Lithium ion battery health state estimation method fusing multi-dimensional characteristic parameters in constant voltage charging stage

Abstract

The invention discloses a lithium ion battery health state estimation method fusing multi-dimensional characteristic parameters in a constant-voltage charging stage. The method comprises the following steps: determining a specific structure of a battery model offline; calculating and extracting current curve dominant candidate characteristic parameters and recessive candidate characteristic parameters corresponding to different equivalent cut-off currents under the constant-voltage working condition of the battery; performing Spearman correlation analysis, and determining a characteristic parameter set representing battery aging and a corresponding effective current interval; constructing and storing battery health state estimation models corresponding to different equivalent constant-voltage cut-off currents; recording and storing a constant-voltage charging time-current sequence of the battery, and calculating dominant characteristic parameters in real time; inquiring to obtain a battery health state estimation model corresponding to the actual constant-voltage charging cut-off current value; an actual state of health of the battery is estimated. Compared with a battery health state estimation method based on dynamic discharge data and constant current charge data, the estimation method provided by the invention has relatively high robustness.

Description

Technical Field

[0001] The present invention belongs to the technical field of batteries, and in particular relates to a method for estimating the health status of a lithium-ion battery by integrating multi-dimensional characteristic parameters in a constant voltage charging stage. Background Art

[0002] Lithium-ion batteries have been widely used in electric vehicle power systems in recent years due to their high energy density, low self-discharge rate, and environmental friendliness. As the mileage of electric vehicles increases, the state of health (SoH) of lithium-ion batteries gradually decreases, which in turn reduces the vehicle's operating performance and increases safety risks. Therefore, accurate estimation of battery SoH is crucial for the efficient and safe operation of the vehicle.

[0003] Compared with the discharge condition, the battery charging condition data is relatively stable. Therefore, the battery SoH estimation method based on the charging condition data has received widespread attention. For the constant current charging condition, the randomness of the initial charging state of the battery will have a certain degree of impact on the accuracy of the battery SoH estimation based on such condition data. In contrast, the battery constant voltage charging condition characteristics are insensitive to the initial charging state and contain more information reflecting the battery aging state. In recent years, they have been increasingly used in battery SoH estimation. However, the existing battery SoH estimation methods based on constant voltage condition characteristics are mainly based on complete constant voltage charging data, and use traditional RC network equivalent circuit model parameters or charging current explicit characteristic parameters to estimate the battery SoH. There is a lack of comprehensive and integrated analysis and application of high-precision battery models and multi-dimensional characteristic parameters of batteries under incomplete constant voltage charging conditions. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a lithium-ion battery health status estimation method that integrates multi-dimensional characteristic parameters of the constant voltage charging stage. After the battery constant voltage charging condition ends, the estimation method integrates the explicit and implicit characteristic parameters extracted based on the constant voltage charging data to accurately estimate the battery health status online.

[0005] The present invention is achieved through the following technical solutions.

[0006] The present invention provides a method for estimating the health status of a lithium-ion battery by integrating multi-dimensional characteristic parameters in a constant voltage charging stage, the method comprising the following steps:

[0007] S1, determining the specific structure of the battery model according to the open circuit voltage characteristics of the battery under constant voltage conditions and the general model structure;

[0008] S2, based on the battery constant voltage operating condition data over the entire life cycle, calculate and extract explicit candidate characteristic parameters and implicit candidate characteristic parameters of the current curve corresponding to different equivalent cut-off currents under the battery constant voltage operating condition;

[0009] S3, performing Spearman correlation analysis on the explicit and implicit characteristic parameters obtained in step S2 to determine a characteristic parameter set characterizing battery aging and a corresponding effective current range;

[0010] S4, combining intelligent optimization algorithm and machine learning algorithm to build and store the battery health status estimation model corresponding to different equivalent constant voltage cut-off currents;

[0011] S5, when the battery enters the constant voltage charging mode, the constant voltage charging time-current sequence of the battery is recorded and stored, and the dominant characteristic parameters determined in step S3 are calculated in real time;

[0012] S6, when the charging process is completed, the actual cut-off current value corresponding to the end of the actual constant voltage charging is recorded, and the corresponding effective implicit characteristic parameters are calculated and extracted according to the effective current interval in which it is located, and based on the explicit characteristic parameters calculated in step S5, the effective explicit characteristic parameters are screened and extracted according to the effective current interval to form a characteristic parameter set;

[0013] S7, according to the relationship between the actual constant voltage charging cut-off current value and the constant voltage charging equivalent cut-off current stored in step S4, query and obtain a battery health status estimation model corresponding to the actual constant voltage charging cut-off current value;

[0014] S8, substituting the characteristic variables obtained in step S6 into the battery actual health state estimation model corresponding to the actual constant voltage charging cut-off current obtained in step S7, to estimate the actual health state of the battery.

[0015] In step S1, the general battery model structure applied to the constant voltage condition includes a plurality of open circuit voltage equivalent voltage sources V connected in parallel and in series. eoc,k , resistor R k and inductor L k , connect multiple series-connected open-circuit voltage equivalent voltage sources V in parallel eoc,k , resistor R k and inductor L k The input end is connected to the positive electrode of the model voltage output end, and multiple series-connected open-circuit voltage equivalent voltage sources V are connected in parallel. eoc,k , resistor R k and inductor L k The output end of is connected to the negative electrode of the voltage output end of the model, and the analytical mathematical expression of the general battery model applied to the constant voltage working condition is:

[0016]

[0017] Where n is the open circuit voltage equivalent voltage source V of the parallel series connection eoc,k , resistor R kand inductor L k The number of branches, that is, the order of the equivalent circuit model; I k (t) is the open-circuit voltage equivalent voltage source V flowing through the series connection at time t eoc,k , resistor R k and inductor L k The current I k (0) is the open circuit voltage equivalent voltage source V eoc,k , resistor R k and inductor L k The initial current of the kth time constant τ k =L k / R k .

[0018] In step S1, the steps of determining the battery model structure are:

[0019] S1.1, extract the OCV-SoC relationship curve corresponding to the SoC range in the constant voltage stage;

[0020] S1.2, piecewise linear approximation of the extracted OCV-SoC relationship curve;

[0021] S1.3, determine the order of the battery model according to the number of sub-function segments contained in the piecewise function.

[0022] In the step S2, the method for extracting the constant voltage working condition data set corresponding to different equivalent cut-off currents is as follows: define the equivalent cut-off current data set as I cut,eq,n =I cut +n*ΔI, where I cut The cut-off current is set for the battery constant voltage condition, ΔI is the equivalent cut-off current change step, n is the number of intervals, and the relationship n<[I(0)-I cut ] / ΔI; extract the data segment (t,I(t)) with the current and corresponding time at the start of constant voltage charging as the starting point and the equivalent cut-off current and corresponding time as the end point, where I cut,eq,n <I(t)<I(0)。

[0023] In step S2, the explicit candidate characteristic parameters of the current curve under the constant voltage condition of the battery mainly include: the battery constant voltage charging time T CV , the current slope k at the end of constant voltage charging I And the constant voltage stage charging capacity Q CV ; Among them, the current slope expression at the end of constant voltage charging is:

[0024]

[0025] Where Δt represents the time interval, satisfying the relationship Δt=m*T s , Ts represents the sampling period, and m is a positive integer. The charging capacity expression in the constant voltage stage is:

[0026]

[0027] In the step S2, the implicit candidate characteristic parameters under the constant voltage condition of the battery mainly include: battery model parameters, the difference characterization coefficient between the initial current curve and the cycle current curve, and the battery capacity current difference value in the constant voltage stage, etc.; wherein the battery model parameter set is [R k ,I k (0),τ k ]; the difference characterization coefficient set between the initial current curve and the circulating current curve is [FoI SSE ,FoI MAE ,FoI MAPE ,FoI RMSE ], the specific expression is:

[0028]

[0029] Among them, I ini and I cyc Respectively represent the initial and cycle current curves, N represents the length of the initial current curve data; the battery capacity current differential value expression in the constant voltage stage is:

[0030]

[0031] In step S3, the expression of the correlation coefficient in the Spearman correlation analysis is:

[0032]

[0033] Among them, ρ FoI,SoH (I cut,eq,n ) is the corresponding equivalent cut-off current I cut,eq,n The Spearman correlation coefficient between the characteristic parameters and the battery health status, n cycle is the number of battery RPT tests in offline testing, d i is the rank difference between the characteristic parameter and the observed value corresponding to the battery health status;

[0034] In the step S3, extract FoI,SoH (I cut,eq,n )|>0.8, and the corresponding equivalent cut-off current. FoI,SoH (I cut,eq,n )|>0.8 The equivalent cut-off current under the condition of effective current interval [I cut,eq,min ,I cut,eq,max], and finally form a set of characteristic variables characterizing the battery SoH and the corresponding effective current range.

[0035] In step S4, a battery health state estimation model corresponding to different equivalent cutoff currents is constructed by using a support vector regression algorithm, and the support vector regression algorithm parameters are optimized by using a gray wolf optimization algorithm to obtain a more accurate battery health state estimation model corresponding to different equivalent cutoff currents. The method for constructing a battery health state estimation model combining the gray wolf optimization algorithm and the support vector regression algorithm is as follows:

[0036] S4.1, Algorithm parameter initialization: Gray wolf population size N GW , the maximum number of iterations T GW ;

[0037] S4.2, Population initialization: Randomly generate N GW Gray wolf individuals x j ,j=1,2,...,N GW ; Train the model through support vector regression algorithm and calculate the fitness value of each individual;

[0038] S4.3, calculate and record the position vector x of the leader wolf σ, the deputy wolf β and the advisor wolf δ σ 、x β and x δ ;

[0039] S4.4, update the position of gray wolves in the gray wolf pack;

[0040] S4.5, determine whether the maximum number of iterations T set in S4.1 has been reached GW Or whether the termination condition is met; if T is not reached GW and the termination condition is not met, execute step S4.2; otherwise, execute step S4.6;

[0041] S4.6, output the optimal gray wolf position and obtain the optimal parameters of the support vector regression algorithm.

[0042] In step S7, the actual constant voltage charging cut-off current value I cut,eq,act The judgment and query process of the constant voltage charging equivalent cut-off current relationship stored in step S4 is as follows: cut,eq,act , when I cut,eq,act Greater than or equal to I cut,eq,max When, according to I cut,eq,max Select the corresponding battery health status estimation model; otherwise, when I cut,eq,act Less than or equal to I cut,eq,min When, according to I cut,eq,min Select the corresponding battery health status estimation model; otherwise, when I cut,eq,act Greater than I cut,eq,min And when Icut,eq,act Less than I cut,eq,max When, according to I cut,eq,act Select the corresponding battery health status estimation model.

[0043] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0044] 1. The present invention comprehensively considers the explicit characteristic parameters and implicit characteristic parameters of the battery under constant voltage conditions. The fusion of the above multi-dimensional characteristic parameters can more comprehensively and accurately characterize the attenuation of the battery health state;

[0045] 2. Compared with the battery health state estimation method under incomplete constant voltage conditions based on data length, the battery health state estimation method based on equivalent cutoff current provided by the present invention is insensitive to the preset constant voltage cutoff condition and has higher flexibility and robustness;

[0046] 3. The battery resistor-inductor network equivalent circuit model and structure determination method constructed by the present invention for constant voltage working conditions establishes the connection between the current characteristics of the battery in the constant voltage charging stage and the open circuit voltage characteristics of the battery, while ensuring the accuracy of the model, it has low complexity and certain interpretability;

[0047] 4. Compared with the battery health status estimation method based on dynamic discharge data and constant current charging data, the estimation method provided by the present invention has higher robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Flow chart of the battery health status estimation algorithm of the present invention.

[0049] Figure 2 It is a structural diagram of the equivalent circuit model of the multi-stage resistor-inductor network of the battery applied to the constant voltage working condition of the present invention.

[0050] Figure 3 It is the open circuit voltage-state of charge relationship curve of the state of charge range corresponding to the constant voltage stage of the ternary lithium ion battery of the present invention.

[0051] Figure 4 This is a comparison and verification diagram of the battery model of the present invention.

[0052] Figure 5 Comparison of the evolution of the root mean square error of current estimation corresponding to the first-order model and the second-order model under different cycle numbers.

[0053] Figure 6 This is the evolution trend diagram of the battery model parameter τ1 identified under different health states with respect to the constant voltage charging cut-off current value.

[0054] Figure 7This is the evolution trend diagram of the battery model parameter τ2 identified under different health states with respect to the constant voltage charging cut-off current value.

[0055] Figure 8 Comparison of the estimated battery health status and the actual measured value corresponding to different constant voltage charging cut-off current values. DETAILED DESCRIPTION

[0056] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0057] The embodiment of the present invention provides a lithium-ion battery health status estimation method integrating multi-dimensional characteristic parameters in a constant voltage charging stage, so as to realize efficient and safe management of vehicle-mounted power batteries and electric energy storage systems.

[0058] A method for estimating the health status of lithium-ion batteries by integrating multi-dimensional characteristic parameters in the constant voltage charging stage is as follows: Figure 1 As shown, the method is mainly divided into two parts; the first part is the offline model construction part, and the second part is the online state estimation part; the two parts will be further explained below.

[0059] The offline model building part includes the following steps:

[0060] 1) Determine the specific structure of the battery model based on the open circuit voltage characteristics of the battery under constant voltage conditions and the general model structure;

[0061] The battery model structure is as follows Figure 2 The general battery model structure applied to constant voltage conditions includes multiple open circuit voltage equivalent voltage sources V connected in parallel and in series. eoc,k , resistor R k and inductor L k , connect multiple series-connected open-circuit voltage equivalent voltage sources V in parallel eoc,k , resistor R k and inductor L k The input end is connected to the positive electrode of the model voltage output end, and multiple series-connected open-circuit voltage equivalent voltage sources V are connected in parallel. eoc,k , resistor R k and inductor L k The output end of is connected to the negative electrode of the voltage output end of the model, and the analytical mathematical expression of the general battery model applied to the constant voltage working condition is:

[0062]

[0063] Where n is the open circuit voltage equivalent voltage source V of the parallel series connection eoc,k , resistor R k and inductor L k The number of branches, that is, the order of the equivalent circuit model; I k (t) is the open-circuit voltage equivalent voltage source V flowing through the series connection at time t eoc,k , resistor R k and inductor L k The current I k (0) is the open circuit voltage equivalent voltage source V eoc,k , resistor R k and inductor L k The initial current of the kth time constant τ k =L k / R k ;

[0064] Among them, the steps for determining the battery model structure are:

[0065] 1.1) Extract the OCV-SoC relationship curve corresponding to the SoC range in the constant voltage stage;

[0066] 1.2) Perform piecewise linear approximation on the extracted OCV-SoC relationship curve;

[0067] 1.3) Determine the order of the battery model according to the number of sub-function segments contained in the piecewise function;

[0068] 2) Based on the battery constant voltage operating condition data over the entire life cycle, calculate and extract the explicit candidate characteristic parameters and implicit candidate characteristic parameters of the current curve corresponding to different equivalent cut-off currents under the battery constant voltage operating condition;

[0069] The method for extracting the constant voltage working condition data set corresponding to different equivalent cut-off currents is as follows: define the equivalent cut-off current data set as I cut,eq,n =I cut +n*ΔI, where I cut The cut-off current is set for the battery constant voltage condition, ΔI is the equivalent cut-off current change step, n is the number of intervals, and the relationship n<[I(0)-I cut ] / ΔI; extract the data segment (t,I(t)) with the current and corresponding time at the start of constant voltage charging as the starting point and the equivalent cut-off current and corresponding time as the end point, where I cut,eq,n <I(t)<I(0);

[0070] The dominant candidate characteristic parameters of the current curve under the constant voltage condition of the battery mainly include: battery constant voltage charging time T CV , the current slope k at the end of constant voltage charging I And the constant voltage stage charging capacity QCV ; Among them, the current slope expression at the end of constant voltage charging is:

[0071]

[0072] Where Δt represents the time interval, satisfying the relationship Δt=m*T s , T s represents the sampling period, and m is a positive integer. The charging capacity expression in the constant voltage stage is:

[0073]

[0074] The implicit candidate characteristic parameters under the constant voltage condition of the battery mainly include: battery model parameters, the difference characterization coefficient between the initial current curve and the cycle current curve, and the battery capacity current difference value in the constant voltage stage; among them, the battery model parameter set is [R k ,I k (0),τ k ], the intelligent optimization algorithm is used to identify the model parameters; the difference characterization coefficient set between the initial current curve and the circulating current curve is [FoI SSE ,FoI MAE ,FoI MAPE ,FoI RMSE ], the specific expression is:

[0075]

[0076] Among them, I ini and I cyc Respectively represent the initial and cycle current curves, N represents the length of the initial current curve data; the battery capacity current differential value expression in the constant voltage stage is:

[0077]

[0078] Among them, the intelligent optimization algorithm includes parameter identification algorithms such as nonlinear least squares method and genetic algorithm.

[0079] 3) performing Spearman correlation analysis on the explicit and implicit characteristic parameters obtained in step 2) to determine a set of characteristic parameters characterizing battery aging and a corresponding effective current range;

[0080] Among them, the expression of the correlation coefficient in Spearman correlation analysis is:

[0081]

[0082] Among them, ρ FoI,SoH (I cut,eq,n ) is the corresponding equivalent cut-off current I cut,eq,n The Spearman correlation coefficient between the characteristic parameters and the battery health status, ncycle is the number of battery RPT tests in offline testing, d i is the rank difference between the characteristic parameter and the observed value corresponding to the battery health status;

[0083] In the step 3), extract FoI,SoH (I cut,eq,n )|>0.8, and the corresponding equivalent cut-off current. FoI,SoH (I cut,eq,n )|>0.8 The equivalent cut-off current under the condition of effective current interval [I cut,eq,min ,I cut,eq,max ], and finally form a set of characteristic variables representing the battery SoH and the corresponding effective current range;

[0084] 4) Combining intelligent optimization algorithm and machine learning algorithm, construct and store the battery health status estimation model corresponding to different equivalent constant voltage cut-off currents;

[0085] Among them, the intelligent optimization algorithm adopts the gray wolf optimization algorithm, and the machine learning algorithm adopts the support vector regression algorithm. The above algorithms can be directly implemented by calling the corresponding algorithm modules in MATLAB. Specifically, the method for constructing a battery health status estimation model combining the gray wolf optimization algorithm and the support vector regression algorithm is:

[0086] S4.1, Algorithm parameter initialization: Gray wolf population size N GW , the maximum number of iterations T GW ;

[0087] S4.2, Population initialization: Randomly generate N GW Gray wolf individuals x j ,j=1,2,...,N GW ; Train the model through support vector regression algorithm and calculate the fitness value of each individual;

[0088] S4.3, calculate and record the position vector x of the leader wolf σ, the deputy wolf β and the advisor wolf δ σ 、x β and x δ ;

[0089] S4.4, update the position of gray wolves in the gray wolf pack;

[0090] S4.5, determine whether the maximum number of iterations T set in S4.1 has been reached GW Or whether the termination condition is met; if T is not reached GW and the termination condition is not met, execute step S4.2; otherwise, execute step S4.6;

[0091] S4.6, output the optimal gray wolf position and obtain the optimal parameters of the support vector regression algorithm.

[0092] The online status estimation part includes the following steps:

[0093] 1) When the battery enters the constant voltage charging mode, the constant voltage charging time-current sequence of the battery is recorded and stored, and the dominant characteristic parameters determined in step 3) in the offline model building part are calculated in real time;

[0094] 2) When the charging process is completed, the actual cut-off current value corresponding to the end of the actual constant voltage charging is recorded, and the corresponding effective implicit characteristic parameters are calculated and extracted according to the effective current interval in which it is located, and based on the explicit characteristic parameters calculated in step 1) in the online state estimation part, the effective explicit characteristic parameters are screened and extracted according to the effective current interval to form a characteristic parameter set;

[0095] 3) According to the relationship between the actual constant voltage charging cut-off current value and the constant voltage charging equivalent cut-off current stored in step 4) in the offline model construction part, query and obtain the battery health status estimation model corresponding to the actual constant voltage charging cut-off current value;

[0096] Among them, the actual constant voltage charging cut-off current value I cut,eq,act The judgment and query process of the constant voltage charging equivalent cut-off current relationship stored in step 4) in the offline model construction part is as follows: Get I cut,eq,act , when I cut,eq,act Greater than or equal to I cut,eq,max When, according to I cut,eq,max Select the corresponding battery health status estimation model; otherwise, when I cut,eq,act Less than or equal to I cut,eq,min When, according to I cut,eq,min Select the corresponding battery health status estimation model; otherwise, when I cut,eq,act Greater than I cut,eq,min And when I cut,eq,act Less than I cut,eq,max When, according to I cut,eq,act Select the corresponding battery health status estimation model;

[0097] 4) Substitute the characteristic variables obtained in step 2) in the online state estimation part into the battery actual health state estimation model corresponding to the actual constant voltage charging cut-off current obtained in step 3) in the online state estimation part to estimate the actual health state of the battery.

[0098] In this embodiment, the implementation object is a ternary lithium-ion battery with a nominal capacity of 3.4Ah, but the actual application is not limited to this. The open circuit voltage-state of charge relationship curve corresponding to the state of charge range in the constant voltage stage of the ternary lithium-ion battery is as follows: Figure 3As shown in Figure 1, it can be found that the relationship curve can be expressed as two linear functions with slopes of tanθ1 and tanθ2, respectively. Therefore, for the target battery, the model order is set to 2. The comparison and verification diagrams of different structural models and the evolution comparison diagrams of the root mean square error of current estimation are shown in Figure 1. Figure 4 and Figure 5 As shown in Figure 2, it can be seen that compared with the first-order model, the current estimation accuracy of the second-order model has been greatly improved under different cycle numbers. The evolution trends of the battery model parameters τ1 and τ2 identified under different health states with respect to the constant voltage charging cut-off current value are shown in Figure 2. Figure 6 and Figure 7 As shown, τ1 and τ2 have a one-to-one correspondence with the constant voltage charging cut-off current value in different health states. The comparison between the estimated value of the battery health state and the actual measured value corresponding to the different constant voltage charging cut-off current values ​​obtained by the present invention is as follows: Figure 8 As shown in Figure 2, it can be found that the estimated battery health status can track the measured value well, so this method can well realize online battery health status estimation.

[0099] In summary, the present invention provides a method for estimating the health status of a lithium-ion battery that integrates multi-dimensional characteristic parameters of a constant-voltage charging stage, including: determining the specific structure of a battery model offline; calculating and extracting explicit candidate characteristic parameters and implicit candidate characteristic parameters of a current curve corresponding to different equivalent cut-off currents under a battery constant-voltage condition; performing a Spearman correlation analysis to determine a set of characteristic parameters characterizing battery aging and a corresponding effective current range; constructing and storing a battery health status estimation model corresponding to different equivalent constant-voltage cut-off currents; recording and storing a battery constant-voltage charging time-current sequence, and calculating explicit characteristic parameters in real time; recording the actual cut-off current value corresponding to the end of actual constant-voltage charging, calculating and extracting the corresponding effective implicit characteristic parameters, screening and extracting effective explicit characteristic parameters, and forming a characteristic parameter set; querying and obtaining a battery health status estimation model corresponding to the actual constant-voltage charging cut-off current value; and estimating the actual health status of the battery. The estimation method proposed in the present invention has the following four advantages: (1) The present invention comprehensively considers the explicit characteristic parameters and implicit characteristic parameters of the battery under constant voltage conditions. The fusion of the above multi-dimensional characteristic parameters can more comprehensively and accurately characterize the attenuation of the battery health state; (2) Compared with the battery health state estimation method under incomplete constant voltage conditions based on data length, the battery health state estimation method based on equivalent cutoff current provided by the present invention is insensitive to the preset constant voltage cutoff condition and has higher flexibility and robustness; (3) A battery resistor-inductor network equivalent circuit model and structure determination method constructed by the present invention for constant voltage conditions establishes the connection between the current characteristics of the battery in the constant voltage charging stage and the open circuit voltage characteristics of the battery. While ensuring the accuracy of the model, it has lower complexity and certain interpretability; (4) Compared with the battery health state estimation method based on dynamic discharge data and constant current charging data, the estimation method provided by the present invention has higher robustness.

[0100] Those skilled in the art will appreciate that the accompanying drawing is only a schematic diagram of a preferred embodiment, and the serial numbers of the embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0101] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0102] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for estimating the health status of a lithium-ion battery by integrating multi-dimensional characteristic parameters of a constant voltage charging stage, characterized in that: The method comprises the following steps: S1, determining the specific structure of the battery model according to the open circuit voltage characteristics of the battery under constant voltage conditions and the general model structure; S2, based on the battery constant voltage operating condition data over the entire life cycle, calculate and extract explicit candidate characteristic parameters and implicit candidate characteristic parameters of the current curve corresponding to different equivalent cut-off currents under the battery constant voltage operating condition; S3, performing Spearman correlation analysis on the explicit and implicit characteristic parameters obtained in step S2 to determine a characteristic parameter set characterizing battery aging and a corresponding effective current range; S4, combining intelligent optimization algorithm and machine learning algorithm to build and store the battery health status estimation model corresponding to different equivalent constant voltage cut-off currents; S5, when the battery enters the constant voltage charging mode, the constant voltage charging time-current sequence of the battery is recorded and stored, and the dominant characteristic parameters determined in step S3 are calculated in real time; S6, when the charging process is completed, the actual cut-off current value corresponding to the end of the actual constant voltage charging is recorded, and the corresponding effective implicit characteristic parameters are calculated and extracted according to the effective current interval in which it is located, and based on the explicit characteristic parameters calculated in step S5, the effective explicit characteristic parameters are screened and extracted according to the effective current interval to form a characteristic parameter set; S7, according to the relationship between the actual constant voltage charging cut-off current value and the constant voltage charging equivalent cut-off current stored in step S4, query and obtain a battery health status estimation model corresponding to the actual constant voltage charging cut-off current value; S8, substituting the characteristic variables obtained in step S6 into the battery actual health state estimation model corresponding to the actual constant voltage charging cut-off current obtained in step S7, to estimate the actual health state of the battery.

2. A method according to claim 1, characterized in that In step S1, the general battery model structure applied to the constant voltage condition includes a plurality of open circuit voltage equivalent voltage sources V connected in parallel and in series. eoc,k , resistor R k and inductor L k , connect multiple series-connected open-circuit voltage equivalent voltage sources V in parallel eoc,k , resistor R k and inductor L k The input end is connected to the positive electrode of the model voltage output end, and multiple series-connected open-circuit voltage equivalent voltage sources V are connected in parallel. eoc,k , resistor R k and inductor L k The output end of is connected to the negative electrode of the voltage output end of the model, and the analytical mathematical expression of the general battery model applied to the constant voltage working condition is: Among them, V t and I(t) are the battery terminal voltage and the charging current at time t respectively; n is the open circuit voltage equivalent voltage source V connected in parallel with the series connection eoc,k , resistor R k and inductor L k The number of branches, that is, the order of the equivalent circuit model; I k (t) is the open-circuit voltage equivalent voltage source V flowing through the series connection at time t eoc,k , resistor R k and inductor L k The current I k (0) is the open circuit voltage equivalent voltage source V eoc,k , resistor R k and inductor L k The initial current of the kth time constant τ k =L k / R k .

3. A method according to claim 1, characterized in that In step S1, the steps of determining the battery model structure are: S1.1, extract the OCV-SoC relationship curve corresponding to the SoC range in the constant voltage stage; S1.2, piecewise linear approximation of the extracted OCV-SoC relationship curve; S1.3, determine the order of the battery model according to the number of sub-function segments contained in the piecewise function.

4. A method according to claim 1, characterized in that In the step S2, the method for extracting the constant voltage working condition data set corresponding to different equivalent cut-off currents is as follows: define the equivalent cut-off current data set as I cut,eq,n =I cut +n*ΔI, where I cut The cut-off current is set for the battery constant voltage condition, ΔI is the equivalent cut-off current change step, n is the number of intervals, and the relationship n<[I(0)-I cut ] / ΔI; extract the data segment (t,I(t)) with the current and corresponding time at the start of constant voltage charging as the starting point and the equivalent cut-off current and corresponding time as the end point, where I cut,eq,n <I(t)<I(0)。 5. A method according to claim 1, characterized in that: In step S2, the explicit candidate characteristic parameters of the current curve under the constant voltage condition of the battery mainly include: the battery constant voltage charging time T CV , the current slope k at the end of constant voltage charging I And the constant voltage stage charging capacity Q CV ; Among them, the current slope expression at the end of constant voltage charging is: Where I is the battery charging current and Δt represents the time interval, satisfying the relationship Δt=m*T s , T s represents the sampling period, m is a positive integer; the charging capacity expression in the constant voltage stage is:

6. A method according to claim 1, characterized in that: In the step S2, the implicit candidate characteristic parameters under the constant voltage condition of the battery mainly include: battery model parameters, the difference characterization coefficient between the initial current curve and the cycle current curve, and the battery capacity current difference value in the constant voltage stage; wherein the battery model parameter set is [R k ,I k (0),τ k ]; the difference characterization coefficient set between the initial current curve and the circulating current curve is [FoI SSE ,FoI MAE ,FoI MAPE ,FoI RMSE ], the specific expression is: Among them, FoI SSE 、FoI RMSE 、FoI MAE and FoI MAPE I represents the characteristic parameters based on the residual sum of squares, root mean square error, mean absolute error and mean absolute percentage error respectively; ini and I cyc Respectively represent the initial and cycle current curves, N represents the length of the initial current curve data; the battery capacity current differential value expression in the constant voltage stage is: Among them, Q CV and T s They represent the battery charging capacity and sampling period in the constant voltage stage respectively.

7. A method according to claim 1, characterized in that In step S3, the expression of the correlation coefficient in the Spearman correlation analysis is: Among them, ρ FoI,SoH (I cut,eq,n ) is the corresponding equivalent cut-off current I cut,eq,n The Spearman correlation coefficient between the characteristic parameters and the battery health status, n cycle is the number of battery RPT tests in offline testing, d i is the rank difference between the characteristic parameter and the observed value corresponding to the battery health status; In the step S3, extract FoI,SoH (I cut,eq,n )|>0.8, and the corresponding equivalent cut-off current. FoI,SoH (I cut,eq,n )|>0.8 The equivalent cut-off current under the condition of effective current interval [I cut,eq,min ,I cut,eq,max ], and finally form a set of characteristic variables characterizing the battery SoH and the corresponding effective current range.

8. A method according to claim 1, characterized in that In the step S4, a support vector regression algorithm is used to construct a battery health state estimation model corresponding to different equivalent cut-off currents, and the support vector regression algorithm parameters are optimized using the gray wolf optimization algorithm to obtain a more accurate battery health state estimation model corresponding to different equivalent cut-off currents.

9. A method according to claim 1, characterized in that In step S7, the actual constant voltage charging cut-off current value I cut,eq,act The judgment and query process of the constant voltage charging equivalent cut-off current relationship stored in step S4 is as follows: cut,eq,act , when I cut,eq,act Greater than or equal to I cut,eq,max When, according to I cut,eq,max Select the corresponding battery health status estimation model; otherwise , when I cut,eq,act Less than or equal to I cut,eq,min When, according to I cut,eq,min Select the corresponding battery health status estimation model; otherwise, when I cut,eq,act Greater than I cut,eq,min And when I cut,eq,act Less than I cut,eq,max When, according to I cut,eq,act Select the corresponding battery health status estimation model.

10. A method according to claim 8, characterized in that The method for constructing a battery health status estimation model combining the gray wolf optimization algorithm and the support vector regression algorithm is as follows: S4.1, Algorithm parameter initialization: Gray wolf population size N GW , the maximum number of iterations T GW ; S4.2, Population initialization: Randomly generate N GW Gray wolf individuals x j ,j=1,2,...,N GW ; Train the model through support vector regression algorithm and calculate the fitness value of each individual; S4.3, calculate and record the position vector x of the leader wolf σ, the deputy wolf β and the advisor wolf δ σ 、x β and x δ ; S4.4, update the position of gray wolves in the gray wolf pack; S4.5, determine whether the maximum number of iterations T set in S4.1 has been reached GW Or whether the termination condition is met; if T is not reached GW If the termination condition is not met, execute step S4.2; Otherwise, execute step S4.6; S4.6, output the optimal gray wolf position and obtain the optimal parameters of the support vector regression algorithm.

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