CN111477981A - Lithium ion battery interval optimization charging method - Google Patents

Abstract

The invention is a method for optimizing charging of lithium ion batteries in different areas. By analyzing the characteristic parameter change characteristics of lithium ion batteries under different rates and different SOC intervals, a capacity decay rate model and an energy consumption model are established, and a comprehensive consideration of the capacity decay rate and the energy consumption model is proposed. A multi-objective function with energy consumption as a penalty term, with capacity decay rate and energy consumption as optimization goals, and SOC as state variable, under the constraints of average charging rate, charging and discharging voltage, maximum charging rate and total charge, the optimization The algorithm calculates a set of optimal current sequences; at the same time, based on the model predictive control theory, the multi-objective optimal charging control of the battery is realized, and the multi-step predictive control method is used to obtain the optimal current sequence. Compared with the traditional charging method, the present invention reduces the energy consumption during the charging process, and has the function of slowing down the capacity decline of the battery and prolonging the service life.

Description

A method for optimal charging of lithium-ion batteries between sub-divisions

technical field

The invention belongs to the technical application field of power batteries, and mainly relates to a method for optimizing charging between sub-divisions of lithium ion batteries.

Background technique

Existing charging methods include constant current and constant voltage charging, current intermittent charging, step-variable current charging, and AC charging. Among them, constant current and constant voltage charging is the most widely used, but the constant voltage stage takes a long time, and the polarization effect generated during the charging process affects the charging efficiency. The current intermittent charging is further divided into constant current intermittent charging and variable current intermittent charging. In the process of constant current intermittent charging, the current amplitude is constant and the duty cycle is unchanged, but the determination of the current amplitude and the duty cycle have not been established. It is solved, the contradiction between charging time and charging polarization cannot be balanced; and the current amplitude of variable current intermittent charging is not unique, which can achieve the purpose of improving charging efficiency, but the control during variable current intermittent charging is complicated, if the current is too large It tends to affect battery life, and if the current is too low, the charging time will increase. Step-variable current charging is the most widely studied charging method. The entire charging process is divided into several stages, and different intelligent algorithms are used to obtain the optimal charging current sequence. However, this method depends on the selection of the type of optimization algorithm, and the current weight The selection of , is relatively arbitrary, and it is not easy to determine the optimal weight coefficient, and the influence of the charging sequence on the battery life is not considered.

SUMMARY OF THE INVENTION

In order to overcome the disadvantages of existing fast charging methods such as adverse effects on battery life and large losses in the charging process, the patent of the present invention proposes an optimized charging method based on an integrated battery capacity decay rate and energy consumption model. The technical scheme adopted by the present invention to solve the technical problem is: an optimized charging method based on the capacity decay rate and energy consumption model of the lithium ion battery. The realization of this method mainly includes two parts, namely the establishment of the model and the realization of the algorithm.

In order to achieve the above purpose, the technical scheme adopted in the present invention is:

A method for optimizing charging between sub-divisions of a lithium-ion battery, comprising the following steps:

S1: According to the relationship between the battery capacity decay rate Q _loss and the equivalent cycle number x, perform differential processing on it to obtain the relationship between the battery capacity decay rate DS and x;

S2: According to the capacity decay rate model of the lithium-ion battery under the full SOC cycle interval of different charging rates and different aging degrees, establish a relationship model between the battery capacity decay rate DS and the SOC interval, the charging rate I _c and the aging degree;

S3: establish a preliminary energy consumption model according to the first-order equivalent circuit model;

S4: The DC internal resistance function R _internal of the battery is established by the DC internal resistance incremental method, and the DC internal resistance curve R _b [SOC, I _c ] with a magnification of 8C is selected as the reference curve of the DC internal resistance, including but not limited to the rate of Set it as the 8C reference curve, and make the difference between the DC internal resistance curve at other magnifications and the reference value to obtain the DC internal resistance incremental curve;

S5: use the fifth-order function to fit the DC internal resistance increment curve obtained by S4, obtain the relational expression of the internal resistance increment under different SOCs and different magnifications, and obtain the final energy consumption model;

S6: According to the capacity decay rate model obtained in S2 and the final energy consumption model in S5, determine the index function and constraint conditions, then select the state variable, the number of charging stages and the decision variable, and use the optimization algorithm to calculate a set of optimized charging current sequences;

S7: Based on the model predictive control theory, the battery SOC change is selected as the prediction model, the energy consumption is used as the optimization goal, the current rate in different SOC intervals is used as the constraint condition, the multi-step prediction with variable step size is selected, and the dynamic programming algorithm is used for optimization. The charging method obtains the optimal current sequence.

On the basis of the above scheme, S1 specifically includes the following steps:

S11: The battery capacity decay rate Q _loss is approximately in a power exponential relationship with the equivalent cycle number x, as shown in formula (1):

Q _loss = β×x ^α (1)

Among them, the equivalent number of cycles is calculated by dividing the actual number of cycles by the number of partitions 5, and α and β are constant and exponential terms respectively;

S12: Deriving the battery capacity decay rate Q _los to obtain formula (2):

DS=d(Q _loss )/dx=α×β×x ^α-1 (2)

Among them, DS represents the battery capacity decay rate.

On the basis of the above scheme, S2 specifically includes the following steps:

S21: The battery capacity decay rate model of the lithium-ion battery under the full SOC cycle interval of different charging rates I _c and different aging degrees, as shown in formula (3):

DS(I _c , Q _loss )=a(Q _loss )×I _c b(Q _loss )+c(Q _loss ) (3)

Among them, a, b, and c are all parameters related to the aging degree of the battery;

S22: The battery capacity decay rate model is coupled to the SOC cycle interval, as shown in formula (4):

DS(soc, I _c , Q _loss )=ΔSOC _k × _g [DS(I _c , Q _loss )] (4)

Among them, ΔSOC _k represents the charge in the kth stage,

ΔSOC _k is shown in formula (5):

Among them, Q is the battery capacity, η is the charging efficiency, Δt _k is the charging time of the k-th stage, I _o is the charging current of the battery, and g[DS(I _c , Q _loss )] refers to each SOC sub-interval and the whole interval Relationship between battery capacity decay rates under cycling conditions;

S23: The establishment process of g[DS(I _c , Q _loss )] is as follows:

Fitting with formula (1), the corresponding α and β are obtained,

Substitute α and β obtained from S1 into Equation (2) to obtain the variation curve of the battery capacity decay rate with the number of cycles in different sub-segmented SOC cycle intervals and in the full SOC cycle interval;

The capacity decay rate in the segmented SOC cycle interval and the capacity decay rate in the full SOC cycle interval approximate a quadratic function relationship, as shown in Equation (6);

g[DS(I _c , Q _loss )]=M+N×DS(I _c , Q _loss )+P×[DS(I _c , Q _loss )] ² (6)

Among them, M, N and P are the relationship parameter values between the segmented SOC cycle interval and the capacity decay rate of the full SOC cycle interval;

At any charging stage k, the calculation model of the battery capacity decay rate L _DS (k) under different SOC cycle intervals, different charging rates I _c and different aging degrees is shown in formula (7):

In formula (7), DS(I _c , Q _loss ) is the full SOC cycle interval, different charging rates I _c and the capacity decay rate of the battery under different aging degrees (as a known quantity), L _DS (k) is the different SOC The cycle interval, different charging rates I _c and the capacity decay rate of the battery under different aging degrees.

On the basis of the above scheme, the preliminary energy consumption model establishment process described in S3 is as follows:

S31: Based on the first-order equivalent circuit model, the stage performance index function L _Eloss (k) of energy consumption is shown in formula (8):

L _Eloss (k)={I _o ² (k)×R _o [SOC(k),I _c (k)]+I _p ² (k)×R _p [SOC(k),I _c (k)] }×△t _k (8)

where R _o [SOC(k), I _c (k)] and R _p [SOC(k), I _c (k)] are the ohmic resistance and polarization at each SOC point at different charging rates I _c , respectively Internal resistance, I _o is the charging current of the battery, I _p is the current flowing through the polarization internal resistance, I _c is the charging rate of the battery, and Δt _k is the charging time of each stage k;

S32: Through the analysis of the identified parameter values, it is found that the polarization capacitance C _p in the equivalent circuit model has a large value. In the process of constant current charging, the current I _p on the polarization internal resistance is considered to be approximately equal to the charging current I _o of the battery , so the stage performance index function of energy consumption is simplified to formula (9):

The sum of the ohmic internal resistance R _o [SOC(k), I _c (k)] and the polarization internal resistance R _p [SOC(k), I _c (k)] is defined as the DC internal resistance R _internal , therefore, the formula (9) is simplified to formula (10), and the preliminary energy consumption model is obtained:

L _Eloss (k)=I _o ² (k)×R _internal [SOC(k),I _c (k)]×Δtk (10).

On the basis of the above scheme, S4 specifically includes the following steps:

S41: According to the relationship between the DC internal resistance and the SOC interval under different charging rates: as the charging rate increases, the DC internal resistance is smaller, and the internal resistance increment method is used to establish the DC internal resistance function of the battery R _internal [SOC(k) ,I _c (k)], and take the DC internal resistance at 8C as the reference value R _b [SOC,I _c ],

The incremental formula of internal resistance is shown in formula (11):

ΔR(SOC, _Ic )=R(SOC, _Ic ) _−Rb (SOC, _Ic ) (11)

The DC internal resistance increment curve under different charging rates is obtained by formula (11).

On the basis of the above scheme, S5 specifically includes the following steps:

S51: Use the fifth-order function to fit the internal resistance increment curve, as shown in formula (12):

ΔR(SOC, _Ic )=r( _Ic )SOC5+s( _Ic ) ^{SOC4 + t} (Ic)SOC3+u( _Ic )SOC2+v( _{Ic )} SOC ⁺ w(I _c )

(12)

Among them, r, s, t, u, v and w are all coefficients related to the charging rate I _c ;

S52: In order to obtain the relationship between the DC internal resistance incremental equation coefficient and the rate, fit the relationship curve between the six coefficients r, s, t, u, v and w in the formula (12) and the charging rate I _c .

Since the relationship between the internal resistance increment coefficient and the magnification is a quadratic function, it is fitted according to formula (13):

x(I _c )=H×I _c ² +Y×I _c +Z (13)

Wherein, x(I _c ) is the relationship between the DC internal resistance incremental equation coefficient and the charging rate, and H, Y, Z are the fitting coefficients;

In order to verify the accuracy of the DC internal resistance function model, the charging rate is 1C and 5C for verification, and the final energy consumption model of the lithium-ion battery is obtained. The final energy consumption model is shown in formula (14):

L _Eloss (k)=I _o ² (k)×{ _Rb [SOC(k), _Ic (k)]+ΔR[SOC(k), _Ic ( _k )]}×Δtk(14).

On the basis of the above scheme, S6 specifically includes the following steps:

S61: Division of stages:

6C is selected as the average charging rate in the optimized charging process, and the charging and discharging process is divided into 15 charging stages after comprehensive consideration;

S62: Selection of state variables:

Select SOC as the state variable;

S63: Determination of decision variables and column writing of state transition equations:

The charging rate I _c is selected as the decision variable:

According to the relationship between the battery state variables SOC, write the state transition equation as shown in equation (15):

Among them, Q is the battery capacity;

S64: Determination of indicator functions and constraints:

The indicator function comprehensively considers the capacity decay rate L _DS (k) and the energy consumption L _Eloss (k) as the objective function f _k [SOC(k)] as the penalty item, as shown in Equation (16):

In order to obtain the optimal charging current sequence, corresponding constraints need to be added, and the constraints are shown in equation (17):

S65: Programming

Since the dynamic programming algorithm follows the algorithm principle of reverse order solution, the optimal index function f(k) of this stage is determined according to formula (18), thereby determining the decision-making current _IL (k) of this stage;

min_f(k+1)+L(k)<f(k)

f(k)=min_f(k+1)+L(k) (18)

The optimal charging current sequence is obtained by the design of the optimal charging method based on the dynamic programming algorithm.

On the basis of the above scheme, S7 specifically includes the following steps:

S71: Selection of prediction model

The first-order equivalent circuit model is selected as the controlled object, and the battery SOC variation model is selected as the prediction model, as shown in equation (19):

Among them, T _s is the charging time of each charging stage;

S72: Determination of stage indicator function:

Only the energy consumption is taken as the optimization objective, and the stage index function is shown in formula (20):

The value of the DC internal resistance R is a function related to the SOC and the charging current value I, and I _k is the charging current in the kth charging stage.

S73: Determination of Constraints

Relevant constraints are added to the charging rate in different SOC intervals, as shown in equation (21):

S74: Selection of prediction step size

The prediction step size is shown in formula (22):

S75: Selection of Optimization Algorithms

The dynamic programming algorithm was chosen as the optimization algorithm.

The beneficial effects of the present invention are as follows:

The beneficial effects of the invention are that the cycle life of the battery is significantly improved under the optimized charging method, and the energy consumption during the charging process is lower than that of the traditional charging method.

Description of drawings

The present invention has the following accompanying drawings:

Fig.1 Variation of capacity decay rate with cycle times in different SOC cycle intervals

Figure 2 Fitting graph of capacity decay rate

Figure 3. The capacity decay rate graph under different SOC cycle intervals

Figure 4. Relationship between partition interval and full interval capacity decay rate

Figure 5 DC internal resistance change curve

Figure 6 Internal resistance incremental change curve

Fig.7 Graph of change of internal resistance increment coefficient with magnification

Figure 8 Comparison of experimental and calculated values of DC internal resistance

Figure 9 Dynamic programming flow chart

Figure 10 Optimized current-voltage sequence diagram

Fig. 11 Control flow chart based on MPC charging method

Figure 12 Optimized current sequence diagram

Figure 13 Energy Consumption Comparison Curve

Figure 14. Capacity Decay Rate vs Cycle Times

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to FIGS. 1 to 14 in the embodiments of the present invention.

A method for optimizing charging between sub-divisions of a lithium-ion battery, comprising the following steps:

S1: According to the relationship between the battery capacity decay rate Q _loss and the equivalent number of cycles x, differentiate it to obtain the relationship between the battery capacity decay rate DS and x:

S1 specifically includes the following steps:

S11: The battery capacity decay rate Q _loss and the equivalent cycle number x are approximately in a power exponential relationship, as shown in formula (1):

Q _loss = β×x ^α (1)

Among them, the equivalent number of cycles is calculated by dividing the actual number of cycles by the number of partitions 5, and α and β are constant and exponential terms respectively;

S12: Deriving the battery capacity decay rate Q _los to obtain formula (2):

DS=d(Q _loss )/dx=α×β×x ^α-1 (2)

Among them, DS represents the battery capacity decay rate.

S2: According to the battery capacity decay rate model of the lithium-ion battery under the full SOC cycle interval of different charging rates and different aging degrees, establish the relationship model between the battery capacity decay rate DS and the SOC interval, the charging rate I _c and the aging degree;

S2 specifically includes the following steps:

S21: The battery capacity decay rate model of the lithium-ion battery under the full SOC cycle interval of different charging rates I _c and different aging degrees, as shown in formula (3):

DS(I _c , Q _loss )=a(Q _loss )×I _c b(Q _loss )+c(Q _loss ) (3)

Among them, a, b, and c are all parameters related to the aging degree of the battery;

S22: The battery capacity decay rate model is coupled to the SOC cycle interval, as shown in formula (4):

DS(soc,I _c ,Q _loss )=ΔSOC _k ×g[DS(I _c ,Q _loss )] (4)

Among them, ΔSOC _k represents the charge in the kth stage,

ΔSOC _k is shown in formula (5):

Among them, Q is the battery capacity, η is the charging efficiency, Δt _k is the charging time of the k-th stage, I _o is the charging current of the battery, and g[DS(I _c , Q _loss )] refers to the distance between each SOC sub-area and the full Relationship between battery capacity decay rates under interval cycling conditions;

S23: g[DS(I _c ,Q _loss )] is established as follows:

Fitting with formula (1), the corresponding α and β are obtained,

Substitute α and β obtained from S1 into Equation (2) to obtain the variation curve of the battery capacity decay rate with the number of cycles in different segmented SOC cycle intervals and full SOC cycle intervals, as shown in Figure 3.

Taking the capacity decay rate in the full SOC cycle interval as the abscissa, the relationship between the battery capacity decay rate in the segmented SOC cycle interval and the capacity decay rate in the whole interval can be obtained as shown in Figure 4.

The capacity decay rate in the segmented SOC cycle interval and the capacity decay rate in the full SOC cycle interval approximate a quadratic function relationship, as shown in Equation (6), and the parameter values of M, N, and P are obtained by fitting in Fig. 4;

g[DS(I _c ,Q _loss )]=M+N×DS(I _c ,Q _loss )+P×[DS(I _c ,Q _loss )] ² (6)

Among them, M, N and P are the relationship parameter values between the segmented SOC cycle interval and the full SOC cycle interval capacity decay rate.

To sum up, for any charging stage k, the calculation model of the battery capacity decay rate L _DS (k) under different SOC cycle intervals, different charging rates I _c and different aging degrees is shown in formula (7):

In formula (7), DS(I _c , Q _loss ) is the full SOC cycle interval, different charging rates I _c and the capacity decay rate of the battery under different aging degrees (as a known quantity), L _DS (k) is the different SOC The cycle interval, different charging rates I _c and the capacity decay rate of the battery under different aging degrees.

S3: Establish a preliminary energy consumption model based on the first-order equivalent circuit model,

The preliminary energy consumption model establishment process described in S3 is as follows:

S31: Based on the first-order equivalent circuit model, the stage performance index function L _Eloss (k) of energy consumption is shown in formula (8):

L _Eloss (k)={I _o ² (k)×R _o [SOC(k),I _c (k)]+I _p ² (k)×R _p [SOC(k),I _c (k)] }×△t _k (8)

where R _o [SOC(k), I _c (k)] and R _p [SOC(k), I _c (k)] are the ohmic resistance and polarization at each SOC point at different charging rates I _c , respectively Internal resistance, I _o is the charging current of the battery, I _p is the current flowing through the polarization internal resistance, I _c is the charging rate of the battery, and Δt _k is the charging time of each stage k;

S32: Through the analysis of the identified parameter values, it can be found that the polarization capacitance C _p in the equivalent circuit model has a large value, and in the process of constant current charging, it can be considered that the current I _p on the polarization internal resistance is approximately the same as the charging current I of the battery _o are equal, so the stage performance index function of energy consumption can be simplified as formula (9):

The sum of the ohmic internal resistance R _o [SOC(k), I _c (k)] and the polarization internal resistance R _p [SOC(k), I _c (k)] is defined as the DC internal resistance R _internal , therefore, the formula (9) is simplified to formula (10), and a preliminary energy consumption model is obtained. :

L _Eloss (k)=I _o ² (k)×R _internal [SOC(k),I _c (k)]×Δt _k (10).

S4: The DC internal resistance function R _internal of the battery is established by the DC internal resistance incremental method, and the DC internal resistance curve R _b [SOC, I _c ] with a charging rate of 8C is selected as the reference curve of the DC internal resistance. The difference between the DC internal resistance curve and the reference value is obtained to obtain the DC internal resistance incremental curve:

S4 specifically includes the following steps:

S41: Figure 5 shows the variation relationship between the DC internal resistance and the SOC interval under different charging rates. As the charging rate increases, the DC internal resistance becomes smaller. Therefore, the DC internal resistance function R _internal [SOC(k), I _c (k)] of the battery is established by the internal resistance increment method, and the DC internal resistance at 8C is used as the reference value R _b [SOC, I _c ].

The incremental formula of internal resistance is shown in formula (11):

△R(SOC, _Ic )=R(SOC, _Ic ) _-Rb (SOC, _Ic ) (11)

The DC internal resistance increment curve under different charging rates is obtained by formula (11), as shown in Figure 6.

S5: Use the fifth-order function to fit the DC internal resistance increment curve obtained by S4, obtain the relational expression of the internal resistance increment under different SOCs and different charging rates, and obtain the final energy consumption model.

S5 specifically includes the following steps:

S51: Use the fifth-order function to fit the internal resistance increment curve, as shown in formula (12):

ΔR(SOC, _Ic )=r( _Ic )SOC5+s( _Ic ) ^{SOC4 + t} (Ic)SOC3+u( _Ic )SOC2+v( _{Ic )} SOC ⁺ w(I _c )

(12)

Here, r, s, t, u, v, and w are all coefficients related to the charging rate _Ic .

S52: In order to obtain the relationship between the DC internal resistance incremental equation coefficient and the rate, fit the relationship curve between the six coefficients r, s, t, u, v and w in the formula (12) and the charging rate I _c , as shown in Figure 7.

Since the relationship between the internal resistance increment coefficient and the magnification is a quadratic function, it is fitted according to formula (13):

x(I _c )=H×I _c ² +Y×I _c +Z (13)

Among them, x(I _c ) is the relationship between the DC internal resistance incremental equation coefficient and the charging rate, and H, Y, and Z are the fitting coefficients.

In order to verify the accuracy of the DC internal resistance function model, the charging rate is 1C and 5C for verification, as shown in Figure 8.

The final energy consumption model of the lithium-ion battery is obtained, and the final energy consumption model is shown in formula (14):

L _Eloss (k)=I _o ² (k)×{R _b [SOC(k),I _c (k)]+△R[SOC(k),I _c (k)]}×△t _k (14 ).

S6: According to the battery capacity decay rate model of S2 and the final energy consumption model of S5, determine the index function and constraint conditions, then select the state variables, the number of charging stages and the decision variables, and use the dynamic programming algorithm to optimize the charging method to obtain a set of optimization Current sequence:

S6 specifically includes the following steps:

S61: Division of stages:

6C is selected as the average charging rate in the optimized charging process, and the charging and discharging process is divided into 15 charging stages after comprehensive consideration.

S62: Selection of state variables:

SOC is selected as the state variable.

S63: Determination of decision variables and column writing of state transition equations:

The charging rate I _c is chosen as the decision variable.

According to the relationship between the battery state variables SOC, write the state transition equation as shown in equation (15):

Among them, Q is the battery capacity;

S64: Determination of indicator functions and constraints:

The indicator function comprehensively considers the capacity decay rate L _DS (k) and the energy consumption L _Eloss (k) as the objective function f _k [SOC(k)] as the penalty item, as shown in Equation (16):

In order to obtain the optimal charging current sequence, corresponding constraints need to be added, and the constraints are shown in equation (17):

S65: Programming

The dynamic programming algorithm flow is obtained from Figure 9.

Since the dynamic programming algorithm follows the algorithm principle of reverse order solution, the initial value of the input stage number k is 15, and the optimal index function f(k) of this stage is determined according to formula (18), thereby determining the decision-making current _IL of this stage (k).

min_f(k+1)+L(k)<f(k)

f(k)=min_f(k+1)+L(k) (18)

The design of the optimal charging method based on the dynamic programming algorithm has obtained the optimal charging current sequence when the balance coefficient M is 100, as shown in Figure 10.

S7: Based on the model predictive control theory, the battery SOC change is selected as the prediction model, the energy consumption is used as the optimization objective, the current rate in different SOC intervals is used as the constraint condition, the multi-step prediction with variable step size is selected, and the dynamic programming algorithm is used to obtain the optimization. Current sequence:

S7 specifically includes the following steps:

S71: Selection of prediction model

The first-order equivalent circuit model is selected as the controlled object, and the battery SOC variation model is selected as the prediction model, as shown in equation (19):

Among them, T _s is the charging time of each charging stage;

S72: Determination of stage indicator function

Only the energy consumption is taken as the optimization objective, and the stage index function is shown in formula (20):

The value of the DC internal resistance R is a function related to the SOC and the charging current value I, and I _k is the charging current in the kth charging stage.

S73: Determination of Constraints

Relevant constraints are added to the charging rate in different SOC intervals, as shown in equation (21):

S74: Selection of prediction step size

The first 10 charging stages use a fixed-step 5-step prediction method. In each optimization stage, only the first element in the control sequence is used as the charging current for this stage. Step prediction method, the prediction step size is shown in formula (22):

S75: Selection of Optimization Algorithms

The design of the model predictive control process has basically been completed. The final step is to select the optimization algorithm. Here, the dynamic programming algorithm is selected as the optimization algorithm. The specific implementation process is shown in Figure 11.

Under the condition that the average charging rate is 6C, based on the above simulation model and using the model predictive control algorithm, the current sequence of each charging stage is obtained as shown in Figure 12. The comparison _curve of the energy consumption change under the conventional charging method with the same average rate and the optimized charging method is shown in Figure 13.

Under different cycle times of lithium-ion batteries, using the constant current and constant voltage charge-discharge capacity test experiments at 1C rate, the relationship between the battery capacity decay rate under the optimized charging method and the traditional charging method with the number of cycles is shown in Figure 14. .

Obviously, the above-mentioned embodiments of the present invention are only examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Changes or changes in other different forms cannot be exhaustively listed here, and all obvious changes or changes derived from the technical solutions of the present invention are still within the protection scope of the present invention.

Contents not described in detail in this specification belong to the prior art known to those skilled in the art.

Claims (8)

1. a lithium ion battery sub-interval optimization charging method, is characterized in that, comprises the following steps: S1: According to the relationship between the battery capacity decay rate Q _loss and the equivalent cycle number x, perform differential processing on it to obtain the relationship between the battery capacity decay rate DS and x; S2: According to the capacity decay rate model of the lithium-ion battery under the full SOC cycle interval of different charging rates and different aging degrees, establish a relationship model between the battery capacity decay rate DS and the SOC interval, the charging rate I _c and the aging degree; S3: establish a preliminary energy consumption model according to the first-order equivalent circuit model; S4: The DC internal resistance function R _internal of the battery is established by the DC internal resistance incremental method, and the DC internal resistance curve R _b [SOC, I _c ] with a magnification of 8C is selected as the reference curve of the DC internal resistance. The difference between the resistance curve and the reference value is obtained to obtain the DC internal resistance incremental curve; S5: use the fifth-order function to fit the DC internal resistance increment curve obtained by S4, obtain the relational expression of the internal resistance increment under different SOCs and different magnifications, and obtain the final energy consumption model; S6: According to the capacity decay rate model obtained in S2 and the final energy consumption model in S5, determine the index function and constraint conditions, then select the state variable, the number of charging stages and the decision variable, and use the optimization algorithm to calculate a set of optimized charging current sequences; S7: Based on the model predictive control theory, the battery SOC change is selected as the prediction model, the energy consumption is used as the optimization goal, the current rate in different SOC intervals is used as the constraint condition, the multi-step prediction with variable step size is selected, and the dynamic programming algorithm is used for optimization. The charging method obtains the optimal current sequence. 2. The lithium-ion battery sub-division optimized charging method as claimed in claim 1, wherein S1 specifically comprises the following steps: S11: The battery capacity decay rate Q _loss is approximately in a power exponential relationship with the equivalent cycle number x, as shown in formula (1): Q _loss = β×x ^α (1) Among them, the equivalent number of cycles is calculated by dividing the actual number of cycles by the number of partitions 5, and α and β are constant and exponential terms respectively; S12: Deriving the battery capacity decay rate Q _los to obtain formula (2): DS=d(Q _loss )/dx=α×β×x ^α-1 (2) Among them, DS represents the battery capacity decay rate. 3. The lithium-ion battery sub-division optimized charging method as claimed in claim 2, wherein S2 specifically comprises the following steps: S21: The battery capacity decay rate model of the lithium-ion battery under the full SOC cycle interval of different charging rates I _c and different aging degrees, as shown in formula (3): DS(I _c , Q _loss )=a(Q _loss )×I _c b(Q _loss )+c(Q _loss ) (3) Among them, a, b, and c are all parameters related to the aging degree of the battery; S22: The battery capacity decay rate model is coupled to the SOC cycle interval, as shown in formula (4): DS(soc,I _c ,Q _loss )=ΔSOC _k ×g[DS(I _c ,Q _loss )] (4) Among them, ΔSOC _k represents the charge in the kth stage, ΔSOC _k is shown in formula (5):

Among them, Q is the battery capacity, η is the charging efficiency, Δt _k is the charging time of the k-th stage, I _o is the charging current of the battery, and g[DS(I _c , Q _loss )] refers to the distance between the SOC partitions and the full Relationship between battery capacity decay rates under interval cycling conditions; S23: g[DS(I _c ,Q _loss )] establishment process is as follows: Fitting with formula (1), the corresponding α and β are obtained, Substitute α and β obtained from S1 into Equation (2) to obtain the variation curve of the battery capacity decay rate with the number of cycles in different sub-segmented SOC cycle intervals and in the full SOC cycle interval; The capacity decay rate in the segmented SOC cycle interval and the capacity decay rate in the full SOC cycle interval approximate a quadratic function relationship, as shown in Equation (6); g[DS(I _c , Q _loss )]=M+N×DS(I _c , Q _loss )+P×[DS(I _c , Q _loss )] ² (6) Among them, M, N and P are the relationship parameter values between the segmented SOC cycle interval and the capacity decay rate of the full SOC cycle interval; At any charging stage k, the calculation model of the battery capacity decay rate L _DS (k) under different SOC cycle intervals, different charging rates I _c and different aging degrees is shown in formula (7):

In formula (7), DS(I _c , Q _loss ) is the full SOC cycle interval, different charging rate I _c and the capacity decay rate of the battery under different aging degrees, L _DS (k) is the different SOC cycle interval, different charging rate I _c and the capacity decay rate of the battery with different aging degrees. 4. lithium-ion battery sub-division optimization charging method as claimed in claim 3, is characterized in that, the described preliminary energy consumption model establishment process of S3 is as follows: S31: Based on the first-order equivalent circuit model, the stage performance index function L _Eloss (k) of energy consumption is shown in formula (8): L _Eloss (k)={I _o ² (k)×R _o [SOC(k),I _c (k)]+I _p ² (k)×R _p [SOC(k),I _c (k)] }×△t _k (8) where R _o [SOC(k), I _c (k)] and R _p [SOC(k), I _c (k)] are the ohmic resistance and polarization at each SOC point at different charging rates I _c , respectively Internal resistance, I _o is the charging current of the battery, I _p is the current flowing through the polarization internal resistance, I _c is the charging rate of the battery, Δt _k is the charging time of each stage k; S32: Through the analysis of the identified parameter values, it is found that the polarization capacitance C _p in the equivalent circuit model has a large value, and it is considered that the current I _p on the polarization internal resistance is approximately equal to the charging current I _o of the battery during the constant current charging process , so the stage performance index function of energy consumption is simplified to formula (9):

The sum of the ohmic internal resistance R _o [SOC(k), I _c (k)] and the polarization internal resistance R _p [SOC(k), I _c (k)] is defined as the DC internal resistance R _internal , therefore, the formula (9) is simplified to formula (10), and the preliminary energy consumption model is obtained: L _Eloss (k)=I _o ² (k)×R _internal [SOC(k),I _c (k)]×Δt _k (10). 5. The lithium-ion battery sub-division optimized charging method as claimed in claim 4, wherein S4 specifically comprises the following steps: S41: According to the relationship between the DC internal resistance and the SOC interval under different charging rates: as the charging rate increases, the DC internal resistance is smaller, and the internal resistance increment method is used to establish the DC internal resistance function of the battery R _internal [SOC(k) , I _c (k)], and take the DC internal resistance at 8C as the reference value R _b [SOC, I _c ], The incremental formula of internal resistance is shown in formula (11): ΔR(SOC, I _c )=R(SOC, I _c )-R _b (SOC, I _c ) (11) Through formula (11), the DC internal resistance increase curve under different charging rates is obtained. 6. The lithium-ion battery sub-division optimized charging method as claimed in claim 5, wherein S5 specifically comprises the following steps: S51: Use the fifth-order function to fit the internal resistance increment curve, as shown in formula (12): ΔR(SOC, _Ic )=r( _Ic )SOC5+s( _Ic ) ^{SOC4 + t} (Ic)SOC3+u( _Ic )SOC2+v( _{Ic )} SOC ⁺ w(I _c ) (12) Among them, r, s, t, u, v and w are all coefficients related to the charging rate I _c ; S52: In order to obtain the relationship between the DC internal resistance incremental equation coefficient and the rate, fit the relationship curve between the six coefficients r, s, t, u, v and w in the formula (12) and the charging rate I _c ; Since the relationship between the internal resistance increment coefficient and the magnification is a quadratic function relationship, it is fitted according to formula (13): x(I _c )=H×I _c ² +Y×I _c +Z (13) Among them, x(I _c ) is the relationship between the DC internal resistance incremental equation coefficient and the charging rate, and H, Y, and Z are the fitting coefficients; In order to verify the accuracy of the DC internal resistance function model, the charging rate is 1C and 5C for verification, and the final energy consumption model of the lithium-ion battery is obtained. The final energy consumption model is shown in formula (14): L _Eloss (k)=I _o ² (k)×{ _Rb [SOC(k), _Ic (k)]+ΔR[SOC(k), _Ic ( _k )]}×Δtk(14). 7. The lithium-ion battery sub-division optimized charging method as claimed in claim 6, wherein S6 specifically comprises the following steps: S61: Division of stages: 6C is selected as the average charging rate in the optimized charging process, and the charging and discharging process is divided into 15 charging stages after comprehensive consideration; S62: Selection of state variables: Select SOC as the state variable; S63: Determination of decision variables and column writing of state transition equations: The charging rate I _c is selected as the decision variable: According to the relationship between the battery state variables SOC, the state transition equation is written as shown in equation (15):

Among them, Q is the battery capacity; S64: Determination of indicator functions and constraints: The indicator function comprehensively considers the objective function f _k [SOC(k)] of the capacity decay rate L _DS (k) and the energy consumption L _Eloss (k) as the penalty item, as shown in Equation (16):

In order to obtain the optimal charging current sequence, corresponding constraints need to be added, and the constraints are shown in equation (17):

S65: Programming Since the dynamic programming algorithm follows the algorithm principle of solving in reverse order, the optimal index function f(k) of this stage is determined according to formula (18), so as to determine the decision-making current _IL (k) of this stage; min_f(k+1)+L(k)<f(k) f(k)=min_f(k+1)+L(k) (18) The optimal charging current sequence is obtained by the design of the optimal charging method based on the dynamic programming algorithm. 8. lithium-ion battery sub-division optimization charging method as claimed in claim 7, is characterized in that, S7 specifically comprises the steps: S71: the selection of prediction model: The first-order equivalent circuit model is selected as the controlled object, and the battery SOC variation model is selected as the prediction model, as shown in equation (19):

Among them, T _s is the charging time of each charging stage; S72: Determination of stage indicator function: Only the energy consumption is taken as the optimization objective, and the stage index function is shown in formula (20):

Among them, the value of the DC internal resistance R is a function related to the SOC and the charging current value I, and I _k is the charging current in the kth charging stage; S73: Determination of Constraints Relevant constraints are added to the charging rate in different SOC intervals, as shown in equation (21):

S74: Selection of prediction step size The prediction step size is shown in formula (22):

S75: Selection of Optimization Algorithms The dynamic programming algorithm was chosen as the optimization algorithm.

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