SOC estimation method and system for lithium battery charging process

Abstract

The invention relates to an SOC estimation method and system for a lithium battery charging process, belongs to the technical field of lithium battery management, and solves the problem of unreasonable distribution of SOC estimation curve graphs in the prior art. The SOC estimation method comprises the following steps: sampling a charging current at a sampling time to obtain a charging current sampled at the current time; the SOC corresponding to the current sampling and the sampling time of the next sampling are determined by the following steps: calculating the charging rate of the current sampling according to the charging current of the current sampling; judging whether a lithium battery model switching condition is met or not according to the charging rate of the current sampling, and determining a lithium battery model of the current sampling; according to the lithium battery model of the current sampling, the SOC corresponding to the current sampling of the lithium battery is obtained in combination with an unscented Kalman particle filtering algorithm; and determining the sampling time of the next sampling according to the SOC of the previous sampling, the SOC of the current sampling and the sampling time of the current sampling. The reasonable distribution of the SOC estimation curve graph is realized, and the practical value is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery management, and in particular to a SOC estimation method and system for a lithium battery charging process. Background Art

[0002] Lithium batteries are widely used in electric vehicles and other fields. Under complex working conditions, the real-time and accurate management of lithium battery packs, ensuring battery safety and extending battery life are of great significance to the development and promotion of industries such as electric vehicles. The control strategy of battery management mainly depends on the estimation accuracy of the lithium battery SOC (State of Charge).

[0003] In the prior art, the SOC estimation of lithium batteries usually adopts a fixed period to estimate the change of SOC, that is, the SOC is estimated once every fixed time. However, the change of SOC during the charging process of lithium batteries is relatively complex. The use of fixed time intervals to estimate SOC results in a large difference in the amount of SOC change in each time period, which further makes the SOC estimation inaccurate, resulting in an unreasonable distribution of the SOC estimation curve and low practical value.

[0004] Therefore, there is an urgent need for a new technical solution for SOC estimation during the lithium battery charging process. Summary of the invention

[0005] In view of the above analysis, an embodiment of the present invention aims to provide a method and system for SOC estimation during a lithium battery charging process, so as to solve the problem of unreasonable distribution of SOC estimation curves in the prior art.

[0006] On the one hand, an embodiment of the present invention provides a SOC estimation method for a lithium battery charging process, the SOC estimation method comprising:

[0007] The charging current is sampled at the sampling time to obtain the charging current of the current sampling;

[0008] The SOC corresponding to the current sampling and the sampling time of the next sampling are determined in the following way:

[0009] Calculate the current sampling charging rate according to the current sampling charging current; determine whether the lithium battery model switching condition is met according to the current sampling charging rate, and determine the current sampling lithium battery model;

[0010] According to the lithium battery model of the current sampling, the SOC corresponding to the current sampling of the lithium battery is obtained in combination with the unscented Kalman particle filter algorithm; the sampling time of the next sampling is determined according to the SOC of the previous sampling, the SOC of the current sampling and the sampling time of the current sampling.

[0011] Based on the further improvement of the above method, the charging rate of the current sampling is calculated as follows:

[0012]

[0013] Wherein, v represents the charging rate of the current sampling, i represents the charging current of the current sampling, and S represents the battery capacity of the lithium battery.

[0014] Based on the further improvement of the above method, the lithium battery model includes a fractional order model and a dual polarization model;

[0015] The step of judging whether the lithium battery model switching condition is met according to the charging rate of the current sampling, and determining the lithium battery model of the current sampling, comprises:

[0016] The charging rate of the current sampling is compared with the model switching charging parameter. If the charging rate of the current sampling is less than the model switching charging parameter, the dual polarization model is used as the lithium battery model of the current sampling; otherwise, the fractional order model is used as the lithium battery model of the current sampling.

[0017] Based on the further improvement of the above method, the sampling time of the next sampling is calculated as follows:

[0018]

[0019] Wherein, k represents the current sampling, T(k) represents the sampling time of the current sampling, T(k+1) represents the sampling time of the next sampling, SOC(k) represents the SOC of the current sampling, and SOC(k-1) represents the SOC of the previous sampling.

[0020] Based on the further improvement of the above method, the dual-polarization model is determined in the following way:

[0021] Obtain the basic parameters of lithium batteries and establish a dual-polarization mathematical model of lithium batteries based on the dual-polarization equivalent circuit model of lithium batteries;

[0022] The online recursive least square method is used to identify the parameters to be identified corresponding to the dual-polarization mathematical model, and the identification parameter results are obtained;

[0023] According to the identification parameter results, the state equation and observation equation of the lithium battery state of charge corresponding to the dual polarization model are established.

[0024] Based on the further improvement of the above method, the online recursive least square method is used to identify the parameters to be identified corresponding to the dual-polarization mathematical model, including:

[0025] According to the state of charge value and the average value of the open circuit voltage of the lithium battery, a functional relationship based on the state of charge value and the average value of the open circuit voltage is established;

[0026] Establish the recursive formula of online recursive least squares method;

[0027] Based on the recursive formula and the functional relationship between the state of charge value and the mean open circuit voltage, the parameters to be identified corresponding to the dual-polarization mathematical model are identified.

[0028] Based on the further improvement of the above method, the functional relationship expression based on the state of charge value and the mean open circuit voltage is:

[0029]

[0030] Among them, Uoc(SOC) represents the relationship between the state of charge value and the average open circuit voltage, E1 represents the open circuit voltage of the lithium battery when it is fully charged, K0, K1, K2 and K3 represent the parameters to be identified corresponding to the dual-polarization mathematical model, and SOC represents the state of charge value.

[0031] Based on the further improvement of the above method, the fractional order model is determined as follows:

[0032] Based on the fractional-order equivalent circuit model of lithium battery, a fractional-order mathematical model of lithium battery is established;

[0033] The hybrid particle swarm algorithm is used to identify the parameters to be identified corresponding to the fractional-order mathematical model and obtain the identification parameter results.

[0034] According to the identification parameter results, the state equation and observation equation of the lithium battery state of charge corresponding to the fractional-order model are established.

[0035] Based on the further improvement of the above method, the fractional-order equivalent circuit model of the lithium battery includes a first resistor, a first CPE element, a second resistor, a second CPE element, a third resistor and a lithium battery;

[0036] The second resistor is connected in parallel with the second CPE element, and one end of the second resistor serves as the positive output electrode of the lithium battery. The first resistor is connected in parallel with the first CPE element, and one end of the first resistor is connected to the positive electrode of the lithium battery, and the other end of the first resistor is connected to the other end of the second CPE element.

[0037] The negative electrode of the lithium battery is output through the third resistor and serves as the output negative electrode of the lithium battery.

[0038] On the other hand, an embodiment of the present invention provides a SOC estimation system for a lithium battery charging process, the SOC estimation system comprising:

[0039] The charging current acquisition module is used to sample the charging current at a sampling time to obtain the charging current of the current sampling;

[0040] The SOC estimation module is used to determine the SOC corresponding to the current sampling and the sampling time of the next sampling in the following manner:

[0041] Calculate the current sampling charging rate according to the current sampling charging current; determine whether the lithium battery model switching condition is met according to the current sampling charging rate, and determine the current sampling lithium battery model;

[0042] According to the lithium battery model of the current sampling, the SOC corresponding to the current sampling of the lithium battery is obtained in combination with the unscented Kalman particle filter algorithm; the sampling time of the next sampling is determined according to the SOC of the previous sampling, the SOC of the current sampling and the sampling time of the current sampling.

[0043] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0044] 1. The present invention continuously adjusts the sampling time of SOC estimation during the lithium battery charging process, so that more SOC estimation is performed when the SOC changes quickly and less SOC estimation is performed when the SOC changes slowly, so that the distribution of the final SOC estimation curve is more reasonable and has more practical value.

[0045] 2. The present invention determines whether the lithium battery model switching condition is met by the charging rate, and reasonably selects the lithium battery model, so that the SOC obtained at each sampling time is more accurate, further improving the accuracy of the SOC estimation curve.

[0046] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0048] Figure 1 A schematic flow chart of a method for estimating SOC during a lithium battery charging process provided by an embodiment of the present invention;

[0049] Figure 2 The SOC estimation record diagram for the lithium battery charging process provided by the embodiment of the present invention;

[0050] Figure 3 A schematic diagram of the structure of a dual-polarization equivalent circuit model of a lithium battery provided in an embodiment of the present invention;

[0051] Figure 4 A schematic diagram of the structure of a fractional-order mathematical model of a lithium battery provided by an embodiment of the present invention;

[0052] Figure 5 A schematic diagram of the structure of a SOC estimation system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0054] The Battery Management System (BMS) is a control system that protects the safe use of power batteries. It monitors the battery's usage status at all times, alleviates the inconsistency of the battery pack through necessary measures, and provides protection for the safe use of new energy vehicles.

[0055] The BMS system hardware consists mainly of various sensors, actuators, algorithm controllers, signal lines and other components, which are used to complete the collection of voltage and current data in the circuit, the detection of battery temperature and coolant temperature, the input of acceleration and braking signals, the transmission of start and stop charging signals, as well as the start and stop of fans and heaters, the switching of balancing circuits, fault alarms and other functions.

[0056] The functions of the BMS software part mainly include: detection of battery parameters such as current, voltage and temperature, measurement of battery status, diagnosis of vehicle faults, battery safety control and alarm, charging control, battery balancing, thermal management, network connection and information storage, etc.

[0057] In order for BMS to achieve the above functions, it is necessary to solve these key problems: accurate measurement of battery voltage, estimation of SOC state, uniformity and balance of batteries, and battery fault diagnosis.

[0058] A specific embodiment of the present invention discloses a SOC estimation method for a lithium battery charging process, such as Figure 1 As shown, the SOC estimation method includes:

[0059] Step S1: sampling the charging current at a sampling time to obtain the charging current of the current sampling;

[0060] Step S2: Determine the SOC corresponding to the current sampling and the sampling time of the next sampling in the following manner:

[0061] Step S21: Calculate the current sampling charging rate according to the current sampling charging current; determine whether the lithium battery model switching condition is met according to the current sampling charging rate, and determine the current sampling lithium battery model;

[0062] Step S22: According to the lithium battery model of the current sampling, the SOC corresponding to the current sampling of the lithium battery is obtained in combination with the unscented Kalman particle filter algorithm; the sampling time of the next sampling is determined according to the SOC of the previous sampling, the SOC of the current sampling and the sampling time of the current sampling.

[0063] Specifically, during the charging process of the lithium battery, the SOC is estimated once at regular intervals, and a curve diagram of the change of the SOC of the lithium battery during the charging process can be obtained.

[0064] Specifically, Figure 2 As shown, the SOC estimation method for the lithium battery charging process provided by the present invention sets a default sampling time t1 in advance when charging starts, that is, the SOC of the lithium battery is estimated at the interval time t1 after the start, as the SOC obtained by the first sampling; then the SOC of the lithium battery is estimated at the sampling time t2 of the next sampling, and so on until the lithium battery is fully charged, thereby obtaining a curve diagram of the SOC change during the lithium battery charging process.

[0065] It is worth noting that when the lithium battery is just charged, the charging rate is large, and the SOC changes rapidly. At this time, the SOC needs to be estimated frequently. When the lithium battery is charged to a certain level, the charging rate decreases and the SOC changes less. At this time, there is no need to estimate the SOC frequently.

[0066] Specifically, Figure 1 As shown, in step S1, the charging current is sampled at a sampling time to obtain the charging current of the current sampling. The sampling time is as follows: Figure 2 As shown, the charging currents i1, i2, i3, ... are obtained for each sampling corresponding to t1, t2, t3, ...

[0067] In step S2, the SOC corresponding to the current sampling and the sampling time of the next sampling are determined by using step S21 and step S22. It can be understood that the sampling time of the first sampling is t1, and there is no previous sampling at this time. Therefore, during the first sampling, only the SOC (1) corresponding to the first sampling needs to be calculated, and the sampling time of the second sampling cannot be calculated; the sampling time of the second sampling is t2, and the SOC (2) corresponding to the second sampling is calculated. At this time, the first sampling exists, and the sampling time of the first sampling is t1. The SOC (1) of the first sampling has been calculated, and the sampling time t3 of the third sampling can be determined at this time; during the third sampling, the sampling time t4 of the fourth sampling and the SOC corresponding to the third sampling can be determined by steps S21 and S22 provided in the embodiment of the present invention.

[0068] It is worth noting that, at the beginning of charging, at least the first sampling time t1 and the second sampling time t2 need to be set in advance.

[0069] Specifically, in step S21, the current sampling charging rate is calculated according to the current sampling charging current; whether the lithium battery model switching condition is met is determined according to the current sampling charging rate, and the current sampling lithium battery model is determined.

[0070] Preferably, the charging rate of the current sampling is calculated as follows:

[0071]

[0072] Wherein, v represents the charging rate of the current sampling, i represents the charging current of the current sampling, and S represents the battery capacity of the lithium battery.

[0073] Specifically, the battery capacity S of the lithium battery remains unchanged as a fixed parameter of the lithium battery; during the charging process of the lithium battery, the charging current i is a variable, which is used to represent the charging current of each sampling; the charging rate obtained by each sampling The charging rate v is calculated. For example, if the battery capacity of the charged lithium battery is 5Ah and the charging current is 1A, then the corresponding charging rate is 0.2C.

[0074] In step S21, it is determined whether the lithium battery model switching condition is met according to the charging rate of the current sampling, and the lithium battery model of the current sampling is determined.

[0075] Preferably, the lithium battery model includes a fractional order model and a dual polarization model;

[0076] The step of judging whether the lithium battery model switching condition is met according to the charging rate of the current sampling, and determining the lithium battery model of the current sampling, comprises:

[0077] The charging rate of the current sampling is compared with the model switching charging parameter. If the charging rate of the current sampling is less than the model switching charging parameter, the dual polarization model is used as the lithium battery model of the current sampling; otherwise, the fractional order model is used as the lithium battery model of the current sampling.

[0078] Specifically, the lithium battery model of the SOC estimation method for the lithium battery charging process provided by the embodiment of the present invention includes a fractional order model and a dual polarization model.

[0079] It can be understood that at the beginning of charging, the charging rate is relatively large, and the fractional-order model is used as the lithium battery model by default. In step S21, the charging rate of the current sampling is compared with the size of the model switching charging parameter. When the charging rate is less than the model switching charging parameter, the dual-polarization model is used as the lithium battery model of the current sampling.

[0080] Preferably, the model switching charging parameter is 0.35-0.45 C. It can be understood that when the charging rate is greater than the model switching charging parameter, the lithium battery model is a fractional order model; when the charging rate is less than the model switching charging parameter, the dual polarization model is used as the lithium battery model.

[0081] Specifically, in step S22, the SOC corresponding to the current sampling of the lithium battery is obtained based on the lithium battery model of the current sampling and combined with the unscented Kalman particle filter algorithm. It can be understood that

[0082] The unscented Kalman particle filter algorithm is a model filtering technology that can be used to estimate the state of an unobservable system. Its basic principle is to use past measurements and system models to estimate the current state of the system. The unscented Kalman particle filter algorithm has a strong anti-noise ability and can extract useful information about the system from the observed signal. It can also suppress the amount of calculation because it does not require a lot of calculations to estimate the current state of the system, but uses limited observation data and models to achieve state estimation.

[0083] Preferably, the dual-polarization model is determined in the following manner:

[0084] Obtain the basic parameters of lithium batteries and establish a dual-polarization mathematical model of lithium batteries based on the dual-polarization equivalent circuit model of lithium batteries;

[0085] The online recursive least square method is used to identify the parameters to be identified corresponding to the dual-polarization mathematical model, and the identification parameter results are obtained;

[0086] According to the identification parameter results, the state equation and observation equation of the lithium battery state of charge corresponding to the dual polarization model are established.

[0087] Specifically, the basic parameters of a lithium battery include the battery capacity and charging current of the lithium battery.

[0088] Specifically, Figure 3 As shown, the dual polarization equivalent circuit model of lithium battery, where Uoc is the open circuit voltage of lithium battery; Rc is the ohmic internal resistance of lithium battery during charging; i is the charging current of lithium battery; Ut is the terminal load voltage of lithium battery; Rm and Cm are the polarization resistance and polarization capacitance of lithium battery, which are used to characterize the slow electrode reaction inside lithium battery; Rn and Cn are the concentration resistance and concentration capacitance of lithium battery, which are used to characterize the fast electrode reaction inside lithium battery; Um is the voltage across Rm and Cm, and Un is the voltage across Rn and Cn. This dual polarization equivalent circuit model is used to simulate the voltage under the sudden change of current i and the rapid change process in the slow and stable process.

[0089] Specifically, according to Figure 3 The dual polarization equivalent circuit model of the lithium battery shown in the figure is based on Kirchhoff's theorem to establish the dual polarization mathematical model of the lithium battery, which is specifically a state space equation as shown below:

[0090]

[0091] Among them, η is the energy conversion efficiency of the lithium battery, Uoc(SOC) is the open circuit voltage corresponding to the state of charge value of the lithium battery, C is the charge of the lithium battery, and SOC is the state of charge value of the lithium battery.

[0092] Preferably, the identifying of the parameters to be identified corresponding to the dual-polarization mathematical model by using an online recursive least squares method includes:

[0093] According to the state of charge value and the average value of the open circuit voltage of the lithium battery, a functional relationship based on the state of charge value and the average value of the open circuit voltage is established;

[0094] Establish the recursive formula of online recursive least squares method;

[0095] Based on the recursive formula and the functional relationship between the state of charge value and the mean open circuit voltage, the parameters to be identified corresponding to the dual-polarization mathematical model are identified.

[0096] Specifically, the functional relationship expression based on the state of charge value and the open circuit voltage average is:

[0097]

[0098] Among them, Uoc(SOC) represents a relationship based on the state of charge value and the average open circuit voltage, E1 represents the open circuit voltage of the lithium battery when it is fully charged, and K0, K1, K2 and K3 represent the parameters to be identified corresponding to the dual-polarization mathematical model.

[0099] It can be understood that K0, K1, K2 and K3 represent the parameters to be identified corresponding to the dual-polarization mathematical model.

[0100] Specifically, the recursive formula of the online recursive least squares method is:

[0101]

[0102] Where θ is the parameter vector to be identified; K(·) is the gain matrix, ε(·) is the observation noise covariance matrix, P(·) is the covariance matrix, y(k) is the output, θ(k) is the terminal voltage error, and I is the unit matrix.

[0103] Specifically, based on the recursive formula and in combination with the functional relationship between the state of charge value and the mean open circuit voltage, the parameters to be identified corresponding to the dual polarization mathematical model are identified to obtain the identification parameter results, including:

[0104] a. Set the initial values ​​θ(0) and P(0);

[0105] b. Sample the current output y(k) and input θ(k-1);

[0106] c. Calculate K(k), θ(k), and P(k);

[0107] d. Set k→k+1, return to step b, and continue the loop.

[0108] Specifically, according to the identification parameter results, the state equation and observation equation of the lithium battery state of charge corresponding to the dual polarization model are established. The state of charge value of the lithium battery is used as the state variable to establish the state equation. The open circuit voltage of the lithium battery is used as the observation variable to establish the observation equation. The state space equation of the lithium battery is obtained in a discretized form:

[0109]

[0110] Where Δt is the sampling period, E1 is the maximum available capacity of the lithium battery when it is fully charged, and w k represents the system error, v k represents the observation error, w 1k ~N(0,Q1),w 2k ~N(0,Q2),w 3k ~N(0,Q3),w k ~N(0,Q),Q=diag{Q1,Q2,Q3} and v k ~N(0,R).

[0111] Among them, H k X(k)+Ψ k i k +w k is the state equation, |yk =g(x k ,i k , v k )=U t，k is the observation equation. In order to facilitate the algorithm implementation, the state quantity is redefined as X(k) = [xk T wk T vk T ] T , the state covariance is defined as Pk=diag{Pk QR}.

[0112] Specifically, in step S22, the SOC corresponding to the current sampling of the lithium battery is obtained according to the state equation and observation equation of the lithium battery state of charge corresponding to the dual-polarization model in combination with the unscented Kalman particle filter algorithm.

[0113] Specifically, particles are extracted according to the initial probability distribution function, including:

[0114] Initialize first, k = 0, particle x0 i+ is randomly generated by the initial probability distribution function p(x0), and the covariance between particles is defined as P0 i+ =P0 + , i=1,2,...N.

[0115] Specifically, the unscented Kalman particle filter algorithm is applied to perform importance sampling, including:

[0116] K = 1, 2, ..., for each sample particle xk i- Apply UKF algorithm to generate new particles xk i+ and the covariance Px,k i + , including the following steps:

[0117] a. Calculate particle sigma and select 2r+1 (r=0, 1, ...) point sets.

[0118]

[0119] Among them, r is the dimension of the state quantity and λ is the composite scale parameter.

[0120] b. Time update:

[0121]

[0122]

[0123]

[0124]

[0125]

[0126] c. Observation update:

[0127]

[0128]

[0129]

[0130]

[0131] Specifically, calculate the posterior probability of the particle:

[0132] Calculate each particle xk,i according to the observed value yk + The posterior probability q i :

[0133]

[0134] After normalization, we have:

[0135]

[0136] We get the posterior particle xk i+ , error covariance Px,k i+ And the corresponding probabilities, i=1,2,…,N.

[0137] Specifically, the state of charge of the lithium battery is estimated:

[0138]

[0139] Specifically, determine whether the number of valid particles after importance sampling is less than the preset particle number threshold:

[0140] Calculate the number of effective particles N after sampling ef : If the number of valid particles is less than the particle number threshold, the judgment result is yes and the SOC estimation result is output; otherwise, the unscented Kalman particle filter algorithm is applied to perform repeated importance sampling.

[0141] The SOC estimation method for the lithium battery charging process provided by the present invention combines particle filtering and unscented Kalman filtering to achieve accurate estimation of the state of charge of the lithium battery, and at the same time has the advantages of fast convergence and strong robustness.

[0142] Preferably, the fractional order model is determined in the following manner:

[0143] Based on the fractional-order equivalent circuit model of lithium battery, a fractional-order mathematical model of lithium battery is established;

[0144] The hybrid particle swarm algorithm is used to identify the parameters to be identified corresponding to the fractional-order mathematical model and obtain the identification parameter results.

[0145] According to the identification parameter results, the state equation and observation equation of the lithium battery state of charge corresponding to the fractional-order model are established.

[0146] Specifically, Figure 4 As shown, the fractional-order equivalent circuit model of the lithium battery includes a first resistor R1, a first CPE element CPE1, a second resistor R2, a second CPE element CPE2, a third resistor and a lithium battery;

[0147] The second resistor R2 is connected in parallel with the second CPE element CPE2, and one end of the second resistor R2 is used as the output positive electrode of the lithium battery; the first resistor R1 is connected in parallel with the first CPE element CPE1, and one end of the first resistor R1 is connected to the positive electrode of the lithium battery, and the other end of the first resistor R2 is connected to the other end of the second CPE element CPE2;

[0148] The negative electrode of the lithium battery is output through the third resistor R0 as the output negative electrode of the lithium battery.

[0149] The third resistor R0 represents the internal resistance of the lithium battery, the first resistor R1 and the second resistor R2 represent pure resistive impedance, and CPE1 and CPE2 are fractional-order constant phase angle elements.

[0150] Specifically, according to Figure 4 The fractional-order equivalent circuit model of the lithium battery shown in the figure is used to establish a fractional-order mathematical model of the lithium battery. The hybrid particle swarm algorithm is used to identify the parameters to be identified corresponding to the fractional-order mathematical model to obtain the identification parameter results. According to the identification parameter results, the state equation and observation equation of the lithium battery state of charge corresponding to the fractional-order model are established.

[0151] Specifically, if the lithium battery model of the current sampling is a fractional-order model, the SOC corresponding to the current sampling of the lithium battery is obtained by combining the unscented Kalman particle filter algorithm. According to the state equation and observation equation of the lithium battery state of charge corresponding to the fractional-order model, the SOC corresponding to the current sampling of the lithium battery is obtained by combining the unscented Kalman particle filter algorithm. The calculation process is the same as the solution method for the dual-polarization model, which will not be repeated here.

[0152] Preferably, the sampling time for the next sampling is calculated as follows:

[0153]

[0154] Wherein, k represents the current sampling, T(k) represents the sampling time of the current sampling, T(k+1) represents the sampling time of the next sampling, SOC(k) represents the SOC of the current sampling, and SOC(k-1) represents the SOC of the previous sampling.

[0155] Specifically, the sampling time of the next sampling is determined by the SOC of two adjacent samples, and the SOC is estimated at each sampling so as to timely control the change of the SOC.

[0156] Another specific embodiment of the present invention discloses a SOC estimation system for a lithium battery charging process, such as Figure 5 As shown, the SOC estimation system includes:

[0157] The charging current acquisition module 501 is used to sample the charging current at a sampling time to obtain the charging current sampled at the current time;

[0158] The SOC estimation module 502 is used to determine the SOC corresponding to the current sampling and the sampling time of the next sampling in the following manner:

[0159] Calculate the current sampling charging rate according to the current sampling charging current; determine whether the lithium battery model switching condition is met according to the current sampling charging rate, and determine the current sampling lithium battery model;

[0160] According to the lithium battery model of the current sampling, the SOC corresponding to the current sampling of the lithium battery is obtained in combination with the unscented Kalman particle filter algorithm; the sampling time of the next sampling is determined according to the SOC of the previous sampling, the SOC of the current sampling and the sampling time of the current sampling.

[0161] Compared with the prior art, the SOC estimation method and system for the lithium battery charging process provided in this embodiment continuously adjusts the sampling time of SOC estimation during the lithium battery charging process, so that more SOC estimation is performed when the SOC changes quickly and less SOC estimation is performed when the SOC changes slowly, so that the final SOC estimation curve has a more reasonable distribution and more practical value; and, by judging whether the lithium battery model switching conditions are met through the charging rate, the lithium battery model is reasonably selected, so that the SOC obtained at each sampling time is more accurate, further improving the accuracy of the SOC estimation curve.

[0162] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0163] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A SOC estimation method for a lithium battery charging process, characterized in that: The SOC estimation method comprises: The charging current is sampled at the sampling time to obtain the charging current of the current sampling; The SOC corresponding to the current sampling and the sampling time of the next sampling are determined in the following manner: Calculate the current sampling charging rate according to the current sampling charging current; determine whether the lithium battery model switching condition is met according to the current sampling charging rate, and determine the current sampling lithium battery model; According to the lithium battery model of the current sampling, the SOC corresponding to the current sampling of the lithium battery is obtained in combination with the unscented Kalman particle filter algorithm; the sampling time of the next sampling is determined according to the SOC of the previous sampling, the SOC of the current sampling and the sampling time of the current sampling.

2. The SOC estimation method according to claim 1, characterized in that: The charging rate of the current sampling is calculated as follows: Wherein, v represents the charging rate of the current sampling, i represents the charging current of the current sampling, and S represents the battery capacity of the lithium battery.

3. The SOC estimation method according to claim 1, characterized in that: Lithium battery models include fractional order models and dual polarization models; The step of judging whether the lithium battery model switching condition is met according to the charging rate of the current sampling, and determining the lithium battery model of the current sampling, comprises: The charging rate of the current sampling is compared with the model switching charging parameter. If the charging rate of the current sampling is less than the model switching charging parameter, the dual polarization model is used as the lithium battery model of the current sampling; otherwise, the fractional order model is used as the lithium battery model of the current sampling.

4. The SOC estimation method according to claim 1, characterized in that: The sampling time for the next sampling is calculated as follows: Wherein, k represents the current sampling, T(k) represents the sampling time of the current sampling, T(k+1) represents the sampling time of the next sampling, SOC(k) represents the SOC of the current sampling, and SOC(k-1) represents the SOC of the previous sampling.

5. The SOC estimation method according to claim 3, characterized in that: The dual polarization model is determined as follows: Obtain the basic parameters of lithium batteries and establish a dual-polarization mathematical model of lithium batteries based on the dual-polarization equivalent circuit model of lithium batteries; The online recursive least square method is used to identify the parameters to be identified corresponding to the dual-polarization mathematical model, and the identification parameter results are obtained; According to the identification parameter results, the state equation and observation equation of the lithium battery state of charge corresponding to the dual polarization model are established.

6. The SOC estimation method according to claim 5, characterized in that: The method of using the online recursive least square method to identify the parameters to be identified corresponding to the dual-polarization mathematical model includes: According to the state of charge value and the average value of the open circuit voltage of the lithium battery, a functional relationship between the state of charge value and the average value of the open circuit voltage is established; Establish the recursive formula of online recursive least squares method; Based on the recursive formula and the functional relationship between the state of charge value and the mean open circuit voltage, the parameters to be identified corresponding to the dual-polarization mathematical model are identified.

7. The SOC estimation method according to claim 6, characterized in that: The functional relationship expression based on the state of charge value and the average open circuit voltage is: Among them, Uoc(SOC) represents the relationship between the state of charge value and the average open circuit voltage, E1 represents the open circuit voltage of the lithium battery when it is fully charged, K0, K1, K2 and K3 represent the parameters to be identified corresponding to the dual-polarization mathematical model, and SOC represents the state of charge value.

8. The SOC estimation method according to claim 7, characterized in that: The fractional order model is determined as follows: Based on the fractional-order equivalent circuit model of lithium battery, a fractional-order mathematical model of lithium battery is established; The hybrid particle swarm algorithm is used to identify the parameters to be identified corresponding to the fractional-order mathematical model and obtain the identification parameter results. According to the identification parameter results, the state equation and observation equation of the lithium battery state of charge corresponding to the fractional-order model are established.

9. The SOC estimation method according to claim 8, characterized in that: The fractional-order equivalent circuit model of the lithium battery includes a first resistor, a first CPE element, a second resistor, a second CPE element, a third resistor and a lithium battery; The second resistor is connected in parallel with the second CPE element, and one end of the second resistor serves as the positive output electrode of the lithium battery. The first resistor is connected in parallel with the first CPE element, and one end of the first resistor is connected to the positive electrode of the lithium battery, and the other end of the first resistor is connected to the other end of the second CPE element. The negative electrode of the lithium battery is output through the third resistor and serves as the output negative electrode of the lithium battery.

10. A SOC estimation system for a lithium battery charging process, characterized in that: The SOC estimation system comprises: The charging current acquisition module is used to sample the charging current at a sampling time to obtain the charging current of the current sampling; The SOC estimation module is used to determine the SOC corresponding to the current sampling and the sampling time of the next sampling in the following manner: Calculate the current sampling charging rate according to the current sampling charging current; determine whether the lithium battery model switching condition is met according to the current sampling charging rate, and determine the current sampling lithium battery model; According to the lithium battery model of the current sampling, the SOC corresponding to the current sampling of the lithium battery is obtained in combination with the unscented Kalman particle filter algorithm; the sampling time of the next sampling is determined according to the SOC of the previous sampling, the SOC of the current sampling and the sampling time of the current sampling.

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