Test method of battery management system and host computer

Abstract

The application belongs to the technical field of battery management system test, and relates to a battery management system test method and an upper computer. A battery system model is constructed, and a periodic iteration mode is adopted to continuously update the battery operation state, so that the battery related parameters evolve with time under a unified framework, and a closed loop updating mechanism is formed. Each parameter is recursively updated based on the last period result, and is jointly affected by the current input, capacity change and heat change, so that the parameter changes from a static relationship to a dynamic relationship that continuously changes with time and is mutually related, thereby ensuring the continuity and consistency of the simulation signal, and avoiding state jumping or inconsistency problems. Meanwhile, the interaction between the electrochemical state and the thermal state is comprehensively considered, so that the thermal effect caused by the current is fed back to the subsequent update, the state of charge change affects the voltage response characteristic, a electro-thermal-state coupling relationship is formed, the simulation process is closer to the actual operation characteristics of the battery, and the reliability and applicability of the BMS test are improved.

Description

Technical Field

[0001] This application belongs to the field of battery management system testing technology, specifically relating to a testing method and host computer for a battery management system. Background Technology

[0002] With the rapid development of new energy vehicles, energy storage systems, and industrial power equipment, the battery management system (BMS), as the core control unit of the battery system, is becoming increasingly complex. The BMS needs to monitor parameters such as voltage, current, temperature, and state of charge of individual battery cells and the battery system in real time, and perform functions such as state estimation, protection control, and fault diagnosis based on the monitoring results. Therefore, before a BMS is put into application, it is usually necessary to verify its response capabilities and functional logic under different operating conditions through a testing platform.

[0003] In existing technologies, to reduce the safety risks and costs associated with real battery testing, battery simulation equipment is typically used to test the Battery Management System (BMS). However, existing battery simulation schemes are mostly based on relatively independent or preset parameter output methods, lacking a unified dynamic correlation mechanism between various operating parameters, making it difficult to reflect the continuous state change characteristics of the battery during charging and discharging.

[0004] Furthermore, during actual battery operation, parameters such as state of charge, temperature, and terminal voltage exhibit interrelationships that change over time and continuously evolve. However, some existing simulation devices typically update parameters in a discrete or discontinuous manner, making it difficult to establish a consistent state evolution process over time. Consequently, there is a certain deviation between the simulation and the actual operating state of the battery under complex conditions.

[0005] Meanwhile, under dynamic charging and discharging conditions and environmental changes, the battery state needs to be continuously updated to reflect the changing trend during operation. However, existing simulation schemes still have shortcomings in terms of unified updating of multiple parameters and continuous change expression, which limits their adaptability to complex test scenarios.

[0006] Therefore, how to achieve continuous updates of battery operating status during BMS testing and uniformly express the dynamic correlation between various operating parameters in order to improve the simulation process's ability to reflect the actual battery operating status, thereby enhancing the reliability and applicability of BMS testing, has become an urgent problem to be solved in this field. Summary of the Invention

[0007] To address the aforementioned issues, this application provides a testing method and host computer for a battery management system, enabling continuous dynamic updates of battery operating status parameters and unified simulation of multi-parameter coupling relationships, thereby improving the realism and reliability of BMS testing.

[0008] To address the aforementioned technical problems, one technical solution adopted in this application is: providing a testing method for a battery management system, the method comprising: constructing a battery system model corresponding to the battery management system under test based on preset battery configuration parameters; wherein, the battery configuration parameters include: initial information of battery charge state, coulombic efficiency, input current, polarization characteristic parameters, open-circuit voltage relationship curve, ohmic internal resistance, electrochemical polarization voltage, battery mass, and thermal characteristic parameters; and executing a simulated battery testing process based on the battery configuration parameters and the battery system model until a preset termination condition is met; wherein, the simulated battery testing process includes: obtaining the battery temperature of the previous cycle in the current cycle, and obtaining the actual battery capacity based on the battery temperature; and obtaining the current cycle's battery capacity based on the initial information of battery charge state, coulombic efficiency, actual battery capacity, and input current. The test process involves: obtaining the battery's state of charge (SOC) information for the current cycle; determining the target polarization resistance and capacitance based on polarization characteristic parameters and battery temperature; obtaining the target polarization voltage based on the target polarization resistance, target polarization capacitance, and input current; obtaining the battery open-circuit voltage based on the relationship curve between the battery SOC information and open-circuit voltage; obtaining simulated voltage information based on the battery open-circuit voltage, input current, ohmic internal resistance, electrochemical polarization voltage, and target polarization voltage; generating and sending a simulated battery signal to the battery management system under test (BMD) based on the simulated battery signal; receiving the first feedback information generated by the BMD based on the simulated battery signal; obtaining the test results of the BMD for the current cycle based on the first feedback information; and obtaining the battery temperature for the next cycle based on the battery mass, input current, ohmic internal resistance, and thermal characteristic parameters.

[0009] To solve the above-mentioned technical problems, another technical solution adopted in the embodiments of this application is: to provide a host computer, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the above-mentioned method.

[0010] Unlike related technologies, this application provides a testing method and host computer for a battery management system. By constructing a battery system model and continuously updating the battery's operating state in a periodic iterative manner, key parameters such as the battery's state of charge, open-circuit voltage, polarization voltage, and temperature can evolve synchronously over time within a unified computational framework, thus logically forming a closed-loop update mechanism for the battery's operating state. Under this mechanism, since each state parameter is recursively calculated based on the results of the previous cycle and is jointly driven by factors such as current input, capacity changes, and heat changes within the same cycle, the physical quantities are no longer mutually independent static mapping relationships, but exhibit dynamic correlation characteristics that change continuously over time and influence each other. This enables the simulated battery signal output to maintain continuity and consistency in the time dimension, avoiding the state jumps or inconsistencies caused by traditional separate parameter update methods. Furthermore, by simultaneously considering the effects of electrochemical and thermal state changes on system parameters within the same calculation cycle, the thermal effects caused by current changes during battery operation can be fed back to the state update in the next cycle. Simultaneously, changes in state of charge influence voltage response characteristics, thus forming a bidirectional coupling relationship between electricity, heat, and state at the system level. The introduction of this coupling relationship allows the simulation process to more realistically reflect the dynamic response of the battery under charging, discharging, and environmental changes. Based on the aforementioned continuous iterative update and multi-parameter coupling mechanism, this application can achieve dynamic evolution simulation of battery operating states within a unified model framework. This makes the output simulation signal closer to the actual battery operating characteristics in terms of change trends, response processes, and state consistency, thereby improving the reliability, consistency, and applicability of BMS test results under complex operating conditions. Attached Figure Description

[0011] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0012] Figure 1 This is a schematic diagram of the structure of a testing system provided in an embodiment of this application; Figure 2 This is a schematic diagram of the hardware structure of a host computer provided in an embodiment of this application; Figure 3 This is a flowchart of a test method for a battery management system provided in an embodiment of this application; Figure 4 This is a flowchart of a simulated battery testing process provided in an embodiment of this application. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and thoroughly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0014] It should be noted that, unless otherwise specified, the various features in the embodiments of this application can be combined with each other, all of which are within the protection scope of this application. When an element is described as "connected" to another element, it can be directly connected to the other element, or there may be one or more intervening elements between them.

[0015] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and are not used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and are not limited in number; for example, a first object can be one or more.

[0016] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application.

[0017] Please see Figure 1 , Figure 1 This is a schematic diagram of the structure of a testing system provided in an embodiment of this application. Figure 1 As shown, the test system 1000 includes an electronic device 100 and a battery management system 200. The electronic device 100 includes a host computer 10, a communication module 20, a simulator body module 30, and a signal output module 40. The communication module 20 is connected to the host computer 10, the simulator body module 30, and the battery management system 200, respectively, and the signal output module 40 is connected to the simulator body module 30 and the battery management system 200, respectively.

[0018] The test system 1000 is used to perform functional testing, sampling verification, and control logic verification on the battery management system 200, including the electronic device 100 and the battery management system 200. The electronic device 100 is used to simulate the battery operating state and provide the battery management system 200 with corresponding simulated test signals such as voltage, current, and temperature, while receiving feedback information from the battery management system 200 to complete closed-loop test verification.

[0019] The electronic device 100 includes a host computer 10, a communication module 20, a simulator body module 30, and a signal output module 40. The modules are connected through standard communication interfaces and signal cables to form a complete battery simulation and testing system architecture.

[0020] The host computer 10 is an industrial computer or embedded computing device, which integrates test control software to execute the battery system model construction and test process control. The host computer 10 includes a battery system model setting unit, an electrothermal parameter setting unit, an input instruction unit, an algorithm processing unit, a data storage unit, an output control unit, and a message simulation unit. It is used to establish a virtual battery system model based on preset battery configuration parameters, and configure the battery series and parallel structure, the number of batteries, as well as virtual relays, virtual loads, and virtual temperature sources through a graphical human-machine interface, thereby generating corresponding control instructions and communication messages.

[0021] Please see Figure 2 This document illustrates a schematic diagram of the hardware structure of a host computer 10 capable of executing the methods described in the embodiments of this application. The host computer 10 includes: at least one processor 11; and a memory 12 communicatively connected to the at least one processor 11. Figure 2 Taking a processor 11 as an example, the memory 12 stores instructions executable by at least one processor 11. These instructions, when executed by the at least one processor 11, enable the at least one processor 11 to perform the test method of the battery management system according to embodiments of this application. The processor 11 and the memory 12 can be connected via a bus or other means. Figure 2 Taking the example of a connection between China and Israel via a bus.

[0022] The memory 12, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the battery management system testing method in the embodiments of this application. The processor 11 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions, and modules stored in the memory 12, thereby implementing the battery management system testing method of the embodiments of this application.

[0023] The memory 12 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computing device. Furthermore, the memory 12 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 12 may optionally include memory remotely located relative to the processor 11, and these remote memories can be connected to the computing device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. One or more modules are stored in the memory 12 and, when executed by one or more processors 11, perform the test method of the battery management system according to the embodiments of this application.

[0024] The communication module 20 is a CAN communication interface device or CAN communication card, including a CAN transceiver, a bus controller, and a communication driver unit. It is used to transmit control commands between the host computer 10 and the simulator main module 30, and to conduct CAN message communication between the simulator main module 30 and the battery management system 200. The communication module 20 is used to issue control commands, transmit simulated data, and receive and parse feedback data from the battery management system 200, thereby forming a bidirectional communication link.

[0025] The simulator module 30 is the main control device for simulating battery operating status. It can be an FPGA control board, an embedded real-time controller, or a dedicated battery simulator main control unit. This module receives battery system control commands from the host computer 10 and generates simulated physical signals such as battery cell voltage, current, and temperature. Internally, the simulator module 30 includes a control signal processing unit, a temperature signal simulation unit, a current signal simulation unit, and a voltage signal simulation unit. The control signal processing unit parses the control commands from the host computer 10 and generates internal calculation parameters. The voltage / current / temperature simulation units perform state calculations and signal generation for different physical quantities of the battery, respectively, and convert the calculation results into continuous analog electrical signals for output via a digital-to-analog converter (DAC).

[0026] The signal output module 40 is a multi-channel signal output and conditioning circuit unit, including multiple isolated output channels, signal amplification circuits, and channel allocation circuits. It receives analog signals output from the simulator main module 30 and performs channel mapping and expansion processing according to the sampling channel configuration of the battery management system 200. The signal output module 40 can be parallel-extended according to the number of BMS channels, distributing and outputting analog signals from different battery cells according to preset channel addresses. This allows analog voltage, analog current, and analog temperature signals to be input to the corresponding sampling ports of the battery management system 200, thereby enabling interface-level signal injection testing of the battery management system 200.

[0027] This test system 1000 collaboratively constructs a host computer 10, a communication module 20, a simulator module 30, and a signal output module 40, forming an integrated battery test architecture covering "model construction - state calculation - communication interaction - signal output". In this architecture, the host computer 10 serves as the core control and computing unit of the test system 1000. It is used to configure the battery system topology, battery configuration parameters, and test conditions. Based on a preset battery system model and a periodic iterative calculation mechanism, it calculates and updates the operating parameters such as battery state of charge, voltage state, temperature state, and polarization state, thereby generating corresponding analog control data and signal output commands. This enables unified modeling, state evolution, and test process scheduling of the virtual battery system operation. The communication module 20 is used to establish a data interaction link between the host computer 10, the simulator module 30, and the battery management system 200. Based on the CAN communication mechanism, it realizes bidirectional transmission of control commands, analog data, and feedback information from the battery management system 200. This allows the test system 1000 to send analog test signals to the battery management system 200 according to the preset test process and receive feedback information generated by the battery management system 200 based on the analog test signals. The simulator module 30 is used to perform corresponding data parsing, signal conversion, and hardware output processing based on the analog control data and signal output instructions output by the host computer 10. It converts the digital battery state data calculated by the host computer 10 into analog voltage, analog current, and analog temperature signals corresponding to the output characteristics of the actual battery system. In some embodiments, the simulator module 30 can also be configured with a real-time processing unit to undertake some high-frequency state calculation tasks according to test requirements, such as high-speed dynamic signal generation, local model calculation, or real-time signal adjustment, thereby improving the real-time performance of the analog signal output. It should be noted that when the simulator module 30 is responsible for the calculation tasks, the host computer only needs to be responsible for parameter configuration, operating condition scheduling, result recording, and human-computer interaction. The signal output module 40 is used to perform channel matching, signal isolation, and interface adaptation of the simulated battery signals generated by the simulator body module 30. According to the sampling channel configuration of the battery management system 200, it outputs the corresponding analog signals such as voltage, current, and temperature to the acquisition interface of the battery management system 200, so that the battery management system 200 can acquire test signals in the manner of actually connecting the battery system, and perform sampling, state estimation, fault diagnosis, and control logic verification based on the test signals.

[0028] Through the synergistic effect of the aforementioned multiple modules, the battery operating state can be continuously mapped and consistently transmitted from the model layer to the signal layer. Simultaneously, because the battery state calculation and signal output processes can be periodically updated according to unified control logic, signals such as battery voltage, current, and temperature possess continuity and dynamic consistency, thus more realistically reflecting the battery's operational changes under different operating conditions. This improves the matching degree between analog signals and actual battery operating characteristics, enhancing the reliability and accuracy of BMS sampling accuracy verification, control logic verification, and fault diagnosis verification under complex operating conditions.

[0029] Please see Figure 3 , Figure 3 This is a flowchart illustrating a testing method for a battery management system provided in an embodiment of this application. The method is applied to the host computer 10 of the aforementioned testing system 1000. For example... Figure 3 As shown, the method includes steps S1-S2: S1: Construct a battery system model corresponding to the battery management system under test based on the preset battery configuration parameters; wherein, the battery configuration parameters include: initial information of battery charge state, coulombic efficiency, input current, polarization characteristic parameters, open circuit voltage relationship curve, ohmic internal resistance, electrochemical polarization voltage, battery mass and thermal characteristic parameters.

[0030] Before the test begins, the operator accesses the battery system configuration interface through the human-machine interface on the host computer, where they select the battery topology and battery model type. The battery topology characterizes the series and parallel connections of the individual battery cells in the simulated battery system. The operator can set the number of series-connected and parallel-connected cells to ensure that the subsequently constructed battery system model matches the actual battery system structure under test.

[0031] Additionally, operators can select the battery model type. The battery model type describes the equivalent model structure of the battery's dynamic electrical behavior, specifically a first-order RC model, a second-order RC model, or a third-order RC model. The RC model uses a resistor and capacitor network to simulate the polarization effect during battery charging and discharging. Different orders of RC models correspond to different numbers of polarization branches to adapt to test scenarios with varying accuracy and computational complexity requirements.

[0032] After selecting the battery topology and model type, the host computer enters the parameter input interface to receive battery configuration parameters. These parameters characterize the initial electrical state, thermal state, and dynamic response characteristics of the battery system, serving as the foundational input parameters for subsequent dynamic battery calculations. The battery configuration parameters must include at least the initial battery charge state information. Coulomb efficiency Input current Polarization characteristic parameters and open-circuit voltage relationship curves Ohmic internal resistance Electrochemical polarization voltage Battery quality And thermal characteristic parameters.

[0033] Understandably, the initial state of charge (SOC) information is used to characterize the battery's initial state of charge at the start of the test, typically expressed as a percentage, and serves as the initial value for subsequent dynamic SOC iteration calculations. Coulomb efficiency characterizes the charge conversion efficiency during battery charging and discharging, reflecting energy loss during this process and participating in SOC dynamic update calculations; it is typically between 0.95 and 1. Input current characterizes the charging and discharging current applied to the battery system during the test. It can be a fixed current value or a dynamic current function that varies over time, including pulse current, step current, periodic function current, or a current based on a preset operating condition curve, thus simulating the battery's operating state under different charging and discharging conditions. The open-circuit voltage relationship curve represents the relationship between SOC and open-circuit voltage. The corresponding curves between these parameters are used to characterize the open-circuit voltage characteristics of the battery under different states of charge (SOC) at rest, thereby obtaining the corresponding open-circuit voltage value based on SOC during subsequent dynamic calculations. Ohmic internal resistance characterizes the instantaneous ohmic voltage drop characteristics generated by the internal conductive path of the battery, corresponding to the instantaneous voltage loss generated when current flows through the internal conductors of the battery, and is included in the calculation of the battery terminal voltage. Electrochemical polarization voltage characterizes the activation polarization effect generated when electrochemical reactions occur at the interface between the battery electrodes and the electrolyte, simulating the voltage shift caused by the limitation of electrochemical reaction kinetics during battery charging and discharging. It should be noted that the electrochemical polarization voltage in this embodiment directly uses the reference value provided by the cell manufacturer. Polarization characteristic parameters are used to describe the dynamic response characteristics of the RC equivalent circuit (taking a second-order RC equivalent circuit as an example), and include at least the polarization resistance reference parameter. Temperature coefficient of resistance Battery reference temperature Polarization capacitor reference parameters and capacitor temperature coefficient The parameters are as follows: polarization resistance reference parameter characterizes the initial value of polarization resistance at a reference temperature (e.g., calibrated value at 25℃ (fitted by electrochemical impedance spectroscopy (EIS)); resistance temperature coefficient describes the variation of polarization resistance with temperature; battery reference temperature serves as the base temperature for temperature correction calculations (e.g., 25℃); polarization capacitance reference parameter characterizes the initial value of polarization capacitance at the reference temperature (e.g., calibrated value at 25℃ (fitted by EIS); capacitance temperature coefficient describes the variation of polarization capacitance with temperature. These parameters are used together to calculate the target polarization resistance and target polarization capacitance at different temperature states. Battery mass characterizes the physical mass parameters of individual battery cells and participates in subsequent calculations of battery thermal capacity and temperature rise. Thermal characteristic parameters describe the heat transfer and heat accumulation characteristics during battery operation, including at least the battery specific heat capacity. Battery reaction heat Heat exchange coefficient and ambient temperature Among them, the specific heat capacity of a battery is used to characterize the amount of heat absorbed by a unit mass of battery to increase its temperature; the heat of reaction of a battery is used to characterize the heat generated or absorbed during the electrochemical reaction of the battery; the heat exchange coefficient is used to characterize the heat exchange capacity between the battery and the external environment; and the ambient temperature is used to characterize the temperature state of the external environment in which the battery is located.

[0034] In addition, battery configuration parameters also include the battery's initial temperature. The initial battery temperature is used to characterize the temperature state of the battery at the initial moment of the test. Since the subsequent SOC calculation and polarization parameter calculation are related to the battery temperature, the initial battery temperature is used as the initial thermal state input parameter for the first calculation cycle.

[0035] It should be noted that the above battery configuration parameters can be obtained through cell testing or from parameter data provided by the cell manufacturer. After receiving the battery configuration parameters, the host computer parses, stores, and maps the parameters, and establishes the corresponding battery system model based on the selected RC model structure.

[0036] The battery system model characterizes the dynamic electrical and thermal behavior of the battery system during charging and discharging. It includes a State of Charge (SOC) model, a voltage response model, and a temperature state model. The SOC model describes the dynamic process of the battery's state of charge changing over time; the voltage response model describes the dynamic relationship between the battery's open-circuit voltage, polarization voltage, and terminal voltage; and the temperature state model describes the battery's heat generation, heat dissipation, and temperature changes. These models are dynamically correlated through the battery configuration parameters, thus providing the foundational model support for subsequent iterative calculations.

[0037] Step S1 establishes a battery system model corresponding to the actual battery system based on preset battery configuration parameters, unifying the battery's electrical state parameters, thermal state parameters, and dynamic response parameters within the same model framework for configuration. Specifically, by uniformly modeling the battery's initial charge state information, input current, open-circuit voltage relationship curves, polarization characteristic parameters, and thermal characteristic parameters, a dynamic correlation is established between changes in the battery's state of charge, voltage response characteristics, and temperature change characteristics. This provides a unified data and model foundation for subsequent periodic iterative calculations of the battery's operating state. Simultaneously, by configuring the battery topology and RC model type, the established battery system model can adapt to test scenarios with different series-parallel structures and varying dynamic accuracy requirements, improving the applicability and scalability of the model construction process. Furthermore, since all parameters can be uniformly configured and mapped before testing, the consistency and completeness of parameter calls during subsequent simulation calculations are improved, reducing model deviations caused by independent processing of different state parameters. This results in simulated battery signals that more closely resemble actual battery operating characteristics, enhancing the realism and reliability of the BMS testing process.

[0038] S2: Based on battery configuration parameters and battery system model, execute simulated battery test process until preset termination conditions are met.

[0039] The preset termination conditions are used to determine the end conditions of the simulated battery test process. These conditions may include reaching a preset simulation duration, reaching a preset number of cycles, reaching a preset SOC threshold, completing the test script execution, or receiving a manual stop command. When the preset termination conditions are not met, the host computer continues to execute steps S21-S29 according to a preset calculation cycle to achieve periodic iterative updates of the battery's operating status.

[0040] Please see Figure 4 , Figure 4 This is a flowchart illustrating a simulated battery testing process provided in an embodiment of this application. For example... Figure 4 As shown, the simulated battery test process includes steps S21-S29: S21: In the current cycle, obtain the battery temperature of the previous cycle and obtain the actual battery capacity based on the battery temperature.

[0041] The battery configuration parameters also include battery health status information, capacity coefficient matrix information, and battery rated capacity. The actual battery capacity is obtained based on the battery temperature, including: obtaining the capacity coefficient based on the battery temperature and capacity coefficient matrix information; and obtaining the actual battery capacity based on the battery health status information, capacity coefficient, and battery rated capacity.

[0042] S22: Based on the initial battery charge state information, coulombic efficiency, actual battery capacity, and input current, obtain the battery charge state information for the current cycle.

[0043] Here, the battery temperature T is used to characterize the current thermal state of the battery, which can be the battery temperature calculated in step S29 of the previous cycle. In the first calculation cycle, since there is no previous cycle, the battery initial temperature pre-configured in step S1 is used as the battery temperature input value for the current cycle.

[0044] Since the battery's release capacity changes with temperature, at the start of the current cycle, the actual battery capacity needs to be recalculated based on the current battery temperature.

[0045] Specifically, the battery configuration parameters also include battery state of health (SOH), capacity coefficient matrix information (K), and battery rated capacity. The battery health status information characterizes the current health of the battery, reflecting the degree of capacity degradation relative to the rated capacity. For example, a state of health (SOH) of 80% indicates that the maximum usable capacity of the battery has decreased to 80% of the rated capacity. The capacity coefficient matrix information characterizes the correction relationship of battery capacity under different temperature conditions; it can be a table showing the correspondence between temperature and capacity coefficients (each coefficient is typically around 0.1 to 1.05). Because battery activity decreases at low temperatures and capacity release characteristics change at high temperatures, the usable capacity of the battery varies at different temperatures. The capacity coefficient is used to correct for temperature-related changes in the rated capacity. The rated capacity characterizes the theoretical rated capacity of the battery under standard operating conditions, usually expressed in ampere-hours (Ah), which can be provided by the cell manufacturer or obtained through battery testing.

[0046] After obtaining the battery temperature for the current cycle, the host computer first looks up the corresponding capacity coefficient K(T) in the capacity coefficient matrix based on the battery temperature. Then, the host computer calculates the actual battery capacity under the current temperature conditions based on the battery health status information, the capacity coefficient, and the battery's rated capacity. The calculation formula is as follows: ; The actual battery capacity characterizes the effective capacity that the battery can actually release under the current temperature and health conditions, serving as the capacity benchmark value for subsequent SOC dynamic updates. Since changes in battery temperature and battery aging both affect the usable battery capacity, the aforementioned capacity correction process ensures that subsequent SOC calculations more closely reflect the actual battery operating conditions.

[0047] After calculating the actual battery capacity for the current cycle, the battery state of charge (SOC) information for the current cycle is obtained based on the initial battery state of charge information, coulombic efficiency, actual battery capacity, and input current. The calculation formula is as follows: ; Among them, the battery charge state information is used to characterize the current battery's remaining charge state, usually expressed as a percentage of the current remaining capacity to the current available capacity; the initial battery charge state information is pre-configured in step S1. Coulomb efficiency characterizes the charge conversion efficiency of a battery during charging and discharging, reflecting the proportion of actual charge change due to energy loss during the process (typically 0.95~1). Input current characterizes the charging and discharging current applied to the battery in the current cycle; it can be a fixed current value or a dynamic current function that varies with time (such as a sine or cosine function, or a pulse function). Discharging current is defined as a negative value, and charging current is defined as a positive value.

[0048] Within the current cycle, the host computer integrates the input current over time to obtain the change in battery charge within the current cycle, and then corrects for the change in charge charge by incorporating the coulombic efficiency. Subsequently, the change in charge charge is normalized based on the actual battery capacity of the current cycle to obtain the SOC change value for the current cycle.

[0049] Finally, the host computer according to The battery charge state information for the current cycle is updated by combining the current cycle's SOC change value.

[0050] Understandably, since the SOC update process takes into account the effects of battery temperature changes, battery aging status, and input current changes on capacity, the SOC result can be dynamically adjusted according to changes in battery operating status, thereby making the subsequent open-circuit voltage calculation and voltage dynamic response calculation more consistent with the actual battery operating characteristics.

[0051] Through steps S21 and S22, the battery's state of charge (SOC) information can be dynamically updated based on battery temperature, battery health status information, and input current. By introducing capacity coefficient matrix information to correct the battery's rated capacity at different temperatures, and combining this with battery health status information to compensate for capacity decay, the obtained actual battery capacity reflects the battery's true usable capacity under current temperature and aging conditions. Subsequently, the SOC is updated integrally based on the corrected actual battery capacity, input current, and coulombic efficiency, allowing the SOC to continuously change with the charging and discharging process. Since the SOC update process simultaneously considers the impact of temperature changes, capacity decay, and changes in charging and discharging conditions on battery capacity characteristics, it avoids the problem of accumulated SOC calculation bias under traditional fixed-capacity models, improving the realism and dynamic consistency of the SOC state simulation. Furthermore, the updated SOC results can provide a state basis for subsequent dynamic calculations of open-circuit voltage and terminal voltage, thereby improving the matching degree between the entire battery dynamic simulation process and the actual battery operating characteristics.

[0052] S23: Based on the polarization characteristic parameters and battery temperature, obtain the target polarization resistance and target polarization capacitance.

[0053] The polarization characteristic parameters include polarization resistance reference parameters, resistance temperature coefficient, battery reference temperature, polarization capacitor reference parameters, and capacitance temperature coefficient. Based on the polarization characteristic parameters and battery temperature, the target polarization resistance and target polarization capacitance are obtained, including: obtaining the target polarization resistance based on the polarization resistance reference parameters, resistance temperature coefficient, battery temperature, and battery reference temperature; and obtaining the target polarization capacitance based on the polarization capacitor reference parameters, capacitance temperature coefficient, battery temperature, and battery reference temperature.

[0054] S24: Obtain the target polarization voltage based on the target polarization resistance, target polarization capacitance, and input current.

[0055] After completing the SOC update for the current cycle, the polarization dynamic response during the battery charging and discharging process is simulated and calculated.

[0056] The polarization characteristic parameters are used to characterize the dynamic response characteristics of the internal polarization branches of the battery. In this embodiment, the battery system model can be a first-order RC model, a second-order RC model, or a third-order RC model. Taking a second-order RC model as an example, the battery includes multiple polarization branches, each used to simulate the polarization response process at different time scales. The target polarization resistance... and target polarization capacitor The calculation formula is as follows: ; ; Among them, the target polarization resistance Used to characterize the dynamic impedance properties of the polarization branch at the current temperature, it mainly reflects the influence of ion migration impedance and concentration diffusion impedance inside the battery on the dynamic voltage response; its value changes with temperature. Target polarization capacitance. It is used to characterize the charge storage capacity of the polarization branch at the current temperature state. It is mainly used to characterize the charge accumulation capacity and dynamic delay characteristics during the polarization process, which also change with temperature. Indicates the degree of temperature deviation from the reference value. Polarization resistance reference parameter. The polarization resistance is the calibration value under reference temperature conditions; the polarization capacitance reference parameters are as follows. These are the polarization capacitance calibration values ​​under reference temperature conditions. The above reference parameters for polarization resistance and polarization capacitance can be obtained through EIS testing and fitting. Temperature coefficient of resistance. Used to characterize the sensitivity of polarization resistance to temperature changes; temperature coefficient of capacitance Used to characterize the change of polarization capacitance with temperature; battery reference temperature The reference temperature used for parameter correction is typically set to 25°C.

[0057] Understandably, within the current cycle, the host computer first obtains the current battery temperature updated in step S21 or S29. Subsequently, based on the polarization resistance reference parameters, the temperature coefficient of resistance, the battery temperature, and the battery reference temperature, a temperature correction calculation is performed on the polarization resistance at the reference temperature to obtain the target polarization resistance at the current temperature. The temperature correction of the polarization resistance is described using an exponential relationship to reflect the impact of temperature changes on ion migration impedance and activation barrier. When the temperature decreases, ion migration ability decreases, and the target polarization resistance increases; when the temperature increases, ion migration ability increases, and the target polarization resistance decreases.

[0058] Subsequently, the host computer performs temperature correction on the polarized capacitor at the reference temperature based on the polarization capacitor reference parameters, capacitor temperature coefficient, battery temperature, and battery reference temperature, thereby obtaining the target polarized capacitor at the current temperature. The temperature correction of the polarized capacitor is described using a power-law form to reflect the changes in charge accumulation capability and polarization delay characteristics under different temperature conditions.

[0059] Through the above process, the RC branch parameters can be updated in real time according to the current temperature state, enabling the RC model to dynamically reflect the changes in battery polarization characteristics under different temperature conditions.

[0060] After obtaining the target polarization resistor and target polarization capacitor, the polarization voltage of the i-th branch is obtained based on the target polarization resistor, target polarization capacitor, and input current. The calculation formula is as follows: ; Understandably, the host computer recursively updates the polarization voltage of each RC branch based on the current cycle input current and the target polarization resistor and target polarization capacitor corresponding to each RC branch. Specifically, the polarization voltage of each RC branch... Used to characterize the dynamic polarization response process at the corresponding time scale. The polarization resistance determines the amplitude of the polarization voltage change, while the polarization capacitance determines the rate of polarization voltage change and the response delay time.

[0061] Within each calculation cycle, the host computer performs dynamic calculations of charging and discharging of the RC branch based on the current input current, so that the polarization voltage of the RC branch changes dynamically with the input current.

[0062] Subsequently, the polarization voltages calculated from each RC branch are superimposed to obtain the target polarization voltage for the current cycle. The target polarization voltage is used to characterize the dynamic polarization voltage generated by the battery during charging and discharging due to concentration diffusion effects and ion transport delays. It is understood that because there is a time delay in ion diffusion within the battery during charging and discharging, the battery terminal voltage will not immediately reach a steady state when the input current changes, but will generate a dynamic polarization response that varies with time. The target polarization voltage is used to describe this dynamic change process.

[0063] Through steps S23 and S24, the polarization parameters in the RC model can be dynamically corrected based on the current battery temperature, and the battery polarization process can be recursively calculated in real time in conjunction with the input current. By performing temperature-related corrections on the polarization resistance and polarization capacitance, the RC branch parameters can be dynamically adjusted with changes in the battery's thermal state, thereby reflecting the changing patterns of ion migration and charge accumulation capabilities under different temperature conditions. Subsequently, based on the corrected RC parameters and the input current, the dynamic polarization voltage of each polarization branch is calculated, and the target polarization voltage is obtained by superimposing multiple branches, enabling the battery terminal voltage to reflect the dynamic hysteresis and diffusion polarization characteristics during the charging and discharging process. Since the polarization parameters are dynamically correlated with the temperature state, and the polarization voltage changes continuously with the input current, the realism and continuity of the battery dynamic voltage response simulation can be improved, avoiding the problem that traditional fixed-parameter models cannot reflect the differences in dynamic response under complex operating conditions. This makes the subsequently generated simulated battery signal closer to the actual battery's operating characteristics under different temperatures and charging and discharging conditions.

[0064] S25: Obtain the battery open-circuit voltage based on the battery charge state information and the open-circuit voltage relationship curve.

[0065] S26: Obtain the simulated voltage information based on the battery open-circuit voltage, input current, ohmic internal resistance, electrochemical polarization voltage, and target polarization voltage.

[0066] After obtaining the SOC calculated in step S22 and the target polarization voltage calculated in step S24, the dynamic terminal voltage of the battery in the current cycle is calculated.

[0067] Since the battery corresponds to different equilibrium voltages at different SOC states, the system pre-establishes the SOC and open-circuit voltage. The correspondence between them, i.e., the open-circuit voltage relationship curve. This relationship curve is typically obtained by performing a static test on the battery cell and is used to reflect the static voltage characteristics of the battery under no-load conditions.

[0068] Within the current calculation cycle, the host computer first reads the current SOC (State of Charge) and then, based on this SOC, searches for the corresponding open-circuit voltage value in the pre-stored open-circuit voltage relationship curve. Additionally, when the current SOC lies between two calibrated SOC points, the host computer can also use interpolation to obtain the corresponding open-circuit voltage, thereby improving the continuity of voltage changes.

[0069] The battery open-circuit voltage for the current cycle is obtained through the above process. This open-circuit voltage is used to characterize the theoretical equilibrium voltage of the battery under resting conditions at the current state of charge (SOC).

[0070] After obtaining the battery open-circuit voltage, simulated voltage information is obtained based on the battery open-circuit voltage, input current, ohmic internal resistance, electrochemical polarization voltage, and target polarization voltage. This simulated voltage information characterizes the actual dynamic terminal voltage output by the battery under current operating conditions, corresponding to the real-time voltage response of the battery during charging and discharging.

[0071] Specifically, the host computer first obtains the input current status for the current cycle. The input current can be the charging current, discharging current, or zero current.

[0072] When the input current is zero, it indicates that the battery is in a static open-circuit state. At this time, the battery terminal voltage gradually approaches the battery open-circuit voltage. Therefore, the analog voltage information can directly use the battery open-circuit voltage as the output result.

[0073] When the input current is in a charging state, the battery terminal voltage will be higher than the open-circuit voltage due to the polarization effect inside the battery during charging. At this time, the host computer adjusts the polarization voltage direction according to the input current direction and calculates the analog voltage information under the current charging condition based on the ohmic voltage drop. The calculation formula is as follows: ; When the input current is in a discharge state, the battery terminal voltage will be lower than the open-circuit voltage due to ohmic voltage drop, electrochemical polarization, and concentration polarization effects within the battery. At this time, the host computer corrects the battery open-circuit voltage based on the input current, ohmic internal resistance, electrochemical polarization voltage, and target polarization voltage, thereby obtaining the simulated voltage information under the current discharge condition. The calculation formula is as follows: ; As the discharge process continues, the target polarization voltage changes dynamically with the RC branch, thus the battery terminal voltage also exhibits dynamic decay characteristics. When the input current gradually decreases and approaches zero, the polarization effect gradually disappears, and the analog voltage information will gradually recover and approach the open-circuit voltage.

[0074] Through steps S25 and S26, the corresponding open-circuit voltage can be dynamically obtained based on the current SOC. Furthermore, by combining the input current, ohmic internal resistance, electrochemical polarization voltage, and target polarization voltage, the battery terminal voltage is dynamically corrected and calculated. By establishing the correlation between SOC and open-circuit voltage, the open-circuit voltage can continuously change with the remaining battery capacity, thus reflecting the battery's static equilibrium voltage characteristics. Subsequently, the ohmic voltage drop, rapid electrochemical polarization effect, and slow concentration polarization effect generated during charging and discharging are comprehensively considered, allowing the battery terminal voltage to dynamically change with the input current and polarization state. Since the terminal voltage calculation process simultaneously correlates the SOC state, current state, and polarization state, the continuity and realism of the simulated voltage information in the dynamic response process can be improved. This avoids the problem that traditional fixed voltage output methods cannot reflect the dynamic charging and discharging process, thus making the generated simulated voltage information closer to the voltage response characteristics of the actual battery under different operating conditions, improving the effectiveness and reliability of BMS voltage sampling and dynamic control testing.

[0075] S27: Based on the analog voltage information, battery temperature, and input current, generate and send analog battery signals to the battery management system under test. The analog battery signals simulate the sampling signals output by a real battery to the BMS under current operating conditions, and include at least analog voltage, analog temperature, and analog current signals.

[0076] S28: Receive the first feedback information generated by the battery management system under test based on the simulated battery signal, and obtain the test result of the battery management system under test for the current cycle based on the first feedback information.

[0077] The host computer will convert the analog voltage information, battery temperature and input current acquired in the current cycle into corresponding control commands and send them to the simulator module through the communication module.

[0078] After receiving the control command, the simulator module calls the corresponding internal signal generation unit to perform signal conversion. Specifically, the voltage simulation unit generates a simulated voltage signal with a corresponding amplitude based on the simulated voltage information; the temperature simulation unit generates a simulated temperature signal in the form of a resistance or voltage based on the battery temperature; and the current simulation unit generates a corresponding simulated current signal based on the input current. In some implementations, the temperature simulation unit can output a simulated temperature signal by simulating the resistance change of an NTC thermistor to adapt to the commonly used temperature sampling interface of the battery management system under test; the current simulation unit can simulate the charging and discharging current state by simulating the output of a Hall sensor or the differential voltage signal of a shunt.

[0079] Subsequently, the simulator module sends the generated analog signals to the signal output module. The signal output module performs channel allocation, electrical isolation, and interface adaptation for the various analog signals, and outputs them according to the corresponding sampling channels of the BMS. Each output channel sends the corresponding analog signal to the battery management system under test based on a preset address or channel mapping relationship.

[0080] After receiving the simulated battery signal, the Battery Management System (BMS) under test acquires and analyzes the corresponding voltage, current, and temperature data based on its internal sampling module. Subsequently, the BMS sends the corresponding first feedback information to the host computer via the communication module. This first feedback information may include: voltage sampling results, current sampling results, temperature sampling results, and SOC estimation results.

[0081] After receiving the first feedback information, the host computer first parses the feedback data and compares and verifies the feedback results according to preset test rules, generating the test results for the battery management system under test (BMS) for the current period. The test results for the BMS can include test pass status, parameter error results, dynamic response time, and function execution results, used to evaluate the current operating status and functional correctness of the BMS under the corresponding operating conditions. For example, the host computer can compare the voltage sampling value fed back by the BMS with the simulated voltage information generated in step S26 to verify the BMS voltage sampling accuracy.

[0082] By setting steps S27 and S28, the simulated battery signal dynamically generated based on voltage, temperature, and current can be stably and continuously output to the battery management system under test (BSD). This realistically reproduces the sampling input characteristics of the battery under different operating conditions at the physical interface level, ensuring that the input conditions of the BSD in actual sampling, calculation, and control logic remain consistent with the actual battery operating state. Simultaneously, by receiving and parsing the first feedback information generated by the BSD based on the simulated battery signal, and further comparing and analyzing it with the preset simulated operating state, the test system can periodically verify the BSD's voltage sampling accuracy, temperature recognition capability, SOC estimation results, and protection and control logic execution. This achieves a comprehensive evaluation of the BSD's functional correctness and dynamic response capability, improving the repeatability, consistency, and coverage of complex operating conditions during the testing process.

[0083] S29: Based on battery mass, input current, ohmic internal resistance, and thermal characteristic parameters, obtain the battery temperature for the next cycle of the current cycle.

[0084] The thermal characteristic parameters include battery specific heat capacity, battery reaction heat, heat exchange coefficient, and ambient temperature. Based on battery mass, input current, ohmic internal resistance, and thermal characteristic parameters, the battery temperature for the next cycle is obtained, including: based on battery mass, battery specific heat capacity, input current, ohmic internal resistance, battery reaction heat, ambient temperature, and heat exchange coefficient, the battery temperature for the next cycle is obtained.

[0085] After completing step S27 and outputting the simulated battery signal to the battery management system under test, the current cycle enters the thermal state update stage. This stage is used to calculate the battery temperature for the next cycle, thereby achieving dynamic recursive updating of the battery's thermal state. The formula for calculating the battery temperature T is as follows: ; Specifically, the host computer acquires the input current I for the current cycle, where the input current characterizes the battery's current charging or discharging state and its operating intensity; it also acquires the battery mass parameter m, which characterizes the thermal inertia of a single cell; and it acquires thermal characteristic parameters, including the battery's specific heat capacity. Battery reaction heat Heat exchange coefficient and ambient temperature Among them, the specific heat capacity of the battery is used to characterize the temperature rise caused by storing a unit mass of heat in the battery, the heat of reaction of the battery is used to characterize the heat released or absorbed during the electrochemical reaction, the heat exchange coefficient is used to characterize the heat dissipation intensity between the battery and the external environment, and the ambient temperature is used to characterize the boundary conditions of the external thermal environment.

[0086] Based on this, the host computer calculates the Joule heating term according to the input current and the ohmic internal resistance. The ohmic internal resistance is used to characterize the equivalent resistance in the electron conduction process inside the battery. It and the input current together determine the Joule heating power generated during the current flow. This part of the heat is always positive and changes with the square of the current, and is used to characterize the internal resistance heating effect of the battery.

[0087] Subsequently, the host computer supplements the thermal effect in the electrochemical process with the battery reaction heat term. The reaction heat is used to characterize the heat power generated by the electrode reaction and ion migration process during the charging and discharging of the battery. This heat may exhibit exothermic or endothermic characteristics.

[0088] In terms of heat dissipation modeling, the host computer calculates the heat dissipation term based on the temperature difference between the current battery temperature and the ambient temperature, combined with the heat exchange coefficient. The heat dissipation term is used to characterize the heat exchange process between the battery and the external environment. When the battery temperature is higher than the ambient temperature, it is heat dissipation; when the battery temperature is lower than the ambient temperature, it is heat absorption, thus forming a bidirectional heat exchange mechanism.

[0089] After calculating the aforementioned thermal power terms, the host computer, based on the law of conservation of energy, performs a comprehensive balance calculation of Joule heat, reaction heat, and heat dissipation. It further normalizes the calculation by incorporating the heat capacity term, which is composed of battery mass and specific heat capacity. Heat capacity characterizes the battery system's ability to buffer temperature changes. By dividing the net thermal power by the heat capacity, the temperature change for the current cycle is obtained. This change is then accumulated and updated based on the battery temperature from the previous cycle to determine the battery temperature for the next cycle.

[0090] By setting the parameters in step S29, the battery temperature update process no longer uses static setting or independent assignment. Instead, it performs periodic recursive calculations based on the energy balance relationship constructed from input current, ohmic internal resistance, battery mass, and thermal characteristic parameters. This allows Joule heating, reaction heat, and heat dissipation to work synergistically within a unified thermal model, resulting in a continuous dynamic evolution of battery temperature under varying charge / discharge conditions and environmental conditions. Since this temperature update result normalizes the thermal power based on heat capacity and is recursively superimposed with the temperature of the previous cycle, it effectively reflects the battery's thermal inertia and thermal hysteresis effects, avoiding the problem of traditional static temperature setting methods failing to accurately reflect the true dynamics of temperature rise / fall. Simultaneously, this temperature result serves as a crucial input for subsequent battery capacity correction, SOC updates, and polarization parameter calculations, creating a closed-loop feedback relationship between temperature changes and electrochemical state changes. This improves the consistency and continuity of the battery thermal-electric coupling simulation, enhancing the realism and reliability of the BMS in temperature sampling, thermal management control, and protection logic testing under complex operating conditions.

[0091] In some embodiments, the battery configuration parameters further include busbar thermal capacity, busbar resistance, and busbar heat dissipation coefficient. The method further includes: obtaining simulated busbar temperature information based on busbar thermal capacity, input current, busbar resistance, ambient temperature, and busbar heat dissipation coefficient; generating and sending a simulated busbar temperature signal to the battery management system under test based on the simulated busbar temperature information; receiving second feedback information generated by the battery management system under test based on the simulated busbar temperature signal, and obtaining busbar test results based on the second feedback information.

[0092] Based on the simulation of battery temperature and voltage, an independent busbar thermal simulation model was further developed and tested to verify the effectiveness of the battery management system under test in monitoring and protecting the high-voltage busbar temperature rise.

[0093] First, obtain the heat capacity of the busbar. Busbar resistance Busbar heat dissipation coefficient and ambient temperature Busbar thermal characteristic parameters. Based on the busbar heat capacity, input current, busbar resistance, ambient temperature, and busbar heat dissipation coefficient, the simulated busbar temperature information is obtained. The calculation formula is as follows: ; Among them, the busbar heat capacity characterizes the ability of the busbar material to absorb heat per unit temperature rise, reflecting its thermal inertia; the busbar resistance characterizes the equivalent resistance characteristic of Joule heating generated when current propagates in the busbar conductor; the busbar heat dissipation coefficient characterizes the intensity of heat exchange between the busbar and the external environment; and the ambient temperature serves as the external thermal boundary condition during the busbar heat exchange process. It should be noted that the above parameters can be obtained through cell testing or from parameter data provided by the cell manufacturer.

[0094] Based on this, the host computer acquires the current input current, which is used to characterize the magnitude of the current flowing through the busbar and its operating state over time. Subsequently, the Joule heat inside the busbar is calculated based on the input current and the busbar resistance. This heat is used to characterize the resistive heating effect generated during the current transmission in the busbar conductors, and its magnitude is positively correlated with the square of the current and the resistance.

[0095] Meanwhile, the host computer calculates the heat dissipation term between the busbar and the external environment by combining the ambient temperature and the busbar heat dissipation coefficient. The heat dissipation term is used to characterize the heat exchange process of the busbar releasing heat to the environment or absorbing heat from the environment, and its direction is determined by the temperature difference between the busbar temperature and the ambient temperature.

[0096] Furthermore, the host computer obtains the busbar temperature change based on the busbar heat capacity, internal Joule heat, and heat dissipation terms, and then recursively updates the busbar temperature in the previous cycle to obtain the simulated busbar temperature information for the current cycle, which is used to characterize the transient thermal state of the busbar under current load and environmental conditions.

[0097] Subsequently, the host computer converts the simulated busbar temperature information into the corresponding simulated busbar temperature signal and sends it to the battery management system under test through the communication module, enabling the battery management system under test to collect the busbar temperature signal in real time and participate in its internal thermal management judgment and protection logic calculation.

[0098] After receiving the simulated busbar temperature signal, the battery management system under test will judge the state of busbar overheating, abnormal temperature rise rate, etc. based on its own algorithm, and output the corresponding second feedback information. This second feedback information is used to characterize its response to changes in busbar temperature and control actions.

[0099] Finally, the host computer analyzes and compares the second feedback information, such as determining whether it triggers derating, protection, or alarm strategies when the busbar temperature exceeds the threshold, and obtains the busbar test results accordingly, thereby verifying the busbar temperature monitoring function and protection logic of the battery management system under test.

[0100] This embodiment allows the busbar temperature to be described not by a fixed value or a simplified static model, but by a periodic recursive calculation based on a dynamic thermal balance relationship constructed from busbar resistance, input current, and heat exchange processes. This enables the Joule heat generated by the busbar under current load and the heat dissipation to the environment to work synergistically within a unified thermal model, thus achieving continuous simulation of the busbar temperature evolution under varying operating conditions. By introducing busbar heat capacity to normalize heat changes, the temperature rise process reflects the actual thermal inertia characteristics of the busbar, avoiding simulation deviations caused by abrupt or discontinuous temperature changes. Simultaneously, by inputting the simulated busbar temperature signal into the battery management system under test (BMS) and obtaining its second feedback information, the system can verify the BMS's response behavior in busbar temperature sampling, over-temperature protection, and thermal management control strategies. This improves the realism of the busbar thermal condition simulation and the system-level test coverage, enhancing the functional reliability assessment of the BMS under high load and complex thermal environments.

[0101] In some embodiments, the method further includes: constructing a high-voltage circuit model and a low-voltage control circuit model matching the battery management system under test based on preset relay configuration parameters; the relay configuration parameters include the topological connection relationship, contact conduction impedance, coil equivalent electrical parameters, relay action delay threshold, high-voltage interlock circuit impedance parameters, pre-charge resistance value, and load equivalent electrical characteristics of the total positive relay, total negative relay, pre-charge relay, charging relay, and heating relay within the battery pack. The high-voltage circuit model is used to characterize the electrical connection topology and dynamic electrical response characteristics between the battery pack, various power relays, pre-charge resistor, and system load; the low-voltage control circuit model is used to equivalently simulate the coil impedance, coil drive voltage, and drive current variation of each relay, and simultaneously complete the real-time simulation of the electrical state of the high-voltage interlock circuit.

[0102] The test system relies on the digital I / O interface and bus communication interface on the simulator module to collect all relay control commands output by the battery management system under test in real time. The collected relay control commands include the main positive relay activation / deactivation command, the main negative relay activation / deactivation command, the precharge relay on / off command, the heating circuit control command, and the high voltage interlock circuit self-test excitation signal.

[0103] The high-voltage circuit model and the low-voltage control circuit model synchronously perform electrical state calculations based on real-time acquired relay control commands, reproducing the actual operation process of the relay contacts. The simulation process can be configured with fault disturbance conditions such as relay action delay, contact sticking, and open circuit. The high-voltage circuit model solves for electrical parameters such as the potential of each node on the high-voltage side, the total battery voltage, the pre-charge branch current, and the load circuit current in real time; the low-voltage circuit model synchronously solves for the circuit current after the relay coil is energized, and the voltage and current states of the high-voltage interlock branch.

[0104] Based on the simulation results of the high-voltage circuit model and the low-voltage control circuit model, the simulator module generates relay status feedback signals to characterize the relay's operating state and the corresponding circuit's operational state. These relay status feedback signals include relay on / off status signals, high-voltage circuit connection signals, pre-charge completion status signals, and high-voltage interlock status signals. The simulator module transmits these relay status feedback signals to the battery management system under test via an analog output board, a hard-wired analog output channel, or a CAN communication channel.

[0105] The battery management system under test continuously collects relay status feedback signals sent by the simulator module and generates corresponding relay control commands based on the internally preset relay control logic. This relay control logic is used to control the engagement, disengagement, and timing of each relay based on the current high-voltage circuit status, low-voltage control status, and fault status. This includes normal power-on control logic, normal power-off control logic, and high-voltage fault protection control logic. For example, after the precharge voltage reaches a set threshold, a command to close the main positive relay is issued; when a high-voltage fault is detected, a command to disconnect all main circuit relays is issued. These commands are continuously sent to the simulator module to form a complete closed-loop control process, fully replicating the battery system's normal power-on process, normal power-off process, and high-voltage fault emergency handling operation under all operating conditions.

[0106] Throughout the test, the host computer continuously receives third-party feedback information uploaded by the battery management system under test via the bus communication channel. This third-party feedback information includes relay operating status messages, relay sticking diagnostic indicators, pre-charge completion status flags, high-voltage interlock fault diagnostic codes, and high-voltage circuit abnormality alarm signals. The host computer synchronously stores the real-time electrical operating status data obtained from the high-voltage and low-voltage circuit models and cross-compares the diagnostic content carried by the third-party feedback information with the actual timing states within the model. The verification dimensions include: whether the relay on / off control timing meets the vehicle functional safety design specifications; the real-time performance and accuracy of the battery management system's fault identification for simulated fault conditions such as relay sticking, coil circuit open circuit, and high-voltage interlock disconnection; and the correct execution of functional safety logic such as high-voltage safety protection and fault power-off.

[0107] The host computer integrates all timing comparison, fault diagnosis and verification, and control timing compliance verification data to generate standardized relay test results. Based on the standardized relay test results, a comprehensive evaluation is completed on the relay timing control logic of the battery management system under test, the full life cycle management capability of the vehicle's high-voltage circuit, and the high-voltage electrical fault diagnosis and safety handling capability.

[0108] This high-voltage circuit and low-voltage control circuit simulation model can be synchronously and sequentially linked with the previously mentioned electro-thermal coupled battery system model, busbar thermal simulation model, equalization simulation model, communication simulation model, and fault simulation model. Within the same simulation cycle, it can synchronously simulate the electrical and thermal evolution of the battery cell, bus communication interaction, equalization energy transfer, high-voltage relay operation, and multiple types of fault coupling conditions, realizing integrated system-level closed-loop joint testing of the battery management system under test, including high-voltage safety, single-cell control, communication interaction, and fault protection.

[0109] In some embodiments, the method further includes: constructing a balancing simulation model corresponding to the battery management system under test according to preset balancing configuration parameters; the balancing configuration parameters include balancing topology type, balancing operating mode, rated balancing current, balancing differential voltage start / stop threshold, maximum balancing duration, and balancing branch fault parameters; the balancing simulation model builds an equivalent calculation framework for energy transfer between cells based on the previously described electro-thermal coupled battery system model, distinguishing between two types of simulation units: passive balancing equivalent topology and active balancing equivalent topology. The passive balancing equivalent topology unit consists of a controllable discharge resistor and switching device connected in parallel to each battery cell; the active balancing equivalent topology unit integrates multiple energy transfer channels, including bidirectional DC / DC, capacitor balancing, and inductor balancing. The balancing simulation model has bidirectional interactive capabilities, can receive balancing control commands issued by the battery management system under test in real time, and synchronously iteratively update the electrical and thermal state parameters of each cell, such as state of charge, open-circuit voltage, and balancing branch heating, according to the balancing control commands.

[0110] The test system captures the equalization control commands output by the battery management system under test (BSD) in real time through digital I / O acquisition channels, SPI / CAN communication interfaces, and analog input interfaces. Understandably, the BSD runs its built-in equalization control strategy based on the acquired individual cell voltages and SOCs, and outputs corresponding control signals. For passive equalization architectures, the BSD outputs digital level signals to drive the switching of the discharge MOSFETs in each cell. For active equalization architectures, the BSD controls the current flow and output amplitude of the energy transfer channel through SPI, CAN communication messages, or PWM pulse signals.

[0111] The equalization simulation model performs closed-loop state calculations based on real-time acquired equalization control commands, simulating the charge increase / decrease effect of the equalization branch current on individual cells, and iteratively updates the real-time SOC and open-circuit voltage of each cell. Simultaneously, the calculation process incorporates battery ohmic internal resistance and polarization characteristic parameters to solve for dynamic terminal voltages, generating updated simulated cell voltage signals. These signals are continuously and in real-time sent to the cell voltage acquisition channel of the battery management system under test (BMD) via a multi-channel analog output board. Simultaneously, the equalization simulation model outputs the equivalent current signal of the equalization branch to the acquisition port of the BMD, supporting the BMD in performing hardware loop self-diagnostic calculations such as open-circuit and short-circuit detection of the equalization branch.

[0112] The battery management system under test continuously collects real-time updated cell voltage signals from the simulation side, re-estimates the SOC of each cell, and dynamically adjusts the equalization control commands based on the updated cell consistency status. It then cyclically executes the equalization control logic until the equalization termination conditions are met. These conditions include the inter-cell voltage difference being less than a set equalization threshold and the cumulative equalization runtime reaching the maximum limit. Through this closed-loop iterative simulation process, the real-world operating conditions of continuous dynamic equalization of battery packs in vehicles and energy storage devices are fully replicated.

[0113] Throughout the test, the host computer continuously receives the fourth feedback information reported by the battery management system under test via bus communication. This fourth feedback information includes data such as the equalization function activation flag, cumulative equalization runtime, equalization branch fault diagnostic codes, and equalization start / stop status messages. The host computer synchronously stores complete operating data within the equalization simulation model, covering dimensions such as the SOC timing curves of each individual cell, dynamic changes in individual cell voltage, the integral value of the equalization current, the timestamps corresponding to the start and stop of the equalization function, and the timing of fault disturbance injection into the equalization branch.

[0114] After the test is completed, the host computer will perform a full-time cross-comparison and verification between the equalization control timing and equalization adjustment target output by the battery management system under test and the actual operating status of the individual cells recorded by the equalization simulation model. The verification includes: whether the equalization strategy starts normally when the individual cell voltage difference reaches the set threshold and exits reliably when the termination condition is met; whether the direction and amplitude of the output current of the equalization branch match the control commands issued by the battery management system; whether there are over-equalization or under-equalization phenomena in individual cells during the equalization process; and the real-time performance and accuracy of the fault identification and diagnosis results of the battery management system for fault disturbances such as open circuit of equalization resistor and abnormal energy transfer channel.

[0115] Based on all timing comparisons, consistency checks, and fault diagnosis verification results, the host computer outputs standardized equalization test results. The test results are used to comprehensively evaluate the correctness of the equalization control strategy of the battery management system under test, the consistency of the multi-cell equalization control response, and the achievement of the battery cell consistency convergence effect.

[0116] This equalization simulation model can be linked with the battery system model and busbar thermal simulation model mentioned above for synchronous time-series operation. It can synchronously simulate the electrical and thermal evolution of the cells and equalize energy transfer under various operating conditions within the same simulation cycle, realizing the integrated closed-loop joint test of the battery management system under test, including single-cell equalization and high-voltage control of the system.

[0117] In some embodiments, a communication simulation model corresponding to the battery management system under test is constructed based on preset communication configuration parameters. The communication configuration parameters include DBC protocol files for charging CAN, LIN bus, external CAN, internal CAN, etc., bus communication matrices, message definitions for each external node, communication cycle thresholds, signal parsing rules, and communication fault judgment thresholds. Based on the above communication configuration parameters, the host computer builds a virtual CAN / LIN bus network within the test system, instantiates multiple types of external virtual communication nodes, and completes the unified configuration of message sending cycles, initial signal assignments, signal callback triggering logic, and bus message transmission and reception buffering rules for each node, resulting in a communication simulation model that can synchronously output multi-node messages and dynamically inject communication anomalies.

[0118] A layered communication testing process is executed based on a communication simulation model. The layered communication testing process is divided into two levels of test conditions: the first communication test (i.e., normal communication function verification) and the second communication test (abnormal communication fault tolerance test).

[0119] (a) Verification of normal communication function The host computer drives the simulator module to continuously send standard messages from each virtual external node to the battery management system under test according to the standard communication timing. During the test, it continuously captures and caches all messages sent by the battery management system under test on the bus as the core data source for communication verification.

[0120] The host computer automatically retrieves the pre-stored DBC protocol file and performs a full-dimensional protocol consistency comparison and verification on the output messages of the battery management system under test. The verification items include: whether the message identifier ID, message data length DLC, and message sending period conform to the protocol definition; whether the start and end positions of single signal bit fields, data endianness format, signal scaling factor, and signal offset match the DBC parsing rules; and whether the physical values ​​of each signal in the message are within the valid value range specified by the protocol.

[0121] Based on verifying the compliance of the static protocol of the message, the response logic of the battery management system under test to the downlink control command is verified simultaneously: the host computer sends standardized command messages such as charging start and stop, and continuously observes the signal value changes and internal control command execution status of the subsequent reported messages of the battery management system under test to determine whether the response logic of the battery management system under test to the external command is accurate.

[0122] (ii) Abnormal communication fault tolerance test The communication simulation model supports injecting various types of abnormal messages into the bus. These abnormal message types include bus error frames, illegal identifier ID messages, data length exceeding the DLC range messages, signal physical value exceeding the limit messages, and message period jitter messages. After the host computer injects various abnormal messages sequentially, it collects bus interaction data to verify the fault-tolerant processing logic of the battery management system: whether abnormal messages are filtered and discarded according to the communication protocol specifications; whether bus faults are identified in real time and reported as Bus-Off or external node loss faults; and whether the fault code DTC generated after the fault message is triggered, the fault occurrence timestamp, and the fault duration correspond one-to-one with the fault injection time.

[0123] Simultaneously verify the communication timeout mechanism: The host computer stops sending key periodic messages such as heartbeat frames, records the start time of message interruption, and monitors whether the battery management system under test triggers the safety control logic within the preset timeout threshold and enters the preset safe operation state such as power limiting and high voltage power-off.

[0124] (iii) Receive the fifth feedback information and generate communication test results During the test, the host computer continuously receives the fifth feedback information output by the battery management system under test through the communication bus. The fifth feedback information consists of bus messages actively reported by the battery management system under test, related to its own communication operation status and communication fault diagnosis results, including bus node status reports, communication fault DTC messages, bus timeout flags, communication fault recovery status frames, and other data.

[0125] The host computer cross-compares and verifies the self-diagnostic status carried by the fifth feedback information with the abnormal operating conditions and message interruption timing actually injected on the bus side: when the simulation side injects a specified communication abnormal message, it verifies whether the battery management system under test generates the corresponding fault code synchronously; whether the node loss timestamp recorded in the battery management system message completely matches the time when the simulator stops sending the corresponding node message.

[0126] After completing the comparison of all verification items, the host computer integrates the protocol consistency verification results, abnormal message fault tolerance processing results, communication timeout response results, and fault diagnosis matching results to generate standardized communication test results. The communication test results are used to comprehensively evaluate the bus protocol compliance, long-term communication stability, communication abnormality fault tolerance handling capability, and bus fault diagnosis accuracy of the battery management system under test.

[0127] This communication simulation model can run synchronously and in conjunction with the battery system model, busbar thermal simulation model, high-voltage circuit model, equalization simulation model, and fault simulation model mentioned above. Within the same simulation time sequence, it can complete the simulation of cell electrical and thermal states, bus communication interaction, and system fault injection, thereby realizing the full-function closed-loop joint testing of the battery management system at the vehicle level.

[0128] In some embodiments, a fault simulation model corresponding to the battery management system under test is constructed based on preset fault configuration parameters. The fault configuration parameters include fault thresholds, fault offsets, fault durations, dynamic fault triggering conditions, and high-voltage circuit linkage constraint parameters for various electrical, thermal, and insulation faults. The fault simulation model is built upon the battery system model described above, integrating multi-dimensional fault disturbance injection units within the battery system model. The host computer can configure and issue various fault disturbance commands to achieve dynamic simulation of multiple types of battery system faults. Fault disturbance types include single-cell voltage overvoltage / undervoltage offset, abnormal battery total voltage, overcurrent during charging / discharging, short circuit in the circuit, cell high / low temperature exceeding limits, and abnormal high-voltage insulation resistance. The fault simulation model has bidirectional closed-loop linkage capability, enabling it to receive control commands from the battery management system under test in real time and synchronously update simulation variables such as high-voltage circuit operating status, simulated voltage output, and simulated current output based on the control commands.

[0129] Based on the fault simulation model, corresponding simulated fault signals are generated and sent to the battery management system under test in real time. The test system synchronously outputs cell voltage, total voltage, loop current, cell temperature, and insulation monitoring signals superimposed with fault disturbances through multiple analog output channels and bus simulation channels. The signals are updated in real time and connected to all hardware acquisition channels of the battery management system under test, driving the internal fault diagnosis logic of the battery management system to start calculation.

[0130] While continuously outputting simulated fault signals to the battery management system under test (BMD), the test system simultaneously acquires all protection-related control commands output by the BMD in real time through hard-wired I / O acquisition channels and bus monitoring units. The acquired signals include the drive level signals corresponding to the main positive relay, main negative relay, and pre-charge relay; the drive level signals of the fault indicator light and audible / visual alarm; and protection-related output signals such as power limit request messages and charge / discharge stop command messages sent out via the CAN / LIN bus.

[0131] The fault simulation model dynamically updates the simulation state of the battery system model based on the real-time acquired protection control commands from the battery management system under test (BSD), forming a closed-loop simulation logic that links fault excitation and protection actions. When the BSD issues a control command to disconnect the main positive relay, the high-voltage circuit model simultaneously sets the simulated total battery voltage output to zero and dynamically updates the total voltage waveform according to the residual voltage decay curve of the high-voltage capacitor, while simultaneously setting the simulated circuit current to zero. When the BSD issues a stop charging command, the simulated operating state of the charging circuit is updated synchronously, and the charging current output is cut off. This forms a complete fault response causal simulation chain: fault disturbance signal injection, BSD fault identification and protection action output, and the simulation model synchronously correcting the electrical output to reproduce the real physical process of fault elimination or change in operating conditions.

[0132] Throughout the test, the sixth feedback information reported by the battery management system under test through the communication bus is continuously collected. The sixth feedback information includes diagnostic message data such as fault diagnostic codes (DTC), fault alarm status indicators, execution status of protection functions at all levels, and fault occurrence and recovery timestamps. The host computer stores and records all feedback messages in a time sequence.

[0133] After the test, the host computer performs a unified time-axis alignment and cross-comparison of the three types of timing data: fault excitation timing data injected from the simulation side, protection control signal timing data output from the hardware port of the battery management system under test, and the sixth feedback information reported by the bus of the battery management system under test. Based on the comparison results, standardized fault test results are generated to fully verify the fault diagnosis function of the battery management system. The evaluation dimensions include: the accuracy and real-time response of fault identification, whether the protection strategies at all levels output matching hardware control actions in the corresponding timing sequence, whether the battery management system completes state recovery or fault latching according to the preset logic after the fault is eliminated, and whether the protection diagnosis status reported by the battery management system bus is completely consistent with the actual output actions of the hardware port, realizing a full-dimensional verification of the fault diagnosis logic and hardware protection execution link.

[0134] This fault simulation model can be synchronously and sequentially linked with the previously mentioned electro-thermal coupled battery system model, busbar thermal simulation model, high-voltage circuit model, equalization simulation model, and communication simulation model. It can synchronously inject electrical faults, thermal faults, and communication anomalies within the same simulation cycle to complete system-level closed-loop testing of the battery management system under multiple fault coupling scenarios.

[0135] This application provides a testing method for a battery management system. By constructing a battery system model and introducing a unified modeling framework for electrochemical state, thermal state, electrical state, and communication state, the testing process of the battery management system under test is transformed from the traditional single-signal static simulation method to a dynamic closed-loop evolution mechanism based on periodic iteration. Under this mechanism, key parameters such as battery state of charge, open-circuit voltage, polarization voltage, and temperature are recursively updated based on the previous cycle state, and form a continuous coupling relationship with input current, capacity changes, and heat exchange processes. This ensures that the physical quantities no longer change in isolation, but exhibit continuous, correlated, and traceable dynamic evolution characteristics in the time dimension. This effectively avoids the state jumps and parameter inconsistencies that easily occur in traditional discrete parameter simulations, improving the physical consistency and engineering realism of the simulated signal in the dynamic process.

[0136] Furthermore, this application introduces the temperature dependence of polarization parameters, electrothermal coupling, and the mapping relationship between open-circuit voltage and SOC into a unified battery model. This allows the changes in the battery's terminal voltage during charging and discharging to fully reflect the combined effects of ohmic voltage drop, polarization, and concentration, thereby significantly improving the accuracy and realism of dynamic voltage simulation. Simultaneously, by modeling the temperature evolution process with energy conservation in relation to current input and thermal characteristic parameters, the battery temperature rise process can realistically reflect the combined effects of Joule heating, reaction heat, and heat dissipation, enhancing the physical rationality of the thermal behavior simulation.

[0137] Furthermore, this application integrates the high-voltage circuit model with the low-voltage control circuit model, busbar thermal simulation model, equalization simulation model, communication simulation model, and fault simulation model into a unified test framework, enabling the battery management system under test to achieve system-level closed-loop testing covering battery state evolution, high-voltage control, energy equalization, communication interaction, and fault protection without connecting to the real battery system and vehicle equipment. Specifically, the system dynamically simulates the state changes of the circuits corresponding to relay control commands using high-voltage and low-voltage control circuit models. Feedback information, including relay on / off states, high-voltage circuit connection states, pre-charge states, and high-voltage interlock states, is sent to the battery management system under test (BSD). This allows the BSD to continuously execute relay control logic based on the simulated operating state, thus achieving complete verification of normal power-on / off processes, high-voltage fault protection processes, and relay anomaly diagnosis processes. A balancing simulation model simulates the energy transfer process between cells based on the balancing control commands output by the BSD, simultaneously updating the cell state of charge, voltage, and balancing branch states. This enables closed-loop verification of the BSD's balancing strategy in a dynamic feedback environment. A communication simulation model constructs a virtual communication node and bus interaction environment corresponding to the real application environment, enabling comprehensive testing of communication protocol parsing, message interaction, communication anomaly identification, and fault tolerance capabilities. A fault simulation model injects electrical faults, thermal faults, and communication anomalies into the battery system model, and synchronously updates the simulation state in conjunction with the protection control commands output by the BSD, forming a complete closed-loop verification process between fault excitation, fault identification, and protection response.

[0138] Based on the aforementioned multi-model collaborative simulation mechanism, this application can synchronously simulate the dynamic changes of battery electrical state, thermal state, equalization state, high-voltage control state, and communication state within the same test platform. This enables the battery management system under test to complete the joint verification of sampling, control, communication, diagnostic, and protection functions under conditions closer to actual operating environments. Compared to static testing using only single voltage, current, or temperature signals, this application not only expands the test coverage and improves the consistency and reliability of the correlation between different states during the test, but also supports the verification of complex operating conditions, multiple fault couplings, and system-level control logic, thereby improving the development and testing efficiency of the battery management system and its reliability in practical applications.

[0139] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them; under the concept of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of this application as described above, which are not provided in detail for the sake of brevity; although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A test method for a battery management system, characterized in that, The method includes: Based on preset battery configuration parameters, a battery system model corresponding to the battery management system under test is constructed; wherein, the battery configuration parameters include: initial battery charge state information, coulombic efficiency, input current, polarization characteristic parameters, open circuit voltage relationship curve, ohmic internal resistance, electrochemical polarization voltage, battery mass, and thermal characteristic parameters. Based on the battery configuration parameters and the battery system model, a simulated battery test process is executed until a preset termination condition is met; wherein, the simulated battery test process includes: In the current cycle, obtain the battery temperature of the previous cycle, and obtain the actual battery capacity based on the battery temperature. Based on the initial state of charge information of the battery, the coulombic efficiency, the actual capacity of the battery, and the input current, the state of charge information of the battery for the current cycle is obtained; Based on the polarization characteristic parameters and the battery temperature, the target polarization resistance and target polarization capacitance are obtained; The target polarization voltage is obtained based on the target polarization resistor, the target polarization capacitor, and the input current; The battery open-circuit voltage is obtained based on the battery charge state information and the open-circuit voltage relationship curve; The simulated voltage information is obtained based on the battery open-circuit voltage, the input current, the ohmic internal resistance, the electrochemical polarization voltage, and the target polarization voltage; Based on the simulated voltage information, the battery temperature, and the input current, a simulated battery signal is generated and sent to the battery management system under test. Receive the first feedback information generated by the battery management system under test based on the simulated battery signal, and obtain the test result of the battery management system under test for the current period based on the first feedback information; The battery temperature for the next cycle is obtained based on the battery mass, the input current, the ohmic internal resistance, and the thermal characteristic parameters.

2. The test method for the battery management system according to claim 1, characterized in that, The battery configuration parameters also include battery health status information, capacity coefficient matrix information, and battery rated capacity. Obtaining the actual battery capacity based on the battery temperature includes: The capacity coefficient is obtained based on the battery temperature and the capacity coefficient matrix information; The actual capacity of the battery is obtained based on the battery health status information, the capacity coefficient, and the battery rated capacity.

3. The test method for the battery management system according to claim 1, characterized in that, The polarization characteristic parameters include a polarization resistance reference parameter, a resistance temperature coefficient, a battery reference temperature, a polarization capacitance reference parameter, and a capacitance temperature coefficient. The step of obtaining the target polarization resistance and target polarization capacitance based on the polarization characteristic parameters and the battery temperature includes: The target polarization resistance is obtained based on the polarization resistance reference parameters, the resistance temperature coefficient, the battery temperature, and the battery reference temperature. The target polarization capacitor is obtained based on the polarization capacitor reference parameters, the capacitor temperature coefficient, the battery temperature, and the battery reference temperature.

4. The test method for the battery management system according to claim 1, characterized in that, The thermal characteristic parameters include battery specific heat capacity, battery reaction heat, heat exchange coefficient, and ambient temperature. The step of obtaining the battery temperature for the next cycle based on the battery mass, input current, ohmic internal resistance, and the thermal characteristic parameters includes: The battery temperature for the next cycle is obtained based on the battery mass, battery specific heat capacity, input current, ohmic internal resistance, battery reaction heat, ambient temperature, and heat exchange coefficient.

5. The test method for the battery management system according to claim 4, characterized in that, The battery configuration parameters also include busbar thermal capacity, busbar resistance, and busbar heat dissipation coefficient; the method further includes: Based on the busbar heat capacity, the input current, the busbar resistance, the ambient temperature, and the busbar heat dissipation coefficient, the simulated busbar temperature information is obtained; Based on the simulated busbar temperature information, a simulated busbar temperature signal is generated and sent to the battery management system under test; The system receives second feedback information generated by the battery management system under test based on the simulated busbar temperature signal, and obtains the busbar test result based on the second feedback information.

6. The test method for the battery management system according to claim 1, characterized in that, The method further includes: Based on the preset relay configuration parameters, construct a high-voltage circuit model and a low-voltage control circuit model corresponding to the battery management system under test; Receive relay control commands sent by the battery management system under test; According to the relay control command, the corresponding relay action state, high-voltage circuit operation state and low-voltage control circuit operation state are simulated based on the high-voltage circuit model and the low-voltage control circuit model, and relay state feedback signals are generated to characterize the relay action state, the high-voltage circuit operation state and the low-voltage control circuit operation state. The relay status feedback signal is sent to the battery management system under test, so that the battery management system under test executes relay control logic based on the relay status feedback signal. The system receives third feedback information uploaded by the battery management system under test and generates standardized relay test results based on the third feedback information.

7. The test method for the battery management system according to claim 1, characterized in that, The method further includes: Based on the preset equalization configuration parameters, an equalization simulation model corresponding to the battery management system under test is constructed. Receive equalization control commands sent by the battery management system under test; The equalization simulation test is performed on the battery management system under test according to the equalization control command and the equalization simulation model. The system receives the fourth feedback information uploaded by the battery management system under test, and generates standardized equalization test results based on the fourth feedback information.

8. The test method for the battery management system according to claim 1, characterized in that, The method further includes: Based on preset communication configuration parameters, a communication simulation model corresponding to the battery management system under test is constructed; wherein, the communication simulation model is used to perform a first communication test and a second communication test on the battery management system under test; The system receives the fifth feedback information uploaded by the battery management system under test and generates standardized communication test results based on the fifth feedback information.

9. The test method for the battery management system according to claim 1, characterized in that, The method further includes: Based on preset fault configuration parameters, a fault simulation model corresponding to the battery management system under test is constructed. Based on the fault simulation model, a simulated fault signal is generated and sent to the battery management system under test, so that the battery management system under test can perform fault testing according to the simulated fault signal; at the same time, the system receives protection control commands sent by the battery management system under test and updates the battery system model according to the protection control commands. The system receives the sixth feedback information uploaded by the battery management system under test and generates standardized fault test results based on the sixth feedback information.

10. A host computer, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1-9.

Images