Simulation test method and device for lithium ion battery voltage

Abstract

The application provides a simulation test method and device for lithium ion battery voltage, relates to the technical field of lithium ion batteries, and aims to improve the terminal voltage simulation precision of a lithium ion battery model under complex working conditions. The method comprises the following steps: constructing an equivalent circuit model of a lithium ion battery; determining a first state parameter for representing the charge state of an electrode core and a second state parameter for representing the charge state of an electrode shell according to the current input to the lithium ion battery through a first part of the equivalent circuit model; determining a first polarization voltage, a second polarization voltage and an ohmic voltage drop according to the current through a second part of the equivalent circuit model; constructing a feedforward neural network model to output the simulated open circuit voltage of the lithium ion battery; and performing superposition calculation according to the simulated open circuit voltage, the first polarization voltage, the second polarization voltage and the ohmic voltage drop to determine the simulation output voltage of the lithium ion battery.

Description

Technical Field

[0001] This application relates to the field of lithium-ion battery technology, and in particular to a simulation test method and equipment for lithium-ion battery voltage. Background Technology

[0002] As a core energy storage component, the high-precision simulation of the internal state of lithium-ion batteries is crucial for the performance of battery management systems. Currently, equivalent circuit models are commonly used to simulate the external characteristics of batteries, with models combining resistive and capacitive networks being widely used to describe the dynamic voltage response of batteries. However, these models typically treat the open-circuit voltage as a static mapping solely related to the state of charge, failing to accurately reflect the actual impact of the key physical mechanism of lithium-ion diffusion within the electrodes on the open-circuit voltage. This results in generally insufficient simulation accuracy and reliability of battery terminal voltage under complex operating conditions. Summary of the Invention

[0003] Based on the above-mentioned technical problems, this application provides a simulation test method and equipment for lithium-ion battery voltage, aiming to improve the simulation accuracy of the terminal voltage of lithium-ion battery models under complex working conditions.

[0004] In a first aspect, this application provides a simulation test method for lithium-ion battery voltage. The method includes: constructing an equivalent circuit model of the lithium-ion battery, the equivalent circuit model comprising a first part characterizing the lithium-ion diffusion process between the electrode active material core and the shell, and a second part characterizing the dynamic output voltage of the battery; using the first part of the equivalent circuit model, determining a first state parameter characterizing the state of charge of the electrode core and a second state parameter characterizing the state of charge of the electrode shell based on the current input to the lithium-ion battery; using the second part of the equivalent circuit model, determining a first polarization voltage, a second polarization voltage, and an ohmic voltage drop based on the current; constructing a feedforward neural network model, using the first state parameter, the second state parameter, the time series of the current, and the temperature time series of the lithium-ion battery as inputs to the feedforward neural network model, and outputting the simulated open-circuit voltage of the lithium-ion battery through nonlinear mapping of the feedforward neural network model; and performing superposition calculations based on the simulated open-circuit voltage, the first polarization voltage, the second polarization voltage, and the ohmic voltage drop to determine the simulated output voltage of the lithium-ion battery.

[0005] In one possible implementation, an equivalent circuit model of a lithium-ion battery is constructed, including: the first part of the equivalent circuit model describes the effect of lithium-ion diffusion on the open-circuit voltage of the battery by setting two active material domains, namely the core domain and the shell domain; the second part of the equivalent circuit model is composed of a set of ohmic resistors and two sets of parallel resistor-capacitor networks connected in series; the set of ohmic resistors is used to characterize the ohmic internal resistance of the battery; the two sets of parallel resistor-capacitor networks are used to characterize the short-term polarization effect and the long-term polarization effect of the battery, respectively.

[0006] In one possible implementation, the first part of the equivalent circuit model determines a first state parameter characterizing the state of charge of the electrode core and a second state parameter characterizing the state of charge of the electrode shell based on the current input to the lithium-ion battery. This includes: calculating the mass transfer current between the electrode active material core and the shell based on the current; and determining the first state parameter and the second state parameter based on the mass transfer current and the current, wherein the first state parameter is the state of charge of the electrode core and the second state parameter is the state of charge of the electrode shell.

[0007] In one possible implementation, the first polarization voltage, the second polarization voltage, and the ohmic voltage drop are determined based on the current through the second part of the equivalent circuit model. This includes: determining the ohmic voltage drop based on the current and the resistance value of the ohmic resistor; determining the first polarization voltage based on the parameters of the current and the parallel network of the first resistor and capacitor, where the first polarization voltage is used to characterize the short-term polarization effect; and determining the second polarization voltage based on the parameters of the current and the parallel network of the second resistor and capacitor, where the second polarization voltage is used to characterize the long-term polarization effect.

[0008] In one possible implementation, a feedforward neural network model is constructed, comprising: an input layer, at least one hidden layer, and an output layer; the input layer is used to receive an input vector, which includes a first state parameter, a second state parameter, a time series of current, and a temperature time series of the lithium-ion battery; at least one hidden layer is used to perform a nonlinear transformation on the received input; and the output layer is used to output a simulated open-circuit voltage.

[0009] In one possible implementation, the feedforward neural network model is trained using a pre-defined training dataset to learn the weight matrix and bias term parameter set in the nonlinear transformation.

[0010] In one possible implementation, the simulated output voltage of the lithium-ion battery is determined by superimposing the simulated open-circuit voltage, the first polarization voltage, the second polarization voltage, and the ohmic voltage drop. This includes subtracting the first polarization voltage, the second polarization voltage, and the ohmic voltage drop sequentially from the simulated open-circuit voltage, and using the resulting voltage value as the simulated output voltage of the lithium-ion battery.

[0011] The technical solution provided in this application brings at least the following beneficial effects: By combining the ability of the equivalent circuit model to characterize the internal polarization and diffusion process of the battery with the dynamic fitting ability of the feedforward neural network to complex nonlinear relationships, this application effectively overcomes the defect of the traditional model inaccurate description of open circuit voltage under varying operating conditions, thereby significantly improving the overall accuracy, adaptability and physical consistency of the lithium-ion battery terminal voltage simulation model.

[0012] Secondly, this application provides a simulation testing device for lithium-ion battery voltage. The device includes: a first model building unit for building an equivalent circuit model of the lithium-ion battery, the equivalent circuit model comprising a first part characterizing the lithium-ion diffusion process between the electrode active material core and the outer shell, and a second part characterizing the dynamic output voltage of the battery; a first determining unit for determining, based on the first part of the equivalent circuit model and the current input to the lithium-ion battery, a first state parameter characterizing the state of charge of the electrode core and a second state parameter characterizing the state of charge of the electrode outer shell; and a second determining unit for determining, based on the current input to the lithium-ion battery, a first state parameter characterizing the state of charge of the electrode core and a second state parameter characterizing the state of charge of the electrode outer shell; and a second determining unit for determining, based on the first part of the equivalent circuit model... The second part involves determining the first polarization voltage, the second polarization voltage, and the ohmic voltage drop based on the current. A second model building unit is used to construct a feedforward neural network model, taking the first state parameter, the second state parameter, the time series of the current, and the temperature time series of the lithium-ion battery as inputs to the feedforward neural network model. Through the nonlinear mapping of the feedforward neural network model, the simulated open-circuit voltage of the lithium-ion battery is output. A third determination unit is used to perform superposition calculations based on the simulated open-circuit voltage, the first polarization voltage, the second polarization voltage, and the ohmic voltage drop to determine the simulated output voltage of the lithium-ion battery.

[0013] Thirdly, this application provides an electronic device, including: a processor and a memory; the memory stores processor-executable instructions; when the processor is configured to execute the instructions, the electronic device implements the method described in the first aspect above.

[0014] Fourthly, this application provides a computer program product that, when run in an electronic device, causes the electronic device to execute the methods related to the first aspect described above, thereby implementing the methods of the first aspect.

[0015] Fifthly, this application provides a computer-readable storage medium comprising: software instructions; which, when executed in an electronic device, cause the electronic device to implement the method described in the first aspect.

[0016] The beneficial effects of the second to fifth aspects mentioned above can be referred to the first aspect, and will not be repeated here. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1A schematic diagram of the structure of a lithium-ion battery voltage simulation test system provided in an embodiment of this application; Figure 2 A schematic diagram illustrating the composition of the electronic device provided in the embodiments of this application; Figure 3 A schematic flowchart illustrating the simulation test method for lithium-ion battery voltage provided in the embodiments of this application; Figure 4 This is a schematic diagram of a second-order equivalent circuit provided in an embodiment of this application; Figure 5 A schematic diagram of the feedforward neural network model provided in the embodiments of this application; Figure 6 A schematic diagram of the 2RCCD model provided in the embodiments of this application; Figure 7 This is a schematic diagram of the composition of a lithium-ion battery voltage simulation test device provided in an embodiment of this application. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0020] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0021] Furthermore, in the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, in the description of the embodiments of this application, "multiple" refers to two or more.

[0022] Before providing a detailed explanation of the embodiments of this application, some related terms and technologies involved in the embodiments of this application will be introduced first.

[0023] 1. Lithium-ion batteries: These are rechargeable batteries that primarily function by the movement of lithium ions between the positive and negative electrodes. The positive electrode is made of lithium-containing metal oxides, such as lithium cobalt oxide (LiCoO2), lithium iron phosphate (LiFePO4), and lithium nickel cobalt manganese oxide (LiNiCoMnO2, or NMC). The negative electrode is typically made of graphite, but lithium metal, lithium alloys, and silicon-carbon anode materials are also used. Major types include lithium cobalt oxide batteries, lithium manganese oxide batteries, lithium iron phosphate batteries, and ternary lithium batteries.

[0024] 2. Working principle of lithium-ion batteries: During discharge, when the battery is connected to an external device, electrons flow from the negative electrode to the positive electrode through the external circuit. Simultaneously, lithium ions are extracted from the negative electrode material, enter the electrolyte, migrate through the micropores of the separator to the positive electrode side, and embed themselves in the crystal structure of the positive electrode material. They combine with electrons from the external circuit, generating current. During charging, when the battery is connected to an external power source, the power source forces electrons to flow from the positive electrode to the negative electrode through the external circuit. Lithium ions are then extracted from the positive electrode material, enter the electrolyte, migrate through the separator to the negative electrode side, and embed themselves in the layered structure of the negative electrode material.

[0025] 3. Battery behavior: This mainly refers to the changes in key parameters such as voltage, capacity, and temperature exhibited by the battery during charging, discharging, storage, and cycling.

[0026] Lithium-ion batteries have been widely used in recent years; however, traditional technologies still cannot effectively observe battery behavior. The main reason is the inability to build models to describe battery behavior and quantify the internal state of the battery.

[0027] Specifically, as a core energy storage component, the high-precision simulation of the internal state of lithium-ion batteries is crucial for the performance of battery management systems. Currently, equivalent circuit models are commonly used to simulate the external characteristics of batteries, with models combining resistive and capacitive networks being widely used to describe the dynamic voltage response of batteries. However, these models typically treat the open-circuit voltage as a static mapping solely related to the state of charge, failing to accurately reflect the actual impact of the key physical mechanism of lithium-ion diffusion within the electrodes on the open-circuit voltage. This results in generally insufficient simulation accuracy and reliability of battery terminal voltage under complex operating conditions.

[0028] In view of the above problems, this application provides a simulation test method for lithium-ion battery voltage. By combining the ability of the equivalent circuit model to characterize the internal polarization and diffusion processes of the battery with the dynamic fitting ability of the feedforward neural network to complex nonlinear relationships, this method effectively overcomes the shortcomings of traditional models in accurately describing open-circuit voltage under varying operating conditions, thereby significantly improving the overall accuracy, adaptability, and physical consistency of the lithium-ion battery terminal voltage simulation model.

[0029] The simulation test method for lithium-ion battery voltage provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0030] The lithium-ion battery voltage simulation test method provided in this application embodiment can be applied to lithium-ion battery voltage simulation test systems. Figure 1 A schematic diagram of a simulation test system for the voltage of this lithium-ion battery is shown. Figure 1 As shown, the lithium-ion battery voltage simulation test system 10 includes a lithium-ion battery voltage simulation test device 11, a server 12, and multiple electrical instruments 13. The lithium-ion battery voltage simulation test device 11 is connected to the server 12 via a wired or wireless connection, and the server 12 is connected to the multiple electrical instruments 13 via a wired or wireless connection.

[0031] Multiple electrical instruments 13 can be specialized tools used to measure electrical quantities such as current and voltage in a circuit, including ammeters, voltmeters, etc.

[0032] Multiple electrical instruments 13 can store the measured battery data in the server 12.

[0033] The lithium-ion battery voltage simulation test device 11 can be used to obtain battery data from each electrical instrument 13 in the charging server 12 and perform lithium-ion battery voltage simulation tests based on these battery data. The specific geological environment monitoring and risk assessment process can refer to the lithium-ion battery voltage simulation test method described in the following method embodiments, which will not be repeated here.

[0034] The lithium-ion battery voltage simulation testing device 11 can be any electronic device with data processing capabilities. For example, the lithium-ion battery voltage simulation testing device 11 can be a server, a computer, or a server cluster consisting of multiple servers. In some embodiments, the server cluster can also be a distributed cluster. Optionally, the server can be a central server, and the server can also be implemented on a cloud platform. For example, the cloud platform can include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, inter-cloud, and multi-cloud, or any combination thereof. This application embodiment does not limit this.

[0035] It should be noted that the above Figure 1The example described uses a lithium-ion battery voltage simulation test device 11 and a server 12 as independent devices. Optionally, the lithium-ion battery voltage simulation test device 11 and the server 12 can also be combined into one device. For example, the lithium-ion battery voltage simulation test device 11 or its corresponding functions, and the server 12 or its corresponding functions can be integrated into one device. This application does not limit this.

[0036] The execution entity of the lithium-ion battery voltage simulation test method provided in this application embodiment can be the aforementioned lithium-ion battery voltage simulation test device 11. As mentioned above, the lithium-ion battery voltage simulation test device 11 can be an electronic device with data processing capabilities, such as a computer or server. Optionally, the lithium-ion battery voltage simulation test device 11 can also be a processor (e.g., a central processing unit, CPU) in the aforementioned electronic device; or, the lithium-ion battery voltage simulation test device 11 can also be an application (APP) with model training capabilities installed in the aforementioned electronic device; or, the lithium-ion battery voltage simulation test device 11 can also be a functional module with model training capabilities in the aforementioned electronic device, etc. This application embodiment does not impose any limitations on this.

[0037] For simplicity, the following description will use the lithium-ion battery voltage simulation test device 11 as an example of electronic equipment.

[0038] Figure 2 This is a schematic diagram illustrating the composition of an electronic device provided in an embodiment of this application. For example... Figure 2 As shown, the electronic device may include: a processor 20, a memory 21, a communication line 22, a communication interface 23, and an input / output interface 24.

[0039] The processor 20, memory 21, communication interface 23 and input / output interface 24 can be connected via communication line 22.

[0040] Processor 20 is used to execute instructions stored in memory 21 to implement the fault analysis method provided in the following embodiments of this application. Processor 20 may be a CPU, a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller (MCU), a programmable logic device (PLD), or any combination thereof. Processor 20 may also be any other device with processing capabilities, such as a circuit, device, or software module; this application embodiment does not limit this. In one example, processor 20 may include one or more CPUs, for example... Figure 2 CPU0 and CPU1 in the example. As an optional implementation, the electronic device may include multiple processors; for example, in addition to processor 20, it may also include processor 25. Figure 2 (The example shown is a dashed line).

[0041] The memory 21 is used to store instructions. For example, the instructions may be computer programs. Optionally, the memory 21 may be a read-only memory (ROM) or other types of static storage devices that can store static information and / or instructions; it may also be a random access memory (RAM) or other types of dynamic storage devices that can store information and / or instructions; it may also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, etc. The embodiments of this application do not limit this.

[0042] It should be noted that the memory 21 can exist independently of the processor 20, or it can be integrated with the processor 20. The memory 21 can be located inside or outside the electronic device, and this embodiment does not impose any restrictions on this.

[0043] Communication line 22 is used to transmit information between the components included in the electronic device.

[0044] The communication interface 23 is used to communicate with other devices (such as the image acquisition device 100 described above) or other communication networks. These other communication networks can be Ethernet, radio access network (RAN), wireless local area network (WLAN), etc. The communication interface 23 can be a module, circuit, transceiver, or any device capable of enabling communication.

[0045] Input / output interface 24 is used to enable human-computer interaction between the user and the electronic device. For example, it enables action interaction or information exchange between the user and the electronic device.

[0046] For example, the input / output interface 24 can be a mouse, keyboard, display screen, or touch screen. Action interaction or information exchange between the user and the electronic device can be achieved through a mouse, keyboard, display screen, or touch screen.

[0047] It should be noted that, Figure 2 The structures shown do not constitute a limitation on electronic devices, except... Figure 2 In addition to the components shown, electronic devices may include more or fewer components than illustrated, or combinations of certain components, or different component arrangements.

[0048] The following describes the simulation test method for lithium-ion battery voltage provided in the embodiments of this application.

[0049] Figure 3 This is a flowchart illustrating a simulation test method for lithium-ion battery voltage provided in an embodiment of this application. Optionally, this method can be performed by someone with the above-described... Figure 2 The electronic device with the hardware structure shown performs, such as Figure 3 As shown, the method includes S301 to S305.

[0050] S301. Construct an equivalent circuit model of a lithium-ion battery.

[0051] The equivalent circuit model includes a first part characterizing the lithium-ion diffusion process between the electrode active material core and the shell, and a second part characterizing the dynamic output voltage of the battery. For example, the equivalent circuit model can be a second-order equivalent circuit model (2RCCD) that considers the electrode diffusion effect.

[0052] In some embodiments, the first part of the equivalent circuit model describes the effect of lithium-ion diffusion on the battery open-circuit voltage by setting two active material domains: the core domain and the shell domain. The second part of the equivalent circuit model consists of a set of ohmic resistors and two sets of parallel resistor-capacitor networks connected in series. The set of ohmic resistors is used to characterize the battery's internal ohmic resistance. The two sets of parallel resistor-capacitor networks are used to characterize the battery's short-term polarization effect and long-term polarization effect, respectively.

[0053] It should be noted that the second-order equivalent circuit model considering the electrode diffusion effect uses a circuit that integrates the mechanism and simulates electrical components to express the diffusion and charging / discharging processes in the battery.

[0054] S302. Using the first part of the equivalent circuit model, determine the first state parameter for characterizing the state of charge of the electrode core and the second state parameter for characterizing the state of charge of the electrode shell based on the current input to the lithium-ion battery.

[0055] As one possible implementation, embodiments of this application can calculate the mass transfer current between the electrode active material core and the shell based on the current. Furthermore, embodiments of this application can determine a first state parameter and a second state parameter based on the mass transfer current and the current, where the first state parameter is the state of charge of the electrode core, and the second state parameter is the state of charge of the electrode shell.

[0056] like Figure 4 As shown, the second-order equivalent circuit includes two coupled parts. The first (left) part simulates the open-circuit voltage Voc by setting two active material domains, the core domain and the shell domain, to describe the effect of lithium-ion diffusion on the battery open-circuit voltage.

[0057] The battery SOC is given by the following formula:

[0058] Mass transfer current Calculated by the following formula:

[0059] in, The electrode equivalent mass transfer coefficient is... The state of charge of the electrode core. For core power, This refers to the volume fraction of the electrode shell. Electrode (battery) capacity , It refers to the number of lithium ions in the outer casing. , , , This represents the battery current.

[0060] State of Charge (SOC) is a core parameter of the Battery Management System (BMS), used to describe the relative ratio of the battery's current remaining charge to its full capacity. It is usually expressed as a percentage (0%-100%) and serves as the basis for battery charge and discharge control, range prediction, and lifespan management.

[0061] The second part (right) simulates the dynamic output voltage of a battery, determined by a resistor. R 0 and R 2 C 2 pairs R 1 C One pair is connected in series. The state-space equation of the model is given by the following equation: (1) (2) V oc , V 2 and V 1 is an open-circuit electrode. C 2 and C The voltage across 1. Lithium-ion equivalent fraction in the electrode shell region.

[0062]

[0063] Among them, internal resistance R 0 is a constant, A is the first state parameter, and B is the second state parameter.

[0064] S303. Using the second part of the equivalent circuit model, determine the first polarization voltage, the second polarization voltage, and the ohmic voltage drop based on the current.

[0065] As one possible implementation, embodiments of this application can determine the ohmic voltage drop based on the current and the resistance value of the ohmic resistor. Further, embodiments of this application can determine a first polarization voltage based on the parameters of the current and the parallel network of a first resistor and capacitor, where the first polarization voltage characterizes the short-term polarization effect. A second polarization voltage can be determined based on the parameters of the current and the parallel network of a second resistor and capacitor, where the second polarization voltage characterizes the long-term polarization effect.

[0066] S304. Construct a feedforward neural network model, taking the first state parameter, the second state parameter, the time series of the current, and the temperature time series of the lithium-ion battery as inputs to the feedforward neural network model, and outputting the simulated open-circuit voltage of the lithium-ion battery through the nonlinear mapping of the feedforward neural network model.

[0067] The feedforward neural network model (FNN) comprises an input layer, at least one hidden layer, and an output layer. The input layer receives an input vector, which includes first state parameters, second state parameters, a time series of the current, and a time series of the lithium-ion battery's temperature. At least one hidden layer performs a nonlinear transformation on the received input. The output layer outputs a simulated open-circuit voltage. The feedforward neural network model is trained using a pre-defined training dataset to learn the weight matrix and bias parameter set for the nonlinear transformation.

[0068] like Figure 5 As shown, in order to simulate the complex and variable open-circuit voltage of battery electrodes, this application embodiment introduces machine learning techniques. The feedforward neural network model (FNN) is an important type of machine learning method designed to approximate complex functions. Its network structure does not contain recurrent or feedback connections, making it the simplest type of neural network, and it is easy to train and implement.

[0069] Given an unknown function g * , make m Dimensional input x Mapped to n Dimensional output y FNNs aim to approximate this function by defining parameterized mappings. y = g ( x , θ ) and through data Learning parameter set θ The FNN structure consists of three concatenated parts: an input layer, hidden layers, and an output layer. The input layer receives input... x This is then passed to the first hidden layer. The hidden layer performs a non-linear transformation on its input. For example, the first hidden layer will transform... x Enter ( W 1, x + b 1), of which It is a selected non-linear mapping, often referred to as an activation function. W 1 is the weight matrix, and b 1 represents the correction term. Then, the following hidden layers sequentially perform similar nonlinear transformations. Finally, the output layer generates an output value to be matched. y . L A multilayer FNN can be described in a general form:

[0070] z l-1 and z l Yes, the input and output are... Layers. For FNN, θ yes W l and b l set l= 1,2,…, – 1.

[0071] like Figure 6 As shown, the state parameters of the 2RCCD model are... V 2. V 1 and V 0 is used as part of the model output, including the current time series and the battery temperature time series. SOCshell, SOCcore The open-circuit voltage is obtained as the input to the FNN. V oc-sim， S305. Based on the simulated open-circuit voltage, first polarization voltage, second polarization voltage, and ohmic voltage drop, perform superposition calculations to determine the simulated output voltage of the lithium-ion battery.

[0072] As one possible implementation, embodiments of this application can sequentially subtract the first polarization voltage, the second polarization voltage, and the ohmic voltage drop from the simulated open-circuit voltage, and use the resulting voltage value as the simulated output voltage of the lithium-ion battery.

[0073] For example, embodiments of this application can... V 2. V 1, V 0 and V and oc-sim Obtain the simulated battery voltage V sim .

[0074] The technical solution provided in this application brings at least the following beneficial effects: By combining the ability of the equivalent circuit model to characterize the internal polarization and diffusion process of the battery with the dynamic fitting ability of the feedforward neural network to complex nonlinear relationships, this application effectively overcomes the defect of the traditional model inaccurate description of open circuit voltage under varying operating conditions, thereby significantly improving the overall accuracy, adaptability and physical consistency of the lithium-ion battery terminal voltage simulation model.

[0075] The foregoing primarily describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the aforementioned functions, it includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0076] In an exemplary embodiment, this application also provides a simulation testing device for lithium-ion battery voltage. Figure 7 This is a schematic diagram illustrating the composition of a lithium-ion battery voltage simulation testing device provided in an embodiment of this application. Figure 7 As shown, the simulation test device for lithium-ion battery voltage includes: a first model building unit 401, a first determination unit 402, a second determination unit 403, a second model building unit 404, and a third determination unit 405.

[0077] The first model building unit 401 is used to build an equivalent circuit model of a lithium-ion battery. The equivalent circuit model includes a first part for characterizing the lithium-ion diffusion process between the core and the shell of the electrode active material, and a second part for characterizing the dynamic output voltage of the battery. The first determining unit 402 is used to determine, through the first part of the equivalent circuit model, a first state parameter for characterizing the state of charge of the electrode core and a second state parameter for characterizing the state of charge of the electrode shell, based on the current input to the lithium-ion battery. The second determining unit 403 is used to determine the first polarization voltage, the second polarization voltage, and the ohmic voltage drop based on the current through the second part of the equivalent circuit model. The second model building unit 404 is used to build a feedforward neural network model. The first state parameter, the second state parameter, the time series of the current and the temperature time series of the lithium-ion battery are used as inputs to the feedforward neural network model. Through the nonlinear mapping of the feedforward neural network model, the simulated open-circuit voltage of the lithium-ion battery is output. The third determining unit 405 is used to perform superposition calculations based on the simulated open-circuit voltage, the first polarization voltage, the second polarization voltage, and the ohmic voltage drop to determine the simulated output voltage of the lithium-ion battery.

[0078] It should be noted that, Figure 7The module division shown is illustrative and represents only one logical functional division; in actual implementation, other division methods are possible. For example, two or more functions can be integrated into a single processing module. These integrated modules can be implemented in hardware or as software functional units.

[0079] In an exemplary embodiment, this application also provides a computer-readable storage medium including software instructions that, when run on an electronic device, cause the electronic device to perform any of the methods provided in the above embodiments.

[0080] In an exemplary embodiment, this application also provides a computer program product containing computer execution instructions, which, when run on an electronic device, causes the electronic device to perform any of the methods provided in the above embodiments.

[0081] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer-executable instructions. When these computer-executable instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer-executable instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer-executable instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a solid-state disk (SSD), etc.

[0082] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0083] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

[0084] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A simulation test method for lithium-ion battery voltage, characterized in that, include: An equivalent circuit model of a lithium-ion battery is constructed. The equivalent circuit model includes a first part for characterizing the lithium-ion diffusion process between the core and the shell of the electrode active material, and a second part for characterizing the dynamic output voltage of the battery. Based on the first part of the equivalent circuit model, a first state parameter for characterizing the state of charge of the electrode core and a second state parameter for characterizing the state of charge of the electrode shell are determined according to the current input to the lithium-ion battery. The first polarization voltage, the second polarization voltage, and the ohmic voltage drop are determined based on the current through the second part of the equivalent circuit model. A feedforward neural network model is constructed, and the first state parameter, the second state parameter, the time series of the current, and the temperature time series of the lithium-ion battery are used as inputs to the feedforward neural network model. The simulated open-circuit voltage of the lithium-ion battery is output through the nonlinear mapping of the feedforward neural network model. The simulated output voltage of the lithium-ion battery is determined by superimposing the simulated open-circuit voltage, the first polarization voltage, the second polarization voltage, and the ohmic voltage drop.

2. The method according to claim 1, characterized in that, The equivalent circuit model for constructing a lithium-ion battery includes: The first part of the equivalent circuit model describes the effect of lithium-ion diffusion on the battery open-circuit voltage by setting two active material domains: the core domain and the shell domain of the electrode active material. The second part of the equivalent circuit model consists of a set of ohmic resistors and two sets of parallel resistor-capacitor networks connected in series. The set of ohmic resistors is used to characterize the internal ohmic resistance of the battery. The two sets of parallel resistor-capacitor networks are used to characterize the short-term and long-term polarization effects of the battery, respectively.

3. The method according to claim 1, characterized in that, The first part of the equivalent circuit model determines a first state parameter characterizing the state of charge of the electrode core and a second state parameter characterizing the state of charge of the electrode shell based on the current input to the lithium-ion battery, including: Calculate the mass transfer current between the core and shell of the electrode active material based on the current. Based on the mass transfer current and the current, the first state parameter and the second state parameter are determined. The first state parameter is the charge state of the electrode core, and the second state parameter is the charge state of the electrode shell.

4. The method according to claim 1, characterized in that, The second part of the process, which uses the equivalent circuit model to determine the first polarization voltage, the second polarization voltage, and the ohmic voltage drop based on the current, includes: The ohmic voltage drop is determined based on the current and the resistance value of the ohmic resistor. The first polarization voltage is determined based on the parameters of the current and the first resistor-capacitor parallel network. The first polarization voltage is used to characterize the short-term polarization effect. The second polarization voltage is determined based on the parameters of the current and the second resistor-capacitor parallel network. The second polarization voltage is used to characterize the long-term polarization effect.

5. The method according to claim 1, characterized in that, The construction of the feedforward neural network model includes: The feedforward neural network model includes an input layer, at least one hidden layer, and an output layer; The input layer is used to receive an input vector, which includes the first state parameter, the second state parameter, the time series of the current, and the temperature time series of the lithium-ion battery. The at least one hidden layer is used to perform a nonlinear transformation on the received input; The output layer is used to output the simulated open-circuit voltage.

6. The method according to claim 5, characterized in that, The feedforward neural network model is trained using a pre-set training dataset to learn the weight matrix and bias term parameter set in the nonlinear transformation.

7. The method according to claim 1, characterized in that, The step of determining the simulated output voltage of the lithium-ion battery by superimposing the simulated open-circuit voltage, the first polarization voltage, the second polarization voltage, and the ohmic voltage drop includes: The first polarization voltage, the second polarization voltage, and the ohmic voltage drop are subtracted sequentially from the simulated open-circuit voltage, and the resulting voltage value is used as the simulated output voltage of the lithium-ion battery.

8. A simulation testing device for lithium-ion battery voltage, characterized in that, include: The first model building unit is used to build an equivalent circuit model of a lithium-ion battery. The equivalent circuit model includes a first part for characterizing the lithium-ion diffusion process between the core and the shell of the electrode active material, and a second part for characterizing the dynamic output voltage of the battery. The first determining unit is used to determine, through the first part of the equivalent circuit model, a first state parameter for characterizing the state of charge of the electrode core and a second state parameter for characterizing the state of charge of the electrode shell, based on the current input to the lithium-ion battery. The second determining unit is used to determine the first polarization voltage, the second polarization voltage, and the ohmic voltage drop based on the current through the second part of the equivalent circuit model. The second model building unit is used to build a feedforward neural network model. The first state parameter, the second state parameter, the time series of the current, and the temperature time series of the lithium-ion battery are used as inputs to the feedforward neural network model. Through the nonlinear mapping of the feedforward neural network model, the simulated open-circuit voltage of the lithium-ion battery is output. The third determining unit is used to perform superposition calculations based on the simulated open-circuit voltage, the first polarization voltage, the second polarization voltage, and the ohmic voltage drop to determine the simulated output voltage of the lithium-ion battery.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

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