Lithium ion battery charging and discharging metering calibration system

Abstract

The invention discloses a lithium ion battery charge and discharge metering calibration system, which comprises a control guide circuit used for guiding voltage, current and temperature signals of a charging pile to an electric energy metering module and guiding a communication signal of the charging pile to a communication module; the electric energy metering module is used for transmitting voltage and current signals to the metering circuit; the power of the charging pile is transmitted to the high-speed processing module and the bidirectional power module; the bidirectional power module is controlled by the high-speed processing module through the communication module, generates real-time power exchange with the charging pile, and carries out real charging and discharging circulation on the lithium ion battery; the high-speed processing module is used for predicting an aging track of the battery in real time by using the lithium ion battery aging track prediction model, taking electrical and aging parameters of a standard battery as a to-be-detected battery, and controlling the bidirectional power module to perform power absorption according to a virtual battery model so as to perform electric energy metering on the charging pile; rapid and accurate metering of charging and discharging electric energy of the lithium ion battery is realized.

Description

Technical Field

[0001] The invention relates to the technical field of electric energy measurement and calibration, and in particular to a lithium-ion battery charge and discharge measurement and calibration system. Background Art

[0002] With the strong support of the state for the development of the electric vehicle industry, the current domestic electric vehicle charging infrastructure is also developing rapidly, but the battery swap market is relatively chaotic. Multiple market participants such as charging piles, battery swap stations, battery owners, and battery users have not yet established a mutual trust mechanism for lithium-ion battery power. The main reason is that there is no consensus on the lithium-ion battery power metering method, and the lithium-ion battery power metering standard device has not yet been perfected. In addition, the distributed energy storage power trading mode is becoming more and more flexible, such as the shared electricity saving model, the "rent-to-sell" model, and the community energy storage model, and the demand for the accuracy of lithium-ion battery power metering is becoming more and more urgent.

[0003] At present, there is still a blank in the lithium-ion battery charging and discharging metering and calibration equipment. Only some scholars have analyzed the sources of measurement uncertainty of lithium-ion battery electric energy according to the requirements of JJF 1059.1-2012 "Evaluation and Expression of Measurement Uncertainty", but there is no research on how to calculate the uncertainty and how to calibrate the lithium-ion battery charging and discharging electric energy metering equipment.

[0004] Based on the above situation, it is urgent to carry out research on lithium-ion battery charging and discharging measurement and calibration technology and demonstration applications, develop standards and calibration technology for lithium-ion battery electric energy measurement, establish a lithium-ion battery measurement standard system, and promote high-quality and low-carbon development of new energy vehicles and distributed energy storage. Summary of the invention

[0005] In view of the above technical problems, the present invention provides a lithium-ion battery charge and discharge measurement and calibration system, comprising: a control and guidance circuit, an electric energy measurement module, a bidirectional power module, a high-speed processing module, a communication module, a display module and a power module;

[0006] A control guide circuit is connected to the charging pile and is used to guide the voltage, current and temperature signals of the charging pile to the electric energy metering module and guide the communication signal of the charging pile to the communication module;

[0007] The electric energy metering module is used to transmit the voltage and current signals of the charging pile to the metering circuit through the analog switch; and transmit the power of the charging pile to the high-speed processing module and the bidirectional power module;

[0008] The bidirectional power module is used to receive the power signal transmitted by the electric energy metering module, and is controlled by the high-speed processing module through the communication module to generate real-time power exchange with the charging pile, so as to perform a real charge and discharge cycle on the lithium-ion battery;

[0009] The high-speed processing module controls the electric energy metering module and the bidirectional power module through the communication module, runs the virtual battery module, and uses the transfer learning strategy to establish a lithium-ion battery aging trajectory prediction model; uses the lithium-ion battery aging trajectory prediction model to predict the battery aging trajectory in real time, uses the electrical and aging parameters of the standard battery as the battery to be tested, and controls the bidirectional power module to absorb power according to the virtual battery model to perform electric energy metering on the charging pile;

[0010] The communication module is connected with the high-speed processing module, the electric energy metering module and the bidirectional power module to realize signal interaction between the high-speed processing module and the electric energy metering module and the bidirectional power module;

[0011] The display module is connected to the high-speed processing module and is used to realize the data display function.

[0012] The power module is connected to a three-phase power supply to supply power to the lithium-ion battery charge and discharge measurement and calibration system.

[0013] Furthermore, the electric energy metering module includes: a sampling circuit, an analog switch, a metering circuit, and a power supply circuit;

[0014] The charging pile voltage and current signals collected by the sampling circuit are transmitted to the metering circuit through the analog switch;

[0015] The power of the charging pile is transmitted to the high-speed processor module and the bidirectional power module;

[0016] The power circuit is connected to the power module to supply power to the above circuit.

[0017] Furthermore, the electric energy metering module has a DC voltage measurement range of 1-1000V, a measurement accuracy of ±0.012%RD+0.008%RG (1V≤U≤1000V), a short-term stability of 0.005% / min, and a measurement resolution of 0.001%RG;

[0018] The DC current measurement range is 600A, the measurement accuracy is ±0.012%RD+0.008%RG (5A≤I≤600A), the short-term stability can reach 0.005% / min, and the measurement resolution is 0.001%RG.

[0019] Furthermore, the bidirectional power module includes an input / output EMI circuit, which ensures that the power supply will not interfere with the power grid or power-consuming equipment through input and output EMI filtering, thereby ensuring the stability of the test; the two dual-channel DSPs in the bidirectional power module ensure that the input filtering, PFC correction, power conversion, and output filtering are controlled by their own independent DSPs.

[0020] Furthermore, the bidirectional power module, which works in two quadrants, can be used as a source to perform a charging test on the battery, and can also be used as a load to perform a discharging test on the battery.

[0021] Furthermore, the bidirectional power module has an adjustable output voltage range of 50V-950V or 10-450V, a rated output of 300V-100V, 50A-60A; and a rated power of 6KW-15Kw.

[0022] Furthermore, the virtual battery module is run and a transfer learning strategy is used to establish a lithium-ion battery aging trajectory prediction model, including:

[0023] A basic empirical aging model is constructed based on the battery electrical characteristics. The basic empirical aging model is used to simulate the capacity aging trajectory of different types of batteries. There are two exponential functions relative to the number of cycles, which are expressed as

[0024]

[0025] Where [θ1, θ2, θ3, θ4] are model parameters, x represents the number of cycles, and C is the battery capacity. The least squares method is used to estimate the parameters [θ1, θ2, θ3, θ4];

[0026] Migrating the basic empirical aging model to the constructed lithium-ion battery aging trajectory prediction model;

[0027] The input variables of the basic empirical aging model correspond to the input variables of the lithium-ion battery aging trajectory prediction model in the following manner:

[0028] x old =ρ0x new +λ0 (10)

[0029] Among them, [ρ0,λ0] are the parameters to be identified;

[0030] The predicted response of the basic empirical aging model is transferred to the lithium-ion battery aging trajectory prediction model in the following manner:

[0031] C new =ρ1C old +λ1+ε (11)

[0032] Among them, [ρ1,λ1] is the parameter to be identified, ε~N(0,σ 2 ) is the experimental error; Combining formulas (1)-(3), the transfer formula from the basic empirical aging model to the lithium-ion battery aging trajectory prediction model is obtained as follows:

[0033]

[0034] Furthermore, it also includes:

[0035] If the lower and upper bounds of the input variables are given, the update process of the parameter estimates can be expressed as

[0036]

[0037] [ρ0,λ0] is calculated by the following formula;

[0038]

[0039] Among them, x old,max is the upper limit of the input variables of the basic empirical aging model, x old,min is the lower limit of the input variable of the basic empirical aging model, x new,max is the upper limit of the input variables of the lithium-ion battery aging trajectory prediction model, x new,min It is the lower limit of the input variables of the lithium-ion battery aging trajectory prediction model;

[0040] The transfer parameters [ρ0, λ0] are calculated. If [ρ0, λ0] are known, the lithium-ion battery aging trajectory prediction model is given by the linear transformation of the basic empirical aging model. The likelihood function is defined by the adaptive Gaussian error, which is used for the difference between the prediction of the lithium-ion battery aging trajectory prediction model and the data observation, and can be expressed as

[0041]

[0042] Finally, make predictions and quantify uncertainty calculations. After estimating the transmission parameters, calculate the untried x * The predicted response at and its density:

[0043]

[0044] Furthermore, the high-speed processing module is equipped with a 2.8GHz quad-core Intel Xeon E3-1515M V5 processor, which can reach 3.7GHz per core after multiplication, dual-channel DDR4 RAM4-2133, 8GB, a standard 512G solid-state drive, 2 USB 3.0 ports, 4 USB 2.0 ports, 2 10 / 100 / 1000BASE-TX Gigabit Ethernet ports, and a 2.5G fiber optic interface compatible with the AURORA protocol.

[0045] Furthermore, the display module is also used for: battery simulation model control to achieve automatic power-on, charging, discharging, power-off, and cycle test control;

[0046] Displays simulation voltage accuracy, monomer series connection, input capacity, initial SOC, SOH, polarization capacitance, polarization resistance, and ambient temperature parameters.

[0047] A lithium-ion battery charge and discharge measurement and calibration system provided by the present invention utilizes the characteristic that batteries have similar degradation mechanisms when operating under similar conditions, combines influencing quantities such as temperature and electrical parameters, establishes a lithium-ion battery aging trajectory prediction model, uses the lithium-ion battery aging trajectory prediction model to predict the battery aging trajectory in real time, uses the electrical and aging parameters of a standard battery as the battery to be tested, controls a bidirectional power module to absorb power according to a virtual battery model, measures the electric energy of a charging pile, and realizes fast and accurate measurement of the charging and discharging electric energy of the lithium-ion battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a structural schematic diagram of a lithium-ion battery charge and discharge measurement and calibration system provided by an embodiment of the present invention;

[0049] Figure 2 It is a flow chart of a bidirectional power module provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] Many specific details are set forth in the following description to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present invention, so the present invention is not limited to the specific implementation disclosed below.

[0051] Lithium-ion batteries are greatly affected by environmental factors, and their electrical, thermal, and aging characteristics will all lead to battery metering errors. In order to achieve accurate metering under charging and discharging conditions, the battery has a similar degradation mechanism when operating under similar conditions, and the battery aging trajectory prediction model is established by combining the influencing variables such as temperature and electrical parameters. Then, a battery charging and discharging metering and calibration method is constructed using a power hardware-in-the-loop (PHIL) simulation platform that is close to the real environment, and a lithium-ion battery charging and discharging metering and calibration system is developed. The output device is demonstrated and applied in scenarios such as new energy vehicle charging and swapping stations and distributed energy storage stations, and the effectiveness is verified. In this way, the rapid and accurate metering of lithium-ion battery charging and discharging energy is achieved, and a mutual trust mechanism for lithium-ion battery energy metering is effectively established among multiple market participants such as charging piles, swapping stations, battery owners, and battery users, providing a foundation for the development of power grid companies' charging and swapping services and distributed energy storage services.

[0052] The present invention provides a lithium-ion battery charge and discharge measurement and calibration system, such as Figure 1 As shown, it mainly includes: control and guidance circuit, electric energy metering module, bidirectional power module, high-speed processing module, communication module, display module and power supply module;

[0053] A control guide circuit is connected to the charging pile and is used to guide the voltage, current and temperature signals of the charging pile to the electric energy metering module and guide the communication signal of the charging pile to the communication module;

[0054] The electric energy metering module is used to transmit the voltage and current signals of the charging pile to the metering circuit through the analog switch; and transmit the power of the charging pile to the high-speed processing module and the bidirectional power module;

[0055] The bidirectional power module is used to receive the power signal transmitted by the electric energy metering module, and is controlled by the high-speed processing module through the communication module to generate real-time power exchange with the charging pile, so as to perform a real charge and discharge cycle on the lithium-ion battery;

[0056] The high-speed processing module controls the electric energy metering module and the bidirectional power module through the communication module, runs the virtual battery module, and uses the transfer learning strategy to establish a lithium-ion battery aging trajectory prediction model; uses the lithium-ion battery aging trajectory prediction model to predict the battery aging trajectory in real time, uses the electrical and aging parameters of the standard battery as the battery to be tested, and controls the bidirectional power module to absorb power according to the virtual battery model to perform electric energy metering on the charging pile;

[0057] The communication module is connected with the high-speed processing module, the electric energy metering module and the bidirectional power module to realize signal interaction between the high-speed processing module and the electric energy metering module and the bidirectional power module;

[0058] The display module is connected to the high-speed processing module and is used to realize the data display function.

[0059] The power module is connected to a three-phase power supply to supply power to the lithium-ion battery charge and discharge measurement and calibration system.

[0060] Furthermore, the electric energy metering module includes: a sampling circuit, an analog switch, a metering circuit, a power supply circuit, etc.; the charging pile voltage and current signals collected by the sampling circuit are transmitted to the metering circuit through the analog switch; the power of the charging pile is transmitted to the high-speed processor module and the bidirectional power module; the power supply circuit is connected to the power supply module to supply power to the above circuit.

[0061] Electric energy metering module, DC voltage measurement range 1-1000V, measurement accuracy ±0.012%RD+0.008%RG (1V≤U≤1000V), short-term stability can reach 0.005% / min, measurement resolution is 0.001%RG; DC current measurement range 600A, measurement accuracy ±0.012%RD+0.008%RG (5A≤I≤600A), short-term stability can reach 0.005% / min, measurement resolution is 0.001%RG. The electric energy output pulse constant (r / kwh) can be set, the setting range is: 1-2,000,000, the maximum pulse output frequency is 10kHz. The setting range of the pulse constant (r / kWh) of the calibrated electric energy meter is: 1-2,000. The setting range of the number of calibration turns is: 1-999,999. The maximum receiving pulse frequency is 5kHz.

[0062] Furthermore, the bidirectional power module includes input / output EMI circuit, three-phase active PFC circuit, DC / DC conversion circuit, input / output detection protection, auxiliary power supply, PFC and DC / DC control drive protection circuit, communication circuit, etc. The input / output EMI circuit ensures that the power supply will not interfere with the power grid or power-consuming equipment through input and output EMI filtering, ensuring the stability of the test; the two dual-channel DSPs in the bidirectional power module ensure that the input filtering, PFC correction, power conversion, and output filtering are controlled by their own independent DSPs, avoiding the shortcomings of the current conventional mainstream design scheme (stacked with a large number of analog circuit controls) with complex circuits, difficult debugging, easy interference, and poor reliability, and also avoiding the characteristics of full software adjustment, time lag, weak adjustment ability, and poor output indicators.

[0063] Bidirectional power module, output voltage range 50V-950V or 10-450V adjustable, rated output 300V-100V, 50A-60A; rated power 6KW-15Kw. Bidirectional power module, working in two quadrants, can be used as a source to charge the battery, and can also be used as a load to discharge the battery. When doing a discharge experiment, the energy released by the battery can be connected to the grid to avoid energy waste.

[0064] The high-speed processing module controls the energy metering module and the bidirectional power module through the communication module. The high-speed processing module itself realizes the battery testing function through program control, runs the virtual battery model, and uses the transfer learning strategy to establish a lithium-ion battery aging trajectory prediction model. In a hardware-in-the-loop environment, the battery aging trajectory is predicted in real time, and the electrical and aging parameters of the corrected standard battery are used as the battery to be tested. The bidirectional power module is controlled to absorb power according to the virtual battery model to measure the energy of the charging pile.

[0065] Among them, since the same type of batteries operating under similar conditions often have similar aging mechanisms, the aging trajectory of one battery can be predicted by the aging trajectory of another battery. Therefore, the cycle life prediction results can be combined with the Bayesian model transfer technology to achieve transfer learning and establish a lithium-ion battery aging trajectory prediction model. The specific establishment method is:

[0066] First, the model migration strategy involved builds a basic empirical aging model based on the battery electrical characteristics. The basic empirical aging model is used to simulate the capacity aging trajectory of different types of batteries. There are two exponential functions relative to the number of cycles, expressed as

[0067]

[0068] Where [θ1, θ2, θ3, θ4] are model parameters, x represents the number of cycles, and C is the battery capacity. The least squares method is used to estimate the parameters [θ1, θ2, θ3, θ4];

[0069] The basic empirical aging model is migrated to the constructed lithium-ion battery aging trajectory prediction model to handle new tasks in the target domain. Since the process in the target domain can be the input and output shift and scaling of the process in the source domain, the input variables of the basic empirical aging model correspond to the input variables of the lithium-ion battery aging trajectory prediction model in the following way:

[0070] x old =ρ0x new +λ0 (18)

[0071] Among them, [ρ0,λ0] are the parameters to be identified;

[0072] Then, the predicted response of the basic empirical aging model is transferred to the lithium-ion battery aging trajectory prediction model in the following manner:

[0073] C new =ρ1C old +λ1+ε (19)

[0074] Among them, [ρ1,λ1] is the parameter to be identified, ε~N(0,σ 2 ) is the experimental error; Combining formulas (1)-(3), the transfer formula from the basic empirical aging model to the lithium-ion battery aging trajectory prediction model is obtained as follows:

[0075]

[0076] Secondly, the estimation of the parameters involved, if the lower and upper limits of the input variables are given, the update process of the parameter estimation is expressed as

[0077]

[0078] [ρ0,λ0] is calculated by the following formula;

[0079]

[0080] Among them, x old,max is the upper limit of the input variables of the basic empirical aging model, x old,min is the lower limit of the input variable of the basic empirical aging model, x new,max is the upper limit of the input variables of the lithium-ion battery aging trajectory prediction model, x new,m i n It is the lower limit of the input variables of the lithium-ion battery aging trajectory prediction model;

[0081] Then the transfer parameters [ρ0, λ0] can be directly calculated. If [ρ0, λ0] are known, the lithium-ion battery aging trajectory prediction model is given by the linear transformation of the basic empirical aging model; the likelihood function is defined by the adaptive Gaussian error, which is used for the difference between the prediction of the lithium-ion battery aging trajectory prediction model and the data observation, and can be expressed as

[0082]

[0083] Finally, make predictions and quantify uncertainty calculations. After estimating the transmission parameters, calculate the untried x * The predicted response at and its density:

[0084]

[0085] High-speed processing module, equipped with 2.8GHz quad-core Intel Xeon E3-1515M V5 processor, each core can reach 3.7GHz after multiplication, dual-channel DDR4 RAM4-2133, 8GB, standard 512G solid-state drive, 2 USB 3.0 ports, 4 USB2.0 ports, 2 10 / 100 / 1000BASE-TX Gigabit Ethernet ports, 2.5G fiber optic interface compatible with AURORA protocol.

[0086] Furthermore, the display module is also used for: battery simulation model control to achieve automatic power-on, charging, discharging, power-off, and cycle test control; display simulation voltage accuracy, single-cell series connection, input capacity, initial SOC, SOH, polarization capacitance, polarization resistance, ambient temperature parameters, and realize data display and storage functions.

[0087] Furthermore, the communication module can transfer CAN frames and signals between the interface and the user program without interrupting the CPU, thereby minimizing message latency and leaving the host processor time to handle complex applications.

[0088] The present invention provides a lithium-ion battery charge and discharge measurement and calibration system, which has the following advantages:

[0089] (1) Real-time online simulation and real-world operating condition verification: By using the power hardware-in-the-loop simulation method to build an online physical simulation battery system, the entire complex dynamic energy metering process can be tested and verified very close to the real operating conditions, and the control strategy and control logic can be simulated and verified.

[0090] (2) Charging test and discharging test are carried out simultaneously: The bidirectional power module can be used as a source to perform charging test on the battery, and can also be used as a load to perform discharging test on the battery. During the discharge test, the energy released by the battery can be connected to the grid to avoid energy waste.

[0091] (3) Comprehensive simulation of various types of batteries: When measuring the charging pile, the device runs a third-party battery model. It can simulate various types of battery packs by initializing the model parameters, and establish an aging trajectory prediction model to measure the power of the charging pile.

[0092] (4) Automatic power-on, charging and discharging of batteries: The lithium-ion battery charge and discharge measurement and calibration system can test lithium-ion batteries. By testing the iterative battery model, it can realize functions such as automatic power-on, charging, discharging, cycle testing, data display, storage, power-off, and completion of testing.

[0093] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0094] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalents that do not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A lithium-ion battery charge and discharge measurement and calibration system, characterized in that: include: Control pilot circuit, electric energy metering module, bidirectional power module, high-speed processing module, communication module, display module and power module; A control guide circuit is connected to the charging pile and is used to guide the voltage, current and temperature signals of the charging pile to the electric energy metering module and guide the communication signal of the charging pile to the communication module; The electric energy metering module is used to transmit the voltage and current signals of the charging pile to the metering circuit through the analog switch; and transmit the power of the charging pile to the high-speed processing module and the bidirectional power module; The bidirectional power module is used to receive the power signal transmitted by the electric energy metering module, and is controlled by the high-speed processing module through the communication module to generate real-time power exchange with the charging pile, so as to perform a real charge and discharge cycle on the lithium-ion battery; The high-speed processing module controls the electric energy metering module and the bidirectional power module through the communication module, runs the virtual battery module, and uses the transfer learning strategy to establish a lithium-ion battery aging trajectory prediction model; uses the lithium-ion battery aging trajectory prediction model to predict the battery aging trajectory in real time, uses the electrical and aging parameters of the standard battery as the battery to be tested, and controls the bidirectional power module to absorb power according to the virtual battery model to perform electric energy metering on the charging pile; The communication module is connected with the high-speed processing module, the electric energy metering module and the bidirectional power module to realize signal interaction between the high-speed processing module and the electric energy metering module and the bidirectional power module; The display module is connected to the high-speed processing module and is used to realize the data display function. The power module is connected to a three-phase power supply to supply power to the lithium-ion battery charge and discharge measurement and calibration system.

2. The system according to claim 1, characterized in that The electric energy metering module includes: a sampling circuit, an analog switch, a metering circuit, and a power supply circuit; The charging pile voltage and current signals collected by the sampling circuit are transmitted to the metering circuit through the analog switch; The power of the charging pile is transmitted to the high-speed processor module and the bidirectional power module; The power circuit is connected to the power module to supply power to the above circuit.

3. The system according to claim 1 or 2, characterized in that: Electric energy metering module, DC voltage measurement range 1-1000V, measurement accuracy ±0.012%RD+0.008%RG (1V≤U≤1000V), short-term stability can reach 0.005% / min, measurement resolution is 0.001%RG; The DC current measurement range is 600A, the measurement accuracy is ±0.012%RD+0.008%RG (5A≤I≤600A), the short-term stability can reach 0.005% / min, and the measurement resolution is 0.001%RG.

4. The system according to claim 1, characterized in that The bidirectional power module includes input and output EMI circuits. Through input and output EMI filtering, it ensures that the power supply will not interfere with the power grid or power-consuming equipment, thereby ensuring the stability of the test. The two dual-channel DSPs in the bidirectional power module ensure that the input filtering, PFC correction, power conversion, and output filtering are controlled by their own independent DSPs.

5. The system according to claim 1, characterized in that The bidirectional power module works in two quadrants and can be used as a source to perform a charging test on the battery, and can also be used as a load to perform a discharge test on the battery.

6. The system according to claim 1 or 5, characterized in that: Bidirectional power module, output voltage range 50V-950V or 10-450V adjustable, rated output 300V-100V, 50A-60A; rated power 6KW-15Kw.

7. The system according to claim 1, characterized in that Run the virtual battery module and use the transfer learning strategy to build a lithium-ion battery aging trajectory prediction model, including: A basic empirical aging model is constructed based on the battery electrical characteristics. The basic empirical aging model is used to simulate the capacity aging trajectory of different types of batteries. There are two exponential functions relative to the number of cycles, which are expressed as Where [θ1, θ2, θ3, θ4] are model parameters, x represents the number of cycles, and C is the battery capacity. The least squares method is used to estimate the parameters [θ1, θ2, θ3, θ4]; Migrating the basic empirical aging model to the constructed lithium-ion battery aging trajectory prediction model; The input variables of the basic empirical aging model correspond to the input variables of the lithium-ion battery aging trajectory prediction model in the following manner: x old =ρ0x new +λ0 (2) Among them, [ρ0,λ0] are the parameters to be identified; The predicted response of the basic empirical aging model is transferred to the lithium-ion battery aging trajectory prediction model in the following manner: C new =ρ1C old +λ1+ε (3) Among them, [ρ1,λ1] is the parameter to be identified, ε~N(0,σ 2 ) is the experimental error; Combining formulas (1)-(3), the transfer formula from the basic empirical aging model to the lithium-ion battery aging trajectory prediction model is obtained as follows: 。 8. The system according to claim 7, characterized in that Also includes: If the lower and upper bounds of the input variables are given, the update process of the parameter estimates can be expressed as [ρ0,λ0] is calculated by the following formula; Among them, x old,max is the upper limit of the input variables of the basic empirical aging model, x old,min is the lower limit of the input variable of the basic empirical aging model, x new,max is the upper limit of the input variables of the lithium-ion battery aging trajectory prediction model, x new,min It is the lower limit of the input variables of the lithium-ion battery aging trajectory prediction model; The transfer parameters [ρ0, λ0] are calculated. If [ρ0, λ0] are known, the lithium-ion battery aging trajectory prediction model is given by the linear transformation of the basic empirical aging model. The likelihood function is defined by the adaptive Gaussian error, which is used for the difference between the prediction of the lithium-ion battery aging trajectory prediction model and the data observation, and can be expressed as Finally, make predictions and quantify uncertainty calculations. After estimating the transmission parameters, calculate the untried x * The predicted response at and its density:

9. The system according to claim 1, characterized in that High-speed processing module, equipped with 2.8GHz quad-core IntelXeon E3-1515M V5 processor, each core can reach 3.7GHz after multiplication, dual-channel DDR4RAM4-2133, 8GB, standard 512G solid-state drive, 2 USB 3.0 ports, 4 USB 2.0 ports, 2 10 / 100 / 1000BASE-TX Gigabit Ethernet ports, 2.5G fiber optic interface compatible with AURORA protocol.

10. The system according to claim 1, characterized in that The display module is also used for: battery simulation model control to achieve automatic power-on, charging, discharging, power-off, and cycle test control; Displays simulation voltage accuracy, monomer series connection, input capacity, initial SOC, SOH, polarization capacitance, polarization resistance, and ambient temperature parameters.

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