of performing multi-objective optimization processing on the discharge capacity of the lithium-ion battery, the percentage value of the capacity attenuation of the lithium-ion battery and the weighted objective function by a multi-objective optimization algorithm to obtain the optimal preheating parameters of the lithium-ion battery specifically includes: The weighted objective function is solved by a multi-objective optimization algorithm to obtain the Pareto optimal solution; Determine target values ​​based on the Pareto optimal solution, wherein the target values ​​include available energy of the lithium-ion battery, capacity loss rate of the lithium-ion battery, and preheating energy consumption of the lithium-ion battery; Perform a maximum available energy index search process on the target value to obtain the optimal preheating power and preheating time; The lithium-ion battery discharge capacity, lithium-ion battery capacity loss rate, lithium-ion battery preheating energy consumption and lithium-ion battery available energy under the optimal preheating power and preheating time are calculated through auxiliary functions. The Pareto frontier diagram is drawn in combination with the discharge capacity of the lithium-ion battery and the percentage value of the lithium-ion battery capacity attenuation to obtain the optimal preheating parameters of the lithium-ion battery.

7. A lithium-ion battery preheating system in a low temperature environment, characterized in that: Includes the following modules: The first module is used to obtain the internal temperature data of the lithium-ion battery through the thermocouple and the external temperature data of the lithium-ion battery through the PI heating film; Based on the internal temperature data and external temperature data of the lithium-ion battery, the preheating power and preheating time of the PI heating film are adjusted, the lithium-ion battery is externally preheated, and the internal and external temperature values ​​of the lithium-ion battery under different ambient temperatures, preheating powers and preheating times are measured to construct a PI heating film preheating polynomial regression equation; Conduct discharge experiments on fully charged lithium-ion batteries at different ambient temperatures and different discharge currents to obtain the value of lithium-ion battery discharge capacity changing with ambient temperature and discharge current; Conduct mixed power pulse characteristic experiments on fully charged lithium-ion batteries at different ambient temperatures, different states of charge, and different discharge currents to obtain lithium-ion battery terminal voltage and current data; Fit the values ​​of lithium-ion battery discharge capacity changing with ambient temperature and discharge current, as well as the lithium-ion battery terminal voltage and current data, to construct the lithium-ion battery RC equivalent circuit model and lithium-ion battery thermal model; The second module is used to perform experimental simulation on the lithium-ion battery RC equivalent circuit model and the lithium-ion battery thermal model to obtain a lithium-ion battery simulation data set; The third module is used to train the neural network regression prediction model based on the lithium-ion battery simulation data set to obtain a trained neural network regression prediction model; Obtaining lithium-ion battery input data and predicting through the trained neural network regression prediction model to obtain the percentage value of the discharge capacity of the lithium-ion battery and the capacity attenuation of the lithium-ion battery; Calculate the preheating energy consumption and available energy of the ion battery, define the available energy weight factor, aging loss rate weight factor and preheating energy consumption weight factor, and construct a weighted objective function; The discharge capacity of the lithium-ion battery, the percentage value of the capacity attenuation of the lithium-ion battery and the weighted objective function are optimized by a multi-objective optimization algorithm to obtain the optimal preheating parameters of the lithium-ion battery; The fourth module is used to perform optimal preheating treatment on the lithium-ion battery based on the optimal preheating parameters of the lithium-ion battery to obtain a preheated lithium-ion battery.

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