A charging strategy optimization method based on battery aging status

Abstract

The present invention discloses a charging strategy optimization method based on the battery aging state, including: S1: obtaining and analyzing battery characteristics; S2: constructing an equivalent circuit model, and performing parameter identification of the open circuit voltage, ohmic internal resistance, polarization internal resistance and polarization capacitance of the equivalent circuit model; S3: verifying the accuracy of the model parameters obtained by identification, analyzing the relationship between the model parameters and the capacity retention rate, and obtaining the battery performance evaluation index of the fusion capacity and internal resistance of the battery in different aging states; S4: combining the obtained battery performance evaluation index, taking the charging time, battery capacity loss and battery score as the optimization target, establishing the charging objective function, optimizing the objective function, obtaining the charging strategy of the optimal charging sequence according to the battery performance evaluation index, and verifying the charging strategy. The constant current and constant voltage charging strategy can overcome the problem of aggravated aging caused by the inability to change with the internal aging state of the battery, while reducing the charging time and extending the battery life.

Description

Technical Field

[0001] The invention relates to a charging strategy optimization method based on battery aging status, belonging to the technical field of automobile power batteries. Background Art

[0002] As the power source of electric vehicles, the performance of power batteries directly affects the overall performance of electric vehicles. Charging is an indispensable part of power battery applications. Charging time and charging loss affect the user experience and economic efficiency of electric vehicles throughout their life cycle. High-rate current charging can shorten the charging time, but it will aggravate battery aging; low charging current can reduce aging, but it will significantly increase the charging time. Considering that the charging performance of the battery will decrease with aging, the battery's acceptance of charging current before and after aging is also different. If the same charging strategy is always used throughout the battery life cycle, it will inevitably further aggravate battery aging, and serious safety issues may occur.

[0003] Battery aging is closely related to external factors such as battery ambient temperature, current, cut-off voltage and state of charge (SOC) working range. The main methods for evaluating battery aging status are: ex situ disassembly diagnosis and in situ observation diagnosis. The former obtains positive and negative electrode samples by destroying the battery, observes the elemental composition and microscopic morphology of the samples by microscopic detection such as X-ray diffractometer and scanning electron microscope, and compares the structure and composition of samples with different aging states to diagnose the battery aging mechanism, but this method cannot obtain continuous diagnosis results within the battery life cycle. The latter performs non-destructive diagnosis of batteries within the battery life cycle without destroying the battery structure. By extracting characteristic parameters from the test data such as the current and voltage of the battery, analyzing the changing rules of the characteristic parameters during the aging process, and establishing a connection with the internal aging state, the battery can be non-destructively diagnosed.

[0004] In the study of charging strategies, capacity loss is usually used as a measure of battery aging, but the internal reactions of the battery are complex, such as the growth and degradation of the solid electrolyte interface (SEI), the degradation of electrolytes and binders, electrode material peeling, lithium plating, and electrode material particle rupture. The complex internal changes correspond to three forms of aging: loss of available lithium and loss of active materials at the positive and negative electrodes. During the use of the battery, the internal reactions of the battery are complex and the internal state of the battery will change with the aging of the battery. The current common charging strategy usually uses capacity loss as a measure of battery aging, but due to the complexity of the internal reactions of the battery, the capacity alone cannot fully measure the battery state; and not considering that changes in the battery state will aggravate the aging of the battery, in severe cases, it will cause the battery to overheat and cause safety problems. Summary of the invention

[0005] Purpose of the invention: In view of the deficiencies in the prior art, the present invention provides a charging strategy optimization method based on the battery aging state. The present invention proposes a battery aging state evaluation based on the fusion of capacity and internal resistance, and optimizes the charging strategy using a multi-objective optimization algorithm considering the aging state, thereby reducing battery aging caused by a decrease in the battery's charging acceptance capacity, reducing the battery charging time and extending the battery life.

[0006] Technical solution: A charging strategy optimization method based on battery aging status, comprising the following steps:

[0007] S1: Obtain and analyze battery characteristics: Obtain battery characteristics through capacity calibration test, mixed power pulse characteristic test and different charging rate cycle test, and analyze the impact of charging rate on battery capacity;

[0008] S2: Construct an equivalent circuit model and identify the parameters of the open circuit voltage, ohmic internal resistance, polarization internal resistance and polarization capacitance of the equivalent circuit model;

[0009] S3: Verify the accuracy of the model parameters identified in S2, analyze the relationship between the model parameters and the capacity retention rate, and obtain the battery performance evaluation index of the fusion capacity and internal resistance of the battery in different aging states;

[0010] S4: Combined with the battery performance evaluation index of fusion capacity and internal resistance obtained in S3, the charging objective function is established with charging time, battery capacity loss and battery score as optimization targets, and the objective function is optimized. According to the battery performance evaluation index, the charging strategy of the optimal charging sequence is obtained and verified.

[0011] S401: Selecting multi-stage charging based on cut-off voltage as a charging strategy and constructing an objective function;

[0012] S402: Optimizing the objective function established in S401 using a multi-objective genetic method to obtain an optimal charging sequence, and using the optimal charging sequence as the final result of the charging strategy optimization;

[0013] S403: Verify the charging strategy obtained in S402.

[0014] The present invention obtains battery characteristics and analyzes the influence of charging rate on battery capacity, constructs an equivalent circuit model and performs parameter identification, verifies parameter accuracy, and analyzes the relationship between model parameters and capacity retention rate, so as to obtain a battery performance evaluation index that evaluates the fusion capacity and internal resistance of batteries in different aging states, and then combines the battery performance evaluation index that evaluates the fusion capacity and internal resistance, takes charging time, battery capacity loss, and battery score as optimization targets, establishes a charging objective function, optimizes the objective function, obtains a charging strategy for the optimal charging sequence according to the battery performance evaluation index, and verifies the charging strategy, thereby overcoming the problem that the constant current and constant voltage charging strategy cannot change with the internal aging state of the battery, which leads to aggravated aging, and at the same time reduces the charging time and prolongs the service life of the battery.

[0015] Preferably, the specific steps of S1 are:

[0016] Capacity calibration test: Place the battery to be tested in a constant temperature environment, set the charge and discharge cut-off voltage and perform constant current charge and discharge cycles, and select the average value of three discharge capacities as the current available capacity of the battery;

[0017] Hybrid power pulse characteristic test: Use 1 / 3C current to discharge the battery to the cut-off voltage, where C is the current density, and then apply constant current to charge the battery to 5% SOC, and then apply 3C rate current to perform short-time pulse charge and discharge on the battery; repeat constant current charging and 3C rate current pulse cycles, set the SOC range and interval, and obtain the pulse response of the battery in the full SOC range;

[0018] Different charging rate cycle test: charge the battery at different rate currents, stop when the cut-off voltage or cut-off current is reached, and then discharge the battery at a constant rate. Set different cycle intervals according to different rates for testing until the battery ages;

[0019] Analyze the impact of charging rate on battery capacity: Analyze the cycle test results at different charging rates, including the changes in battery available capacity and capacity loss rate with the number of cycles at different charging rates.

[0020] Preferably, the specific steps of constructing the equivalent circuit model in S2 are:

[0021] Based on Kirchhoff's current-voltage law, an equivalent circuit model is established as follows:

[0022]

[0023] Among them, U ocv is the battery open circuit voltage, R o is the equivalent internal resistance of the battery, R p and C pare polarization resistance and polarization capacitance respectively, U p is the capacitance C p Terminal voltage, U and I are the terminal voltage and current of the circuit respectively.

[0024] Preferably, the parameter identification in S2 is specifically:

[0025] Open circuit voltage identification: Perform a mixed power pulse characteristic test on the battery that has not been subjected to an aging test at room temperature, and use the voltage at the end of the static state after the pulse current is discharged as the open circuit voltage at the corresponding SOC;

[0026] Identification of ohmic internal resistance: Calculate the ohmic internal resistance by using the change in voltage from U1 to U2 caused by the instantaneous pulse current charging of the battery, and the change in voltage from U3 to U4 caused by the instantaneous removal of the charging current. The calculation formula is as follows:

[0027]

[0028] Among them, U1 to U2 is the voltage caused by the moment of charging, and U3 to U4 is the voltage caused by the moment of removing the charging current;

[0029] Polarization internal resistance and polarization capacitance identification: Parameter identification of polarization internal resistance and polarization capacitance is performed based on the pulse current discharge stage in the mixed power pulse characteristic test:

[0030] During the pulse discharge phase, when the battery is discharged, the current flows through the RC circuit in the equivalent circuit, and the capacitor C p Voltage across the terminals U p Gradually increasing, U p The change over time is shown in the formula:

[0031]

[0032] Since the voltage value of the two ends of the RC circuit is 0 in the steady state, the RC circuit is in a zero state response at this time. Solving the formula, we get the formula:

[0033]

[0034] At this time, the battery terminal voltage is expressed as follows:

[0035]

[0036] In the static stage after the discharge is completed, when the battery is discharged, the polarized capacitor is discharged, and the battery terminal voltage shows a slow upward trend. At this time, the RC circuit has a zero input response, and the battery terminal voltage is shown by the following formula:

[0037]

[0038] The nonlinear least square method is used to fit the terminal voltage in the static stage after the discharge is completed according to formula (6) to obtain the time constant τ of the RC circuit:

[0039] τ=R p C p (7)

[0040] Substitute the fitting time constant (7) into formula (5), and fit the terminal voltage during the pulse discharge phase to obtain the polarization internal resistance R p , use formula (8) to get C p :

[0041]

[0042] Preferably, the specific steps of verifying the model parameters in S3 are:

[0043] Substitute the open circuit voltage, ohmic internal resistance, polarization internal resistance and polarization capacitance identified in S2 into the equivalent circuit model to obtain the battery simulation model:

[0044]

[0045] The experimental current is input into the battery simulation model to obtain the output terminal voltage of the battery simulation model, which is compared with the terminal voltage of the actual battery to verify the accuracy of the open circuit voltage, ohmic internal resistance, polarization internal resistance and polarization capacitance identified in S2.

[0046] Preferably, the relationship between the analysis model parameters and the capacity retention rate in S3 is specifically:

[0047] By using the mixed power pulse characteristic test, the average value of the SOC parameters in the range of 0.2-0.8 is taken as the equivalent parameter of the current state, and the change trend of the ohmic internal resistance, polarization internal resistance and polarization capacitance under different charging rate cycles during the aging process is obtained. In this way, the relationship between the growth of equivalent circuit parameters caused by different charging rate cycles and the capacity retention rate is analyzed.

[0048] Preferably, the battery performance evaluation index for evaluating different aging states of the battery obtained in S3 is specifically:

[0049] Based on the three characteristic parameters of ohmic internal resistance, polarization internal resistance and time constant, the weights are calculated respectively, and then the battery performance evaluation index under different aging conditions is obtained;

[0050] The three characteristic parameters of ohmic internal resistance, polarization internal resistance and time constant are expressed in the form of a matrix S(i,j) = {R o ,R p,τ}, where i = 1, 2, ..., m; where m is the number of batteries, j = 1, 2, 3, S(i, j) represents the jth characteristic parameter of the i-th battery;

[0051]

[0052] The characteristic parameters are standardized. According to the changing trend of the characteristic parameters during the aging process, the ohmic internal resistance and polarization internal resistance are defined as negative indicators, and the time constant is defined as a positive indicator, which represents the standardized characteristic parameter matrix.

[0053]

[0054] Calculate the weight P of the jth index in the ith battery ij :

[0055]

[0056] Calculate the entropy value and variance coefficient of the jth indicator:

[0057]

[0058] g j =1-e j (14)

[0059] In the formula, e j is the entropy value of the jth indicator, g j is the coefficient of difference of the j-th index, and the constant

[0060] Calculate the weight α of the jth feature parameter j :

[0061]

[0062] Among them, g i is the coefficient of difference of the i-th indicator;

[0063] Calculate the evaluation value P of each battery sample i :

[0064]

[0065] Evaluation value P = {P1, P2, ..., P m The changes in the ohmic internal resistance, polarization internal resistance and time constant parameters of m batteries during the aging process were quantitatively evaluated; since the equivalent circuit parameters change with SOC, the average values ​​of the ohmic internal resistance, polarization internal resistance and time constant in the SOC range of 0.2-0.8 were taken as the evaluation index Pi of the fusion capacity and internal resistance.

[0066] Preferably, the specific steps of S401 are:

[0067] The objective function of charging time can be defined as:

[0068]

[0069] Where, T i represents the charging time of the i-th stage;

[0070] The objective function of capacity fading can be defined as:

[0071]

[0072] Where, L i represents the sum of the available capacity loss and the available lithium loss of the i-th order;

[0073] Since the battery capacity cannot fully reflect the battery status, the ohmic internal resistance, polarization internal resistance and time constant obtained in S3 are used to combine their characteristic parameters into a matrix to score the obtained battery status;

[0074] The objective function based on equivalent circuit parameter scoring is defined as:

[0075] J score =P(S good ,{R o_age ,R p_age ,τ age}) (19)

[0076] Where P is the battery rating, R o_age is the ohmic internal resistance value calculated based on the predicted ohmic internal resistance growth rate, R p_age is the predicted polarization internal resistance value, τ age is the predicted time constant value, S good is the matrix of feature parameter combinations;

[0077] In the optimization process, the constraints of current, voltage, and SOC also need to be considered. The complete charging optimization objective function is as follows:

[0078]

[0079]

[0080] Where V max and V min is the voltage upper and lower limits; I max and I min U is the upper and lower limits of the charging rate; i is the voltage of each level, I i is the charging current at each stage, SOCi The SOC obtained by charging each stage.

[0081] Preferably, the specific steps of S402 are:

[0082] Based on the elite non-dominated sorting genetic method, the elite retention strategy is selected on the basis of Pareto efficiency. The charging time, battery capacity loss, battery score based on ohmic internal resistance, polarization internal resistance and time constant are used as multi-objective optimization factors, and substituted into the elite non-dominated sorting genetic method to find the optimal charging sequence:

[0083] S4021: Population initialization, randomly generate an initial population, the population size is N, and the evolutionary generation number Gen=1;

[0084] S4022: Execute selection, crossover, and mutation operators to determine whether the first generation offspring population is formed: if it is formed, update the evolutionary generation number Gen=2; if it is not formed, perform a non-dominated sorting operation on the initial population and execute selection, crossover, and mutation operators to generate the first generation offspring population and set the evolutionary generation number Gen=2;

[0085] S4023: Merge the parent and child populations into a new population:

[0086] First, determine whether a new parent population has been formed. If not, calculate the objective function of the new population individuals, and perform fast non-dominated sorting, calculate crowding, and elite strategy to generate a new parent population. Perform selection, crossover, and mutation operators on the generated parent population to generate a child population. If it has been formed, directly perform selection, crossover, and mutation operators on the generated parent population to generate a child population.

[0087] S4024: Determine whether the evolutionary generation is less than the maximum evolutionary generation. If so, make the evolutionary generation Gen=Gen+1 and return to S4023; if it is greater than or equal to the maximum evolutionary generation, the operation ends, that is, the optimal charging sequence is obtained.

[0088] Preferably, the specific steps of S403 are:

[0089] The optimal charging sequence obtained by S402 is verified as the charging strategy and compared with the constant current and constant voltage strategy. A single simulation is performed on the non-aged battery, and the charging time, charging capacity loss and battery score are solved using a multi-objective genetic method. The entropy weight method is used to select the optimal charging strategy from the solution set, and the battery is charged and discharged using the optimal charging strategy, which is compared with the charging strategy of S402 to verify the optimized charging strategy obtained by S402.

[0090] Beneficial effects: The present invention evaluates the battery aging state based on the fusion of ohmic internal resistance, polarization internal resistance, time constant and battery capacity, and evaluates the battery state from the perspectives of capacity attenuation and power attenuation respectively. Compared with a single capacity evaluation index, it can better characterize the true aging state of the battery; the charging time, capacity loss and battery score based on ohmic internal resistance, polarization internal resistance and time constant are taken together as multi-index charging optimization targets; taking multiple optimization targets as factors, the elite non-dominated sorting genetic method is used to optimize the charging strategy to obtain the optimal charging sequence, thereby overcoming the problem that the constant current and constant voltage charging strategy cannot change with the internal aging state of the battery, which leads to aggravated aging, and at the same time reduces the charging time and prolongs the service life of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0092] Figure 1 is a flow chart of the method of the present invention;

[0093] Figure 2 The voltage curve and current curve obtained from the mixed power pulse characteristic test;

[0094] Figure 3 The available capacity and capacity loss rate of the battery under different charging rate tests are compared with the number of cycles: (a) available capacity, (b) capacity loss rate;

[0095] Figure 4 The open circuit voltage curve at the corresponding SOC is obtained by testing the hybrid power pulse characteristics of the unaged battery at room temperature;

[0096] Figure 5 Partial pulse response curve for mixed power pulse characteristics;

[0097] Figure 6 The curve diagram of the ohmic internal resistance changing with SOC obtained by equivalent circuit model identification;

[0098] Figure 7 This is a curve diagram of the polarization internal resistance changing with SOC obtained by equivalent circuit model identification;

[0099] Figure 8 This is a curve diagram of the polarization capacitance changing with SOC obtained by equivalent circuit model identification;

[0100] Fig. 9Comparison of the model predicted voltage and the actual measured terminal voltage: (a) simulation result, (b) simulation error;

[0101] Fig.10 The graph is a graph showing the change of ohmic internal resistance with capacity retention rate caused by different charging rate cycles;

[0102] Fig.11 The graph is a graph showing the change of polarization internal resistance with capacity retention rate caused by different charging rate cycles;

[0103] Fig.12 The graph is a graph showing the change of polarization capacitance with capacity retention rate caused by different charging rate cycles;

[0104] Fig.13 The Pareto front diagram of charging optimization is used to optimize the established objective function by using the elite non-dominated sorting genetic method;

[0105] Fig.14 Comparison diagram between “Scheme 1” and 1C-CCCV obtained by multi-objective optimization of elite non-dominated sorting genetic method: (a) charging current, (b) charging voltage;

[0106] Fig.15 Comparison diagram between “Scheme 2” and 2C-CCCV obtained by multi-objective optimization of elite non-dominated sorting genetic method: (a) charging current, (b) charging voltage;

[0107] Fig.16 Comparison diagram between “Scheme 3” obtained by elite non-dominated sorting genetic method multi-objective optimization and 3C-CCCV: (a) charging current, (b) charging voltage. DETAILED DESCRIPTION

[0108] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0109] In the description of the present invention, it is necessary to understand that the terms "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0110] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may include that the first and second features are in direct contact, or may include that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, a first feature being "above", "above" and "above" a second feature includes that the first feature is directly above and obliquely above the second feature, or simply indicates that the first feature is higher in level than the second feature. A first feature being "below", "below" and "below" a second feature includes that the first feature is directly below and obliquely below the second feature, or simply indicates that the first feature is lower in level than the second feature.

[0111] like Figure 1 As shown, a charging strategy optimization method based on battery aging status includes the following steps:

[0112] S1: Obtain and analyze battery characteristics: Obtain battery characteristics through capacity calibration test, mixed power pulse characteristic test and different charging rate cycle test, and analyze the impact of charging rate on battery capacity;

[0113] like Figure 2 As shown, the capacity calibration test: the battery to be tested is placed in a constant temperature environment, the charge and discharge cut-off voltage is set, and a constant current charge and discharge cycle is performed, and the average value of the three discharge capacities is selected as the current available capacity of the battery;

[0114] Place the power battery in a 25℃ constant temperature box for 4 hours, use 1 / 3C current to charge the battery to a cut-off voltage of 4.2V, use constant voltage charging to reduce the current to the cut-off current 1 / 20C, and let it stand for 1 hour, use 1 / 3C current to discharge the battery to a cut-off voltage of 2.75V, and let it stand for 1 hour, repeat the steps three times, and take the average of the three discharge capacities as the current available capacity of the battery.

[0115] Hybrid power pulse characteristic test: Use 1 / 3C current to discharge the battery to the cut-off voltage, where C is the current density, and then apply constant current to charge the battery to 5% SOC, and then apply 3C rate current to perform short-time pulse charge and discharge on the battery; repeat constant current charging and 3C rate current pulse cycles, set the SOC range and interval, and obtain the pulse response of the battery in the full SOC range;

[0116] Use 1 / 3C current to discharge the battery to a cut-off voltage of 2.75V and let it stand for 1 hour, use 1 / 2C current to charge the battery for 6 minutes, adjust the battery SOC to 5%, let it stand for 1 hour, use 3C pulse current to charge for 10 seconds, let it stand for 40 seconds, then use 3C pulse current to discharge for 10 seconds, let it stand for 40 seconds; repeat the above process to obtain voltage responses of SOC at 5%, 10%, 15%, ..., 95%, respectively, until the battery is fully charged.

[0117] Different charging rate cycle test: charge the battery at different rate currents, stop when the cut-off voltage or cut-off current is reached, and then discharge the battery at a constant rate. Set different cycle intervals according to different rates for testing until the battery ages;

[0118] The battery was fully charged at constant current and constant voltage with 1C, 2C, and 3C as the charging rate, the cut-off voltage was 4.2V, the cut-off current was 1 / 20C, and the 1C discharge rate was used for discharge. The test was performed with 90 cycles, 20 cycles, and 5 cycles as the cycle intervals of each charging rate until the battery capacity decayed to 80% of the initial capacity.

[0119] Analyze the impact of charging rate on battery capacity: Analyze the cycle test results at different charging rates, including the changes in battery available capacity and capacity loss rate with the number of cycles at different charging rates.

[0120] like Figure 3As shown in the figure, the available capacity and capacity loss rate of the battery under different charging rates change with the number of cycles. The 1C-CCCV, 2C-CCCV, and 3C-CCCV shown in the figure are all cycle scheme numbers, where the cycle scheme "1C-CCCV" refers to 1C charging rate, 1C discharge rate, and a cycle interval of 90 cycles; "2C-CCCV" refers to 2C charging rate, 1C discharge rate, and a cycle interval of 20 cycles; "3C-CCCV" refers to 3C charging rate, 1C discharge rate, and a cycle interval of 5 cycles. As can be seen from the figure, there are obvious differences in capacity decay caused by different charging rates, and the differences are mainly reflected in the decay rate and the number of cycles at which the capacity reaches 80%. As the rate increases, the number of cycles that the available capacity of the battery decays to 80% of the initial value decreases. For the 1C charging cycle, the capacity decays to less than 80% after more than 600 cycles, while 2C charging and 3C charging cycle 200 and 60 cycles respectively. The capacity decay rate of batteries cycled at different charging rates increased at the end. For the 1C-CCCV charging method, the average capacity decayed by 0.0290% before 450 cycles, and by 0.0345% after 450 cycles. For the 2C-CCCV charging method, the average capacity decayed by 0.0883% in the first 140 cycles, and by 0.1357% after 140 cycles. For the 3C-CCCV charging method, the first 45 cycles decayed by 0.3385%, and by 0.3812% after 45 cycles. For 1C charging and discharging, the performance is not stable at the beginning of battery aging, and the capacity loss mainly comes from the formation of the passivation film on the surface of the lithium-ion battery electrode, that is, the SEI film. As the cycle progresses, the growth of the SEI film on the negative electrode surface gradually stabilizes, the internal performance of the battery tends to be stable, and the capacity decay rate gradually stabilizes; at the end of battery aging, the battery's acceptance of charging weakens and the capacity decays rapidly. The reason is that lithium plating occurs at the negative electrode, resulting in a large loss of available lithium. When the battery is cycled using high-rate charging, the initial capacity decay is faster, and the decay rate increases with the rate. At the same time, the turning point of capacity decay moves forward. The reason is that as the charging rate increases, the battery reaction rate accelerates, causing the SEI film to form quickly. Unlike 1C charging and discharging, the capacity loss rate of high-rate cycling increases further at the end of aging. The reason is that as the charging rate increases, lithium ions have greater stress when embedded in and out of the electrode material, resulting in loss of active materials and increasing the capacity loss rate.

[0121] S2: Construct an equivalent circuit model and identify the parameters of the open circuit voltage, ohmic internal resistance, polarization internal resistance and polarization capacitance of the equivalent circuit model;

[0122] Based on Kirchhoff's current-voltage law, an equivalent circuit model is established as follows:

[0123]

[0124] Among them, U ocv is the battery open circuit voltage, R o is the equivalent internal resistance of the battery, R p and C p are polarization resistance and polarization capacitance respectively, U p is the capacitance C p Terminal voltage, U and I are the terminal voltage and current of the circuit respectively.

[0125] like Figure 4 The figure shows the open circuit voltage of the battery without aging test at 25°C.

[0126] Open circuit voltage identification: Perform a mixed power pulse characteristic test on the battery that has not been subjected to an aging test at room temperature. The battery SOC is adjusted by 5% each time, and the voltage after standing for 1 hour is taken as the open circuit voltage at the corresponding SOC;

[0127] Identification of ohmic internal resistance: Calculate the ohmic internal resistance by using the change in voltage from U1 to U2 caused by the instantaneous 3C pulse current charging of the battery, and the change in voltage from U3 to U4 caused by the instantaneous removal of the charging current. The calculation formula is as follows:

[0128]

[0129] Among them, U1 to U2 is the voltage caused by the moment of charging, and U3 to U4 is the voltage caused by the moment of removing the charging current;

[0130] like Figure 5 As shown in the figure, it shows part of the pulse voltage response in the mixed power pulse characteristic test. At the moment of discharging the battery and the moment of removing the current, the terminal voltage of the battery will change suddenly, corresponding to the U1 to U2 and U3 to U4 segments in the figure. At this time, the voltage change is caused by the ohmic internal resistance.

[0131] like Figure 6-Figure 8 As shown, the results of the identified ohmic internal resistance, polarization internal resistance, and polarization capacitance are shown;

[0132] Polarization internal resistance and polarization capacitance identification: Parameter identification of polarization internal resistance and polarization capacitance is performed based on the 10s stage of 3C pulse current discharge in the mixed power pulse characteristic test:

[0133] During the pulse discharge phase, when the battery is discharged, the current flows through the RC circuit in the equivalent circuit, and the capacitor C p Voltage across the terminals U p Gradually increasing, U p The change over time is shown in the formula:

[0134]

[0135] Since the voltage value of the two ends of the RC circuit is 0 in the steady state, the RC circuit is in a zero state response at this time. Solving the formula, we get the formula:

[0136]

[0137] At this time, the battery terminal voltage is expressed as follows:

[0138]

[0139] In the static stage after the discharge is completed, when the battery is discharged, the polarized capacitor is discharged, and the battery terminal voltage shows a slow upward trend. At this time, the RC circuit has a zero input response, and the battery terminal voltage is shown by the following formula:

[0140]

[0141] The nonlinear least square method is used to fit the terminal voltage in the static stage after the discharge is completed according to formula (6) to obtain the time constant τ of the RC circuit:

[0142] τ=R p C p (7)

[0143] Substitute the fitting time constant (7) into formula (5), and fit the terminal voltage during the pulse discharge phase to obtain the polarization internal resistance R p , use formula (8) to get C p :

[0144]

[0145] S3: Verify the accuracy of the model parameters identified in S2, analyze the relationship between the model parameters and the capacity retention rate, and obtain the battery performance evaluation index of the fusion capacity and internal resistance of the battery in different aging states;

[0146] Verify model parameters:

[0147] Substitute the open circuit voltage, ohmic internal resistance, polarization internal resistance and polarization capacitance identified in S2 into the equivalent circuit model to obtain the battery simulation model:

[0148]

[0149] The experimental current is input into the battery simulation model to obtain the output terminal voltage of the battery simulation model, which is compared with the terminal voltage of the actual battery to verify the accuracy of the open circuit voltage, ohmic internal resistance, polarization internal resistance and polarization capacitance identified in S2.

[0150] like Fig. 9As shown, the comparison result of the model predicted voltage and the actual measured terminal voltage is shown. Observing the model prediction error results, it can be seen that there is a large error at low SOC. The error comes from the pulse discharge stage. The reason is that the ohmic internal resistance of the battery during discharge has a large change at low SOC. This embodiment mainly considers the charging stage, so it is believed that the overall error is small and can meet the needs.

[0151] Analyze the relationship between model parameters and capacity retention rate:

[0152] By using the mixed power pulse characteristic test, the average value of the SOC parameters in the range of 0.2-0.8 is taken as the equivalent parameter of the current state, and the change trend of the ohmic internal resistance, polarization internal resistance and polarization capacitance under different charging rate cycles during the aging process is obtained. In this way, the relationship between the growth of equivalent circuit parameters caused by different charging rate cycles and the capacity retention rate is analyzed.

[0153] like Figure 10-12 As shown, the relationship between the growth of equivalent circuit parameters and capacity retention rate caused by different charging rate cycles is demonstrated. The capacity retention rate is obtained by dividing the current available capacity of the battery by the initial available capacity. For ohmic internal resistance and time constant, the growth rates of the three charging rates are similar only when the capacity retention rate is close to 100%. After that, as the capacity retention rate decreases, the difference in growth rates gradually increases. At the same capacity retention rate, the smaller the rate, the greater the parameter growth rate. For polarization internal resistance, batteries cycled at different rates have high similarity before the capacity retention rate is 91%, and then the growth rate of 1C charging cycle gradually increases, while the growth rates of 2C charging cycle and 3C charging cycle are always close.

[0154] It can be concluded that although the batteries have the same capacity retention rate, the internal parameters of the batteries caused by different charging methods are quite different. The capacity retention rate can only evaluate the available capacity of the battery, and cannot comprehensively measure the internal state of the battery. If only capacity loss is used as the optimization target of the battery in the charging strategy, it is not possible to measure the changes in the internal performance of the battery caused by charging differences, so additional battery performance evaluation standards are needed.

[0155] Get battery performance evaluation indicators for evaluating different battery aging states:

[0156] Based on the three characteristic parameters of ohmic internal resistance, polarization internal resistance and time constant, the weights are calculated respectively, and then the battery performance evaluation index under different aging conditions is obtained;

[0157] The three characteristic parameters of ohmic internal resistance, polarization internal resistance and time constant are expressed in the form of a matrix S(i,j) = {R o ,R p,τ}, where i = 1, 2, ..., m; where m is the number of batteries, j = 1, 2, 3, S(i, j) represents the jth characteristic parameter of the i-th battery;

[0158]

[0159] The characteristic parameters are standardized. According to the changing trend of the characteristic parameters during the aging process, the ohmic internal resistance and polarization internal resistance are defined as negative indicators, that is, the smaller the internal resistance, the better, and the time constant is a positive indicator, that is, the larger the time constant, the better, which represents the standardized characteristic parameter matrix;

[0160]

[0161] Calculate the weight P of the jth index in the ith battery ij :

[0162]

[0163] Calculate the entropy value and variance coefficient of the jth indicator:

[0164]

[0165] g j =1-e j (14)

[0166] In the formula, e j is the entropy value of the jth indicator, g j is the coefficient of difference of the j-th index, and the constant

[0167] Calculate the weight α of the jth feature parameter j :

[0168]

[0169] Among them, g i is the coefficient of difference of the i-th indicator;

[0170] Calculate the evaluation value P of each battery sample i :

[0171]

[0172] Evaluation value P = {P1, P2, ..., P m The changes of ohmic internal resistance, polarization internal resistance and time constant parameters during the aging process of m batteries were quantitatively evaluated. Since the equivalent circuit parameters change with SOC, the average values ​​of ohmic internal resistance, polarization internal resistance and time constant in the SOC range of 0.2-0.8 were taken as the evaluation index P of fusion capacity and internal resistance. i .

[0173] Considering that the above equivalent circuit parameters change with SOC, in order to reflect the overall performance of the battery, the average values ​​of the ohmic internal resistance, polarization internal resistance and time constant in the SOC range of 0.2-0.8 are taken, and the scoring results are as follows:

[0174] Under the current density of 1C, the score value after 0 cycle is 0.9143, the score value after 60 cycles is 0.8201, and the score value after 180 cycles is 0.5901; under the current density of 2C, the score value after 0 cycle is 0.9450, the score value after 60 cycles is 0.7980, and the score value after 180 cycles is 0.0781; under the current density of 3C, the score value after 0 cycle is 0.9147, and the score value after 60 cycles is 0.1046.

[0175] In summary, for the same charging rate, the battery score will gradually decrease with the increase in the number of cycles, and the higher the charging rate, the faster the score decreases. The difference in the score of the unaged battery is due to the difference in the parameters of different batteries. For the battery after 60 cycles, the battery score of the 1C rate charge and discharge cycle is the highest, and the battery score of the 3C rate charge and discharge cycle is much lower than the other two rates. At this time, the battery with the 3C rate is already in an aged state. For the battery after 180 cycles, the battery score of the 1C rate cycle is still higher than the 2C rate cycle. At this time, the 1C rate battery still has a high capacity, while the 2C rate is close to aging. It can be seen that the score value is positively correlated with the battery state. The better the state of the battery, the higher the score value. Under the same number of cycles, the battery equivalent circuit parameter change rate of the large rate charge and discharge cycle is larger, resulting in a lower score value.

[0176] S4: Combined with the battery performance evaluation index of fusion capacity and internal resistance obtained in S3, the charging objective function is established with charging time, battery capacity loss and battery score as optimization targets, and the objective function is optimized. According to the battery performance evaluation index, the charging strategy of the optimal charging sequence is obtained and verified.

[0177] S401: Select multi-stage charging based on cut-off voltage as the charging strategy. The charging stages are divided into 5 stages I1 to I5, and the charging cut-off voltage is 4.2V. Construct an objective function that takes into account both battery capacity loss and fast charging. Fast charging is to complete charging of the battery in the shortest time, that is, the sum of the accumulated charging time of each stage is the shortest.

[0178] The objective function of charging time can be defined as:

[0179]

[0180] Where, T i represents the charging time of the i-th stage;

[0181] The objective function of capacity fading can be defined as:

[0182]

[0183] Where, L i represents the sum of the available capacity loss and the available lithium loss of the i-th order;

[0184] Since the battery capacity cannot fully reflect the battery status, the ohmic internal resistance, polarization internal resistance and time constant obtained in S3 are used to combine their characteristic parameters into a matrix to score the obtained battery status;

[0185] The objective function based on equivalent circuit parameter scoring is defined as:

[0186] J score =P(S good ,{R o_age ,R p_age ,τ age}) (19)

[0187] Where P is the battery rating, R o_age is the ohmic internal resistance value calculated based on the predicted ohmic internal resistance growth rate, R p_age is the predicted polarization internal resistance value, τ age is the predicted time constant value, S good is the matrix of feature parameter combinations;

[0188] In the optimization process, the constraints of current, voltage, and SOC also need to be considered. The complete charging optimization objective function is as follows:

[0189]

[0190]

[0191] Where V max and V min is the voltage upper and lower limits; I max and I min U is the upper and lower limits of the charging rate; i is the voltage of each level, I i is the charging current at each stage, SOC i The SOC obtained by charging each stage.

[0192] S402: Optimizing the objective function established in S401 using a multi-objective genetic method to obtain an optimal charging sequence, and using the optimal charging sequence as the final result of the charging strategy optimization;

[0193] Based on the elite non-dominated sorting genetic method, the elite retention strategy is selected on the basis of Pareto efficiency. The charging time, battery capacity loss, battery score based on ohmic internal resistance, polarization internal resistance and time constant are used as multi-objective optimization factors, and substituted into the elite non-dominated sorting genetic method to find the optimal charging sequence:

[0194] S4021: Population initialization, randomly generate an initial population, the population size is N, and the evolutionary generation number Gen=1;

[0195] S4022: Execute selection, crossover, and mutation operators to determine whether the first generation offspring population is formed: if it is formed, update the evolutionary generation number Gen=2; if it is not formed, perform a non-dominated sorting operation on the initial population and execute selection, crossover, and mutation operators to generate the first generation offspring population and set the evolutionary generation number Gen=2;

[0196] S4023: Merge the parent and child populations into a new population:

[0197] First, determine whether a new parent population has been formed. If not, calculate the objective function of the new population individuals, and perform fast non-dominated sorting, calculate crowding, and elite strategy to generate a new parent population. Perform selection, crossover, and mutation operators on the generated parent population to generate a child population. If it has been formed, directly perform selection, crossover, and mutation operators on the generated parent population to generate a child population.

[0198] S4024: Determine whether the evolutionary generation is less than the maximum evolutionary generation. If so, make the evolutionary generation Gen=Gen+1 and return to S4023; if it is greater than or equal to the maximum evolutionary generation, the operation ends, that is, the optimal charging sequence is obtained.

[0199] The elite non-dominated sorting genetic method optimizes the established objective function and obtains the Pareto front diagram of charging optimization. Each solution on the front is a charging scheme that comprehensively considers the charging time, battery capacity loss, and battery rating. The obtained charging current is analyzed. In order to avoid the influence of subjective factors when selecting the optimal scheme from the charging scheme, the entropy weight method is used to select the charging scheme. The three charging schemes with the highest weights in the three intervals of charging rate less than 1.75C, 1.75C-2.5C, and 2.5C-3C in the first stage are selected as the optimal charging sequence;

[0200] like Fig.13As shown in the figure, the Pareto front diagram of charging optimization is shown by the elite non-dominated sorting genetic method to optimize the established objective function. The figure includes 28 solutions. It can be seen from the schemes in the figure that charging time and capacity loss are two contradictory goals. Shortening the charging time will cause a large capacity loss, and the battery score will also be reduced. Therefore, shortening the charging time requires a large charging current to be applied to the battery, and excessive charging current will cause great damage to the battery capacity, and will cause a large change in the ohmic internal resistance, polarization internal resistance and time constant, resulting in a decrease in the battery score value. Analysis of the obtained charging current shows that the first-stage charging rate of each charging scheme is mainly concentrated around 2C and 1.5C; the first-stage rate of the scheme with the shortest charging time is 3C, and the first-stage rate of the scheme with the longest charging time is 1.5C.

[0201] S403: Verify the charging strategy obtained in S402;

[0202] The optimal charging sequence obtained by S402 is verified as the charging strategy and compared with the constant current and constant voltage strategy. A single simulation is performed on the non-aged battery, and the charging time, charging capacity loss and battery score are solved using a multi-objective genetic method. The entropy weight method is used to select the optimal charging strategy from the solution set, and the battery is charged and discharged using the optimal charging strategy, which is compared with the charging strategy of S402 to verify the optimized charging strategy obtained by S402.

[0203] like Figure 14-16 As shown, the comparison results of three solutions with the constant current and constant voltage strategy are shown:

[0204] "Scheme 1, 2, 3" are the charging sequences after optimization by the elite non-dominated sorting genetic method, that is, "Scheme 1" is [1.5463, 0.6636, 0.5248, 0.4948, 0.3398]; "Scheme 2" is [2.0552, 1.0519, 0.8584, 0.5048, 0.3309]; "Scheme 3" is [2.9923, 2.0029, 1.2767, 1.1373, 0.8952].

[0205] It takes 3047s to charge to the cut-off condition with Scheme 1, charging 93.43% of the initial capacity; it takes 5025s to charge 1C-CCCV to the cut-off condition, charging 97.86% of the initial capacity. Compared with the case of charging the same amount of electricity, 1C-CCCV takes 3766s, and Scheme 1 shortens the time by 19.09%; for the case of charging to the cut-off condition, Scheme 1 shortens the time by 39.36%, while only charging 4.43% less electricity.

[0206] It takes 2633s to charge to the cut-off condition with Scheme 2, charging 94.81% of the initial capacity; it takes 3771s to charge to the cut-off condition with 2C-CCCV, charging 97.57% of the initial capacity. Compared with the case of charging the same amount of electricity, 2C-CCCV takes 2723s, and Scheme 2 shortens the time by 3.31%; for the case of charging to the cut-off condition, Scheme 2 shortens the time by 30.10%, while only charging 2.76% less electricity.

[0207] It takes 1335s to charge to the cutoff condition with Scheme 3, charging 84.43% of the initial capacity; it takes 3507s to charge to the cutoff condition with 3C-CCCV, charging 97.29% of the initial capacity. Compared with the case of charging the same amount of electricity, 3C-CCCV takes 2186s, and Scheme 3 shortens the time by 38.93% compared with 3C-CCCV. Scheme 3 reduces the charging time by 61.93% compared with 3C-CCCV, but charges 12.86% less electricity.

[0208] Taking into account the charging time and battery aging, the scheme 1 with the highest weight is used as the long-cycle charging strategy, and the discharge strategy adopts 1C discharge, and 50 charge and discharge cycles are performed. The battery capacity retention rate after the cycle is 97.65%, which is equivalent to 57.91 cycles of 1C-CCCV. The average charging time of each cycle of scheme 1 is 2875s, and the average charging time of each cycle of 1C-CCCV is 4983s. Scheme 1 shortens the charging time by 42.30%. The aged battery obtained by scheme 1 is tested for mixed power pulse characteristics and parameter identification. The battery score calculated based on the entropy weight method is 0.7121, which is less than the predicted 0.7003. Compared with the score of 0.8201 of 1C-CCCV in 60 cycles, the battery score obtained by scheme 1 cycle is reduced by 14.61%. Based on the above analysis, it can be seen that the obtained charging strategy can effectively reduce the charging time without causing too much damage to the battery.

[0209] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0210] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A charging strategy optimization method based on battery aging status, characterized in that: The following steps are involved: S1: Obtain and analyze battery characteristics: Obtain battery characteristics through capacity calibration test, mixed power pulse characteristic test and different charging rate cycle test, and analyze the impact of charging rate on battery capacity; The specific steps of S1 are: Capacity calibration test: Place the battery to be tested in a constant temperature environment, set the charge and discharge cut-off voltage and perform constant current charge and discharge cycles, and select the average value of three discharge capacities as the current available capacity of the battery; Hybrid power pulse characteristic test: Use 1 / 3C current to discharge the battery to the cut-off voltage, where C is the current density, then apply constant current to charge the battery to 5% SOC, and then apply 3C rate current to perform short-term pulse charge and discharge on the battery; Repeat constant current charging and 3C rate current pulse cycles, set the SOC range and interval, and obtain the pulse response of the battery in the full SOC range; Different charging rate cycle test: charge the battery at different rate currents, stop when the cut-off voltage or cut-off current is reached, and then discharge the battery at a constant rate. Set different cycle intervals according to different rates for testing until the battery ages; Analyze the impact of charging rate on battery capacity: Analyze the cycle test results at different charging rates, including the changes in battery available capacity and capacity loss rate with the number of cycles at different charging rates; S2: Construct an equivalent circuit model and identify the parameters of the open circuit voltage, ohmic internal resistance, polarization internal resistance and polarization capacitance of the equivalent circuit model; The specific steps of constructing the equivalent circuit model in S2 are: Based on Kirchhoff's current-voltage law, an equivalent circuit model is established as follows: Among them, U ocv is the battery open circuit voltage, R o is the equivalent internal resistance of the battery, R p and C p are polarization resistance and polarization capacitance respectively, U p is the capacitance C p Terminal voltage, U and I are the terminal voltage and current of the circuit respectively; The parameter identification in S2 is specifically as follows: Open circuit voltage identification: Perform a mixed power pulse characteristic test on the battery that has not been subjected to an aging test at room temperature, and use the voltage at the end of the static state after the pulse current is discharged as the open circuit voltage at the corresponding SOC; Identification of ohmic internal resistance: Calculate the ohmic internal resistance by using the change in voltage from U1 to U2 caused by the instantaneous pulse current charging of the battery, and the change in voltage from U3 to U4 caused by the instantaneous removal of the charging current. The calculation formula is as follows: Among them, U1 to U2 is the voltage caused by the moment of charging, and U3 to U4 is the voltage caused by the moment of removing the charging current; Polarization internal resistance and polarization capacitance identification: Parameter identification of polarization internal resistance and polarization capacitance is performed based on the pulse current discharge stage in the mixed power pulse characteristic test: During the pulse discharge phase, when the battery is discharged, the current flows through the RC circuit in the equivalent circuit, and the capacitor C p Voltage across the terminals U p Gradually increasing, U p The change over time is shown in the formula: Since the voltage value of the two ends of the RC circuit is 0 in the steady state, the RC circuit is in a zero state response at this time. Solving the formula, we get the formula: At this time, the battery terminal voltage is expressed as follows: In the static stage after the discharge is completed, when the battery is discharged, the polarized capacitor is discharged, and the battery terminal voltage shows a slow upward trend. At this time, the RC circuit has a zero input response, and the battery terminal voltage is shown by the following formula: The nonlinear least square method is used to fit the terminal voltage in the static stage after the discharge is completed according to formula (6) to obtain the time constant τ of the RC circuit: τ=R p C p (7) Substitute the fitting time constant (7) into formula (5), and fit the terminal voltage during the pulse discharge phase to obtain the polarization internal resistance R p , use formula (8) to get C p : S3: Verify the accuracy of the model parameters identified in S2, analyze the relationship between the model parameters and the capacity retention rate, and obtain the battery performance evaluation index of the fusion capacity and internal resistance of the battery in different aging states; The specific steps of verifying the model parameters in S3 are: Substitute the open circuit voltage, ohmic internal resistance, polarization internal resistance and polarization capacitance identified in S2 into the equivalent circuit model to obtain the battery simulation model: The experimental current is input into the battery simulation model to obtain the output terminal voltage of the battery simulation model, which is compared with the terminal voltage of the actual battery to verify the accuracy of the open circuit voltage, ohmic internal resistance, polarization internal resistance and polarization capacitance identified in S2; The relationship between the analysis model parameters and the capacity retention rate in S3 is specifically: Using the mixed power pulse characteristic test, the average value of the SOC parameters in the range of 0.2-0.8 is taken as the equivalent parameter of the current state, and the change trend of the ohmic internal resistance, polarization internal resistance and polarization capacitance under different charging rate cycles during the aging process is obtained, so as to analyze the relationship between the growth of equivalent circuit parameters and capacity retention rate caused by different charging rate cycles; The battery performance evaluation index for evaluating different battery aging states obtained in S3 is specifically: Based on the three characteristic parameters of ohmic internal resistance, polarization internal resistance and time constant, the weights are calculated respectively, and then the battery performance evaluation index under different aging conditions is obtained; The three characteristic parameters of ohmic internal resistance, polarization internal resistance and time constant are expressed in the form of a matrix S(i,j) = {R o ,R p ,τ}, where i = 1, 2, ..., m; where m is the number of batteries, j = 1, 2, 3, S(i, j) represents the jth characteristic parameter of the i-th battery; The characteristic parameters are standardized. According to the changing trend of the characteristic parameters during the aging process, the ohmic internal resistance and polarization internal resistance are defined as negative indicators, and the time constant is defined as a positive indicator, which represents the standardized characteristic parameter matrix. Calculate the weight P of the jth index in the ith battery ij : Calculate the entropy value and variance coefficient of the jth indicator: g j =1-e j (14) In the formula, e j is the entropy value of the jth indicator, g j is the coefficient of difference of the j-th index, and the constant Calculate the weight α of the jth feature parameter j : Among them, g i is the coefficient of difference of the i-th indicator; Calculate the evaluation value P of each battery sample i : Evaluation value P = {P1, P2, ..., P m The changes of ohmic internal resistance, polarization internal resistance and time constant parameters during the aging process of m batteries were quantitatively evaluated. Since the equivalent circuit parameters change with SOC, the average values ​​of ohmic internal resistance, polarization internal resistance and time constant in the SOC range of 0.2-0.8 were taken as the evaluation index P of fusion capacity and internal resistance. i ; S4: Combined with the battery performance evaluation index of fusion capacity and internal resistance obtained in S3, the charging objective function is established with charging time, battery capacity loss and battery score as optimization targets, and the objective function is optimized. According to the battery performance evaluation index, the charging strategy of the optimal charging sequence is obtained and verified. S401: Selecting multi-stage charging based on cut-off voltage as a charging strategy and constructing an objective function; S402: Optimizing the objective function established in S401 using a multi-objective genetic method to obtain an optimal charging sequence, and using the optimal charging sequence as the final result of the charging strategy optimization; S403: Verify the charging strategy obtained in S402.

2. The method for optimizing the charging strategy based on the battery aging state according to claim 1, characterized in that: The specific steps of S401 are: The objective function of charging time can be defined as: Where, T i represents the charging time of the i-th stage; The objective function of capacity fading can be defined as: Where, L i represents the sum of the available capacity loss and the available lithium loss of the i-th order; Since the battery capacity cannot fully reflect the battery status, the ohmic internal resistance, polarization internal resistance and time constant obtained in S3 are used to combine their characteristic parameters into a matrix to score the obtained battery status; The objective function based on equivalent circuit parameter scoring is defined as: J score =P(S good ,{R o_age ,R p_age ,τ age }) (19) Where P is the battery rating, R o_age is the ohmic internal resistance value calculated based on the predicted ohmic internal resistance growth rate, R p_age is the predicted polarization internal resistance value, τ age is the predicted time constant value, S good is the matrix of feature parameter combinations; In the optimization process, the constraints of current, voltage, and SOC also need to be considered. The complete charging optimization objective function is as follows: Where V max and V min is the voltage upper and lower limits; I max and I min The upper and lower limits of the charging rate; U i is the voltage of each level, I i is the charging current at each stage, SOC i The SOC obtained by charging each stage.

3. The method for optimizing the charging strategy based on the battery aging state according to claim 2, characterized in that: The specific steps of S402 are: Based on the elite non-dominated sorting genetic method, the elite retention strategy is selected on the basis of Pareto efficiency. The charging time, battery capacity loss, battery score based on ohmic internal resistance, polarization internal resistance and time constant are used as multi-objective optimization factors, and substituted into the elite non-dominated sorting genetic method to find the optimal charging sequence: S4021: Population initialization, randomly generate an initial population, the population size is N, and the evolutionary generation number Gen=1; S4022: Execute selection, crossover, and mutation operators to determine whether the first generation of offspring population has been formed: if it has been formed, update the evolutionary generation number Gen=2; If not, perform a non-dominated sorting operation on the initial population and perform selection, crossover, and mutation operators to generate the first generation of offspring population and set the evolutionary generation number Gen = 2; S4023: Merge the parent and child populations into a new population: First, determine whether a new parent population has been formed. If not, calculate the objective function of the new population individuals, and perform fast non-dominated sorting, calculate crowding, and elite strategy to generate a new parent population. Perform selection, crossover, and mutation operators on the generated parent population to generate a child population. If it has been formed, directly perform selection, crossover, and mutation operators on the generated parent population to generate a child population. S4024: Determine whether the evolutionary generation is less than the maximum evolutionary generation. If so, make the evolutionary generation Gen=Gen+1 and return to S4023; if it is greater than or equal to the maximum evolutionary generation, the operation ends, that is, the optimal charging sequence is obtained.

4. The method for optimizing charging strategy based on battery aging status according to claim 3 is characterized in that: The specific steps of S403 are: The optimal charging sequence obtained by S402 is verified as the charging strategy and compared with the constant current and constant voltage strategy. A single simulation is performed on the non-aged battery, and the charging time, charging capacity loss and battery score are solved using a multi-objective genetic method. The entropy weight method is used to select the optimal charging strategy from the solution set, and the battery is charged and discharged using the optimal charging strategy, which is compared with the charging strategy of S402 to verify the optimized charging strategy obtained by S402.

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