Battery cell simulation test method and device and electronic equipment

Abstract

The invention relates to a battery cell simulation test method and device and electronic equipment, and the method comprises the steps: building a battery cell data model according to the experimental data and historical operation data of a target battery cell in different working stages, mapping the battery cell data model into an equivalent circuit model, and generating a battery cell equivalent circuit model for simulation; a battery cell simulation device is controlled to output voltage, current and charge state changes of a simulation battery cell according to a battery cell equivalent circuit model, output end data are collected in real time in the simulation process, and dynamic correction and parameter adjustment are conducted on a battery cell data model and the equivalent circuit model. Accurate simulation of dynamic behaviors of the target battery cell under different charging and discharging working conditions is realized. According to the invention, data-driven modeling and equivalent circuit modeling can be fused, and a dynamic adjustment and model correction mechanism is introduced in the simulation process, so that the battery cell simulation result can be continuously close to the real state of the target battery cell along with the change of the working condition.

Description

Technical Field

[0001] This application relates to the field of battery cell testing technology, and more specifically to a battery cell simulation testing method, apparatus, electronic device, and computer-readable storage medium. Background Technology

[0002] With the rapid development of new energy vehicles, energy storage systems and related new energy industries, battery systems, as key energy units, have seen their performance, safety and lifespan become core factors restricting the stability of battery systems. The battery cell is the core unit of the battery system, and its characteristics such as voltage, current, state of charge changes and temperature directly affect the control strategy and operational safety of the battery system. Therefore, in the development, design and verification stages of battery systems, it is usually necessary to test, analyze and verify the behavior characteristics of the battery cell under various charge and discharge conditions, environmental conditions and load conditions in order to evaluate its performance and provide a basis for the formulation of control strategies.

[0003] Currently, the testing of battery cell performance still mainly relies on actual battery cell charge-discharge experiments. This involves conducting multiple rounds of charge-discharge tests on real battery cells on test benches or battery packs to obtain operational data under different conditions. While this method can obtain relatively realistic test results, in practical applications, on the one hand, actual battery cell charge-discharge experiments usually require a long testing cycle, making it difficult to meet the needs of rapid product iteration and accelerated R&D pace; on the other hand, the experiments require a large number of battery cells, resulting in high costs, and the testing process has high requirements for the experimental environment and safety protection. Especially in high-power, large-capacity battery pack testing scenarios, safety risks such as overheating and overcurrent are prone to occur, which not only increases experimental costs but also limits the repeated verification of complex and extreme operating conditions in a laboratory environment. Summary of the Invention

[0004] This application provides a cell simulation test method, apparatus, and electronic device that can integrate data-driven modeling and equivalent circuit modeling and introduce dynamic adjustment and model correction mechanisms during the simulation process, so that the cell simulation results can continuously closely approximate the real state of the target cell as the operating conditions change.

[0005] In a first aspect, this application provides a cell simulation testing method, the method comprising: generating multi-dimensional cell feature parameters based on experimental data and historical operating data of the target cell at different operating stages; constructing a cell data model that reflects the dynamic behavior of the cell using machine learning or statistical modeling methods based on the multi-dimensional cell feature parameters; mapping the cell data model to an equivalent circuit model and generating an equivalent circuit model of the cell by setting preset circuit parameters; controlling a cell simulation device to output simulated changes in cell voltage, current, and state of charge based on the equivalent circuit model of the cell, thereby realizing real-time simulation of the target cell under different charging and discharging conditions; wherein, during the real-time simulation process, preset data is collected in real time from the output of the cell simulation device, and the cell data model is dynamically corrected and its parameters are adjusted based on the real-time collected preset data, so that the cell simulation device truly simulates the application characteristics of the target cell.

[0006] In one optional embodiment of the first aspect, the step of generating multi-dimensional cell feature parameters based on experimental data and historical operating data of the target cell at different operating stages, and constructing a cell data model reflecting the dynamic behavior of the cell using machine learning or statistical modeling methods based on the multi-dimensional cell feature parameters, includes: acquiring experimental data and historical operating data of the target cell at different operating stages, wherein the experimental data includes voltage, current, temperature, and state change data of the target cell under different states of charge, and the historical operating data includes voltage, current, temperature, and state change data of the target cell in historical records; after acquiring the experimental data and historical operating data, performing data denoising, outlier removal, time alignment, and normalization processing, and extracting voltage change rate, current response characteristics, and temperature change characteristics to characterize the cell state. The process involves: 1) ...

[0007] In one alternative embodiment of the first aspect, the battery cell simulation device includes: a microprocessor, a digital-to-analog converter circuit, a data acquisition circuit, an operational amplifier buffer, a signal comparator, a signal integrator, a power operational amplifier circuit, a sampling resistor, a communication interface, a Sense interface, and a VBAT power output interface.

[0008] In one optional embodiment of the first aspect, the control cell simulation device outputs simulated cell voltage, current, and state of charge changes based on the cell equivalent circuit model, comprising: controlling a microprocessor to generate corresponding digital control signals based on target voltage, current, and state of charge parameters set in the cell equivalent circuit model, and sending the digital control signals to a digital-to-analog converter circuit; controlling the digital-to-analog converter circuit to convert the digital control signals into continuously changing analog voltage signals and stabilizing and buffering the analog voltage signals via an operational amplifier buffer; controlling a signal comparator to compare the buffered analog voltage signals with the actual output voltage signals acquired by the load end through the Sense interface in real time to obtain a deviation signal between the target output signal and the actual output signal; and controlling a signal integrator to integrate the deviation signal and send the integrated control signal to a power operational amplifier circuit, thereby outputting the corresponding simulated cell voltage and current via the power operational amplifier circuit.

[0009] In one optional embodiment of the first aspect, the step of acquiring preset data from the output of the cell simulation device in real time during the real-time simulation process and dynamically correcting and adjusting the parameters of the cell data model based on the acquired preset data includes: controlling a sampling resistor to acquire analog signals from the output of the simulated cell in real time and processing the acquired analog signals to a preset input range through a signal conditioning circuit, then converting the conditioned analog signals into digital signals and transmitting the digital signals to a microprocessor; controlling the microprocessor to determine the operating condition of the target cell in the current control cycle based on the analog signals and obtaining predicted data matching the operating condition from the cell data model; and controlling the microprocessor to compare the digital signals with the predicted data item by item and calculate the corresponding output voltage deviation. The microprocessor is controlled to determine whether the target output parameters calculated by the cell equivalent circuit model in the current control cycle deviate from a preset threshold based on the deviation values. If so, it is determined that the target output parameters deviate from the actual behavior of the target cell, and a dynamic correction and parameter adjustment process is performed. If not, it is determined that the target output parameters of the cell equivalent circuit model are within a reasonable range. The cell data model is corrected according to the deviation values, and the model parameters, feature mapping relationship, and / or model weight are corrected. The corrected cell data model is then remapped to the cell equivalent circuit model, and the relevant circuit parameters in the cell equivalent circuit model are updated synchronously. The changes in voltage, current, and state of charge of the simulated cell are then recalculated, and the output parameters of the cell simulation device are updated.

[0010] In one alternative embodiment of the first aspect, during the dynamic correction process, the method further includes: determining whether the dynamic correction of the cell data model and the cell equivalent circuit model has achieved a preset effect based on a preset error threshold and a convergence criterion; if so, the microprocessor stops adjusting the parameters of the cell data model and the cell equivalent circuit model and applies the current parameters as stable parameters to subsequent control cycles; if not, the microprocessor continues to execute the dynamic correction process based on the error evaluation index, and incrementally adjusts the parameters until the convergence criterion is met or the preset number of corrections is reached.

[0011] In one alternative embodiment of the first aspect, during the real-time simulation process, the method further includes: setting target state-of-charge parameters and balancing strategy parameters for multiple cell simulation devices, wherein the balancing strategy parameters include one or more of active balancing parameters or passive balancing parameters; collecting the output voltage, current, and state-of-charge parameters of the multiple cell simulation devices and calculating the state difference between different cell simulation devices; controlling the multiple cell simulation devices to adjust their output voltage or output current during the simulation process according to the balancing strategy parameters, thereby creating a voltage difference or charge difference between different cell simulation devices; wherein during the balancing control process, the changes in voltage, current, and state of charge of the multiple cell simulation devices are continuously monitored and the balancing strategy parameters are dynamically adjusted to simulate the balancing process between cells in a real battery pack.

[0012] In one alternative embodiment of the first aspect, during the real-time simulation process, the method further includes: setting overvoltage, overcurrent, short circuit, and overtemperature thresholds in the cell simulation device and monitoring the output terminal status in real time through a data acquisition circuit; when the output voltage, current, or temperature parameter is detected to exceed the preset threshold, a protection control command is triggered to stop the output or enter a controlled load reduction state, and at the same time, the abnormal status information is transmitted to the target terminal.

[0013] Secondly, this application provides a battery cell simulation testing device, comprising: a simulation terminal, which transmits setting parameter instructions and transmits real-time parameter data of each individual battery cell in the battery cell simulation device; a battery cell simulation device, comprising a microprocessor, a digital-to-analog converter circuit, a data acquisition circuit, an operational amplifier buffer, a signal comparator, a signal integrator, a power operational amplifier circuit, a sampling resistor, a communication interface, a Sense interface, and a VBAT power output interface; and a control unit, used to execute the battery cell simulation testing method according to any one of the first aspects, comprising: a data processing module, used to generate multi-dimensional battery cell characteristic parameters based on experimental data and historical operating data of the target battery cell at different operating stages; and a model building module, used to... The multidimensional cell feature parameters are constructed using machine learning or statistical modeling methods to create a cell data model that reflects the dynamic behavior of the cell. This cell data model is then mapped to an equivalent circuit model, and preset circuit parameters are set to generate an equivalent circuit model. A simulation control module controls the cell simulation device to output simulated cell voltage, current, and state of charge changes based on the equivalent circuit model, thereby achieving real-time simulation of the target cell under different charging and discharging conditions. A dynamic correction module is used to collect preset data from the output of the cell simulation device in real-time during the simulation process and dynamically correct and adjust the parameters of the cell data model based on the collected preset data.

[0014] Thirdly, this application provides an electronic device, characterized in that it comprises: a processor adapted to execute a computer program; and a computer-readable storage medium storing a computer program, wherein when the computer program is executed by the processor, it implements the method according to any one of the first aspects.

[0015] Fourthly, this application provides a computer-readable storage medium, characterized in that it is used to store a computer program that, when the computer program is run on a computer, causes the computer to perform the method according to any one of the first aspects.

[0016] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this application. Attached Figure Description

[0017] The accompanying drawings, which are incorporated herein and form part of this specification, illustrate one or more embodiments of the present application and, together with the description, serve to explain the principles of the present application and to enable those skilled in the art to make and use the present application.

[0018] Figure 1 This is a schematic structural diagram of an exemplary battery cell simulation testing apparatus according to some embodiments of this application.

[0019] Figure 2 This is a schematic structural diagram of an exemplary battery cell simulation device according to some embodiments of this application.

[0020] Figure 3 This is a circuit diagram of an exemplary signal conditioning circuit according to some embodiments of this application.

[0021] Figure 4 This is a schematic flowchart of an exemplary cell simulation testing method according to some embodiments of this application.

[0022] Figure 5 This is a schematic flowchart of an exemplary method for simulating changes in cell voltage, current, and state of charge according to some embodiments of this application.

[0023] Figure 6 This is a flowchart illustrating an exemplary dynamic correction and parameter adjustment method according to some embodiments of this application.

[0024] Figure 7 This is a flowchart illustrating an exemplary method for equalization control of multiple battery cell simulation devices according to some embodiments of this application.

[0025] Figure 8 This is a schematic flowchart of an exemplary cell simulation device anomaly handling method according to some embodiments of this application.

[0026] Figure 9 This is a schematic block diagram of an exemplary control unit according to some embodiments of this application.

[0027] Figure 10 This is a schematic structural diagram of an exemplary electronic device according to some embodiments of this application. Detailed Implementation

[0028] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments may be implemented in various forms and should not be construed as limited to the examples set forth herein; rather, the description of these embodiments is intended to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to provide a deeper understanding of embodiments of this application.

[0029] To facilitate understanding of the technical solutions provided in this application, the relevant terms are explained below.

[0030] It should be noted that the terminology used in the implementation section of this application is only for explaining the embodiments of this application and is not intended to limit this application.

[0031] For example, the term "and / or" in this article simply describes the relationship between related objects, indicating that three relationships can exist. For instance, A and / or B can represent: A alone, A and B simultaneously, and B alone. The term "at least one" simply describes the combination relationship of listed objects, indicating that one or more can exist. For instance, at least one of the following: A, B, C can represent the following combinations: A alone, B alone, C alone, A and B simultaneously, A and C simultaneously, B and C simultaneously, and A, B, and C simultaneously. The term "multiple" refers to two or more. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0032] For example, the term "correspondence" can indicate a direct or indirect correspondence between two things, or an association between them, or a relationship of instruction and being instructed, configuration and being configured, etc. The term "instruction" can be direct, indirect, or indicate an association. For example, A instructing B can mean A directly instructs B, for example, B can be obtained through A; it can also mean A indirectly instructs B, for example, A instructs C, B can be obtained through C; or it can mean an association between A and B. The terms "predefined" or "preconfigured" can refer to pre-stored codes, tables, or other relevant information that can be used for instruction in the device, or it can refer to something agreed upon by a protocol. "Protocol" can refer to standard protocols in the field. The term "when..." can be interpreted as "if," "when," or "in response," etc. Similarly, depending on the context, the phrases "if determined" or "if detected (the condition or event of the statement)" can be interpreted as "when determined" or "in response to determined" or "when detected (the condition or event of the statement)" or "in response to detected (the condition or event of the statement)" and similar descriptions. The terms "first," "second," "third," "fourth," "A," "B," etc., are used to distinguish different objects, not to describe a specific order. The terms "includes" and "has," and any variations thereof, are intended to cover non-exclusive inclusion.

[0033] Figure 1 A schematic structural diagram of an exemplary cell simulation testing apparatus according to some embodiments of this application is shown.

[0034] refer to Figure 1 As shown. The battery cell simulation test device 100 of this application includes at least a simulation terminal 101, a communication HUB 104, a battery cell simulation device 102, and a control unit 103, wherein the components are connected by wired or wireless means to form an overall device for performing battery cell state simulation tests.

[0035] The simulation terminal 101 is used to transmit setting parameter instructions related to cell simulation to the control unit 103 or the cell simulation device 102, and to receive real-time parameter data of each individual cell transmitted by the cell simulation device 102, and display them in the form of curves, tables or numerical values. It can be one or more of a smartphone, host computer, industrial control computer, tablet computer, laptop computer or terminal device with data processing capabilities.

[0036] The communication hub 104 uses routing to assign ID addresses, enabling data communication and management between the analog terminal 101 and multiple battery cell simulation devices 102, and buffering transmitted data. Specifically, the communication hub 104 assigns a unique ID address to each connected battery cell simulation device 102 via routing, thus distinguishing the individual battery cells corresponding to different simulation devices 102. It forwards setting parameter commands issued by the analog terminal 101 and buffers and organizes real-time parameter data transmitted by the battery cell simulation devices 102 before sending it to the analog terminal 101, thereby avoiding data conflicts or loss caused by simultaneous communication from multiple devices. The communication hub 104 supports one or more communication protocols, including but not limited to CAN, RS485, Ethernet, or serial communication, to achieve stable communication.

[0037] Figure 2 A schematic structural diagram of an exemplary cell simulation device according to some embodiments of this application is shown.

[0038] The cell simulation device 102 is used to simulate and output the voltage, current, and electrical characteristics of a single cell. (Reference) Figure 2 As shown, the battery cell simulation device 102 includes a power supply section 102a, a data acquisition circuit 102b, a microprocessor 102c, a digital-to-analog converter circuit 102d, an operational amplifier buffer 102e, a signal comparator 102f, a signal integrator 102g, a power operational amplifier circuit 102h, a sampling resistor 102i, a communication interface 102j, a Sense interface 102k, and a VBAT power output interface 102L. Among them:

[0039] The power supply section 102a is used to manage the power supply of various functional modules inside the battery cell simulation device 102. It takes the externally input DC24V power supply and performs multi-stage voltage conversion through the isolated power supply module to provide different isolated and stable operating voltages to the acquisition circuit 102b, microprocessor 102c, digital-to-analog conversion circuit 102d, operational amplifier and power drive circuit, etc.

[0040] The acquisition circuit 102b is used to acquire the voltage and current signals of the analog output terminal of the battery cell in real time. The acquired analog signal is filtered, amplified and range matched by the signal conditioning circuit, and the processed analog signal is converted into a digital signal and transmitted to the microprocessor 102c for subsequent calculation and status judgment.

[0041] Figure 3 A circuit diagram of an exemplary signal conditioning circuit according to some embodiments of this application is shown.

[0042] For example, refer to Figure 3As shown, the connection relationship of the signal conditioning circuit in this application is described as follows: Input ports TS- and TS+ are connected to R1 and R4 respectively; the other end of R1 is connected to U1-2, and the other end of R4 is connected to pin U1-3; both ends of C3 are connected to U1-2 and U1-3 respectively; U1-4 is connected to the -9V power supply; U1-5 is connected to GND, and U1-7 is connected to the +9V power supply; R2 is connected to U1-6, and the other end of R2 is connected to U2-2; U2-3 is connected to the input port DAC_VO, U2-4 is connected to -9V, and U2-5 is connected to GND. Connect U2-6 to R3, and U2-7 to the +9V power supply. Connect R3 to U2-6, and the other end to U3A-2. Connect U3A-1 to the output port VoltageIn. Connect U3A-3 to GND. Connect U3A-8 to C2. Connect U3A-4 to C4. Connect C1 to U3A-2, and the other end to R5. Connect R5 to C1, and the other end to U3A-1. Connect C2 to U3A-8 and +9V, and the other end to GND. Connect C4 to U3A-4 and -9V, and the other end to GND.

[0043] The microprocessor 102c is used to receive setting parameter instructions from the analog terminal 101 or the control unit 103 and perform logical control and calculation processing on the cell simulation process according to the setting parameters; at the same time, it has a certain length of running data storage function, and the microprocessor 102c is also used to monitor the output status in real time and control the output to shut down and generate a corresponding warning signal when abnormal conditions such as short circuit, overcurrent, overvoltage or overtemperature are detected to reach a set threshold.

[0044] The digital-to-analog converter circuit 102d is used to convert the digital control signal output by the microprocessor 102c into a corresponding analog voltage signal and output the analog voltage signal to the operational amplifier buffer 102e.

[0045] The operational amplifier buffer 102e is used to buffer and stabilize the output analog voltage signal, improve the signal's driving capability and anti-interference capability, and transmit the processed signal to the signal comparator 102f.

[0046] The signal comparator 102f receives the signal from the operational amplifier buffer 102e and the load voltage signal from the Sense interface 102k, respectively, compares the voltage difference between the two, and outputs the comparison result to the signal integrator 102g.

[0047] The signal integrator 102g is used to integrate the voltage difference output by the signal comparator 102f and adjust it in conjunction with the set value of the digital-to-analog converter circuit 102d to achieve stable control of the output voltage and output the integrated signal to the power operational amplifier circuit 102h.

[0048] The power operational amplifier circuit 102h receives the output signal of the signal integrator 102g and outputs the corresponding simulated cell VBAT voltage and current values ​​according to the control signal, so as to realize the simulation of the working state of the real cell.

[0049] The sampling resistor 102i accurately samples the output current by detecting the voltage difference across the resistor and outputs the sampling result to the acquisition circuit 102b.

[0050] The communication interface 102j is used to realize data communication between the cell simulation device 102 and the communication HUB 104, the control unit 103 or the smart terminal, so as to complete functions such as parameter configuration, operation status monitoring and data transmission.

[0051] The Sense interface 102k is used to connect the positive and negative voltage signals at the load end, enabling the detection of the analog cell terminal voltage and providing feedback signals to the signal comparator 102f.

[0052] The VBAT power output interface 102L serves as a cell analog output terminal, used to output analog cell voltage and current signals.

[0053] The control unit 103 is connected to the simulation terminal 101 and the cell simulation device 102 respectively. It is used to receive the test control commands issued by the simulation terminal 101, parse the test control commands and send them to the corresponding cell simulation device 102, manage the operation status of the cell simulation device 102 in a unified manner, and execute each step in the cell simulation test method during the test process.

[0054] Some embodiments of this application provide a cell simulation testing method that enables the cell simulation device 102 to continuously and dynamically simulate the electrochemical characteristics and working state of the target cell in real application scenarios.

[0055] Figure 4 A schematic diagram of an exemplary cell simulation testing method according to some embodiments of this application is shown. This cell simulation testing method can be interactively executed by a simulation terminal 101, a control unit 103, and a cell simulation device 102. The simulation terminal 101 can be one or more of a smartphone, desktop computer, tablet computer, and laptop computer.

[0056] refer to Figure 4 As shown. This cell simulation test method includes at least the following steps 201 to 204.

[0057] 201: Based on experimental data and historical operating data of the target cell at different working stages, generate multi-dimensional cell feature parameters and, based on these multi-dimensional cell feature parameters, use machine learning methods or statistical modeling methods to construct a cell data model that can reflect the dynamic behavior of the cell.

[0058] Specifically, in step 201, the experimental data is obtained through controlled experimental testing of the target battery cell. The controlled experiment includes at least one or more of the following: constant current charging stage, constant voltage charging stage, constant current discharging stage, resting stage, charging and discharging stage at different rates, and charging and discharging stage under different ambient temperature conditions. During the experiment, the target battery cell is sampled in real time by a battery cell testing device. The sampled experimental data includes at least the battery cell terminal voltage data, battery cell charging and discharging current data, battery cell surface or internal temperature data, battery cell state of charge change data, and battery cell operating status identification data. Furthermore, the experimental data is collected according to a sampling period preset by the tester. This sampling period includes, but is not limited to, milliseconds, seconds, or minutes to ensure that the dynamic characteristics of the battery cell under different operating stages can be reflected.

[0059] The historical operating data comes from the historical data of the target cell during actual use. This historical operating data can be obtained by the background monitoring. The historical operating data includes at least historical cell terminal voltage data, historical charging and discharging current data, historical temperature change data, historical state of charge change data, and timestamp information related to the cell's operating status. By introducing historical operating data, the constructed cell data model can not only reflect the cell characteristics under experimental conditions, but also characterize the cell's behavior in real operating scenarios.

[0060] After acquiring experimental and historical operational data, data denoising, outlier removal, time alignment, and normalization are performed. Data denoising involves filtering the collected voltage, current, and temperature data to eliminate sampling noise. This filtering includes, but is not limited to, moving average filtering, low-pass filtering, or median filtering. Outlier removal involves eliminating data that significantly deviates from the normal operating range based on pre-set thresholds or statistical rules. This outlier includes abnormal information caused by sampling interruptions, sensor distortion, or instantaneous impacts. Time alignment aligns the timestamps of data from different sampling sources to ensure data consistency across the same time dimension. Normalization normalizes or standardizes the processed data to eliminate the influence of different units and numerical ranges on subsequent modeling processes.

[0061] Therefore, after data preprocessing, multi-dimensional cell characteristic parameters for characterizing the cell's operating state are extracted from the processed data. These multi-dimensional cell characteristic parameters include at least the cell terminal voltage change rate characteristics, charge and discharge current response characteristics, temperature change rate characteristics, cell state of charge characteristics, cell internal resistance change characteristics, and state characteristics under different operating stages. These multi-dimensional cell characteristic parameters can be obtained by calculating the data of voltage, current, and temperature changes over time and combining features according to the time window preset by the tester to form features that can reflect the dynamic behavior of the cell.

[0062] After generating multidimensional cell feature parameters, these parameters are used as model inputs, and the output voltage, current changes, and state of charge changes of the target cell are used as model outputs. A cell data model is constructed using machine learning or statistical modeling methods. The machine learning methods include, but are not limited to, regression models, support vector machine models, neural network models, or time series-based prediction models. The statistical modeling methods can employ parametric regression models. During the training process of the cell data model, the acquired experimental data and historical operating data are divided into training datasets and validation datasets. The parameters of the cell data model are optimized through iterative training, enabling the cell data model to characterize the dynamic behavior of the target cell at different operating stages.

[0063] 202: Map the cell data model to the equivalent circuit model and generate the cell equivalent circuit model by setting preset circuit parameters.

[0064] Specifically, in step 201, the equivalent circuit model of the battery cell is described using an equivalent circuit structure, which includes at least one or more of the following: a voltage source characterizing the open-circuit voltage characteristics of the battery cell, a series resistor characterizing the ohmic internal resistance characteristics of the battery cell, and a parallel resistor and capacitor network characterizing the polarization characteristics of the battery cell.

[0065] After determining the structure of the equivalent circuit model of the battery cell, a correspondence is established between the circuit parameters in the battery cell data model and the battery cell equivalent circuit model based on the dynamic characteristics reflected in the battery cell data model. Specifically, the voltage value of the voltage source in the battery cell equivalent circuit model is determined based on the steady-state voltage characteristics output in the battery cell data model; the ohmic internal resistance parameter in the battery cell equivalent circuit model is determined based on the transient voltage drop characteristics reflected in the battery cell data model; and the resistance and capacitance values ​​in the parallel resistor and capacitor branches are determined based on the voltage recovery or decay characteristics over time reflected in the battery cell data model. Thus, the battery cell equivalent circuit model of this application can reflect the electrical behavior of the target battery cell under different operating conditions.

[0066] Based on the characteristic parameters output by the cell data model, the initial settings of each circuit parameter in the cell equivalent circuit model are performed. The initial settings include at least determining the initial voltage value of the voltage source in the cell equivalent circuit model based on the correspondence between the state of charge and voltage output by the cell data model; determining the initial value of the series resistance in the cell equivalent circuit model based on the voltage change characteristics of the cell under sudden current change conditions; and determining the initial values ​​of the parallel resistance and the resistance and capacitance in the capacitor branch based on the dynamic response characteristics of the voltage change with time during the charging and discharging process of the cell.

[0067] After initial setting of circuit parameters, the circuit parameters of the cell equivalent circuit model are solved and calibrated to ensure consistency between the cell equivalent circuit model and the actual cell behavior. Specifically, the output voltage of the cell equivalent circuit model under the same input current condition is compared with the output result of the cell data model. The difference between the two is calculated, and the circuit parameters are adjusted using parameter optimization methods, including but not limited to least squares method, gradient optimization method, or recursive parameter identification method. Through multiple iterative calculations, the output result of the cell equivalent circuit model gradually approaches the output result of the cell data model.

[0068] After the circuit parameters are calibrated, the generated cell equivalent circuit model is verified. This verification includes at least voltage response verification under different charge / discharge rates, dynamic response verification within different state of charge ranges, and model stability verification under different temperature conditions. Therefore, if the verification results do not meet the preset error range, the circuit parameters in the cell equivalent circuit model are readjusted until the output error between the cell equivalent circuit model and the cell data model meets the requirements preset by the tester, thereby generating a cell equivalent circuit model for real-time simulation.

[0069] In some examples of this application, the equivalent circuit model can be a first-order or multi-order RC equivalent circuit model. If a first-order RC equivalent circuit model is adopted, it includes at least one parallel resistor and capacitor branch. If a multi-order RC equivalent circuit model is adopted, it includes multiple parallel resistor and capacitor branches, thereby characterizing the dynamic response characteristics of the target cell at different time scales.

[0070] 203: The control cell simulation device 102 outputs simulated cell voltage, current and state of charge changes according to the cell equivalent circuit model, thereby realizing real-time simulation of the target cell under different charging and discharging conditions.

[0071] Specifically, in step 203, the control cell simulation device 102 performs cell state calculation and simulation output according to the cell equivalent circuit model generated in step 202, within each control cycle preset by the tester. The control cycle is in the millisecond or sub-millisecond range.

[0072] Figure 5 A schematic flowchart illustrating an exemplary method for simulating changes in cell voltage, current, and state of charge according to some embodiments of this application is shown. (Reference) Figure 2 As shown, the battery cell simulation device 102 includes at least a microprocessor 102c, a digital-to-analog converter circuit 102d, a data acquisition circuit 102b, an operational amplifier buffer 102e, a signal comparator 102f, a signal integrator 102g, a power operational amplifier circuit 102h, a sampling resistor 102i, a communication interface 102j, a Sense interface 102k, and a VBAT power output interface 102L.

[0073] refer to Figure 5 As shown. In step 203, the control cell simulation device 102 outputs simulated cell voltage, current and state of charge changes according to the cell equivalent circuit model, including at least the following steps 203a to 203d.

[0074] 203a: The control microprocessor 102c performs discretization calculations on the equivalent circuit model of the battery cell using the control cycle set above. That is, in each control cycle, the microprocessor 102c calculates the target output parameters of the target battery cell in the current control cycle based on the battery cell state parameters of the previous control cycle and the current charging and discharging conditions. The target output parameters include at least the target battery cell output voltage value, the target charging and discharging current value, and the target state of charge parameter. The target output voltage value is jointly determined by the state variables of the voltage source, resistor, and capacitor branches in the equivalent circuit model of the battery cell. The target state of charge parameter is updated based on the current integration result to reflect the real-time charge change of the battery cell.

[0075] After completing the real-time state calculation, the microprocessor 102c generates a corresponding digital control signal based on the calculated target cell output voltage, current and state of charge parameters. The digital control signal includes at least a voltage control signal representing the target output voltage amplitude and a current control signal representing the target output current limit or direction. Then, the microprocessor 102c sends the generated digital control signal to the digital-to-analog converter circuit 102d.

[0076] 203b: After the digital control signal is sent to the digital-to-analog converter circuit 102d, the control circuit 102d converts the received digital control signal into a continuously changing analog voltage signal. Then, the generated analog voltage signal is buffered and stabilized by the operational amplifier buffer 102e to reduce the signal source impedance and improve the driving capability, thereby ensuring the stable transmission of the analog signal in the subsequent circuit.

[0077] 203c: The buffered analog voltage signal is input to the signal comparator 102f, which controls the signal comparator 102f to compare it with the actual output voltage signal acquired from the load end through the Sense interface 102k in real time. Based on the comparison result, the deviation signal between the target output signal and the actual output signal is obtained. The deviation signal is used to characterize the degree of deviation between the current output state and the calculation result of the equivalent circuit model.

[0078] 203d: The control signal integrator 102g integrates the deviation signal to eliminate steady-state error and improve output accuracy. Then, the integrated control signal is sent to the power operational amplifier circuit 102h. The power operational amplifier circuit 102h outputs the analog cell voltage and current corresponding to the amplitude and direction, so that the output result gradually approaches the target output parameters calculated by the equivalent circuit model.

[0079] 204: During the real-time simulation, preset data from the output of the cell simulation device 102 is collected in real time, and the cell data model is dynamically corrected and its parameters are adjusted based on the real-time collected preset data, so that the cell simulation device 102 can dynamically simulate the characteristic state of the target cell in a real application scenario.

[0080] Specifically, in step 204, based on the simulated output described in step 203, the cell data model and the cell equivalent circuit model are dynamically corrected by constructing a real-time feedback and correction mechanism.

[0081] Figure 6 A flowchart illustrating an exemplary dynamic correction and parameter adjustment method according to some embodiments of this application is shown.

[0082] refer to Figure 6 As shown. In step 204, during the real-time simulation, preset data from the output of the cell simulation device is collected in real time, and the cell data model is dynamically corrected and its parameters are adjusted based on the collected preset data, including at least the following steps 204a to 204f.

[0083] 204a: During the process of outputting the analog cell voltage and current in the power operational amplifier circuit 102h, the current signal at the output terminal of the analog cell is acquired in real time by controlling the sampling resistor 102i set in the output circuit. At the same time, the voltage signal at the output terminal of the analog cell is acquired in real time through the Sense interface 102k. The acquired analog signal is then processed by the signal conditioning circuit, which includes at least amplification, filtering and limiting units, so as to adjust the acquired analog signal to the preset input range that the acquisition circuit 102b can recognize. The conditioned analog signal is then converted into the corresponding digital signal by the acquisition circuit 102b and transmitted to the microprocessor 102c according to the sampling period preset by the tester.

[0084] 204b: The control microprocessor 102c performs real-time analysis on the received digital signals to determine the operating conditions of the target cell within the current control cycle, and then obtains prediction data matching the operating conditions from the cell data model; wherein, the operating conditions include at least the operating parameters of the current charge / discharge state, the current charge / discharge current range, the current state of charge range, and the current temperature range; the prediction data includes at least the predicted cell output voltage, the predicted output current, and the predicted state of charge change.

[0085] 204c: The control microprocessor 102c compares the digital signal with the predicted data item by item and calculates the corresponding output voltage deviation value, output current deviation value and state of charge deviation value. The deviation value can be calculated using absolute deviation, relative deviation or average deviation within a preset time window, so as to reduce the impact of instantaneous fluctuations on the correction process.

[0086] 204d: The control microprocessor 102c determines whether the target output parameters calculated by the cell equivalent circuit model in the current control cycle deviate from the threshold preset by the tester based on the calculated deviation value. If any deviation value exceeds the corresponding threshold, it is determined that the target output parameters deviate from the actual behavior of the target cell, and a dynamic correction process is executed. When all deviation values ​​do not deviate from the threshold preset by the tester, it is determined that the target output parameters of the cell equivalent circuit model are within a reasonable range. The target output parameters include at least the equivalent internal resistance parameter, the equivalent capacitance parameter, the open circuit voltage parameter, and the temperature correction parameter.

[0087] 204e: During the dynamic correction process, one or more of the following adjustments are made to the cell data model based on the deviation value: model parameter correction, feature mapping relationship correction, and model weight correction. The model parameter correction involves incrementally adjusting the internal resistance-related parameters, polarization parameters, or open-circuit voltage-related parameters in the cell data model based on the magnitude and trend of the deviation value. The feature mapping relationship correction involves locally correcting the mapping relationship between the multi-dimensional cell feature parameters and the cell data model output to enhance the cell data model's adaptability to the current operating conditions. The model weight correction involves fine-tuning the weight parameters related to the current operating conditions without changing the overall structure of the cell data model to reduce prediction errors. This correction is an online parameter-level correction, rather than retraining the cell data model.

[0088] 204f: After completing the correction of the cell data model, the corrected cell data model is remapped to the cell equivalent circuit model and the relevant circuit parameters in the cell equivalent circuit model are updated synchronously. Then, based on the updated cell data model parameters and cell equivalent circuit model parameters, the changes in simulated cell voltage, current and state of charge are recalculated and the output parameters of cell simulation device 102 are updated.

[0089] After updating the output parameters of the cell simulation device 102, the real-time data acquisition steps are re-executed and the above-mentioned working condition matching, deviation calculation, parameter determination and model correction processes are repeated. When the deviation value is less than the threshold preset by the tester in multiple consecutive control cycles, the current dynamic correction process is terminated and the current parameters are kept for subsequent cell simulation processes.

[0090] Therefore, through the above control and state update process, the cell simulation device 102 can continuously output simulated cell voltage, current and state of charge changes that conform to the characteristics of the cell equivalent circuit model under different charge and discharge rates, different state of charge ranges and different load conditions, thereby realizing real-time simulation of the target cell under different charge and discharge conditions.

[0091] In some embodiments of this application, in order to ensure the correction effect of the cell simulation device 102, an error threshold and a convergence criterion are preset to determine whether the dynamic correction of the cell data model and the cell equivalent circuit model has achieved the expected effect.

[0092] Specifically, the error threshold includes at least one or more of the following: voltage error threshold, current error threshold, and state of charge error threshold. The voltage error threshold is set based on the deviation between the simulated cell output voltage and the target voltage calculated from the cell equivalent circuit model. The voltage error threshold can be set as an absolute error threshold or a relative error threshold. The current error threshold is set based on the deviation between the simulated cell output current and the target current calculated from the cell equivalent circuit model. The state of charge error threshold is set based on the difference between the real-time calculated state of charge parameters and the model-predicted state of charge parameters.

[0093] In actual implementation, the error threshold can be pre-set and stored in the microprocessor 102c according to the rated voltage, rated capacity, rated current of the target battery cell and the target application scenario, and used as the judgment benchmark in the dynamic correction process.

[0094] Specifically, the convergence criteria include one or more of the following: error amplitude convergence criteria, error rate of change convergence criteria, and maximum iteration count criteria. The error amplitude convergence criteria is used to determine that the cell data model and the cell equivalent circuit model have reached convergence when the error evaluation indicators of the simulated cell output voltage, current, and state of charge are all less than the corresponding error threshold in multiple consecutive control cycles. The error rate of change convergence criteria is used to determine that the dynamic correction process tends to be stable when the rate of change of the error evaluation indicators is less than the preset rate of change threshold in multiple consecutive control cycles. The maximum iteration count criteria is used to terminate the current correction process and keep the most recently stable model parameters as the model parameters for the current control cycle when the dynamic correction has been executed up to the preset maximum number of corrections.

[0095] Therefore, when any of the above convergence criteria are met, the microprocessor 102c stops further adjusting the parameters of the cell data model and the cell equivalent circuit model and applies the current model parameters as stable parameters to subsequent control cycles.

[0096] When the above convergence criteria are not met, the microprocessor 102c continues to execute the dynamic correction process based on the magnitude and trend of the error evaluation index, and makes incremental adjustments to the model parameters until the convergence criteria are met or the number of corrections preset by the tester is reached.

[0097] In some embodiments of this application, when updating the parameters of the battery cell equivalent circuit model, a progressive parameter update method is adopted so that the updated battery cell equivalent circuit model parameters take effect gradually over multiple control cycles, in order to avoid abnormalities caused by sudden changes in the output voltage or current of the simulated battery cell.

[0098] Specifically, the microprocessor 102c decomposes the parameter changes generated by model correction into multiple sub-update quantities and applies them gradually to the equivalent circuit model of the battery cell over multiple consecutive control cycles, so that the updated resistance, capacitance, and open-circuit voltage parameters gradually approach the target values. During this gradual update process, the microprocessor 102c monitors the rate of change of the simulated battery cell output voltage and current in real time. When the rate of change of the output exceeds the smoothing threshold preset by the tester, it automatically reduces the parameter update step size or suspends the update operation. In this way, while realizing adaptive correction of the dynamic behavior of the battery cell, it avoids discontinuous changes in the simulated output due to abrupt changes in model parameters, thereby ensuring that the battery cell simulation device 102 is controllable at the hardware output level.

[0099] In some embodiments of this application, a time constraint mechanism may also be introduced during the dynamic correction and parameter adjustment process.

[0100] Specifically, before determining whether to trigger dynamic correction, the microprocessor 102c performs a multi-control-cycle joint evaluation of the output voltage deviation, output current deviation, and state-of-charge deviation. Only when the deviation value continuously meets the correction triggering conditions preset by the tester for multiple consecutive control cycles and the direction of deviation change remains consistent within the consecutive control cycles will the parameter correction of the cell data model and the cell equivalent circuit model be triggered. When the deviation value fluctuates abnormally only in a single control cycle or for a short period of time, the microprocessor 102c does not perform dynamic correction and maintains the current model parameters unchanged. In this way, the dynamic correction process keeps the time scale consistent with the actual physical behavior changes of the cell, thereby avoiding miscorrection caused by sampling noise, transient disturbances, or rapid load changes.

[0101] Figure 7 A flowchart illustrating an exemplary method for equalization control of multiple battery cell simulation devices according to some embodiments of this application is shown.

[0102] refer to Figure 7 As shown. In some embodiments of this application, during real-time simulation, the method (specifically step 204) further includes at least the following steps 205a to 205d to simulate the balancing behavior of multiple cells in a real battery pack under different states of charge.

[0103] 205a: Set target state of charge parameters and equalization strategy parameters for multiple cell simulation devices 102 respectively. The target state of charge parameters are used to characterize the expected state of charge value of each cell simulation device 102 under the current simulation conditions. The equalization strategy parameters include at least one or more of active equalization parameters or passive equalization parameters.

[0104] Specifically, the active balancing parameters include at least the balancing trigger threshold, balancing current amplitude, balancing time parameter, and balancing priority parameter; the passive balancing parameters include at least the balancing start threshold, voltage release amplitude, and equivalent dissipation power parameter; the target state of charge parameter and the balancing strategy parameter are both issued by the analog terminal 101 and stored in the microprocessor 102c of each cell simulation device 102 as the basic parameters for subsequent balancing control.

[0105] 205b: During the real-time simulation, the simulation terminal 101 collects the output voltage, current and state of charge parameters of each cell simulation device 102 in real time and calculates the state difference between different cell simulation devices 102 based on the collection results.

[0106] Specifically, the state difference includes at least the voltage difference, the state of charge difference, and the equivalent charge difference. When any state difference exceeds the equalization trigger threshold preset by the tester, it is determined that the equalization control process needs to be executed.

[0107] 205c: After determining that equalization control needs to be performed, the analog terminal 101 controls each cell simulation device 102 to adjust its output voltage or output current during the simulation process according to the equalization strategy parameters, so as to form a voltage difference or power difference between different cell simulation devices 102 under human control.

[0108] Specifically, according to the active balancing strategy, the output current direction or amplitude is adjusted by controlling the corresponding cell simulation device 102 to simulate an energy transfer-type balancing process; according to the passive balancing strategy, the output voltage of the corresponding cell simulation device 102 is reduced or its output current is limited to simulate an energy dissipation-type balancing process. These adjustments are achieved by dynamically adjusting the equivalent internal resistance parameters, current limiting parameters, or target voltage parameters in the cell's equivalent circuit model.

[0109] 205d: During the equalization control process, the analog terminal 101 continuously monitors the output voltage, current and state of charge parameters of each cell simulation device 102 and dynamically adjusts the equalization strategy parameters according to the real-time monitored voltage, current and state of charge change parameters.

[0110] Specifically, the dynamic adjustment includes at least incremental or decremental adjustment of the equalization current amplitude, extension or shortening of the equalization duration, and switching or reordering of the cell simulation devices 102 participating in the equalization, so that the voltage difference or state of charge difference between multiple cell simulation devices 102 gradually decreases, thereby simulating the equalization process between cells in a real battery pack.

[0111] When the state difference between each cell simulation device 102 is less than the equalization termination threshold preset by the tester for multiple consecutive control cycles, the equalization process is determined to be complete, the equalization control is stopped, and each cell simulation device 102 resumes normal simulation output according to its respective target state of charge parameters and cell equivalent circuit model.

[0112] In some embodiments of this application, when multiple cell simulation devices 102 perform equalization simulation, different initial parameters or aging correction parameters are configured for different cell simulation devices 102 to simulate the impact of cell differences in a real battery pack on the equalization process.

[0113] Figure 8 A flowchart illustrating an exemplary method for handling abnormalities in a battery cell simulation device according to some embodiments of this application is shown.

[0114] refer to Figure 8 As shown. In some embodiments of this application, during real-time simulation, the method (specifically step 204) further includes at least the following steps 206a to 206d, thereby improving the safety of the cell simulation device 102 during long-term testing.

[0115] 206a: Overvoltage, overcurrent, short circuit and overtemperature thresholds are set in the cell simulation device 102 and the output status is monitored in real time through the acquisition circuit 102b.

[0116] Specifically, the overvoltage threshold is used to limit the maximum allowable value of the simulated cell output voltage, the overcurrent threshold is used to limit the maximum allowable value of the simulated cell output current, the short-circuit determination threshold is used to determine whether a short circuit has occurred at the output terminal based on the sudden change amplitude of the output current or the voltage drop characteristics, and the overtemperature threshold is used to limit the maximum allowable operating temperature inside or at the output terminal of the cell simulation device 102. These protection thresholds can be issued by the simulation terminal 101 or preset and stored in the microprocessor 102c of the cell simulation device 102 by the tester during testing.

[0117] Therefore, in the real-time simulation process, at least one temperature sensor is set in the cell simulation device 102. The acquisition circuit 102b monitors the output voltage, current and temperature parameters in real time through the sampling resistor 102i, the Sense interface and the temperature sensor, and transmits the acquisition results to the microprocessor 102c according to the preset sampling period.

[0118] 206b: After receiving the monitoring data transmitted by the acquisition circuit 102b, the microprocessor 102c judges the output voltage, current and temperature parameters. When it is determined that the output voltage, current and temperature parameters exceed the preset threshold, the cell simulation device 102 is determined to be in an abnormal state.

[0119] Specifically, the preset thresholds include any one of the following: output voltage greater than overvoltage threshold, output current greater than overcurrent threshold, monitored temperature greater than overtemperature threshold, and output voltage rapidly decreasing and output current suddenly increasing within a short period of time, satisfying any one of the short circuit determination thresholds.

[0120] In actual implementation, to avoid false triggering caused by momentary interference, anomaly determination can be confirmed based on the judgment results of multiple consecutive sampling periods. These multiple sampling periods are set by the testers according to actual needs.

[0121] 206c: When the cell simulation device 102 is determined to be in an abnormal state, the microprocessor 102c immediately triggers the protection control command corresponding to the abnormal state.

[0122] Specifically, the protection control command includes at least a stop output command, a controlled load reduction command, and a current and voltage limiting command. The stop output command controls the power operational amplifier circuit 102h to shut down its output, thereby cutting off the voltage and current output of the simulated battery cell. The controlled load reduction command gradually reduces the output current or output voltage without immediately cutting off the output, causing the battery cell simulation device 102 to enter a controlled load reduction state. The current and voltage limiting command sets dynamic limits on the output current or output voltage to prevent further escalation of the abnormality. This protection control command is issued by the microprocessor 102c to the power operational amplifier circuit 102h or related circuit modules for execution.

[0123] 206d: While executing protection control instructions, the microprocessor 102c transmits the current abnormal status information to the analog terminal 101.

[0124] Specifically, the abnormal status information includes at least the abnormality type, the time of the abnormality, the output voltage, current and temperature parameters at the time of the abnormality, and the currently executed protection control command, thereby enabling testers to monitor the operating status of the cell simulation device 102 in real time.

[0125] In actual implementation, after the abnormal state is resolved, the microprocessor 102c determines whether to allow the cell simulation device 102 to re-enter the normal simulation process according to the recovery strategy preset by the tester. The recovery strategy includes at least allowing the output to be restarted after the abnormal parameters have recovered to the safe threshold range and remained stable for a preset period of time; allowing the cell simulation device 102 to re-enter the simulation state after the tester issues a recovery command through the simulation terminal 101; and before resuming the output, the microprocessor 102c performs state reset or parameter verification on the cell data model and the cell equivalent circuit model.

[0126] In some embodiments of this application, during real-time simulation, when the output voltage, output current, or temperature parameter is detected to exceed the protection threshold preset by the tester and a protection control command is triggered, the microprocessor 102c synchronously enters the model freeze state.

[0127] Specifically, in this model-frozen state, parameter updates for the cell data model and the cell equivalent circuit model are paused, and the model parameters that most recently met the stability requirements are used as a temporary output reference.

[0128] After the abnormal state is resolved and the output parameters are restored to a safe range and remain stable for a period of time until the recovery time is set by the tester, the microprocessor 102c gradually releases the model from the frozen state according to the recovery strategy set by the tester and limits the update range of model parameters in the early stage of recovery, so that the dynamic correction process is restarted under controlled conditions; thereby ensuring hardware safety when the cell simulation device 102 switches between abnormal and normal operating conditions.

[0129] The preferred embodiments of this application have been described in detail above with reference to the accompanying drawings. However, this application is not limited to the specific details of the embodiments described above. Within the scope of the technical concept of this application, various simple modifications can be made to the technical solutions of this application, and these simple modifications all fall within the protection scope of this application. For example, the various specific technical features described in the specific embodiments described above can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this application will not describe the various possible combinations separately. Furthermore, various different embodiments of this application can also be arbitrarily combined, as long as they do not violate the spirit of this application, they should also be considered as the content disclosed in this application.

[0130] It should also be understood that, in the various method embodiments of this application, the order of the processes mentioned above does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0131] thus, Figure 9 A schematic block diagram of an exemplary control unit 103 according to some embodiments of this application is shown.

[0132] like Figure 9 As shown, the control unit 103 may include: a data acquisition module 103a, used to generate multi-dimensional battery cell characteristic parameters based on experimental data and historical operating data of the target battery cell at different operating stages; a model building module 103b, used to construct a battery cell data model that reflects the dynamic behavior of the battery cell using machine learning or statistical modeling methods based on the multi-dimensional battery cell characteristic parameters; map the battery cell data model to an equivalent circuit model and generate an equivalent circuit model of the battery cell by setting preset circuit parameters; a simulation control module 103c, used to control the battery cell simulation device 102 to output simulated changes in battery cell voltage, current and state of charge based on the equivalent circuit model of the battery cell, thereby realizing real-time simulation of the target battery cell under different charging and discharging conditions; and a dynamic correction module 103d, used to collect preset data from the output of the battery cell simulation device 102 in real time during the real-time simulation process and dynamically correct and adjust the parameters of the battery cell data model based on the real-time collected preset data.

[0133] It should be understood that the device embodiments and method embodiments can correspond to each other, and similar descriptions can be referred to the method embodiments. To avoid repetition, they will not be repeated here. Specifically, the cell simulation testing device 100 can correspond to the corresponding subject in the cell simulation testing method of the embodiments of this application, and each unit in the cell simulation testing device 100 is for implementing the corresponding process in the cell simulation testing method. For the sake of brevity, they will not be repeated here.

[0134] It should also be understood that the various units in the cell simulation testing device 100 involved in the embodiments of this application are based on logical functional division. In practical applications, the function of one unit can also be implemented by multiple units, or the function of multiple units can be implemented by one unit. Furthermore, these functions can also be implemented with the assistance of one or more other units. For example, some or all of the cells in the cell simulation testing device 100 can be merged into one or more additional units. Furthermore, some units(s) in the cell simulation testing device 100 can be further divided into multiple functionally smaller units, which can achieve the same operation without affecting the technical effects of the embodiments of this application. Moreover, the cell simulation testing device 100 may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented collaboratively by multiple units.

[0135] It should also be understood that the terms "module" or "unit" used in the embodiments of this application refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0136] For example, the cell simulation testing device 100 according to the embodiments of this application, and the method of the embodiments of this application, can be constructed and implemented by running a computer program (including program code) capable of executing the steps involved in the corresponding method on a general-purpose computing device including processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable storage medium and loaded into an electronic device through the computer-readable storage medium. The computer program is used to implement the corresponding method of the embodiments of this application. In other words, the units mentioned above can be implemented in hardware, in software instructions, or in a combination of hardware and software. Specifically, the steps of the method embodiments in the embodiments of this application can be completed by the integrated logic circuits of the hardware in the processor and / or in software instructions. The steps of the method disclosed in the embodiments of this application can be directly embodied as being executed by a hardware decoding processor, or by a combination of hardware and software in the decoding processor. Optionally, the software can be located in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The software in the memory can be run by the processor to perform the steps described in the method embodiments above.

[0137] Figure 10 A schematic structural diagram of an exemplary electronic device 300 according to some embodiments of this application is shown.

[0138] like Figure 10 As shown, the electronic device 300 includes at least a processor 310 and a computer-readable storage medium 320. The processor 310 and the computer-readable storage medium 320 can be connected via a bus or other means. The computer-readable storage medium 320 stores a computer program 321, which includes computer instructions. The processor 310 executes the computer instructions stored in the computer-readable storage medium 320. The processor 310 is the computing and control core of the electronic device 300, and is suitable for implementing one or more computer instructions, specifically for loading and executing one or more computer instructions to achieve a corresponding method flow or function.

[0139] As an example, processor 310 may also be referred to as a central processing unit (CPU). Processor 310 may include, but is not limited to: general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete component gate or transistor logic devices, discrete hardware components, etc.

[0140] As an example, the computer-readable storage medium 320 may be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device; optionally, it may also be at least one computer-readable storage medium located remotely from the aforementioned processor 310. Specifically, the computer-readable storage medium 320 includes, but is not limited to, volatile memory and / or non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0141] like Figure 10 As shown, the electronic device 300 may also include a transceiver 330.

[0142] The processor 310 can control the transceiver 330 to communicate with other devices; specifically, it can send information or data to other devices or receive information or data sent by other devices. The transceiver 330 may include a transmitter and a receiver. The transceiver 330 may further include antennas, and the number of antennas may be one or more.

[0143] It should be understood that the various components in the electronic device 300 are connected via a bus system, which includes a data bus, a power bus, a control bus, and a status signal bus. It is worth noting that the electronic device 300 can be any type of electronic device with data processing capabilities; the computer-readable storage medium 320 stores first computer instructions; the processor 310 loads and executes the first computer instructions stored in the computer-readable storage medium 320 to achieve… Figure 2 The corresponding steps in the method embodiment shown; in a specific implementation, the first computer instruction in the computer-readable storage medium 320 is loaded by the processor 310 and the corresponding steps are executed. To avoid repetition, they will not be described again here.

[0144] According to another aspect of this application, embodiments of this application provide a chip. This chip can be an integrated circuit chip with signal processing capabilities, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The chip can also be referred to as a system-on-a-chip (SoC), system-on-a-chip (SoC), chip system, or system-on-chip, etc. This chip can be applied to various electronic devices capable of mounting chips, enabling the device with the chip mounted to execute the corresponding steps in the methods or logic block diagrams disclosed in the embodiments of this application. For example, the chip may be suitable for implementing one or more computer instructions, specifically suitable for loading and executing one or more computer instructions to achieve a corresponding method flow or corresponding function.

[0145] According to another aspect of this application, embodiments of this application provide a computer-readable storage medium (Memory). This computer-readable storage medium is a computer's memory device used to store programs and data. It is understood that the computer-readable storage medium here may include both built-in storage media within the computer and, of course, extended storage media supported by the computer. The computer-readable storage medium provides storage space that stores the operating system of an electronic device. This storage space contains computer instructions suitable for loading and execution by a processor. When these computer instructions are read and executed by the processor of the computer device, they cause the computer device to perform the corresponding steps in the methods or logic diagrams disclosed in the embodiments of this application.

[0146] According to another aspect of this application, embodiments of this application provide a computer program product or computer program. The computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform corresponding steps in the methods or logic block diagrams disclosed in the embodiments of this application. In other words, when the solutions provided in this application are implemented using software, they can be implemented in whole or in part as a computer program product or computer program. The computer program product or computer program includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes of the embodiments of this application are run or the functions of the embodiments of this application are implemented.

[0147] It is worth noting that the computer involved in this application can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions involved in this application can be stored in a computer-readable storage medium, or can be transmitted from one computer-readable storage medium to another. For example, the computer 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.

[0148] Those skilled in the art will recognize that the units and process steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. In other words, those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of protection of this application.

[0149] Finally, it should be noted that the above content is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology 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. For example, the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. Furthermore, various different embodiments of this application can also be arbitrarily combined, as long as they do not violate the basic idea of ​​this application, and they should also be considered as the content disclosed in this application.

[0150] List of reference numerals in the attached diagram:

[0151] 100. Cell simulation testing device

[0152] 101. Analog Terminal

[0153] 102. Battery cell simulation equipment

[0154] 102a. Power Supply Section

[0155] 102b. Acquisition Circuit

[0156] 102c, microprocessor

[0157] 102d, digital-to-analog converter circuit

[0158] 102e, Op-amp Buffer

[0159] 102f, Signal comparator

[0160] 102g, signal integrator

[0161] 102h, power operational amplifier circuit

[0162] 102i, sampling resistor

[0163] 102j, Communication Interface

[0164] 102k, Sense interface

[0165] 102l, VBAT power output interface

[0166] 103. Control Unit

[0167] 103a. Data Acquisition Module

[0168] 103b, Model Building Module

[0169] 103c, Analog Control Module

[0170] 103d, Dynamic Correction Module

[0171] 104. Communication HUB

[0172] 300. Electronic equipment

[0173] 310. Processor

[0174] 320. Computer-readable storage media

[0175] 321. Computer Programs

[0176] 330. Transceiver.

Claims

1. A cell simulation test method integrating regulation function, characterized in that, The method includes: Based on experimental data and historical operating data of the target battery cell at different working stages, multi-dimensional battery cell feature parameters are generated, and based on the multi-dimensional battery cell feature parameters, a battery cell data model that can reflect the dynamic behavior of the battery cell is constructed using machine learning methods or statistical modeling methods. The battery cell data model is mapped to the equivalent circuit model, and the battery cell equivalent circuit model is generated by setting preset circuit parameters. The control cell simulation equipment outputs simulated changes in cell voltage, current, and state of charge based on the cell's equivalent circuit model, thereby achieving real-time simulation of the target cell under different charging and discharging conditions; wherein... During the real-time simulation, preset data is collected from the output of the cell simulation device in real time, and the cell data model is dynamically corrected and its parameters are adjusted based on the real-time collected preset data, so that the cell simulation device can realistically simulate the application characteristics of the target cell.

2. The method according to claim 1, characterized in that, The process of generating multi-dimensional cell feature parameters based on experimental data and historical operating data of the target cell at different operating stages, and constructing a cell data model that reflects the dynamic behavior of the cell using machine learning or statistical modeling methods based on the multi-dimensional cell feature parameters, includes: Acquire experimental data and historical operating data of the target battery cell at different operating stages. The experimental data includes voltage, current, temperature and state change data of the target battery cell under different states of charge. The historical operating data includes voltage, current, temperature and state change data of the target battery cell in historical records. After acquiring the experimental data and historical operating data, data denoising, abnormal data removal, time alignment and normalization are performed, and multi-dimensional feature parameters for characterizing the cell state, such as voltage change rate, current response characteristics, temperature change characteristics, state of charge and internal resistance change, are extracted. Based on the multidimensional cell feature parameters, a cell data model is constructed using machine learning or statistical modeling methods. The cell data model is trained by using the multidimensional cell feature parameters as model input and the changes in the target cell's output voltage, current, and state of charge as model output. The step of mapping the cell data model to an equivalent circuit model and generating a cell equivalent circuit model by setting preset circuit parameters includes: Based on the preset characteristics reflected in the cell data model, the circuit parameters of the resistor, capacitor and voltage source in the equivalent circuit model are determined, and then a cell equivalent circuit model for real-time simulation is constructed.

3. The method according to claim 1 or 2, characterized in that, The battery cell simulation device includes: a microprocessor, a digital-to-analog converter circuit, a data acquisition circuit, an operational amplifier buffer, a signal comparator, a signal integrator, a power operational amplifier circuit, a sampling resistor, a communication interface, a Sense interface, and a VBAT power output interface.

4. The method according to claim 3, characterized in that, The control cell simulation device outputs simulated cell voltage, current, and state of charge changes based on the cell equivalent circuit model, including: The control microprocessor generates corresponding digital control signals based on the target voltage, current and state of charge parameters set in the equivalent circuit model of the battery cell, and sends the digital control signals to the digital-to-analog converter circuit. The digital-to-analog converter circuit is controlled to convert the digital control signal into a continuously changing analog voltage signal and the analog voltage signal is stabilized and buffered by an operational amplifier buffer. The control signal comparator compares the buffered analog voltage signal with the actual output voltage signal acquired by the load through the Sense interface in real time to obtain the deviation signal between the target output signal and the actual output signal; and, The control signal integrator integrates the deviation signal and sends the integrated control signal to the power operational amplifier circuit, which then outputs the corresponding analog cell voltage and current.

5. The method according to claim 4, characterized in that, During the real-time simulation, preset data from the output of the cell simulation device is collected in real time, and the cell data model is dynamically corrected and its parameters adjusted based on the collected preset data, including: The sampling resistor is controlled to acquire the analog signal at the output terminal of the analog cell in real time, and the acquired analog signal is processed by the signal conditioning circuit to the preset input range. Then, the conditioned analog signal is converted into a digital signal and the digital signal is transmitted to the microprocessor. The microprocessor is controlled to determine the operating conditions of the target cell in the current control cycle based on the analog signal and to obtain predictive data matching the operating conditions from the cell data model. The microprocessor controls the digital signal to compare the predicted data item by item and calculate the corresponding output voltage deviation value, output current deviation value and state of charge deviation value. The microprocessor controls whether the target output parameters calculated by the cell equivalent circuit model in the current control cycle deviate from the preset threshold based on the deviation value. If so, it determines that the target output parameters deviate from the actual behavior of the target cell and performs dynamic correction and parameter adjustment. If not, it determines that the target output parameters of the cell equivalent circuit model are within a reasonable range. Based on the deviation value, the cell data model is adjusted by modifying model parameters, feature mapping relationships, and / or model weights; and... The corrected cell data model is remapped into the cell equivalent circuit model, and the relevant circuit parameters in the cell equivalent circuit model are updated synchronously. Then, the changes in the simulated cell voltage, current and state of charge are recalculated, and the output parameters of the cell simulation device are updated.

6. The method according to claim 5, characterized in that, During the real-time simulation process, the method further includes: Based on the preset error threshold and convergence criterion, determine whether the dynamic correction of the cell data model and the cell equivalent circuit model has achieved the preset effect. If so, the microprocessor will stop adjusting the parameters of the cell data model and the cell equivalent circuit model and apply the current parameters as stable parameters to subsequent control cycles; If not, the microprocessor continues to execute the dynamic correction process based on the error evaluation index, making incremental adjustments to the parameters until the convergence criterion is met or the preset number of corrections is reached.

7. The method according to claim 1 or 5, characterized in that, During the real-time simulation process, the method further includes: Set target state of charge parameters and balancing strategy parameters for multiple cell simulation devices, wherein the balancing strategy parameters include one or more of active balancing parameters or passive balancing parameters; The output voltage, current, and state of charge parameters of the multiple cell simulation devices are collected, and the state difference between different cell simulation devices is calculated. Based on the balancing strategy parameters, the multiple cell simulation devices are controlled to adjust their output voltage or output current during the simulation process, thereby creating a voltage difference or power difference between different cell simulation devices; wherein... During the equalization control process, the voltage, current, and state of charge changes of the multiple cell simulation devices are continuously monitored and the equalization strategy parameters are dynamically adjusted to simulate the equalization process between cells in a real battery pack.

8. The method according to claim 1 or 5, characterized in that, During the real-time simulation process, the method further includes: Overvoltage, overcurrent, short circuit, and overtemperature thresholds are set in the battery cell simulation equipment, and the output status is monitored in real time through a data acquisition circuit; and... When the output voltage, current or temperature parameters are detected to exceed the preset threshold, a protection control command is triggered to stop the output or enter a controlled load reduction state, and at the same time the abnormal status information is transmitted to the target terminal.

9. A cell simulation testing device integrating regulation function, characterized in that, include: The simulation terminal transmits setting parameter commands and real-time parameter data of each individual cell in the cell simulation device. The battery cell simulation device includes a microprocessor, a digital-to-analog converter circuit, a data acquisition circuit, an operational amplifier buffer, a signal comparator, a signal integrator, a power operational amplifier circuit, a sampling resistor, a communication interface, a Sense interface, and a VBAT power output interface. as well as A control unit, configured to control and execute the cell simulation test method according to any one of claims 1-9, comprising: The data processing module is used to generate multi-dimensional cell characteristic parameters based on experimental data and historical operating data of the target cell at different working stages. The model building module is used to construct a cell data model that reflects the dynamic behavior of the cell based on the multi-dimensional cell feature parameters using machine learning or statistical modeling methods; and to map the cell data model to an equivalent circuit model and generate an equivalent circuit model of the cell by setting preset circuit parameters. The simulation control module is used to control the cell simulation equipment to output simulated cell voltage, current, and state of charge changes based on the cell equivalent circuit model, thereby achieving real-time simulation of the target cell under different charging and discharging conditions; and, The dynamic correction module is used to collect preset data from the output of the cell simulation device in real time during the real-time simulation process and to dynamically correct and adjust the parameters of the cell data model based on the collected preset data.

10. An electronic device, characterized in that, include: Processor, adapted to execute computer programs; and, A computer-readable storage medium storing a computer program that, when executed by the processor, implements the method of any one of claims 1 to 8.

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