Intelligent charging method for lithium ion power battery

Abstract

The application discloses a kind of lithium ion power battery intelligent charging methods, by constructing battery fast charging simulation model and obtaining parameter;The initial charging safety boundary of battery is obtained;The initial charging safety boundary is verified by lithium ion power battery full life cycle data test platform, and the target charging safety boundary is obtained;According to the target charging safety boundary, the final optimal solution of battery fast charging multi-objective is calculated by improved genetic algorithm, and the first fast charging strategy is obtained;The first fast charging strategy is optimized according to pulse width, and the final target fast charging strategy is obtained, and the final target fast charging strategy obtained through the above overall steps can shorten the charging time of lithium ion power battery, reduce temperature rise, delay battery life and reduce energy loss during charging.The application can be widely applied in the field of battery fast charging optimization technology.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery fast charging optimization, and particularly to an intelligent charging method for a lithium-ion power battery. BACKGROUND

[0002] For the pure electric passenger vehicle industry, "slow charging" is still one of the core pain points. Taking some pure electric vehicles supporting fast charging as an example, the average theoretical charging rate thereof is about 1C, that is, 30 minutes are needed to achieve charging of 30% to 80% SoC. However, the charging rate of most electric vehicles is still not high, and about 40 to 50 minutes are needed to achieve charging of 30% to 80% SoC. Therefore, improving the charging speed becomes an important link to accelerate the development of pure electric passenger vehicles. At the present stage, improving the charging speed and guaranteeing the safety of battery charging are always two important goals of the formulation of a lithium-ion power battery charging strategy, and formulating a scientific and reasonable charging strategy can shorten the charging time while guaranteeing the service life of the battery.

[0003] The charging strategy of a battery mainly includes two kinds of strategies based on experience and based on a model. The charging strategy based on experience generally shortens the charging time by increasing the charging rate, however, the chemical reaction mechanism inside the battery is not deeply considered in the charging strategy, and the service life of the battery is inevitably accelerated while the charging rate is improved. Therefore, three kinds of optimization schemes of the charging strategy of a lithium-ion power battery are proposed by some scholars: improving the current waveform (such as pulse charging, multi-stage charging, etc.), finding the optimal charging current and current frequency under different constraints by using an optimized estimation algorithm, or improving the charging process according to the chemical reaction mechanism inside the battery. SUMMARY

[0004] In view of this, the embodiments of the present application provide an intelligent charging method for a lithium-ion power battery, which aims to optimize and shorten the charging time, reduce the temperature rise, prolong the service life of the battery, and reduce the energy loss during charging.

[0005] One aspect of the embodiments of the present application provides an intelligent charging method for a lithium-ion power battery, comprising the following steps: constructing a battery fast charging simulation model and obtaining model parameters; determining an initial charging safety boundary of the battery according to the model parameters; verifying the initial charging safety boundary through a lithium-ion power battery full-life cycle data test platform to obtain a target charging safety boundary; wherein the lithium-ion power battery full-life cycle data test platform comprises a fast charging test platform and an alternating current impedance test platform; calculating the final optimal solution of a battery fast charging multi-objective through an improved genetic algorithm according to the target charging safety boundary to obtain a first fast charging strategy; and optimizing the first fast charging strategy according to the pulse width to obtain a final target fast charging strategy.

[0006] Optionally, the constructing a battery fast charging simulation model and obtaining model parameters comprises: taking a lithium battery pseudo two-dimensional model as a prototype, adding solid-liquid phase diffusion and ohmic polarization process on the basis of a lithium ion thermal coupling model to construct a battery fast charging simulation model; obtaining model parameters of the battery fast charging simulation model, and reducing the model parameters.

[0007] Optionally, the determining the initial charging safety boundary of the battery according to the model parameters comprises: obtaining simulation of a negative electrode potential in battery operation through the battery fast charging simulation model according to the model parameters; and determining an initial charging safety boundary according to a simulation result.

[0008] Optionally, the verifying the initial charging safety boundary through a lithium ion power battery full life cycle data test platform to obtain a target charging safety boundary comprises: configuring a first battery group and a second battery group; wherein the first battery group and the second battery group each comprise a plurality of new batteries with the same performance; configuring a lithium ion power battery full life cycle data test platform to monitor data of a battery impedance and a constant temperature and humidity box during a charging and discharging process; obtaining a first processing result of the first battery group in a constant temperature box at a first environmental temperature during a cycle working condition processing through the data monitoring; obtaining a second processing result of the second battery group in a constant temperature box at a second environmental temperature during a cycle working condition processing through the data monitoring; verifying the initial charging safety boundary according to the first processing result and the second processing result to obtain a target charging safety boundary.

[0009] Optionally, the calculating a final optimal solution of a battery fast charging multi-objective according to the target charging safety boundary through an improved genetic algorithm to obtain a first fast charging strategy comprises: initializing a genetic population according to the target safety boundary; wherein an individual of the genetic population is a current sequence of a lithium ion power battery charging process; determining a fast charging optimization target and a fitness function of the lithium ion power battery; performing cross and mutation on the genetic population, and calculating an intermediate optimal solution of the battery fast charging multi-objective according to the fast charging optimization target and the fitness function; repeating the step of performing cross and mutation on the genetic population until a maximum iteration number reaches a predetermined threshold value, or the intermediate optimal solution calculated in a predetermined continuous iteration number changes by not more than a predetermined optimal solution change threshold value, and then obtaining the final optimal solution of the battery fast charging multi-objective; and taking the optimal solution of the battery fast charging multi-objective as the first fast charging strategy.

[0010] Optionally, the crossing and mutation of the genetic population, the intermediate optimal solution of the battery fast charging multi-objective is calculated according to the fast charging optimization target and the fitness function, comprising: determining a first individual and a second individual in the genetic population according to a crossover probability; crossing the gene positions of the first individual and the second individual randomly through simulated binary crossover to obtain a first parent population and a first offspring population; performing a mutation operation on the first offspring population through an equal-probability mutation method to obtain a second parent population and a second offspring population; recombining the second parent population and the second offspring population to obtain a solution of the battery fast charging multi-objective; wherein the solution of the battery fast charging multi-objective is one or more; determining an intermediate optimal solution based on a tournament algorithm according to the fitness function and the solution of the battery fast charging multi-objective.

[0011] Optionally, the first fast charging strategy is optimized according to the pulse width to obtain a final target fast charging strategy, comprising: determining a plurality of candidate pulse widths based on a weight coefficient; calculating the first fast charging strategy under each candidate pulse width to obtain a first fast charging strategy set; comparing the fitness function convergence results of all the first fast charging strategies in the first fast charging strategy set to obtain a final target fast charging strategy.

[0012] Embodiments of the present application also provide a lithium-ion power battery intelligent charging system, comprising: a first module for building a battery fast charging simulation model and obtaining parameters; a second module for obtaining an initial charging safety boundary of the battery; a third module for verifying the initial charging safety boundary through a lithium-ion power battery full life cycle data test platform to obtain a target charging safety boundary; wherein the lithium-ion power battery full life cycle data test platform comprises a fast charging test platform and an alternating current impedance test platform; a fourth module for solving an ultimate optimal solution of a battery fast charging multi-objective according to the target charging safety boundary through an improved genetic algorithm to obtain a first fast charging strategy; and a fifth module for optimizing the fast charging strategy according to a pulse width to obtain a final target fast charging strategy.

[0013] Embodiments of the present application also provide an electronic device, comprising a processor and a memory; the memory is used for storing a program; the processor executes the program to realize the method as described above.

[0014] Embodiments of the present application also provide a computer readable storage medium, the storage medium stores a program, the program is executed by a processor to realize the method as described above.

[0015] The embodiment of the present application has the following beneficial effects: by constructing a battery fast charging simulation model and obtaining parameters; obtaining an initial charging safety boundary of the battery; verifying the initial charging safety boundary through a lithium ion power battery full life cycle data test platform to obtain a target charging safety boundary; wherein the lithium ion power battery full life cycle data test platform comprises a fast charging test platform and an alternating current impedance test platform; according to the target charging safety boundary, calculating an ultimate optimal solution of the battery fast charging multi-objective through an improved genetic algorithm to obtain a first fast charging strategy; optimizing the first fast charging strategy according to pulse width to obtain a final target fast charging strategy, and the final target fast charging strategy obtained through the above overall steps can shorten the charging time of the lithium ion power battery, reduce temperature rise, prolong the service life of the battery and reduce energy loss during charging. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0017] Figure 1 is a method step flow chart provided by the embodiment of the present application;

[0018] Figure 2 is a negative electrode potential-SoC relationship curve of the battery at 5 DEG C provided by the embodiment of the present application;

[0019] Figure 3 is a negative electrode potential-SoC relationship curve of the battery at 25 DEG C provided by the embodiment of the present application;

[0020] Figure 4 is a charging safety boundary curve at 5 DEG C provided by the embodiment of the present application;

[0021] Figure 5 is a charging safety boundary curve at 25 DEG C provided by the embodiment of the present application;

[0022] Figure 6 is a charging safety boundary curve of the battery at different temperatures provided by the embodiment of the present application;

[0023] Figure 7 is a charging condition curve at 25 DEG C provided by the embodiment of the present application;

[0024] Figure 8 is a charging condition curve at 5 DEG C provided by the embodiment of the present application;

[0025] Figure 9is a flow chart of the improved genetic algorithm provided by the embodiment of the present application;

[0026] Figure 10 is a first variation mode schematic diagram provided by the embodiment of the present application;

[0027] Figure 11 is a second variation mode schematic diagram provided by the embodiment of the present application;

[0028] Figure 12 is a 3s, 5s, 10s three pulse width fitness function convergence process curve diagram provided by the embodiment of the present application;

[0029] Figure 13 is a 3s, 5s, 10s three pulse width charging current optimization result curve diagram provided by the embodiment of the present application;

[0030] Figure 14 is a 3s, 5s, 10s three pulse width simulation terminal voltage optimization result curve diagram provided by the embodiment of the present application;

[0031] Figure 15 is a 3s, 5s, 10s three pulse width polarization voltage optimization result curve diagram provided by the embodiment of the present application;

[0032] Figure 16 is a 3s, 4s, 5s, 7s, 10s five pulse width irreversible heat optimization target result column diagram provided by the embodiment of the present application;

[0033] Figure 17 is a 3s, 4s, 5s, 7s, 10s five pulse width charging time consumption optimization target result column diagram provided by the embodiment of the present application. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical scheme and advantages of the present application clearer and more apparent, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0035] In order to optimize the fast charging problem of the lithium ion power battery, the embodiment of the present application provides a lithium ion power battery intelligent charging method, comprising: constructing a battery fast charging simulation model and obtaining parameters; obtaining an initial charging safety boundary of the battery; verifying the initial charging safety boundary through a lithium ion power battery full life cycle data test platform to obtain a target charging safety boundary; wherein the lithium ion power battery full life cycle data test platform comprises a fast charging test platform and an alternating current impedance test platform; according to the target charging safety boundary, calculating an optimal solution of the battery fast charging multi-objective through an improved genetic algorithm to obtain a first fast charging strategy; and optimizing the first fast charging strategy according to the pulse width to obtain a final target fast charging strategy.

[0036] Specifically, the lithium ion power battery intelligent charging method of the embodiment of the present application comprises the following steps S100-S500:

[0037] S100, constructing a battery fast charging simulation model and obtaining model parameters.

[0038] Specifically, before the introduction, the involved nouns and technical terms are explained as follows:

[0039] SoC (State of charge): that is, the state of charge, used to reflect the remaining capacity of the battery, which is defined as the ratio of the remaining capacity to the battery capacity, commonly expressed in percentage.

[0040] Charging rate: the charging rate (C) is a measure of the charging speed, which refers to the current value required by the battery to charge to its rated capacity in a specified time. The charge and discharge rate = charge and discharge current / rated capacity, 1C represents the current intensity when the battery is completely discharged in one hour.

[0041] Reaction basic process: refers to the deintercalation and intercalation process of lithium ions in the positive and negative electrode particles, which is used to describe the open circuit voltage E ocv (t) of the battery.

[0042] Solid phase diffusion process: refers to the diffusion process of lithium ions in the active particles during the working process, which causes the concentration difference between the surface and the inside of the active particles.

[0043] Liquid phase diffusion process: refers to the movement process of lithium ions in the electrolyte between the positive and negative electrodes. During the working process of the battery, lithium ions are intercalated and deintercalated on the surface of the active particles of the positive and negative electrodes, causing the concentration difference inside the positive liquid phase, inside the negative liquid phase and between the positive and negative electrodes, causing the diffusion of lithium ions in the electrolyte, resulting in concentration polarization.

[0044] Electrochemical polarization process: when the current is suddenly added, the rate of the electrochemical reaction inside the battery lags behind the rate of electron movement, at this time the electrode potential deviates, forming electrochemical polarization.

[0045] Ohmic polarization process: refers to the polarization effect generated by the equivalent internal resistance of each part of the lithium ion power battery, which is mainly caused by the internal resistance of the solid electrolyte interface film (SEI), positive and negative electrode materials, electrolyte, etc.

[0046] Step S100 includes the following steps S110-S120:

[0047] S110, taking a lithium battery pseudo two-dimensional model as a prototype, adding solid-liquid phase diffusion and Ohmic polarization process on the basis of the lithium ion thermal coupling model, and constructing a battery fast charging simulation model.

[0048] Specifically, taking a lithium battery pseudo two-dimensional model as a prototype, adding solid-liquid phase diffusion and Ohmic polarization process on the basis of the lithium ion thermal coupling model (SP), and constructing a battery fast charging simulation model. The lithium battery pseudo two-dimensional model (P2D) is a model used to simulate solid-state lithium batteries, and is also widely used in the research of lithium ion batteries due to its universality.

[0049] S120, obtaining model parameters of the battery fast charging simulation model, and simplifying the parameters.

[0050] Specifically, the model parameters required by the battery fast charging simulation model are obtained, and the obtained model parameters are summarized and simplified. Among them, the model parameters include the inherent open circuit potential of the negative electrode, the lithium intercalation concentration on the surface of the negative active particle, the concentration polarization overpotential, the Ohmic polarization overpotential, etc.

[0051] S200, determining the initial charging safety boundary of the battery according to the model parameters.

[0052] Specifically, the model parameters will change at different temperatures, and the Ohmic polarization process can determine that the battery negative potential is related to the model parameters, that is, the charging safety boundary of the lithium ion power battery will change with the change of temperature. Step S200 includes the following steps S210-S220:

[0053] S210, according to the model parameters, obtaining the simulation of the negative potential in the battery working through the battery fast charging simulation model.

[0054] Specifically, the expression of the negative potential U terneg (t) is:

[0055]

[0056] Wherein, U terneg (t) is the negative potential; U neg is the inherent open circuit potential of the negative electrode; x out(t) is the lithium intercalation concentration on the negative active particle surface, U liq (t) is the concentration polarization overpotential; U ohm (t) is the electrochemical polarization overpotential; U reaneg (t) is the ohmic polarization overpotential.

[0057] S220, determining the initial charging safety boundary according to the result of the simulation.

[0058] Specifically, for steps S210-S220, the obtained model parameter result is substituted into the battery fast charging simulation model, and the relationship between the negative electrode potential of the battery and the charging rate and SoC at different temperatures is obtained according to the ohmic polarization process. Optionally, taking the first ambient temperature as 25℃ and the second ambient temperature as 5℃ as an example, referring to Figure 2 and Figure 3 , Figure 2 is a negative electrode potential-SoC relationship curve of the battery at 5℃ provided by an embodiment of the present application, Figure 3 is a negative electrode potential-SoC relationship curve of the battery at 25℃ provided by an embodiment of the present application.

[0059] For different charging rates, the negative electrode potential simulation is performed by using the battery fast charging simulation model, referring to Figure 4 and Figure 5 , Figure 4 is a charging safety boundary curve at 5℃ provided by an embodiment of the present application, Figure 5 is a charging safety boundary curve at 25℃ provided by an embodiment of the present application. When the negative electrode potential intersects with the x-axis (i.e. 0V), the SoC corresponding to the point is obtained, and the charging rate at this time is the maximum safe charging rate that the battery can accept at the SoC.

[0060] Referring to Figure 6 , Figure 6 is a charging safety boundary curve of the battery at different temperatures provided by an embodiment of the present application, according to Figure 6 It can be known that the maximum safe charging rate that the battery can accept is high, which meets the high-rate charging demand.

[0061] When the temperature decreases, the charging safety boundary of the battery decreases, which indicates that the charging rate that the battery can accept decreases at low temperature, and lithium is more likely to be precipitated at high rate. This is because the temperature decrease will cause the battery internal resistance to increase obviously.

[0062] When the temperature increases, the charging safety boundary increases, and the safety boundaries at different temperatures are close at above 30℃. This is because the battery ohmic internal resistance changes little at above 25℃, and it can be known from the ohmic polarization process that the charging safety boundary is mainly affected by the ohmic internal resistance.

[0063] It should be noted that the initial charging safety boundary of the embodiment of the present application can have multiple boundaries according to the difference of temperature.

[0064] S300, verify the initial charging safety boundary through the lithium ion power battery full life cycle data test platform to obtain a target charging safety boundary; wherein the lithium ion power battery full life cycle data test platform includes a fast charging test platform and an alternating current impedance test platform.

[0065] Specifically, step S300 includes the following steps S310-S350:

[0066] S310, configure a first battery pack and a second battery pack; wherein the first battery pack and the second battery pack are both composed of a plurality of new batteries with the same performance.

[0067] Specifically, the first battery pack and the second battery pack with the same performance are configured, and optionally, new batteries with similar performance in the same batch can also be configured as the first battery pack and the second battery pack, and the number of batteries of the two battery packs is equal. The batteries of the first battery pack and the second battery pack are numbered to facilitate the recording of battery data. The first battery pack is divided into group 1, group 2 and group 3, and the second battery pack is divided into group 4, group 5 and group 6, wherein the number of batteries in each group of the second battery pack corresponds to the number of batteries in each group of the first battery pack. Taking the first environmental temperature of 25℃ and the second environmental temperature of 5℃ as an example, the first battery pack is placed in a constant temperature box of 25℃, and the second battery pack is placed in a constant temperature box of 5℃, and all the batteries are placed in the corresponding constant temperature box for a period of time in advance to ensure that the temperature inside and outside the battery is consistent. The above-mentioned setting of different cycle conditions for comparison test groups inside and outside the charging safety boundary is beneficial to the verification and analysis of the rationality of the initial charging safety boundary.

[0068] S320, configure the lithium ion power battery full life cycle data test platform to monitor the battery impedance and the data of the constant temperature and humidity box during the charging and discharging process.

[0069] Specifically, the configured lithium ion power battery full life cycle data test platform includes:

[0070] The fast charging test platform is composed of a switch, a battery charging and discharging tester, a programmable constant temperature and humidity box, and a computer for data recording, connected with the upper computer through an RJ-45 interface, and different working conditions can be set through the matched upper computer software, and the charging and discharging state, current, voltage and capacity of the battery can be detected and controlled in real time. It can be used for setting different temperatures for multiple constant temperature boxes, simultaneously charging and discharging multiple batteries, and verifying the simulation model parameters of different environmental temperatures.

[0071] An alternating current impedance test platform is linked with a computer through an interface by an electrochemical workstation, and a matched host computer software can set a sine voltage excitation of different frequencies and different test durations, and is used for obtaining an ohmic internal resistance in a battery.

[0072] The lithium ion power battery full life cycle data test platform is configured to monitor the battery impedance and the constant temperature and humidity box data during the charging and discharging process, and is beneficial to collect the battery data information in the verification process.

[0073] S330, a first processing result of a first battery group in a constant temperature box at a first environmental temperature is obtained through data monitoring.

[0074] S340, a second processing result of a second battery group in a constant temperature box at a second environmental temperature is obtained through data monitoring.

[0075] Specifically, for steps S330-S340, the step of performing the cycle condition processing includes the following steps one to step six:

[0076] Step one, a predetermined rate is configured, all batteries are charged at a predetermined rate to a state of a cut-off voltage 4.2V and a cut-off current 0.05C, and then are left for a period of time.

[0077] Step two, the batteries are discharged at a predetermined rate to make the battery SoC drop to 20%, and then are left for a period of time.

[0078] Since the battery fast charging method of the embodiment of the application mainly aims at the charging condition of the battery from 20% SoC to 80% SoC, only the charging safety boundary corresponding to the SoC is verified, and the accuracy is improved.

[0079] Step three, a first charging condition and a second charging condition are configured according to the initial charging safety boundary of the first environmental temperature, and a third charging condition and a fourth charging condition are configured according to the initial charging safety boundary of the second environmental temperature. Optionally, taking 25℃ as the first environmental temperature and 5℃ as the second environmental temperature as an example, the first charging condition and the second charging condition are configured as Figure 7 , Figure 7 is a charging condition curve diagram provided by the embodiment of the application at 25℃, and the third charging condition and the fourth charging condition are configured as Figure 8 , Figure 8 is a charging condition curve diagram provided by the embodiment of the application at 5℃.

[0080] Step four, charge group 1 according to the first charging condition, charge group 2 according to the second charging condition, and charge group 3 with a predetermined high charging rate constant current; charge group 4 according to the third charging condition, and charge group 5 according to the fourth charging condition, and charge group 6 with the same charging rate as group 3. After the above charging, the battery is kept in a resting state for a period of time to allow the concentration polarization to recover.

[0081] The battery in step four is charged from 20% SoC to 80% SoC, wherein the first charging condition and the third charging condition are set to decrease to 95% of the current maximum current when the current reaches 98% of the current charging safety boundary, and the second charging condition and the fourth charging condition are set to decrease to 97% of the current maximum current when the current reaches 102% of the current charging safety boundary. From Figure 7 and Figure 8 As can be seen from the above, the current rate of the first charging condition and the third charging condition is always below the charging safety boundary, and the charging rate of the second charging condition and the fourth charging condition exceeds the boundary at different conversion stages. As a control experiment, setting a high charging rate constant current for constant current charging can more clearly observe the lithium precipitation phenomenon.

[0082] Step five, constant current discharge the battery at a predetermined discharge rate to reduce the battery SoC to 20%, and enter a resting state.

[0083] Step six, set the number of cycles, repeat steps four to five according to the number of cycles, and finally execute step four again to charge the battery to 80% SoC to observe the lithium precipitation phenomenon.

[0084] S350, verifying the initial safety charging boundary according to the first processing result and the second processing result to obtain a target charging safety boundary.

[0085] If the detection results of all the batteries in the first battery group and the second battery group meet the judgment conditions (1)-(3), the charging safety boundary obtained by using the battery fast charging simulation model has high accuracy, wherein the judgment conditions are:

[0086] (1) The negative electrode surface of the battery processed according to the first charging condition and the third charging condition has only black negative electrode material;

[0087] (2) The negative electrode surface of the battery processed according to the second charging condition and the third charging condition has a small amount of lithium dendrite formation;

[0088] (3) The negative electrode surface of the battery processed according to the high rate constant current charging condition has a large area of lithium dendrite formation.

[0089] The initial charging safety boundary with the highest accuracy is taken as the target charging safety boundary.

[0090] S400, according to the target charging safety boundary, the final optimal solution of the battery fast charging multi-objective is calculated by the improved genetic algorithm, and a first fast charging strategy is obtained.

[0091] Specifically, referring to Figure 9 , Figure 9 is a flowchart of the improved genetic algorithm provided by the embodiment of the application, and it should be noted that Figure 9 The maximum sequence length in step S400 includes the following steps S410-S450:

[0092] S410, initializing the genetic population according to the target safety boundary; wherein the individual of the genetic population is the current sequence of the lithium-ion power battery charging process.

[0093] Specifically, when initializing the genetic population, the parameters of the genetic algorithm are configured, the initial SoC of the battery is configured as 20%, the SoC at the charging stop moment is configured as 80%, and the maximum length of the sequence is set according to the actual situation. Each individual of the genetic population is the current sequence of the battery from 20% SoC to 80% SoC, each value in the sequence is the current of each second, and the length of the sequence is the charging time required. When generating the sequence, the upper limit of the current of each second is the target charging safety boundary corresponding to the current SoC, and the lower limit of the current is set as -1C (i.e. 1C discharge) considering the influence of the negative pulse on the charging effect. Since the genetic algorithm needs to ensure that the sequence lengths of different individuals are consistent, and the SoC may reach 80% in advance when different individuals are randomly generated, zero padding is performed on these sequences to ensure that the sequence length is equal to the maximum length of the sequence. In addition, there may be a case that the sequence reaches the end while the SoC does not reach 80% when generating the individual, therefore the missing part is converted into a rate value and evenly distributed to all values in the sequence.

[0094] S420, determining the fast charging optimization target and the fitness function of the lithium-ion power battery.

[0095] The embodiment of the application formulates the optimization target of the fast charging strategy to evaluate the individual fitness, and the optimization target can be specifically expressed as the following two single targets:

[0096] (1) Minimizing irreversible heat, and the expression of the irreversible heat J1 is:

[0097]

[0098] (2) Minimizing the battery charging time, and the expression of the charging time J2 is:

[0099]

[0100] Among them, u * (·) represents the control variable, i.e., different current sequences; time total The total sequence length is the same for all individuals, and the charging time depends on the number of zeros padded at the end of the sequence. Therefore, in this invention, the final charging time is the length of each sequence minus the number of zeros padded at the end. liq (t) represents the concentration polarization overpotential; U rea (t) represents the electrochemical polarization overpotential; U ohm (t) Ohmic polarization overpotential; U liq (t)+U rea (t)+U ohm (t) represents the total polarization voltage.

[0101] For irreversible heat generation, the least amount of heat is generated during 1C constant current charging, while the maximum amount of heat is generated when charging along the charging safety boundary. Optionally, in this embodiment of the invention, the irreversible heat generated during charging can be Q at 1C. 1c A score of 100 is given, and the heat generated during charging, Q, is within the charging safety boundary. max The score is 60 points, using linear scoring, and the irreversible thermal Q during the charging process is considered. total The score J1 is:

[0102]

[0103] Regarding charging time, the shortest time is when charging along the safety boundary, while the longest time is when charging at a constant current of 1C. Therefore, the charging time is the time required to charge along the safety boundary. max 100 points, time taken to charge at 1C 1C With a score of 60, and using linear scoring, the charging time is... total The score J2 is:

[0104]

[0105] Furthermore, subsequent crossover, mutation, and other operations may cause the current at certain locations within an individual to exceed the current charging safety boundary. Therefore, boundary judgment is performed on each current value in the individual, and the total number of those exceeding the boundary is denoted as J3. This J3 is coupled into the fitness function with a given weight and used as a penalty function.

[0106] By using weight coefficients ω1, ω2, and ω3 to transform the multi-objective combination into a single objective, the established fitness function is:

[0107]

[0108]

[0109] wherein, u * (·) is a control variable, ω1, ω2 are optimization objective weight coefficients, which can be adjusted according to requirements. ω3 is an adjustment coefficient of the number of current exceeding the boundary, in order to prevent the fitness function from being affected too much, the application sets ω3 = 0.1. It should be noted that ω3 can also be set to other values, such as 0.2, 0.3, etc.

[0110] S430, performing crossover and mutation on the genetic population, and calculating the intermediate optimal solution of the battery fast charging multi-objective according to the fast charging optimization objective and the fitness function.

[0111] Specifically, step S430 includes the following S431-S435:

[0112] S431, determining a first individual and a second individual in the genetic population according to a crossover probability.

[0113] Specifically, the first individual and the second individual are determined according to a crossover probability P c The first individual and the second individual are randomly determined. Alternatively, the first individual can be set as the father, and the second individual can be set as the mother.

[0114] S432, performing crossover on the gene bits of the first individual and the second individual by simulating binary crossover to obtain a first father population and a first offspring population.

[0115] Specifically, the current father is traversed, and a random number r c When r c <P c , two unequal individuals are randomly selected as the "father" and the "mother", and the gene bits are randomly selected by simulating binary crossover (SBX) to obtain a first father population and a first offspring population. Simulated binary crossover is a crossover operation suitable for real number coding, and in simulated binary crossover, each offspring is created from parent chromosomes.

[0116] S433, performing mutation operation on the first offspring population by an equal probability mutation method to obtain a second father population and a second offspring population.

[0117] Specifically, the first offspring population is subjected to mutation operation by an equal probability mutation method to obtain a second father population and a second offspring population. The above mutation operation can have two mutation methods, which are specifically:

[0118] (1) The first mutation method refers to Figure 10 , Figure 10is a first variation mode schematic diagram provided by the embodiment of the present application, from the first non-zero value at the end of the sequence of the first sub-population to take m values, divide the sum of the m values into the front sequence, and then set the values of the m positions to zero.

[0119] (2) the second variation mode, refer to Figure 11 , Figure 11 is a second variation mode schematic diagram provided by the embodiment of the present application, according to the random determination of the offspring of a certain gene site for variation operation. Random number judgment is carried out for each gene site of the offspring, when the random number is less than or equal to 0.04, the value of the current gene site of the individual is changed, and another gene site not in the end of the all-zero sequence is randomly selected for change.

[0120] S434, the second parent population and the second sub-population are recombined to obtain a battery fast charging multi-objective solution; wherein the battery fast charging multi-objective solution is one or more.

[0121] Specifically, the battery fast charging multi-objective solution is the individual fitness.

[0122] S435, based on the tournament algorithm, the intermediate optimal solution is determined according to the fitness function and the battery fast charging multi-objective solution.

[0123] Specifically, the tournament algorithm is a method of selection operation, which is an algorithm for finding the maximum value and the second maximum value or the minimum value and the second minimum value. The tournament algorithm can achieve the minimum average query times. The present application can determine the intermediate optimal solution according to the fitness function and the battery fast charging multi-objective solution by using the tournament algorithm, and the intermediate optimal solution is used to determine the final optimal solution in the iteration process.

[0124] S440, the steps of crossing and mutating the genetic population are repeatedly executed until the maximum iteration number reaches a predetermined threshold value, or the change of the intermediate optimal solution calculated in the predetermined consecutive iteration times does not exceed a predetermined optimal solution change threshold value, then the final optimal solution of the battery fast charging multi-objective is obtained.

[0125] Specifically, the final optimal solution is the current population when the iteration is stopped.

[0126] S450, the optimal solution of the battery fast charging multi-objective is used as the first fast charging strategy.

[0127] Specifically, the optimal solution of the battery fast charging multi-objective obtained by the above iteration is determined as the first fast charging strategy.

[0128] S500, the first fast charging strategy is optimized according to the pulse width to obtain the final target fast charging strategy.

[0129] Specifically, step S500 includes steps S510-S530.

[0130] S510, determine a plurality of candidate pulse widths based on the weight coefficient.

[0131] S520, calculate the first fast charging strategy under each candidate pulse width to obtain a first fast charging strategy set.

[0132] Specifically, the genetic algorithm is solved under each pulse width to obtain the first fast charging strategy under each candidate pulse width, and all candidate pulse width combinations are referred to as the first fast charging strategy set.

[0133] S530, compare the fitness function convergence results of all first fast charging strategies in the first fast charging strategy set to obtain the final target fast charging strategy.

[0134] Specifically, the fitness function convergence results of all first fast charging strategies in the first fast charging strategy set are compared, and the target pulse width is determined according to the convergence results, and the first fast charging strategy with the best fitness function convergence effect and the target pulse width are taken as the final target fast charging strategy.

[0135] The embodiment of the present application also provides a lithium ion power battery intelligent charging system, comprising: a first module, the first module is used for constructing a battery fast charging simulation model and obtaining parameters; a second module, the second module is used for obtaining an initial charging safety boundary of the battery; a third module, the third module is used for verifying the initial charging safety boundary through a lithium ion power battery full life cycle data test platform to obtain a target charging safety boundary; wherein the lithium ion power battery full life cycle data test platform comprises a fast charging test platform and an alternating current impedance test platform; a fourth module, the fourth module is used for solving the final optimal solution of the battery fast charging multi-objective according to the target charging safety boundary through an improved genetic algorithm to obtain a first fast charging strategy; a fifth module, the fifth module is used for optimizing the fast charging strategy according to the pulse width to obtain a final target fast charging strategy.

[0136] The embodiment of the present application also provides an electronic device, comprising a processor and a memory; the memory is used for storing a program; the processor executes the program to realize the method as described above.

[0137] The embodiment of the present application also provides a computer readable storage medium, the storage medium stores a program, and the program is executed by a processor to realize the method as described above.

[0138] The embodiment of the present application has the beneficial effects that the overall steps of the embodiment of the present application consider the optimization target of minimizing irreversible heat and minimizing battery charging time, the final target fast charging strategy calculated can shorten the charging time of the lithium ion power battery, reduce temperature rise, delay battery life and reduce energy loss during charging, and is beneficial to fast charging of the lithium ion power battery.

[0139] An application scenario of the embodiment of the present application is introduced as follows:

[0140] According to step S100 of the embodiment of the present application, a battery fast charging simulation model is constructed and model parameters are obtained; then the initial charging safety boundary of the battery is determined according to the model parameters, and the determined initial charging safety boundary is as shown in Figure 3 and Figure 5 The target charging safety boundary is obtained by verifying the initial charging safety boundary through a lithium ion power battery full life cycle data test platform; the charging safety boundary is verified and analyzed, different cycle working conditions are set in and outside the safety boundary for comparison test groups, and the battery is processed, and the specific steps for processing the battery are as follows:

[0141] (1) Select 6 new batteries with similar performance from the same batch, number them and divide them into two groups, one group (including No. 1, 3 and 5 batteries) is tested in a constant temperature box at 25℃, and the other group (including No. 2, 4 and 6 batteries) is tested in a constant temperature box at 5℃.

[0142] All batteries are placed in the corresponding constant temperature box for 2 hours in advance to ensure that the temperature inside and outside the battery is consistent.

[0143] (2) Charge all batteries at 1C rate to the state of cut-off voltage 4.2V and cut-off current 0.05C, and then stand for 30 minutes.

[0144] (3) Discharge the battery at 1C rate for 48 minutes to make the battery SoC drop to 20%, and then stand for 30 minutes.

[0145] (4) Charge No. 1 and No. 3 batteries according to the first charging condition and the second charging condition of Figure 7 respectively, and charge No. 2 and No. 4 batteries according to the third charging condition and the fourth charging condition of 8 respectively. At the same time, charge No. 5 and No. 6 batteries at 6C rate for 6 minutes. Then keep the battery in a standby state for 30 minutes to restore the concentration polarization.

[0146] (5) Discharge the battery at 2C rate for 18 minutes to make the battery SoC drop to 20%, and then stand for 30 minutes.

[0147] (6) Repeat steps (4)-(5) for 20 times, and then perform step (4) once again to charge the battery to 80% SoC to observe the lithium precipitation phenomenon. Then, disassemble and detect the above two groups of 6 batteries, if the negative electrode surface of the 1st and 2nd batteries under the charging condition 1 is only black negative electrode material, the negative electrode surface of the 3rd and 4th batteries under the charging condition 2 has a little lithium dendrite, and the negative electrode surface of the 5th and 6th batteries under the high-rate constant current condition can observe obvious large-area lithium dendrite, it is indicated that the charging safety boundary obtained by using the model has high accuracy, and the initial charging safety boundary with the highest accuracy is taken as the target charging safety boundary.

[0148] According to the target charging safety boundary, the final optimal solution of the battery fast charging multi-objective is calculated by the improved genetic algorithm in step S400 of the embodiment of the application, and a first fast charging strategy is obtained.

[0149] When the population is initialized, each individual is a current sequence of the battery from 20% SoC to 80% SoC, each value in the sequence is the current amplitude per second, and the length of the sequence is the charging time required. The time required for the battery to be charged from 20% SoC to 80% SoC by 1C rate is 2160s, in order to ensure that the fast charging time is shortened by more than 40% compared with 1C constant current charging, the maximum length of the sequence is set to 1296.

[0150] When the sequence is generated, the upper limit of the current per second is the charging safety boundary corresponding to the current SoC, and the lower limit of the current is set to -1C (that is, 1C discharge) considering the influence of the negative pulse on the charging effect. Since the genetic algorithm needs to ensure that the lengths of different sequences are consistent, and the SoC may reach 80% in advance when different individuals are randomly generated, zero padding is performed on these sequences to ensure that the length is equal to 1296. In addition, the sequence may reach the end while the SoC does not reach 80% when the individual is generated, therefore, the missing part is converted into a rate value and evenly distributed in all values of the sequence.

[0151] The optimal fast charging target and the fitness function of the lithium ion power battery are determined according to step S440, and the iteration of crossover and mutation is performed according to step S430, when the maximum iteration number is reached or the optimal value of the fitness function in the last 10 generations changes by no more than 0.0001, it is considered that the optimal solution is found, the optimization is stopped, and the final optimal solution, that is, the first fast charging optimization strategy, is obtained.

[0152] Based on the weight coefficient collocation, the genetic algorithm is solved under five conditions of pulse width of 3s, 4s, 5s, 7s and 10s, and the influence of different pulse widths on strategy optimization is discussed.

[0153] The convergence processes of the fitness functions of the three pulse widths of 3s, 5s and 10s are as followsFigure 12 , the charging current optimization result reference Figure 13 , the simulation end voltage optimization result reference Figure 14 , the polarization voltage optimization result reference Figure 15 .

[0154] 3s, 4s, 5s, 7s, 10s, the optimization target result reference of the five cases Figure 16 and Figure 17 .

[0155] From the optimization results, when the pulse width is 3, the optimization strategy tends to let the positive pulse follow the safety boundary while increasing the number of negative pulses. When the pulse width increases, the optimization strategy tends to reduce or even remove negative pulses while keeping the positive pulse amplitude as close to 3C as possible. Because when the pulse width increases, the number of times the high-rate current exceeds the safety boundary increases, thus the penalty term fitness in S420 increases, ultimately leading to a decrease in the score. However, the optimal selection method based on the tournament algorithm eliminates individuals with low scores, and the current amplitude of the individuals that ultimately survive and iterate tends to be the middle value (3C), so that there is no need for too many negative pulses to suppress the increase of polarization voltage. Therefore, when the pulse width increases, the charging time as a whole still shows a downward trend, although the amplitude of the positive pulse also decreases, due to the decrease in the number of negative pulses. Among them, the charging time of different pulse widths is shortened by 54.17%, 55.74%, 57.17%, 58.84% and 60.65% respectively compared with 1C constant current charging; the irreversible heat is reduced by 35.84%, 37.94%, 40.28%, 36.07% and 33.15% respectively compared with the heat generated by following the safety boundary charging.

[0156] From the optimization perspective of irreversible heat, with the increase of pulse width, the irreversible heat shows a downward trend first and then an upward trend, and it is relatively small when the pulse width is 5. When the pulse width is 3, although the growth of polarization voltage is suppressed by increasing negative pulses, the charging time becomes relatively long, resulting in a larger total irreversible heat; while when the pulse width is 10, although the charging time decreases, the decrease in negative pulses leads to a relatively large polarization voltage, and the total irreversible heat also increases accordingly. When the pulse width is 5, the charging time and the irreversible heat can be well balanced.

[0157] According to the above results, it can be determined that the pulse width of 5 is the target fast charging strategy.

[0158] In addition, the optimal strategy calculated after increasing the pulse width is better than the solution calculated when the pulse width is 1 in terms of charging time and irreversible heat, indicating that it is easier to find the optimal strategy by appropriately increasing the pulse width.

[0159] In some alternative embodiments, the function / operations mentioned in the block diagrams can not occur in the order mentioned in the operational illustrations. For example, depending on the involved function / operation, two blocks shown in succession can in fact be executed substantially concurrently or the blocks can sometimes be executed in reverse order, depending upon the functionality / operations involved. Furthermore, embodiments presented and described in the flowcharts are only examples of implementing the present application. Alternative embodiments are possible where some of the steps are omitted, wherein additional steps are added, or wherein some of the steps are performed in a different order. It should be understood that the order of steps presented and described in the flowcharts illustrates implementations of the present application. The steps presented and described in the flowcharts are not necessarily performed in the order presented and described. Steps from one exemplary flowchart can be performed in a different order.

[0160] Furthermore, although the present application has been described in the context of functional modules, it is to be understood that one or more of the functions and / or features described can be integrated in a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It will also be appreciated that detailed discussion of the actual implementation of each module is not necessary for an understanding of the present application. Rather, the actual implementation of the modules, in light of the description of the properties, functions and internal relationships of the various functional modules disclosed herein, will be apparent to the skilled artisan in view of the present disclosure. Thus, the present application is not limited to the embodiments described herein which can be considered as illustrative only. Indeed, the scope of the present application is to be determined only by the appended claims and equivalents thereto. It is therefore contemplated to this effect that the particular conceptual aspects disclosed are merely illustrative and are not intended to limit the scope of the present application, which is to be determined by the full scope of the appended claims and equivalents thereto.

[0161] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0162] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be embodied in non-transitory computer-readable media, executed by one or more computing devices, and / or in any other way. The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered a list of executable instructions for implementing logic functions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor- containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber (optical), and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that can be later executed by a computer. In this context, a "computer-readable medium" can be any means that can store the program for use by or in connection with the instruction execution system, apparatus, or device.

[0163] The foregoing description of various embodiments of the application has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the application to the precise form disclosed, and various modifications and variations are possible in light of the above teachings. It is intended that the scope of the application be limited not with this detailed description, but rather by the claims appended hereto.

[0164] It is understood that various portions of the application can be implemented in hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, implementation can be with any or a combination of the following technologies, which are all well-known in the art: a discrete logic circuit(s) having logic gates for implementing logic functions upon an application of data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA), etc.

[0165] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the application. In the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate way in one or more embodiments or examples.

[0166] While the embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary and are not to be construed as limiting the scope of the application. The scope of the application is defined by the appended claims and their equivalents.

[0167] The above is a specific description of the preferred embodiment of the present application, but the present application is not limited to the described embodiment, and those skilled in the art can make various equivalent modifications or replacements without departing from the spirit of the present application, and these equivalent modifications or replacements are all included in the scope defined by the claims of the present application.

Claims

1. A method for intelligent charging of a lithium-ion power cell, characterized in that, The application relates to a method for determining a fast charging strategy of a lithium ion battery, comprising the following steps: constructing a battery fast charging simulation model and obtaining model parameters; determining an initial charging safety boundary of the battery according to the model parameters; verifying the initial charging safety boundary through a lithium ion power battery full life cycle data test platform to obtain a target charging safety boundary; wherein the lithium ion power battery full life cycle data test platform comprises a fast charging test platform and an alternating current impedance test platform; calculating a final optimal solution of a battery fast charging multi-objective through an improved genetic algorithm according to the target charging safety boundary to obtain a first fast charging strategy; optimizing the first fast charging strategy according to pulse width to obtain a final target fast charging strategy; the step of calculating the final optimal solution of the battery fast charging multi-objective through the improved genetic algorithm according to the target charging safety boundary to obtain the first fast charging strategy comprises the following steps: initializing a genetic population according to the target safety boundary; wherein an individual of the genetic population is a current sequence of a lithium ion power battery charging process; determining a fast charging optimization objective and a fitness function of the lithium ion power battery; carrying out cross and mutation on the genetic population, and calculating an intermediate optimal solution of the battery fast charging multi-objective according to the fast charging optimization objective and the fitness function; repeating the step of carrying out cross and mutation on the genetic population until a maximum iteration number reaches a predetermined threshold value, or the change of the intermediate optimal solution calculated in a predetermined continuous iteration number is not more than a predetermined optimal solution change threshold value, so that the final optimal solution of the battery fast charging multi-objective is obtained; taking the optimal solution of the battery fast charging multi-objective as the first fast charging strategy; the fast charging optimization objective comprises minimizing irreversible heat and minimizing battery charging time.

2. The intelligent charging method of lithium-ion power battery according to claim 1, characterized in that, the step of constructing the battery fast charging simulation model and obtaining the model parameters comprises the following steps: taking a lithium battery pseudo two-dimensional model as a prototype, adding solid-liquid phase diffusion and ohmic polarization process on the basis of a lithium ion thermal coupling model to construct a battery fast charging simulation model; obtaining model parameters of the battery fast charging simulation model, and simplifying the model parameters.

3. The intelligent charging method of lithium-ion power battery according to claim 1, characterized in that, the step of determining the initial charging safety boundary of the battery according to the model parameters comprises the following steps: simulating a negative electrode potential in a battery working process through the battery fast charging simulation model according to the model parameters; determining an initial charging safety boundary according to a simulation result.

4. The intelligent charging method of lithium-ion power battery according to claim 1, characterized in that, the step of verifying the initial charging safety boundary through the lithium ion power battery full life cycle data test platform to obtain the target charging safety boundary comprises the following steps: configuring a first battery group and a second battery group; wherein the first battery group and the second battery group each comprise a plurality of new batteries with the same performance; configuring a lithium ion power battery full life cycle data test platform to monitor data of battery impedance and a constant temperature and humidity box in a charging and discharging process; obtaining a first processing result of the first battery group in a constant temperature box at a first environmental temperature through the data monitoring; obtaining a second processing result of the second battery group in a constant temperature box at a second environmental temperature through the data monitoring; Verify the initial safe charging boundary according to the first processing result and the second processing result to obtain a target charging safe boundary.

5. The intelligent charging method of lithium-ion power battery according to claim 1, characterized in that, The step of performing crossover and mutation on the genetic population to calculate an intermediate optimal solution of the battery fast charging multi-objective according to the fast charging optimization objective and the fitness function comprises: determining a first individual and a second individual in the genetic population according to a crossover probability; randomly selecting gene positions of the first individual and the second individual for crossover through simulated binary crossover to obtain a first parent population and a first offspring population; performing mutation operation on the first offspring population through an equal-probability mutation mode to obtain a second parent population and a second offspring population; performing individual recombination on the second parent population and the second offspring population to obtain a solution of the battery fast charging multi-objective; wherein the solution of the battery fast charging multi-objective is one or more; determining an intermediate optimal solution according to the fitness function and the solution of the battery fast charging multi-objective based on a tournament algorithm.

6. The intelligent charging method of lithium-ion power battery according to claim 5, characterized in that, The step of optimizing the first fast charging strategy according to the pulse width to obtain a final target fast charging strategy comprises: determining a plurality of candidate pulse widths based on a weight coefficient; calculating the first fast charging strategy under each of the candidate pulse widths to obtain a first fast charging strategy set; comparing the fitness function convergence results of all the first fast charging strategies in the first fast charging strategy set to obtain a final target fast charging strategy.

7. A lithium-ion power cell intelligent charging system, characterized in that, comprise: a first module configured to construct a battery fast charging simulation model and obtain parameters; a second module configured to obtain an initial charging safe boundary of the battery; a third module configured to verify the initial charging safe boundary through a lithium-ion power battery full-life cycle data test platform to obtain a target charging safe boundary; wherein the lithium-ion power battery full-life cycle data test platform comprises a fast charging test platform and an alternating current impedance test platform; a fourth module configured to solve a final optimal solution of a battery fast charging multi-objective according to the target charging safe boundary through an improved genetic algorithm to obtain a first fast charging strategy; a fifth module configured to optimize the fast charging strategy according to a pulse width to obtain a final target fast charging strategy; the fourth module specifically performs the following steps: initializing a genetic population according to the target safe boundary; wherein an individual of the genetic population is a current sequence of a lithium-ion power battery charging process; determining a fast charging optimization objective and a fitness function of the lithium-ion power battery; performing crossover and mutation on the genetic population to calculate an intermediate optimal solution of the battery fast charging multi-objective according to the fast charging optimization objective and the fitness function; repeating the step of performing crossover and mutation on the genetic population until a maximum iteration number reaches a predetermined threshold value, or the change of the intermediate optimal solution calculated in a predetermined number of continuous iterations does not exceed a predetermined optimal solution change threshold value, to obtain a final optimal solution of the battery fast charging multi-objective; taking the optimal solution of the battery fast charging multi-objective as a first fast charging strategy; the fast charging optimization objective comprises minimizing irreversible heat and minimizing battery charging time.

8. An electronic device, comprising: comprising a processor and a memory; the memory is configured to store a program; the processor executes the program to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 6.

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