Pulse cycling current based lithium ion battery capacity loss free low temperature self heating optimization method

Abstract

The application discloses a lithium ion battery capacity-loss-free low-temperature self-heating optimization method based on pulse cycle current and belongs to the technical field of lithium ion battery self-heating. The application solves the problem of lithium precipitation of a battery caused by improper selection of current amplitude and frequency in the existing pulse cycle self-heating method. The application comprises the following steps: obtaining monomer battery parameters, establishing an electrochemical-thermal coupling model for calculating the temperature rise and lithium precipitation of a battery under the condition of high-frequency pulse cycle current; replacing multiple pulse cycle periods with one pulse cycle period to optimize the calculation efficiency of the electrochemical-thermal coupling model; combining a Bayesian optimization algorithm to establish a pulse cycle self-heating optimization method; adjusting the initial temperature of the electrochemical-thermal coupling model to determine the optimal pulse cycle parameters of the battery at different temperatures, and realizing the real-time adjustment of the pulse cycle current parameters with the battery temperature in the self-heating process. The application can reduce the capacity decline of the battery caused by lithium precipitation on the basis of ensuring a high self-heating temperature rise rate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lithium ion battery self-heating, and particularly relates to a lithium ion battery self-heating optimization method without capacity loss at low temperature based on pulse circulation current. BACKGROUND

[0002] Lithium ion batteries have played a huge role in solving energy crisis and environmental pollution due to their high energy density, cyclic charge and discharge, and no emissions during use. However, low temperature environment will seriously affect the charge and discharge performance of the battery, not only causing a large decrease in the discharge capacity of the battery, but also causing irreversible capacity decay of the battery due to the problem of negative electrode lithium precipitation generated by long-term charging in a low temperature environment. More seriously, the dendritic metal lithium precipitation may pierce the separator, causing serious safety problems. Therefore, heating the battery before use is a feasible means to solve the adverse effects of low temperature on the battery.

[0003] The low temperature heating technology of the battery can be divided into external heating method and self-heating method according to the different sources of heat. The external heating method mainly wraps the battery pack with heating pipes, electric heating plates, phase change materials, PTC heaters and high temperature gas or liquid, etc. outside the battery pack, and transmits the heat outside the battery to the battery through heat conduction to achieve the heating of the battery. This heating method is easy to implement, but has the problems of poor heating uniformity, high heating energy consumption, and the additional space occupied by the heating accessories, which reduces the energy density of the battery pack. The self-heating technology utilizes the characteristic of increased impedance of the battery at low temperature, and heats the battery from the inside by applying current to the battery to generate heat. This heating method does not require additional heating accessories, and the uniformity of the heat generated by the battery is better. In the self-heating technology, compared with the one-way current self-heating technology which is easy to cause overcharging and overdischarging of the battery, the self-heating technology based on pulse circulation current has greater advantages, which can keep the SOC (State of Charge) of the battery stable. However, the current amplitude and frequency selection of the current pulse circulation self-heating method is not appropriate, which easily leads to the problem of lithium precipitation and capacity decrease of the battery. SUMMARY

[0004] The purpose of the present application is to solve the problem of lithium precipitation and capacity decrease of the battery caused by the inappropriate current amplitude and frequency selection of the current pulse circulation self-heating method, and a lithium ion battery self-heating optimization method without capacity loss at low temperature based on pulse circulation current is proposed.

[0005] The technical scheme of the present application is: a lithium ion battery self-heating optimization method without capacity loss at low temperature based on pulse circulation current, comprising the following steps:

[0006] Step one: select a single lithium ion battery, obtain its electrochemical parameters and thermal physical parameters, and establish an electro-thermal coupling model for calculating the temperature rise and lithium precipitation of lithium ion batteries under high-frequency pulse cycle current conditions;

[0007] Step two: optimize the calculation efficiency of the electro-thermal coupling model in step one for the temperature rise and lithium precipitation of lithium ion batteries by replacing the calculation results of multiple pulse cycle periods with the calculation results of one pulse cycle period in the electro-thermal coupling model;

[0008] Step three: use the Bayesian optimization algorithm to combine the electro-thermal coupling model optimized for calculation efficiency in step two to establish a pulse cycle self-heating optimization method, and realize the rapid determination of the optimal pulse cycle current amplitude and frequency;

[0009] Step four: adjust the initial temperature of the electro-thermal coupling model in step two multiple times, use the pulse cycle self-heating optimization method in step three to obtain the optimal pulse cycle current amplitude and frequency, and obtain the optimal pulse cycle parameters of the battery at different temperatures;

[0010] Step five: use the optimal pulse cycle parameters of the battery at different temperatures obtained in step four to adjust the amplitude and frequency of the pulse cycle current in real time according to the temperature of the lithium ion battery, and realize the maximization of the battery temperature rise and the minimization of the lithium precipitation amount.

[0011] Further, the electro-thermal coupling model in step one specifically includes a lithium ion battery quasi-two-dimensional model and a one-dimensional thermal model;

[0012] The lithium ion battery quasi-two-dimensional model specifically includes mass conservation equations, charge conservation equations in the solid-liquid phase of the lithium ion battery, and electrochemical reaction current density equations on the solid-liquid phase interface;

[0013] The solid-phase lithium ion conservation equation is:

[0014]

[0015] The solid-phase charge conservation equation is:

[0016]

[0017] The liquid-phase lithium ion conservation equation is:

[0018]

[0019] The liquid-phase charge conservation equation is:

[0020]

[0021] Wherein, c sD is the solid phase concentration of lithium ions in the electrode particles, s D is the diffusion coefficient of lithium ions in the solid phase, φ is the effective solid phase conductivity, s a is the solid phase potential, v i is the effective reaction area of the positive and negative electrode particles, sum is the total electrochemical reaction current density on the particle surface, ε e is the liquid phase volume fraction, c e is the liquid phase concentration of lithium ions in the electrolyte, is the effective liquid phase diffusion coefficient of lithium ions, t + is the lithium ion transference number, φ is the effective liquid phase conductivity, e a is the liquid phase potential, F is the Faraday constant, R is the ideal gas constant, x is the model x-axis coordinate, r is the model spherical particle radial r-coordinate, t is time, and T is the battery temperature.

[0022] In the electrochemical reaction current density equation on the solid-liquid phase interface, the total electrochemical reaction current density on the negative electrode solid-liquid phase interface specifically includes lithium ion intercalation / deintercalation reaction current density, lithium deposition reaction current density, and double-layer current density, and the total electrochemical reaction current density on the positive electrode solid-liquid phase interface specifically includes lithium ion intercalation / deintercalation reaction current density and double-layer current density.

[0023] wherein the total electrochemical reaction current density on the negative electrode surface is:

[0024] i sum_neg = i Li_int + i Li_pla + i DL

[0025] The total electrochemical reaction current density on the positive electrode surface is:

[0026] i sum_pos = i Li_int + i DL

[0027] The lithium ion intercalation / deintercalation reaction current density is:

[0028]

[0029] The lithium deposition reaction current density is:

[0030]

[0031] The double-layer current density is:

[0032]

[0033] wherein i0,int exchange current density for lithium intercalation / deintercalation reaction, α int transfer coefficient for lithium intercalation / deintercalation reaction, η int overpotential for lithium intercalation / deintercalation reaction, i 0,pla exchange current density for lithium deposition reaction, α pla transfer coefficient for lithium deposition reaction, η pla overpotential for lithium deposition reaction, C DL double-layer capacitance, φ DL double-layer potential;

[0034] The one-dimensional thermal model specifically includes an energy conservation equation for calculating the battery temperature change and an Arrhenius correction formula for correcting the electrochemical parameters and reaction kinetics parameters at the reference temperature;

[0035] The energy conservation equation is:

[0036]

[0037] The Arrhenius correction formula is:

[0038]

[0039] wherein, ρ is the battery density, c p is the specific heat capacity of the battery, q J is the total heat source, h is the convective heat transfer coefficient of the battery and the environment, A is the convective heat transfer surface area of the battery, T0 is the ambient temperature, Arreh is the temperature correction coefficient, E a is the activation energy of the parameter, T r is the reference temperature.

[0040] Further, the electrochemical-thermal coupling model after the calculation efficiency optimization in step two can quickly calculate the temperature rise and lithium precipitation amount of the lithium ion battery under the condition of high-frequency pulse cycle current; wherein the specific method for optimizing the calculation efficiency of the electrochemical-thermal coupling model is to set an acceleration factor t fac , and replace the lithium precipitation amount of t fac pulse cycle with the lithium precipitation amount of one pulse cycle, so that the calculation period is shortened by t fac times; correspondingly, the energy conservation equation is rewritten to match the temperature change of the battery with the accelerated time scale:

[0041]

[0042] Further, the specific implementation process of the pulse cycle self-heating optimization method in step three is:

[0043] Step three one: input any set of pulse cycle parameter combination in the pulse cycle parameter space into the calculated efficiency-optimized electrochemical-thermal coupling model, add the calculated battery temperature rise index and capacity attenuation index to the observation data set of the Bayesian optimization algorithm to initialize the probability proxy model;

[0044] Step three two: the Bayesian optimization algorithm automatically selects the next pulse cycle parameter combination to be observed according to the acquisition function;

[0045] Step three three: input the pulse cycle parameter combination obtained in step three two into the calculated efficiency-optimized electrochemical-thermal coupling model to calculate the battery temperature and lithium precipitation amount after pulse cycle self-heating;

[0046] Step three four: input the battery temperature and lithium precipitation amount after pulse cycle self-heating obtained in step three three into the Bayesian optimization algorithm, and repeat steps three two to three four after updating the probability proxy model.

[0047] Further, the probability proxy model specifically includes a prior probability model p(f) and an observation model p(D|f). According to Bayes' theorem, updating the probability proxy model is to obtain the posterior probability p(f|D) containing more data information; wherein f represents the mapping relationship between temperature rise and lithium precipitation amount and pulse cycle current parameters, D represents the data set of the observed temperature rise and lithium precipitation amount under the pulse cycle current parameters, p(f) represents the prior probability of f, and p(D|f) represents the posterior probability of D.

[0048] Further, the acquisition function determines the next set of pulse cycle current parameters to be observed by maximizing the temperature rise and minimizing the acquisition function of lithium precipitation amount.

[0049] Further, the specific implementation process of obtaining the optimal pulse cycle parameters of the battery at different temperatures in step four is as follows:

[0050] Step four one: use the pulse cycle self-heating optimization method in step three to obtain the amplitude and frequency of the optimal pulse cycle current at the current temperature;

[0051] Step four two: modify the initial temperature of the calculated efficiency-optimized electrochemical-thermal coupling model and repeat step four one

[0052] The beneficial effects of the present application are that the present application discloses a lithium ion battery capacity loss-free low-temperature self-heating optimization method based on pulse cycle current, compared with the prior art, the improvement of the present application is that: the present application calculates the temperature rise and lithium precipitation of the battery under the condition of high-frequency pulse cycle current through the establishment of electrochemical-thermal coupling model; replace multiple pulse cycle periods with one pulse cycle period, optimize the calculation efficiency of the electrochemical-thermal coupling model; combined with the bayesian optimization algorithm, establish the pulse cycle self-heating optimization method, quickly obtain the amplitude and frequency of the optimal pulse cycle current; adjust the initial temperature of the electrochemical-thermal coupling model, obtain the optimal pulse cycle parameters of the battery at different temperatures, realize the real-time adjustment of the pulse cycle current parameters with the battery temperature in the self-heating process. The present application provides a lithium ion battery capacity loss-free low-temperature self-heating optimization method based on pulse cycle current, which can make the battery adjust the pulse cycle current parameters to the optimal value in real time according to the temperature in the self-heating process, ensure that the battery capacity decline caused by lithium precipitation is reduced on the basis of high self-heating temperature rise rate. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 The flowchart of the present application is a lithium ion battery capacity loss-free low-temperature self-heating optimization method based on pulse cycle current;

[0054] Figure 2 The schematic diagram of the electrochemical-thermal coupling model of the lithium ion battery in the embodiment of the present application is shown in the figure;

[0055] Figure 3 The specific implementation flowchart of the pulse cycle self-heating optimization method in the embodiment of the present application is shown in the figure;

[0056] Figure 4 The specific implementation flowchart of obtaining the optimal pulse cycle parameters of the battery at different temperatures in the embodiment of the present application is shown in the figure;

[0057] Figure 5 is the change of the pulse cycle current parameters in space of the battery in the process of using the pulse cycle self-heating optimization method at-20℃ in the embodiment of the present application. Figure 5(a) is the 10th iteration; Figure 5(b) is the 50th iteration; Figure 5(c) is the 100th iteration;

[0058] Figure 6 The optimal pulse cycle parameters of the battery at different temperatures in the embodiment of the present application are shown in the figure;

[0059] Figure 7 The simulation and experimental results of the battery temperature rise using the optimization method for self-heating in the embodiment of the present application are shown in the figure;

[0060] Figure 8 The simulation and experimental results of the battery capacity change after using the optimization method for self-heating 90 times in the embodiment of the present application are shown in the figure. DETAILED DESCRIPTION

[0061] In order to facilitate the understanding of the present application, the present application is further described below in conjunction with the accompanying drawings and specific implementation examples, but the protection scope of the present application is not limited to the following specific examples.

[0062] Embodiment: A lithium ion battery capacity loss-free low-temperature self-heating optimization method based on pulse cycle current, the implementation process is as shown in Figure 1 , including: obtaining single battery parameters, establishing an electrochemical-thermal coupling model for calculating the temperature rise and lithium precipitation of the battery under high-frequency pulse cycle current conditions; replacing multiple pulse cycle periods with one pulse cycle period to optimize the calculation efficiency of the electrochemical-thermal coupling model; combining the Bayesian optimization algorithm to establish a pulse cycle self-heating optimization method; adjusting the initial temperature of the electrochemical-thermal coupling model to determine the optimal pulse cycle parameters of the battery at different temperatures, and realizing the real-time adjustment of the pulse cycle current parameters during the self-heating process. The specific implementation steps are as follows:

[0063] Step one: select a single lithium ion battery, obtain its electrochemical parameters and thermal physical parameters, and establish an electrochemical-thermal coupling model for calculating the temperature rise and lithium precipitation of the lithium ion battery under high-frequency pulse cycle current conditions.

[0064] The electrochemical-thermal coupling model specifically includes a lithium ion battery quasi-two-dimensional model (P2D model) and a one-dimensional thermal model. The lithium ion battery quasi-two-dimensional model specifically includes the mass conservation equation, the charge conservation equation in the solid-liquid phase of the lithium ion battery, and the electrochemical reaction current density equation on the solid-liquid phase interface. The mass conservation equation and the charge conservation equation in the solid-liquid phase of the lithium ion battery are shown in Table 1. Wherein, c s is the solid phase concentration of lithium ions in the electrode particles, D s is the diffusion coefficient of lithium ions in the solid phase, is the effective solid phase conductivity, φ s is the solid phase potential, a v is the effective reaction area of the positive and negative electrode particles, i sum is the total electrochemical reaction current density on the particle surface, ε e is the liquid phase volume fraction, c e is the liquid phase concentration of lithium ions in the electrolyte, is the effective liquid phase diffusion coefficient of lithium ions, t + is the lithium ion transference number, is the effective liquid phase conductivity, φ e is the liquid phase potential, F is the Faraday constant, R is the ideal gas constant, x is the model x-axis coordinate, r is the model spherical particle radial r-coordinate, t is the time, and T is the battery temperature.

[0065] Table 1 Charge and mass conservation equations in lithium-ion battery solid-liquid phase

[0066]

[0067] The total electrochemical reaction current density on the solid-liquid interface includes lithium ion intercalation / deintercalation reaction current density, lithium deposition reaction current density and double layer current density, as shown in formula (5) in Table 2; the total electrochemical reaction current density on the solid-liquid interface includes lithium ion intercalation / deintercalation reaction current density and double layer current density, as shown in formula (6) in Table 2. Wherein, the lithium ion intercalation / deintercalation reaction current density, lithium deposition reaction current density and double layer current density are shown in formula (7)-(9) in Table 2. Wherein, i 0,int is the lithium ion intercalation / deintercalation reaction exchange current density, a int is the lithium ion intercalation / deintercalation reaction transfer coefficient, η int is the lithium ion intercalation / deintercalation reaction overpotential, i 0,pla is the lithium deposition reaction exchange current density, a pla is the lithium deposition reaction transfer coefficient, η pla is the lithium deposition reaction overpotential, C DL is the double layer capacitance, φ DL is the double layer potential.

[0068] Table 2 Electrode reaction kinetics equations of lithium-ion battery

[0069]

[0070] The one-dimensional thermal model includes energy conservation equation and Arrhenius correction formula, wherein the battery temperature change is calculated by the energy conservation equation, and the electrochemical parameters and reaction kinetics parameters at the reference temperature are corrected by the Arrhenius formula, as shown in formula (10)-(11) in Table 3. Wherein, ρ is the battery density, c p is the specific heat capacity of the battery, q J is the total heat source, h is the convective heat transfer coefficient of the battery and the environment, A is the convective heat transfer surface area of the battery, T0 is the environmental temperature, Arreh is the temperature correction coefficient, E a is the activation energy of the parameter, T r is the reference temperature.

[0071] Table 3 Thermal model of lithium-ion battery

[0072]

[0073] Taking a commercial lithium iron phosphate (LFP) / graphite 18650 cylindrical single-cell battery with a rated capacity of 1200mAh as an example, an electrochemical-thermal coupling model was established using COMSOL Multiphysics, and the schematic diagram of the model is shown in Figure 2. All parameter values ​​were obtained through experimental measurements, literature citations, and fitting experimental data, as shown in Table 4.

[0074] Table 4 Parameters of the Electrochemical-Thermocoupled Model

[0075]

[0076]

[0077] Step Two: Optimize the computational efficiency of the electrochemical-thermal coupling model in Step One for calculating lithium-ion battery temperature rise and lithium plating by replacing the calculation results from multiple pulse cycles with the results obtained from a single pulse cycle in the electrochemical-thermal coupling model. Specifically, this is achieved by setting an acceleration factor t. fac The amount of lithium plating in one pulse cycle is used instead of t. fac The amount of lithium deposited per pulse cycle shortens the calculation cycle by t. fac Accordingly, the energy conservation equation is rewritten to match the battery's temperature change to the accelerated time scale: Optionally, t fac The value is set to 10Freq, where Freq is the selected frequency of the pulsed cycling current. This means that the calculation result obtained in one pulse cycle is used instead of the calculation result obtained in 10Freq pulse cycles. The electrochemical-thermal coupling model, optimized for computational efficiency, can quickly calculate the temperature rise and lithium deposition of lithium-ion batteries under high-frequency pulsed cycling current conditions.

[0078] Step 3: Using the Bayesian optimization algorithm, combined with the electrochemical-thermal coupling model optimized for computational efficiency in Step 2, a pulsed cyclic self-heating optimization method is established to rapidly determine the optimal pulsed cyclic current amplitude and frequency. Preferably, the specific implementation process of the pulsed cyclic self-heating optimization method is as follows: Figure 3 As shown, it includes:

[0079] Step 301: Select any set of pulse cycle parameters from the pulse cycle parameter space and input them into the electrochemical-thermal coupling model after computational efficiency optimization. Add the calculated battery temperature and lithium plating amount after five minutes of pulse cycle self-heating along with the parameter combination to the observation data set of the Bayesian optimization algorithm to initialize the probabilistic surrogate model.

[0080] Step 302: The Bayesian optimization algorithm automatically selects the next pulse cycle parameter combination to be observed based on the acquisition function.

[0081] Step 303: input the pulse cycle parameter set into the calculated efficiency-optimized electro-thermal coupling model to calculate the battery temperature and lithium precipitation amount after five minutes of pulse cycle self-heating.

[0082] Step 304: input the battery temperature and lithium precipitation amount after five minutes of pulse cycle self-heating into the Bayesian optimization algorithm, and repeat steps 302 to 304 after updating the probabilistic surrogate model.

[0083] The probabilistic surrogate model specifically includes a prior probability model p(f) and an observation model p(D|f). According to Bayes' theorem, updating the probabilistic surrogate model is to obtain the posterior probability p(f|D) containing more data information; wherein f represents the mapping relationship between temperature rise and lithium precipitation amount and pulse cycle current parameters, D represents the data set of observed temperature rise and lithium precipitation amount under the pulse cycle current parameters, p(f) represents the prior probability of f, and p(D|f) represents the posterior probability of D. The acquisition function determines the next set of pulse cycle current parameters to be observed by maximizing the temperature rise and minimizing the acquisition function of lithium precipitation amount.

[0084] Step four: adjust the initial temperature of the electro-thermal coupling model in step two multiple times, use the pulse cycle self-heating optimization method in step three to obtain the optimal pulse cycle current amplitude and frequency, and obtain the optimal pulse cycle parameters of the battery at different temperatures. Preferably, the specific implementation process of obtaining the optimal pulse cycle parameters of the battery at different temperatures is as shown in Figure 4 , which includes:

[0085] Step 401: set the initial temperature of the calculated efficiency-optimized electro-thermal coupling model to -20°C.

[0086] Step 402: use the pulse cycle self-heating optimization method to obtain the optimal pulse cycle current amplitude and frequency at the current temperature.

[0087] Step 403: modify the initial temperature T of the calculated efficiency-optimized electro-thermal coupling model to T+2, repeat steps 402 to 403 until the temperature is 0°C.

[0088] Figure 5 is the change of pulse cycle current parameters and corresponding temperature rise and lithium precipitation amount in space obtained at the 10th iteration, the 50th iteration and the 100th iteration in the process of using the pulse cycle self-heating optimization method at an initial temperature of -20°C. In the iteration process, the optimization method makes the pulse cycle current parameters quickly move towards the direction of maximizing the temperature rise and minimizing the lithium precipitation amount. The optimal pulse cycle parameters of the battery at different temperatures are as shown in Figure 6 .

[0089] Step 5: Using the optimal pulse cycling parameters obtained in Step 4 for the battery at different temperatures, adjust the amplitude and frequency of the pulse cycling current in real time according to the temperature of the lithium-ion battery to maximize battery temperature rise and minimize lithium deposition. Simulation and experimental results of the self-heating battery temperature rise using the optimization method are shown below. Figure 7 As shown, the battery can be rapidly heated from -20°C to 11.1°C within five minutes, and to 0°C in just 160 seconds. Simulation and experimental results of battery capacity changes after 90 self-heating cycles using the optimized method are shown below. Figure 8 As shown, the battery capacity did not decrease after 90 cycles of low-temperature heating. Both simulation and experiments confirm that the low-temperature self-heating optimization method for lithium-ion batteries based on pulsed cycling current proposed in this invention, which eliminates capacity loss, can effectively solve the problem of lithium plating and capacity reduction caused by improper selection of current amplitude and frequency in existing pulsed cycling self-heating methods, while ensuring a high self-heating temperature rise rate.

[0090] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A pulse cycling current based lithium ion battery capacity lossless low temperature self heating optimization method characterized in that, The method comprises the following steps: Step one: selecting a single lithium ion battery, obtaining its electrochemical parameters and thermal physical parameters, and establishing an electro-thermal coupling model for calculating the temperature rise and lithium precipitation of the lithium ion battery under high-frequency pulse cycle current conditions; Step two: optimizing the calculation efficiency of the electro-thermal coupling model in step one for the temperature rise and lithium precipitation of the lithium ion battery by replacing the calculation results of multiple pulse cycle periods with the calculation results of one pulse cycle period in the electro-thermal coupling model; Step three: using a Bayesian optimization algorithm and combining the electro-thermal coupling model with optimized calculation efficiency in step two to establish a pulse cycle self-heating optimization method and realize rapid determination of the optimal pulse cycle current amplitude and frequency; Step four: adjusting the initial temperature of the electro-thermal coupling model in step two multiple times, using the pulse cycle self-heating optimization method in step three to obtain the optimal pulse cycle current amplitude and frequency, and obtaining the optimal pulse cycle parameters of the battery at different temperatures; Step five: using the optimal pulse cycle parameters of the battery at different temperatures obtained in step four, adjusting the amplitude and frequency of the pulse cycle current in real time according to the temperature of the lithium ion battery to realize maximization of the battery temperature rise and minimization of the lithium precipitation amount; The electro-thermal coupling model in step one specifically comprises a quasi-two-dimensional model of the lithium ion battery and a one-dimensional thermal model; The quasi-two-dimensional model of the lithium ion battery specifically comprises mass conservation equations, charge conservation equations in the solid-liquid phases of the lithium ion battery, and an electrochemical reaction current density equation on the solid-liquid phase interface; The solid-phase lithium ion conservation equation is: The solid-phase charge conservation equation is: The liquid-phase lithium ion conservation equation is: The liquid-phase charge conservation equation is: where c s is the solid-phase concentration of lithium ions in the electrode particles, D s is the diffusion coefficient of lithium ions in the solid phase, is the effective solid-phase conductivity, φ s is the solid-phase potential, a v is the effective reaction area of the positive and negative electrode particles, i sum is the total electrochemical reaction current density on the particle surface, ε e is the volume fraction of the liquid phase, c e is the liquid-phase concentration of lithium ions in the electrolyte, is the effective liquid-phase diffusion coefficient of lithium ions, t + is the lithium ion transference number, is the effective liquid-phase conductivity, φ e is the liquid-phase potential, F is the Faraday constant, R is the ideal gas constant, x is the model x-axis dimension coordinate, r is the model spherical particle radial r-dimension coordinate, t is time, and T is the battery temperature. In the electrochemical reaction current density equation on the solid-liquid phase interface, the total electrochemical reaction current density on the negative electrode solid-liquid phase interface specifically comprises lithium ion intercalation / deintercalation reaction current density, lithium precipitation reaction current density, and double-layer current density, and the total electrochemical reaction current density on the positive electrode solid-liquid phase interface specifically comprises lithium ion intercalation / deintercalation reaction current density and double-layer current density; The total electrochemical reaction current density on the negative electrode surface is: i sum_neg = i Li_int + i Li_pla + i DL The total electrochemical reaction current density on the positive electrode surface is: i sum_pos = i Li_int + i DL The lithium ion intercalation / deintercalation reaction current density is: The lithium precipitation reaction current density is: The double-layer current density is: wherein, i 0,int is the exchange current density for the lithium ion intercalation / deintercalation reaction, a int is the transfer coefficient for the lithium ion intercalation / deintercalation reaction, η int is the overpotential for the lithium ion intercalation / deintercalation reaction, i 0,pla is the exchange current density for the lithium deposition reaction, a pla is the transfer coefficient for the lithium deposition reaction, η pla is the overpotential for the lithium deposition reaction, C DL is the double layer capacitance, φ DL is the double layer potential; The one-dimensional thermal model specifically comprises an energy conservation equation for calculating the temperature change of the battery and an Arrhenius correction formula for correcting the electrochemical parameters and reaction kinetics parameters at a reference temperature; The energy conservation equation is: The Arrhenius correction formula is: where p is the battery density, c p is the specific heat capacity of the battery, q J is the total heat source, h is the convective heat transfer coefficient between the battery and the environment, A is the convective heat transfer surface area of the battery, To is the ambient temperature, Arreh is the temperature correction coefficient, E a is the activation energy of the parameter, T r is the reference temperature; In step two, the electrochemical-thermal coupling model, after computational efficiency optimization, can quickly calculate the temperature rise and lithium deposition of lithium-ion batteries under high-frequency pulsed cycling current conditions. Specifically, the method for optimizing the computational efficiency of the electrochemical-thermal coupling model involves setting an acceleration factor t. fac The amount of lithium plating in one pulse cycle is used instead of t. fac The amount of lithium deposited per pulse cycle shortens the calculation cycle by t. fac Accordingly, the energy conservation equation is rewritten to match the battery's temperature change to the accelerated time scale: The specific implementation process of the pulse cycle self-heating optimization method in step three is as follows: Step three one: selecting an arbitrary pulse cycle parameter combination in the pulse cycle parameter space and inputting the pulse cycle parameter combination into the electro-thermal coupling model with optimized calculation efficiency, adding the calculated battery temperature rise index and capacity attenuation index to the observation data set of the Bayesian optimization algorithm to initialize the probability proxy model; Step three two: the Bayesian optimization algorithm automatically selects the next pulse cycle parameter combination to be observed according to the sampling function; Step three three: input the pulse cycle parameter set obtained in step three two into the calculated efficiency-optimized electrochemical-thermal coupling model to calculate the battery temperature and lithium precipitation amount after pulse cycle self-heating; Step three four: input the battery temperature and lithium precipitation amount after pulse cycle self-heating obtained in step three three into the Bayesian optimization algorithm, and repeat steps three two to three four after updating the probability proxy model.

2. The method for lithium-ion battery self-heating optimization without capacity loss at low temperature based on pulsed cyclic current according to claim 1, characterized in that, The probability proxy model specifically includes a prior probability model p(f) and an observation model p(D|f). According to Bayes' theorem, updating the probability proxy model is to obtain the posterior probability p(f|D) containing more data information; wherein f represents the mapping relationship between temperature rise and lithium precipitation amount and pulse cycle current parameters, D represents the data set of the observed temperature rise and lithium precipitation amount under the pulse cycle current parameters, p(f) represents the prior probability of f, and p(D|f) represents the posterior probability of D. 3.The lithium-ion battery capacity-loss-free low-temperature self-heating optimization method based on pulsed cyclic current according to claim 1, wherein, The acquisition function determines the next set of pulse cycle current parameters that need to be observed by maximizing the temperature rise and minimizing the acquisition function of the lithium precipitation amount.

4. The pulsed cyclic current based lithium ion battery capacity-less loss low temperature self-heating optimization method according to claim 1, wherein, The specific implementation process of obtaining the optimal pulse cycle parameters of the battery at different temperatures in step four is as follows: Step four one: use the pulse cycle self-heating optimization method in step three to obtain the amplitude and frequency of the optimal pulse cycle current at the current temperature; Step four two: modify the initial temperature of the calculated efficiency-optimized electrochemical-thermal coupling model and repeat step four one.

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