Lithium battery multi-time scale step-by-step parameter identification method, system, device and medium

Abstract

The present invention discloses a multi-time scale step-by-step parameter identification method, system, device and medium for lithium battery, wherein the method includes: differentiating each parameter in a second-order RC equivalent circuit model into two different time scales, FD and SD; writing the terminal voltage belonging to SD into the circuit equation as a whole to obtain an autoregressive model, performing parameter identification on the autoregressive model, and calculating the parameters belonging to FD; removing the parameters belonging to FD from the second-order RC equivalent circuit model to obtain a new equivalent circuit model containing only SD parameters, calculating the terminal voltage of the new equivalent circuit model, and calculating the parameters belonging to SD; obtaining a complete second-order RC equivalent circuit model according to the parameters belonging to FD and the parameters belonging to SD. The present invention can accurately track the battery terminal voltage, has high adaptability and robustness to different working conditions and temperatures, has high accuracy for the identification of OCV and internal resistance, and can be widely used in the field of battery management.

Description

Technical Field

[0001] The present invention relates to the field of battery management, and in particular to a multi-time scale step-by-step parameter identification method, system, device and medium for lithium batteries. Background Art

[0002] Lithium-ion batteries are currently widely used in electric vehicles. As an important tool for electric vehicle energy management and control, BMS plays an important role in ensuring the safe and stable driving of electric vehicles. As one of the most important parts of BMS, accurate identification of model parameters is the basis for the good operation of BMS. Model parameter identification is mainly carried out for equivalent circuit models. The equivalent circuit model of lithium batteries is a variable parameter system. The use of online parameter identification can effectively solve the problem of time-varying parameters.

[0003] However, lithium-ion batteries also have multi-time scale problems. Due to the multi-time scale characteristics of lithium batteries, if you want to model lithium batteries more accurately, you must consider how to distinguish model components that represent chemical properties at different time scales. The current mainstream solution is to distinguish the characteristics of lithium batteries on different time scales into fast dynamics (FD) characteristics and slow dynamics (SD) characteristics. Among them, FD mainly includes resistance characteristics and charge transfer characteristics, and SD mainly includes charge diffusion characteristics. However, most traditional parameter identification algorithms identify all model parameters at the same time, which can easily lead to inaccurate or even unreasonable identification results of some SD parameters. In addition, most of the current identification algorithms require measuring the open circuit voltage (OCV) of the battery in advance, which will require a lot of experimental time. Summary of the invention

[0004] In order to solve at least one of the technical problems existing in the prior art to a certain extent, the purpose of the present invention is to provide a lithium battery multi-time scale step-by-step parameter identification method, system, device and medium.

[0005] The technical solution adopted by the present invention is:

[0006] A multi-time scale step-by-step parameter identification method for a lithium battery comprises the following steps:

[0007] Differentiate the parameters in the second-order RC equivalent circuit model (ECM) into two different time scales: FD and SD.

[0008] Write the terminal voltage belonging to SD into the circuit equation as a whole to obtain the autoregressive model (Auto Regressive with exogenous input, ARX), perform parameter identification on the autoregressive model, and calculate the parameters belonging to FD;

[0009] The parameters belonging to FD are removed from the second-order RC equivalent circuit model to obtain a new equivalent circuit model containing only SD parameters, the terminal voltage of the new equivalent circuit model is calculated, and the parameters belonging to SD are calculated;

[0010] A complete second-order RC equivalent circuit model is obtained based on the parameters belonging to FD and the parameters belonging to SD.

[0011] Furthermore, the second-order RC equivalent circuit model includes 6 parameters, namely: ideal voltage source U oc , internal resistance R0 and two RC links R j , C j , j = 1, 2; where U oc represents the open circuit voltage (OCV) of the battery, u0 is the terminal voltage of the internal resistance, u1 and u2 are the terminal voltages of the two RC links respectively, and u t is the terminal voltage of the lithium battery, i t is the load current of the lithium battery; when i t When it is positive, it means the lithium battery is charging, and when it is negative, it means the lithium battery is discharging;

[0012] In the second-order RC equivalent circuit model, R0, R1, and C1 belong to FD; U oc , R2 and C2 belong to SD; the terminal voltages corresponding to the six parameters are divided into two different time scales, FD and SD, among which the terminal voltages u0 and u1 belong to the FD part, and the voltage source U oc The terminal voltage u2 belongs to the SD part; define two voltage values ​​u SD and u FD , represent the voltage of the SD part and the voltage of the FD part respectively.

[0013] Furthermore, the terminal voltage belonging to SD is written into the circuit equation as a whole to obtain an autoregressive model, and the parameters of the autoregressive model are identified to calculate the parameters belonging to FD, including:

[0014] The terminal voltage u belonging to the SD part SD Written into the circuit equation as a whole, we get the transfer function;

[0015] The transfer function is discretized using the inverse difference transformation method;

[0016] The autoregressive model is obtained according to the discretized transfer function;

[0017] The autoregressive model is converted into a matrix form and brought into the fixed memory recursive least squares (FMRLS) algorithm to calculate the parameters R0, R1 and C1 belonging to FD.

[0018] Furthermore, the expression of the transfer function is:

[0019]

[0020] The discretization of the transfer function is:

[0021]

[0022]

[0023] Where T s is the sampling time of the system, c1, c2 and c3 are coefficients composed of the equivalent circuit model parameters, respectively:

[0024]

[0025] Where τ1=R1C1;

[0026] The expression of the autoregressive model is:

[0027] u t (k) = c1u t (k-1)+c2i t (k)+c3i t (k-1)+u SD (k)-c1u SD (k-1)

[0028] The autoregressive model is converted into a matrix form:

[0029]

[0030] In the formula, y(k) is the output of the model at time k, Φ(k) is the data vector of the model at time k, and θ(k) is the parameter vector of the model at time k;

[0031] Substitute the autoregressive model into the least squares recursive algorithm with limited memory to obtain the parameter vector θ(k) of the model at all times. After obtaining c1, c2 and c3, the parameters R0, R1 and C1 of the FD part are solved. The expressions are as follows:

[0032]

[0033] Furthermore, the parameters belonging to FD are removed from the second-order RC equivalent circuit model to obtain a new equivalent circuit model containing only SD parameters, the terminal voltage of the new equivalent circuit model is calculated, and the parameters belonging to SD are calculated, including:

[0034] The parameters belonging to FD are removed from the second-order RC equivalent circuit model to obtain a new equivalent circuit model containing only SD parameters; the state transfer matrix and observation matrix of the new equivalent circuit model are calculated, and the parameters U belonging to SD are calculated using the fading extended Kalman filter (FEKF) filtering algorithm. oc , R2 and C2.

[0035] Furthermore, the parameters belonging to FD are removed from the second-order RC equivalent circuit model to obtain a new equivalent circuit model containing only SD parameters; the state transfer matrix and the observation matrix of the new equivalent circuit model are calculated, and the parameters U belonging to SD are calculated using the fading extended Kalman filter algorithm. oc , R2 and C2, including:

[0036] Remove the parameters belonging to FD from the second-order RC equivalent circuit model to obtain a new equivalent circuit model containing only SD parameters, and calculate the terminal voltage u of the new equivalent circuit model SD :

[0037] u SD =u t -u FD (1)

[0038] In the formula, u t is the terminal voltage of the battery;

[0039] According to the new equivalent circuit model, the expression of u2 is:

[0040]

[0041] Dividing both sides of formula (2) by R2, we get the expression of the current i2 passing through R2:

[0042]

[0043] i2,U oc , R2 and C2 are four variables as state variables, and the current i t As the model input, and considering the actual situation U oc , R2 and C2 change very little within the unit sampling time, so it can be assumed that:

[0044]

[0045] According to formula (3) and formula (4), the state equation of the new equivalent circuit model is written as:

[0046]

[0047] In the formula, x k Represents the four state quantities at the kth moment, u k The model input i at the kth moment t (k);

[0048] According to the linearized state equation, the state transfer matrix is:

[0049]

[0050] in is the state quantity that has been corrected at time k-1;

[0051]

[0052] will u SD The output equation of the new equivalent circuit model obtained as the model output is:

[0053] g(x k ,u k )=u SD,k =U oc,k +R 2,k ·i 2,k (8)

[0054] According to the output equation, the linearized observation matrix is:

[0055]

[0056] in is the predicted value of the state quantity at time k;

[0057] Get the state transfer matrix and the observation matrix Then, the fading extended Kalman filter algorithm is used to adjust the parameters U of the SD part. oc , R2 and C2 are identified.

[0058] Furthermore, the method further comprises the following steps:

[0059] Acquire the terminal current information and terminal voltage information of the lithium battery in real time, and obtain the actual measured battery terminal voltage;

[0060] The terminal voltage is estimated using a complete second-order RC equivalent circuit model, and the estimated terminal voltage is compared with the actual measured battery terminal voltage.

[0061] Another technical solution adopted by the present invention is:

[0062] A multi-time scale step-by-step parameter identification system for lithium batteries, comprising:

[0063] Parameter division module, used to divide various parameters in the second-order RC equivalent circuit model into two different time scales: FD and SD;

[0064] The first calculation module is used to write the terminal voltage belonging to SD into the circuit equation as a whole, obtain an autoregressive model, perform parameter identification on the autoregressive model, and calculate the parameters belonging to FD;

[0065] The second calculation module is used to remove the parameters belonging to FD from the second-order RC equivalent circuit model, obtain a new equivalent circuit model containing only SD parameters, calculate the terminal voltage of the new equivalent circuit model, and calculate the parameters belonging to SD;

[0066] The model application module is used to obtain a complete second-order RC equivalent circuit model according to the parameters belonging to FD and the parameters belonging to SD.

[0067] Another technical solution adopted by the present invention is:

[0068] A multi-time scale step-by-step parameter identification device for a lithium battery, comprising:

[0069] at least one processor;

[0070] at least one memory for storing at least one program;

[0071] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0072] Another technical solution adopted by the present invention is:

[0073] A computer-readable storage medium stores a program executable by a processor, wherein the program executable by the processor is used to execute the method described above when executed by the processor.

[0074] The beneficial effects of the present invention are as follows: the mathematical model established by the present invention can effectively distinguish the information of different time scales contained in the terminal voltage, avoiding the problem of inaccurate identification of parameters of large time scales in traditional parameter identification algorithms. It is not necessary to measure OCV in advance, but OCV can be identified synchronously in the parameter identification of the SD part, which can greatly reduce the test time and solve the problem of OCV curve changes caused by changes in the external environment and battery aging. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the embodiments of the present invention or the drawings of related technical solutions in the prior art are introduced below. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0076] Figure 1 is a second-order RC equivalent circuit model diagram in an embodiment of the present invention;

[0077] Figure 2 is a circuit model diagram after parameter classification in an embodiment of the present invention;

[0078] Figure 3 is a structural diagram of a multi-time scale step-by-step parameter identification method for a lithium battery in an embodiment of the present invention;

[0079] Figure 4 is a circuit model diagram of the SD part in an embodiment of the present invention;

[0080] Figure 5 is a voltage and current curve diagram of the battery operating in the FUDS condition in the data set used in the embodiment of the present invention; wherein Figure 5 (a) is the complete testing process, Figure 5 (b) is the voltage-current curve of the lithium battery when running the FUDS operating current;

[0081] Figure 6 is a comparison diagram of the model terminal voltage and the actual terminal voltage under FUDS working conditions in an embodiment of the present invention; wherein Figure 6 (a) is the initial voltage of 80%, Figure 6 (b) is the initial voltage of 50%;

[0082] Figure 7 is a comparison diagram of the model terminal voltage and the actual terminal voltage under the DST condition in an embodiment of the present invention; wherein Figure 7 (a) is the initial voltage of 80%, Figure 7 (b) The initial voltage is 50%;

[0083] Figure 8 are the model parameter curves identified under FUDS and DST conditions respectively in the embodiment of the present invention; wherein Figure 8 (a) is the FUDS condition, Figure 8 (b) is the DST condition;

[0084] Fig. 9 is an OCV test diagram of the data used in the embodiment of the present invention; wherein Fig. 9 (a) is the complete testing process, Fig. 9 (b) is the OCV curve obtained by the test;

[0085] Fig.10 is a comparison diagram of the COV-SOC curve identified in the embodiment of the present invention and the COV-SOC curve obtained by testing; wherein Fig.10 (a) is the FUDS condition, Fig.10 (b) is the DST condition. DETAILED DESCRIPTION

[0086] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limitations of the present invention. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0087] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0088] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0089] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0090] This embodiment provides a multi-time scale step-by-step parameter identification method for lithium batteries, which solves the impact of multi-time scale problems in lithium battery parameter identification. OCV is identified synchronously during parameter identification, which can greatly reduce the test time and solve the problem of OCV curve changes caused by external environment changes and battery aging.

[0091] The mathematical model established by the present invention can effectively distinguish the information of different time scales contained in the terminal voltage, avoiding the problem of inaccurate identification of large time scale parameters in traditional parameter identification algorithms. In view of the problem that the OCV-SOC curve used in traditional identification algorithms is easily changed due to aging and temperature changes, the present invention estimates the OCV curve in real time. The results show that the new algorithm proposed by the present invention can accurately track the battery terminal voltage and has high adaptability and robustness to different operating conditions and temperatures. It also has very high accuracy in the identification of OCV and internal resistance.

[0092] In a preferred embodiment of the present application, a data set published by the Center for Advanced Life Cycle Engineering (CALCE) of the University of Maryland was used for simulation verification. We selected the experimental data of the research center on INR 18650-20R lithium-ion batteries for simulation verification. The test process and results are as follows: Figure 5 As shown. The battery is a nickel-cobalt-manganese ternary lithium battery with a rated capacity of 2Ah, a charge and discharge cut-off voltage of 4.2V and 2.5V respectively, a maximum current of 7.5A, and an operating temperature range of 0-50°C. The test conditions are dynamic stress conditions (DST) and the US Federal City Driving Conditions (FUDS). The measured terminal voltage value is used as a reference value and compared with the model terminal voltage value estimated by the method as a verification to illustrate the effectiveness and accuracy of the method.

[0093] The embodiment of the present invention provides a multi-time scale step-by-step parameter identification method for a lithium battery, the specific structure of which is as follows: Figure 3 As shown, the specific steps include:

[0094] Step 1: Obtain the terminal current and terminal voltage information of the lithium battery in real time. In this example, data is obtained from a data set.

[0095] Step 2: According to Figure 1 and Figure 2 The terminal voltage u belonging to the SD part SD The circuit equation is written as a whole to obtain the ARX model, and the parameters of the ARX model are identified using the FMRLS algorithm to calculate the parameters R0, R1 and C1 belonging to the FD.

[0096] Wherein, step 2 specifically includes the following steps:

[0097] Step 2.1: Set the terminal voltage u belonging to the SD part SD Writing the circuit equation as a whole gives the transfer function:

[0098]

[0099] Step 2.2: Discretize the transfer function using the inverse difference transformation method:

[0100]

[0101]

[0102] Where T s is the sampling time of the system, which is 1s in this example, and c1, c2, and c3 are coefficients composed of the equivalent circuit model parameters, which are:

[0103]

[0104] Where τ1=R1C1.

[0105] Step 2.3: According to the discretized transfer function, the ARX model is obtained as:

[0106] u t (k) = c1u t (k-1)+c2i t (k)+c3i t (k-1)+u SD (k)-c1u SD (k-1)

[0107] Step 2.4: Write the ARX model in matrix form:

[0108]

[0109] Among them, y(k) is the output of the model at time k, Φ(k) is the data vector of the model at time k, and θ(k) is the parameter vector of the model at time k.

[0110] Step 2.5: Substitute the ARX model into the FMRLS algorithm to obtain the parameter vector θ(k) of the model at all times. After obtaining c1, c2 and c3, the parameters R0, R1 and C1 of the FD part can be solved, and their expressions are:

[0111]

[0112] Step 3: Remove the identified FD parameters from the ECM to obtain a circuit model containing only SD parameters, and calculate the model terminal voltage u SD . And calculate the state transfer matrix and observation matrix of the circuit model, and then use the FEKF algorithm to calculate the SD parameter U oc , R2 and C2.

[0113] Wherein, step 3 specifically includes the following steps:

[0114] Step 3.1: Remove the identified FD parameters from the ECM and Figure 2Calculate the new model Figure 4 The terminal voltage u SD for:

[0115] u SD =u t -u FD

[0116] where u t is the terminal voltage of the battery.

[0117] Step 3.2: According to Figure 4 The expression of u2 is:

[0118]

[0119] Step 3.3: Divide both sides of the above equation by R2 to get the expression of the current i2 passing through R2:

[0120]

[0121] Step 3.4: Set i2, U oc , R2 and C2 are four variables as state variables, and the current i t As the model input, and considering the actual situation U oc , R2 and C2 change very little within the unit sampling time, so it can be assumed that:

[0122]

[0123] Step 3.5: Based on the two equations obtained in steps 3.3 and 3.4, the state equation of the new model is written as:

[0124]

[0125] Among them, x k Represents the four state quantities at the kth moment, u k The model input i at the kth moment t (k).

[0126] Step 3.6: According to the linearized state transfer matrix of the state equation in step 3.5, it is:

[0127]

[0128] in is the state quantity that has been corrected at time k-1;

[0129]

[0130] Step 3.7: Put u SD The output equation of the model obtained as the model output is:

[0131] g(x k ,u k )=u SD,k =U oc,k +R 2,k ·i 2,k

[0132] Step 3.8: According to the output equation of step 3.7, the linearized observation matrix can be obtained as:

[0133]

[0134] in is the predicted value of the state quantity at time k.

[0135] Step 3.9: According to step 3.6 and step 3.8, the above state transfer matrix is ​​obtained and the observation matrix Then, the FEKF algorithm is used to adjust the parameters U of the SD part. oc , R2 and C2 are identified.

[0136] Step 4: After obtaining the complete ECM model parameters, the model terminal voltage is calculated according to the circuit equation and compared with the actual lithium battery terminal voltage. In order to prove the superiority of this method, the terminal voltage results estimated by the Forgetting Factor Recursive Least Square (FFRLS) are compared together. The results are as follows: Figure 6 and Figure 7 In order to prove the adaptability of this method, the parameter identification results obtained under different working conditions are shown in Figure 8 At the same time, the accuracy of the OCV parameter is verified. The test process curve and test results of this parameter are shown in Fig. 9 The comparison result of this parameter is as follows Fig.10 shown.

[0137] Depend on Figure 6 and Figure 7 It can be seen that the terminal voltage estimated by this method has very high accuracy and is almost consistent with the actual value. COV also has very high accuracy and is very close to the test value.

[0138] In summary, the method of this embodiment has the following advantages and beneficial effects compared with the prior art:

[0139] (1) The work in this paper does not require the measurement of OCV in advance, but can identify OCV synchronously during the parameter identification of the SD part. This can greatly reduce the test time and solve the problem of OCV curve changes caused by external environment changes and battery aging.

[0140] (2) The model established in this paper can effectively separate the information of FD and SD, which can effectively prevent their mutual interference and thus improve the recognition accuracy.

[0141] This embodiment also provides a lithium battery multi-time scale step-by-step parameter identification system, including:

[0142] Parameter division module, used to divide various parameters in the second-order RC equivalent circuit model into two different time scales: FD and SD;

[0143] The first calculation module is used to write the terminal voltage belonging to SD into the circuit equation as a whole, obtain an autoregressive model, perform parameter identification on the autoregressive model, and calculate the parameters belonging to FD;

[0144] The second calculation module is used to remove the parameters belonging to FD from the second-order RC equivalent circuit model, obtain a new equivalent circuit model containing only SD parameters, calculate the terminal voltage of the new equivalent circuit model, and calculate the parameters belonging to SD;

[0145] The model application module is used to obtain a complete second-order RC equivalent circuit model according to the parameters belonging to FD and the parameters belonging to SD.

[0146] A lithium battery multi-time scale step-by-step parameter identification system of this embodiment can execute a lithium battery multi-time scale step-by-step parameter identification method provided by the method embodiment of the present invention, can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0147] This embodiment also provides a lithium battery multi-time scale step-by-step parameter identification device, comprising:

[0148] at least one processor;

[0149] at least one memory for storing at least one program;

[0150] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0151] A lithium battery multi-time scale step-by-step parameter identification device of this embodiment can execute a lithium battery multi-time scale step-by-step parameter identification method provided by the method embodiment of the present invention, can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0152] This embodiment also provides a storage medium storing instructions or programs that can execute a lithium battery multi-time scale step-by-step parameter identification method provided by the method embodiment of the present invention. When the instructions or programs are run, any combination of implementation steps of the method embodiment can be executed, and the corresponding functions and beneficial effects of the method can be obtained.

[0153] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0154] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0155] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0156] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0157] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0158] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0159] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. 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.

[0160] Although 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.

[0161] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A multi-time scale step-by-step parameter identification method for lithium batteries, characterized in that: The following steps are involved: Differentiate each parameter in the second-order RC equivalent circuit model into two different time scales: FD and SD; Write the terminal voltage belonging to SD into the circuit equation as a whole to obtain the autoregressive model, perform parameter identification on the autoregressive model, and calculate the parameters belonging to FD; The parameters belonging to FD are removed from the second-order RC equivalent circuit model to obtain a new equivalent circuit model containing only SD parameters, the terminal voltage of the new equivalent circuit model is calculated, and the parameters belonging to SD are calculated; A complete second-order RC equivalent circuit model is obtained based on the parameters belonging to FD and the parameters belonging to SD; The second-order RC equivalent circuit model includes 6 parameters, namely: ideal voltage source U oc , internal resistance R0 and two RC links R j , C j , j = 1, 2; where u0 is the terminal voltage of the internal resistance, u1, u2 are the terminal voltages of the two RC links respectively, and u t is the terminal voltage of the lithium battery, i t is the load current of the lithium battery; when i t When it is positive, it means the lithium battery is charging, and when it is negative, it means the lithium battery is discharging; In the second-order RC equivalent circuit model, R0, R1, and C1 belong to FD, U oc , R2 and C2 belong to SD; the terminal voltages corresponding to the six parameters are divided into two different time scales, FD and SD, among which the terminal voltages u0 and u1 belong to the FD part, and the voltage source U oc The terminal voltage u2 belongs to the SD part; define two voltage values ​​u SD and u FD , represent the voltage of the SD part and the voltage of the FD part respectively; The terminal voltage belonging to SD is written into the circuit equation as a whole to obtain an autoregressive model, and the parameters of the autoregressive model are identified to calculate the parameters belonging to FD, including: The terminal voltage u belonging to the SD part SD Written into the circuit equation as a whole, we get the transfer function; The transfer function is discretized using the inverse difference transformation method; The autoregressive model is obtained according to the discretized transfer function; The autoregressive model is converted into a matrix form and brought into the least squares recursive algorithm with limited memory to calculate the parameters R0, R1 and C1 belonging to FD; The expression of the transfer function is: The discretization of the transfer function is: Where T s is the sampling time of the system, c1, c2 and c3 are coefficients composed of the equivalent circuit model parameters, respectively: Where τ1=R1C1; The expression of the autoregressive model is: u t (k)=c1u t (k-1)+c2i t (k)+c3i t (k-1)+u SD (k)-c1u SD (k-1) The autoregressive model is converted into a matrix form: In the formula, y(k) is the output of the model at time k, Φ(k) is the data vector of the model at time k, and θ(k) is the parameter vector of the model at time k; Substitute the autoregressive model into the least squares recursive algorithm with limited memory to obtain the parameter vector θ(k) of the model at all times. After obtaining c1, c2 and c3, the parameters R0, R1 and C1 of the FD part are solved. The expressions are as follows: The method removes the parameters belonging to FD from the second-order RC equivalent circuit model to obtain a new equivalent circuit model containing only SD parameters, calculates the terminal voltage of the new equivalent circuit model, and calculates the parameters belonging to SD, including: The parameters belonging to FD are removed from the second-order RC equivalent circuit model to obtain a new equivalent circuit model containing only SD parameters; the state transfer matrix and observation matrix of the new equivalent circuit model are calculated, and the parameters U belonging to SD are calculated using the fading extended Kalman filter algorithm. oc , R2 and C2.

2. A lithium battery multi-time scale step-by-step parameter identification method according to claim 1, characterized in that: The following steps are also included: Acquire the terminal current information and terminal voltage information of the lithium battery in real time, and obtain the actual measured battery terminal voltage; The terminal voltage is estimated using a complete second-order RC equivalent circuit model, and the estimated terminal voltage is compared with the actual measured battery terminal voltage.

3. A lithium battery multi-time scale step-by-step parameter identification system, applied to a lithium battery multi-time scale step-by-step parameter identification method according to claim 1 or 2, characterized in that: include: Parameter division module, used to divide various parameters in the second-order RC equivalent circuit model into two different time scales: FD and SD; The first calculation module is used to write the terminal voltage belonging to SD into the circuit equation as a whole, obtain an autoregressive model, perform parameter identification on the autoregressive model, and calculate the parameters belonging to FD; The second calculation module is used to remove the parameters belonging to FD from the second-order RC equivalent circuit model, obtain a new equivalent circuit model containing only SD parameters, calculate the terminal voltage of the new equivalent circuit model, and calculate the parameters belonging to SD; The model application module is used to obtain a complete second-order RC equivalent circuit model according to the parameters belonging to FD and the parameters belonging to SD.

4. A multi-time scale step-by-step parameter identification device for lithium batteries, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 2.

5. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to perform the method according to any one of claims 1 to 2 when executed by the processor.

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