Lithium battery parameter identification method and device based on residual compensation

Abstract

The embodiment of the invention discloses a lithium battery parameter identification method and device based on residual compensation, and the method comprises the steps: constructing an equivalent circuit with deviation of a lithium battery, and determining a mathematical model corresponding to the equivalent circuit; performing parameter identification on the mathematical model by applying a steepest descent optimization method to obtain identification parameters of the mathematical model; and determining the parameters of the lithium battery according to the obtained identification parameters of the mathematical model. By means of the mode, the deviation serves as the model variable to be identified, the influence of the model deviation on the model parameters is compensated, the accuracy of model parameter identification is improved, and the reliability and stability of parameter identification are improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of vehicle batteries, and in particular to a method and device for identifying lithium battery parameters based on residual compensation. Background Art

[0002] Lithium-ion batteries are the most important energy storage devices for current mobile devices, electronic products, energy storage vehicles, and energy storage power stations. In particular, lithium iron phosphate batteries and ternary lithium batteries have been widely used in electric vehicles. The range and safety of lithium batteries are the decisive factors in the current development and popularization of electric vehicles. To achieve safe and reliable operation of lithium batteries, the internal state must be accurately detected. These states include state of charge (SOC), health state, power state, etc. However, these states cannot be measured, and the only quantities that can be measured are current and voltage. Therefore, the internal state is generally obtained indirectly by modeling lithium batteries and using parameter identification and state estimation methods. Accurate identification of lithium battery model parameters is the key.

[0003] Parameter identification is divided into two categories: offline identification and online identification. Offline identification must implement specific tests to obtain specific current and voltage data, and calculate the model parameters offline through the identification algorithm. This method is only suitable for testing or verification, and is not suitable for actual application scenarios. The parameters of the lithium battery model will change with changes in temperature and aging process. Therefore, online parameter identification must be performed during use to ensure reliable estimation of the internal state. The online parameter identification algorithm of the lithium battery model is the key. The algorithm must ensure the accuracy of the identification and meet the requirements of low computational complexity.

[0004] The accuracy and reliability of the online identification method of lithium battery model parameters are closely related to the model and parameter identification method. Because the complexity and accuracy of the equivalent circuit model of lithium batteries can achieve a good balance, it has been widely proven to be suitable for real-time state estimation. A key point of parameter identification is how to convert the equivalent circuit model into a mathematical model or regression model that can perform parameter identification. The commonly used regression model does not consider the influence of model deviation, but ignores the model deviation. This will lead to inaccurate model parameters, especially when the current and voltage sampling values ​​contain large noise, the identification accuracy will be greatly reduced. At the same time, the commonly used regression model does not consider the pole problem that occurs during the parameter identification process, resulting in the need to add many additional identification constraints. The identification method of lithium battery model parameters directly determines the accuracy and computational complexity of the identification results. The recursive least squares method is currently widely used. However, this method will have the problem of data saturation. For time-varying parameter systems such as lithium battery parameters, complex forgetting factors must be designed. At the same time, the recursive least squares method must ensure that the input data is continuous and valid, which is almost impossible to achieve in actual use. Summary of the invention

[0005] In view of the above problems, an embodiment of the present invention provides a lithium battery parameter identification method and device based on residual compensation, which overcomes the above problems or at least partially solves the above problems.

[0006] According to one aspect of an embodiment of the present invention, a lithium battery parameter identification method based on residual compensation is provided, the method comprising: constructing an equivalent circuit with deviation of the lithium battery, and determining a mathematical model corresponding to the equivalent circuit; applying a steepest descent optimization method to perform parameter identification on the mathematical model, and obtaining identification parameters of the mathematical model; and determining the parameters of the lithium battery according to the identification parameters of the obtained mathematical model.

[0007] Optionally, the equivalent circuit includes: a self-discharge resistor, a charging capacitor, an SOC deviation, a controllable current source, a controllable voltage source, a series resistor, an open circuit voltage deviation, a polarization voltage deviation, a polarization capacitor and a polarization resistor. The self-discharge resistor, the charging capacitor and the SOC deviation are respectively connected in parallel on both sides of the controllable current source, the controllable voltage source is connected to the controllable current source, one end of the controllable voltage source is connected in series with the series resistor and the open circuit voltage deviation in sequence, the other end of the controllable voltage source is connected to the polarization resistor and the polarization capacitor, the polarization capacitor is connected in series with the polarization voltage deviation and is associated on both sides of the polarization resistor, and the end of the polarization resistor away from the controllable voltage source and the end of the open circuit voltage deviation away from the series resistor are respectively positive and negative output terminals.

[0008] Optionally, the determining of the mathematical model corresponding to the equivalent circuit includes: the mathematical model determined according to the deviation of the equivalent circuit of the lithium battery is:

[0009]

[0010] V b (k) is the terminal voltage at the kth moment, V oc (k) is the open circuit voltage at the kth moment, I b (k) is the current at the kth moment, Δf oc (k) is the battery open circuit voltage deviation at the kth moment, Δf p (k) is the battery concentration polarization deviation at the kth moment, T s is the sampling period, R p is the polarization resistance, C p is the polarization capacitance, R s is the series resistance.

[0011] Optionally, the application of the steepest descent optimization method to perform parameter identification on the mathematical model to obtain identification parameters of the mathematical model includes: establishing a regression model for parameter identification based on the mathematical model; obtaining a parameter identification equation including model deviation based on the regression model, and constructing an optimization index; and applying the steepest descent method according to the parameter identification equation to solve the identification parameters that minimize the optimization index.

[0012] Optionally, the establishing of a regression model for parameter identification based on the mathematical model includes: obtaining a regression model for parameter identification by differentiating and discretizing the mathematical model:

[0013]

[0014] Where, ΔV b (k) is the output of the regression model, ΔV b (k) = V b (k)-V b (k-1), ΔI b (k) = I b (k)-I b (k-1), is the input of the regression model, θ(k) is the identification parameter to be identified, T represents the transformation rank, and e(k) is the model residual.

[0015] Optionally, the step of obtaining a parameter identification equation including a model deviation based on the regression model and constructing an optimization index includes: obtaining a parameter identification equation including a model deviation based on the regression model:

[0016]

[0017] The constructed optimization index J is:

[0018]

[0019] in, represents the model bias, ρ is the weight factor, and λ is the Lagrange multiplier.

[0020] Optionally, determining the parameters of the lithium battery according to the obtained identification parameters of the mathematical model includes: calculating the parameters of the lithium battery by applying the following relationship according to the solved identification parameters:

[0021]

[0022] Among them, T s is the sampling period, R p is the polarization resistance, C p is the polarization capacitance, R s is the series resistance, a1, a2, a3 are the items in the identification parameters.

[0023] Based on the same inventive concept, a lithium battery parameter identification device based on residual compensation is also provided, including: a mathematical model construction unit, used to construct an equivalent circuit with deviation of the lithium battery and determine the mathematical model corresponding to the equivalent circuit; a model parameter identification unit, used to apply the steepest descent optimization method to perform parameter identification on the mathematical model and obtain identification parameters of the mathematical model; a battery parameter acquisition unit, used to determine the parameters of the lithium battery according to the identification parameters of the acquired mathematical model.

[0024] Based on the same inventive concept, an embodiment of the present invention further proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the aforementioned method when executing the program.

[0025] Based on the same inventive concept, an embodiment of the present invention further proposes a computer storage medium, in which at least one executable instruction is stored, and the executable instruction enables a processor to execute the aforementioned method.

[0026] The embodiment of the present invention constructs an equivalent circuit of a lithium battery with a deviation, and determines a mathematical model corresponding to the equivalent circuit; applies the steepest descent optimization method to perform parameter identification on the mathematical model to obtain identification parameters of the mathematical model; determines the parameters of the lithium battery according to the identification parameters of the obtained mathematical model, and identifies the deviation as a model variable to compensate for the influence of the model deviation on the model parameters, thereby improving the accuracy of model parameter identification and increasing the reliability and stability of parameter identification.

[0027] The above description is only an overview of the technical solution of the embodiment of the present invention. In order to more clearly understand the technical means of the embodiment of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0029] Figure 1 A schematic diagram of the flow of a lithium battery parameter identification method based on residual compensation provided by an embodiment of the present invention is shown;

[0030] Figure 2 A schematic diagram of an equivalent circuit of a lithium battery provided by an embodiment of the present invention is shown;

[0031] Figure 3 A schematic diagram of the structure of a lithium battery parameter identification device based on residual compensation provided by an embodiment of the present invention is shown;

[0032] Figure 4 A schematic diagram of an electronic device in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0033] 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.

[0034] Figure 1 A schematic flow chart of a lithium battery parameter identification method based on residual compensation provided in an embodiment of the present invention is shown.

[0035] like Figure 1 As shown, the lithium battery parameter identification method based on residual compensation is applied to a server, including:

[0036] Step S11: constructing an equivalent circuit of the lithium battery with a deviation, and determining a mathematical model corresponding to the equivalent circuit.

[0037] In the embodiment of the present invention, an equivalent circuit of a lithium battery with a model deviation is established, and the equivalent circuit takes into account the polarization voltage deviation, the open circuit voltage deviation, and the SOC deviation. Figure 2As shown, the equivalent circuit includes: self-discharge resistor R sd , charging capacitor C n , SOC deviation Δf soc , controllable current source, controllable voltage source V oc , series resistance R s , open circuit voltage deviation Δf oc , polarization voltage deviation Δf p , polarization capacitance C p And the polarization resistance R p , the self-discharge resistor R sd , the charging capacitor C n and the SOC deviation Δf soc are connected in parallel on both sides of the controllable current source, and the controllable voltage source V oc The controllable current source is connected to the controllable voltage source V oc One end of the series resistor R s and the open circuit voltage deviation Δf oc , the controllable voltage source V oc The other end of the polarization resistor R p And the polarization capacitance C p connect, the polarization capacitor C p The pole voltage deviation Δf p in series and associated with the polarization resistor R p On both sides, the polarization resistance R p of the controllable voltage source V oc The open circuit voltage deviation Δf oc of the series resistor R s The output voltage of the positive and negative output terminals of the equivalent circuit is V b , the output current is I b The current of the controllable current source is I bat .

[0038] After determining the equivalent circuit of the lithium battery, further determine the mathematical model corresponding to the equivalent circuit. The optional mathematical model determined according to the equivalent circuit of the lithium battery with deviation is:

[0039] V b (k) = V oc (k)-a1V oc (k-1)+a1V b (k-1)+a2I b (k)+a3I b (k-1)+Δf oc (k)+Δf p (k)

[0040]

[0041] V b (k) is the terminal voltage at the kth moment, V oc (k) is the open circuit voltage at the kth moment, I b (k) is the current at the kth moment, Δf oc (k) is the battery open circuit voltage deviation at the kth moment, Δf p (k) is the battery concentration polarization deviation at the kth moment, T s is the sampling period, R p is the polarization resistance, C p is the polarization capacitance, R s is the series resistance.

[0042] Step S12: applying the steepest descent optimization method to perform parameter identification on the mathematical model to obtain identification parameters of the mathematical model.

[0043] In the embodiment of the present invention, optionally, a regression model for parameter identification is first established based on the mathematical model. Specifically, the following regression model for parameter identification is obtained by differentiating and discretizing the mathematical model:

[0044]

[0045] Where, ΔV b (k) is the output of the regression model, ΔV b (k) = V b (k)-V b (k-1), ΔI b (k) = I b (k)-I b (k-1), is the input of the regression model, θ(k) is the identification parameter to be identified, T represents the transfer rank, and e(k) is the model residual, which is also identified as a model parameter. By differential means, the decoupling of the model and the open circuit voltage can be achieved; by identifying the model residual as a model parameter, the influence of the deviation on the model parameter can be compensated.

[0046] Then, a parameter identification equation including model deviation is obtained based on the regression model, and an optimization index is constructed. The parameter identification equation including model deviation is obtained based on the regression model as follows:

[0047]

[0048] The constructed optimization index J is:

[0049]

[0050] in, represents the model deviation, ρ is the weight factor, and λ is the Lagrange multiplier. The optimization index J consists of two parts: model deviation and parameter increment.

[0051] Then, the steepest descent method is applied according to the parameter identification equation to solve the identification parameter θ(k) that minimizes the optimization index.

[0052] Step S13: Determine the parameters of the lithium battery according to the obtained identification parameters of the mathematical model.

[0053] According to the identification parameter θ(k) identified in step S12, since θ(k)=[a1(k),a2(k),a3(k),e(k)] T , the parameters of the lithium battery are calculated using the following relationship according to the solved identification parameters:

[0054]

[0055] Among them, T s is the sampling period, R p is the polarization resistance, C p is the polarization capacitance, R s is the series resistance, a1, a2, a3 are the items in the identification parameters. So far, the parameter identification is completed.

[0056] The embodiment of the present invention converts the equivalent circuit into a regression model capable of parameter identification, and identifies the model deviation as the identification parameter of the regression model, thereby compensating for the influence of the model deviation on the model parameters and improving the accuracy of model parameter identification; by applying the parameter identification method based on the steepest descent optimization method to perform parameter identification, the influence of invalid data on the identification process can be avoided, thereby increasing the reliability and stability of parameter identification; the problems of model deviation, invalid data and large amount of calculation in lithium battery parameter estimation can be solved, thereby realizing fast and accurate lithium battery model parameter identification.

[0057] In summary, the lithium battery parameter identification method based on residual compensation in an embodiment of the present invention constructs an equivalent circuit with deviation of the lithium battery and determines the mathematical model corresponding to the equivalent circuit; applies the steepest descent optimization method to perform parameter identification on the mathematical model to obtain the identification parameters of the mathematical model; determines the parameters of the lithium battery according to the identification parameters of the obtained mathematical model, and identifies the deviation as a model variable to compensate for the influence of the model deviation on the model parameters, thereby improving the accuracy of model parameter identification and increasing the reliability and stability of parameter identification.

[0058] The above specific embodiments of the present invention are described. In some cases, the actions or steps recorded in the embodiments of the present invention can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0059] Based on the same concept, the embodiment of the present invention also provides a lithium battery parameter identification device based on residual compensation. Applicable to a server. Figure 3 As shown, the lithium battery parameter identification device based on residual compensation includes: a mathematical model construction unit, a model parameter identification unit and a battery parameter acquisition unit.

[0060] A mathematical model building unit, used to build an equivalent circuit of a lithium battery with a deviation, and determine a mathematical model corresponding to the equivalent circuit;

[0061] A model parameter identification unit, used to apply the steepest descent optimization method to perform parameter identification on the mathematical model to obtain identification parameters of the mathematical model;

[0062] A battery parameter acquisition unit is used to determine the parameters of the lithium battery according to the acquired identification parameters of the mathematical model.

[0063] For the convenience of description, the above device is described as various modules according to their functions. Of course, when implementing the embodiment of the present invention, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0064] The above-mentioned device is applied to the corresponding method in the aforementioned embodiment and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0065] Based on the same inventive concept, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in any one of the above embodiments is implemented.

[0066] An embodiment of the present invention provides a non-volatile computer storage medium, wherein the computer storage medium stores at least one executable instruction, and the computer executable instruction can execute the method described in any of the above embodiments.

[0067] Figure 4A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 401, a memory 402, an input / output interface 403, a communication interface 404, and a bus 405. The processor 401, the memory 402, the input / output interface 403, and the communication interface 404 are connected to each other in communication within the device through the bus 405.

[0068] The processor 401 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solution provided by the method embodiment of the present invention.

[0069] The memory 402 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 402 can store an operating system and other application programs. When the technical solution provided by the method embodiment of the present invention is implemented by software or firmware, the relevant program code is stored in the memory 402 and called and executed by the processor 401.

[0070] The input / output interface 403 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0071] The communication interface 404 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0072] The bus 405 comprises a pathway for transmitting information between the various components of the device (eg, the processor 401 , the memory 402 , the input / output interface 403 , and the communication interface 404 ).

[0073] It should be noted that, although the above device only shows the processor 401, the memory 402, the input / output interface 403, the communication interface 404 and the bus 405, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiment of the present invention, and does not necessarily include all the components shown in the figure.

[0074] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present invention as described above, which are not provided in detail for the sake of simplicity.

[0075] This application is intended to cover all such substitutions, modifications and variations that fall within the broad scope of all embodiments. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the scope of protection of this disclosure.

Claims

1. A lithium battery parameter identification method based on residual compensation, characterized in that: The method comprises: Constructing a biased equivalent circuit of a lithium battery and determining a mathematical model corresponding to the equivalent circuit; Applying the steepest descent optimization method to perform parameter identification on the mathematical model to obtain identification parameters of the mathematical model; The parameters of the lithium battery are determined according to the obtained identification parameters of the mathematical model.

2. The method according to claim 1, characterized in that: The equivalent circuit includes: a self-discharge resistor, a charging capacitor, an SOC deviation, a controllable current source, a controllable voltage source, a series resistor, an open circuit voltage deviation, a polarization voltage deviation, a polarization capacitor and a polarization resistor. The self-discharge resistor, the charging capacitor and the SOC deviation are respectively connected in parallel on both sides of the controllable current source, the controllable voltage source is connected to the controllable current source, one end of the controllable voltage source is sequentially connected in series with the series resistor and the open circuit voltage deviation, the other end of the controllable voltage source is connected to the polarization resistor and the polarization capacitor, the polarization capacitor is connected in series with the polarization voltage deviation and is associated with both sides of the polarization resistor, and one end of the polarization resistor away from the controllable voltage source and one end of the open circuit voltage deviation away from the series resistor are respectively positive and negative output ends.

3. The method according to claim 2, characterized in that Determining the mathematical model corresponding to the equivalent circuit includes: The mathematical model determined according to the equivalent circuit with deviation of the lithium battery is: V b (k)=V oc (k)-a1V oc (k-1)+a1V b (k-1)+a2I b (k)+a3I b (k-1)+Δf oc (k)+Δf p (k) <h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr"> a2=-R<h2 style=";text-align:left;direction:ltr"> s <h2 style=";text-align:left;direction:ltr"> a3 = a1R<h2 style=";text-align:left;direction:ltr"> s <h2 style=";text-align:left;direction:ltr"> -(1-a1)R<h2 style=";text-align:left;direction:ltr"> p V b (k) is the terminal voltage at the kth moment, V oc (k) is the open circuit voltage at the kth moment, I b (k) is the current at the kth moment, Δf oc (k) is the battery open circuit voltage deviation at the kth moment, Δf p (k) is the battery concentration polarization deviation at the kth moment, T s is the sampling period, R p is the polarization resistance, C p is the polarization capacitance, R s is the series resistance.

4. The method according to claim 3, characterized in that The applying the steepest descent optimization method to perform parameter identification on the mathematical model to obtain the identification parameters of the mathematical model includes: Establishing a regression model for parameter identification based on the mathematical model; Obtaining a parameter identification equation including a model deviation based on the regression model, and constructing an optimization index; The steepest descent method is applied according to the parameter identification equation to solve the identification parameter that minimizes the optimization index.

5. The method according to claim 4, characterized in that The step of establishing a regression model for parameter identification based on the mathematical model comprises: By differentiating and discretizing the mathematical model, a regression model for parameter identification is obtained: Where, ΔV b (k) is the output of the regression model, ΔV b (k) = V b (k)-V b (k-1), ΔI b (k) = I b (k)-I b (k-1), is the input of the regression model, θ(k) is the identification parameter to be identified, T represents the transformation rank, and e(k) is the model residual.

6. The method according to claim 4, characterized in that The step of obtaining a parameter identification equation including a model deviation based on the regression model and constructing an optimization index comprises: Based on the regression model, a parameter identification equation including model deviation is obtained: The constructed optimization index J is: in, represents the model bias, ρ is the weight factor, and λ is the Lagrange multiplier.

7. The method according to claim 1, characterized in that Determining the parameters of the lithium battery according to the obtained identification parameters of the mathematical model includes: The parameters of the lithium battery are calculated using the following relationship based on the identified parameters: Among them, T s is the sampling period, R p is the polarization resistance, C p is the polarization capacitance, R s is the series resistance, a1, a2, a3 are the items in the identification parameters.

8. A lithium battery parameter identification device based on residual compensation, characterized in that: The device comprises: A mathematical model building unit, used to build an equivalent circuit of a lithium battery with a deviation, and determine a mathematical model corresponding to the equivalent circuit; A model parameter identification unit, used to apply the steepest descent optimization method to perform parameter identification on the mathematical model to obtain identification parameters of the mathematical model; A battery parameter acquisition unit is used to determine the parameters of the lithium battery according to the acquired identification parameters of the mathematical model.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A computer storage medium, characterized in that: The storage medium stores at least one executable instruction, and the executable instruction enables the processor to execute the method according to any one of claims 1 to 7.

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