CN-121995132-A - BMS hardware in-loop test method and test system

CN121995132ACN 121995132 ACN121995132 ACN 121995132ACN-121995132-A

Abstract

The invention relates to the technical field of testing, and provides a testing method and a testing system for BMS hardware in-loop testing, wherein the testing system comprises the steps of generating a test case set covering normal, boundary and fault scenes by adopting a multi-objective optimization algorithm according to a testing target; scheduling and executing a test case set, running an electrothermal coupling battery model calibrated by experimental data, injecting faults into the BMS to be tested, simultaneously recording response data of the BMS in real time, dynamically adjusting the priority and execution sequence of the subsequent test case according to the real-time test result, analyzing the response data, evaluating the BMS performance and generating a test report.

Inventors

GUO XIANGWEN
Sha Yingdi
GUO XIAOTIAN

Assignees

重庆同沃汽车科技有限公司

Dates

Publication Date: 20260508
Application Date: 20251201

Claims (10)

1. A method for testing hardware of a Battery Management System (BMS) in a loop, comprising: Generating a test case set covering normal, boundary and fault scenes by adopting a multi-objective optimization algorithm according to a test target; Scheduling and executing the test case set, running an electrothermal coupling battery model calibrated by experimental data, injecting faults into the BMS to be tested, and simultaneously recording response data of the BMS in real time, wherein the priority and execution sequence of the subsequent test case are dynamically adjusted according to the real-time test result; And analyzing the response data, evaluating BMS performance and generating a test report.
2. The test method of claim 1, wherein before generating the test case set covering normal, boundary and fault scenarios by using a multi-objective optimization algorithm according to the test objective, the test method further comprises: and configuring battery pack parameters, communication protocols and test targets according to the specifications of the BMS to be tested.
3. The test method of claim 1, wherein generating a test case set covering normal, boundary and fault scenarios using a multi-objective optimization algorithm according to a test objective comprises: and analyzing BMS control logic, identifying critical conditions, generating corresponding boundary test scenes, and adding the boundary test scenes into the test case set.
4. The test method according to claim 1, wherein the multi-objective optimization algorithm comprises a genetic algorithm or a particle swarm optimization algorithm, and the objectives of the multi-objective optimization algorithm optimization comprise test scenario coverage, test time, and computing resource utilization.
5. The method of claim 1, wherein the injecting faults into the BMS under test comprises injecting at least one of the following faults: the intelligent triggering system comprises a hard fault, a soft fault, a progressive fault, an intermittent fault and an intelligent triggering fault based on a model state, wherein the hard fault comprises an open-circuit fault, a short-circuit fault and abnormal connection impedance, and the soft fault comprises sensor precision drift, performance attenuation, communication delay, packet loss and protocol error.
6. The testing method of claim 1, wherein the injecting faults into the BMS under test further comprises triggering a plurality of associated faults sequentially or simultaneously according to preset logic to simulate a composite fault scenario, and/or, And configuring faults according to fault configuration data input by a user through a man-machine interaction interface, wherein the fault configuration data comprises fault types, severity and occurrence time.
7. The test method of claim 1, further comprising automatically triggering and executing a complete test flow from test case generation to report generation after code submission by a developer through integration with an external continuous integration/continuous deployment CI/CD tool, implementing an automated regression test.
8. The testing method according to claim 1, wherein the electric coupling battery model calibrated by experimental data is operated to simulate the electric characteristics of the battery by adopting a double-time-constant equivalent circuit model, and/or the electric coupling battery model calibrated by experimental data is operated to describe the heat exchange process inside the battery and with the environment by adopting a three-dimensional thermal network model, and/or the electric coupling battery model calibrated by experimental data is operated to introduce an aging factor for simulating the performance attenuation of the battery and a temperature gradient effect for simulating the uneven temperature distribution in the battery pack.
9. A test system for BMS hardware-in-loop testing, characterized by a test method for performing the BMS hardware-in-loop testing according to any of claims 1 to 8, comprising: The test management platform is used as a control core of the system and is internally provided with an intelligent test case generation engine; the real-time simulation system is in communication connection with the test management platform and is used for running a high-precision electrothermal coupling battery model; The fault injection unit is in communication connection with the test management platform and the real-time simulation system and is used for receiving instructions and simulating various types of hardware and software faults; The parameter configurable BMS interface module is used for adapting BMS to be tested with different architectures and communication protocols and realizing interaction of simulation data and instructions; And the result analysis and evaluation system is in communication connection with the test management platform and is used for analyzing the test data, evaluating BMS performance and generating a test report.
10. The test system of claim 9, wherein the parameter configurable BMS interface module adapts to a centralized or distributed BMS architecture by way of software configuration; The parameter configurable BMS interface module supports at least one communication protocol of a controller area network CAN, a controller area network FD of flexible data rate and a daisy chain; the parameter configurable BMS interface module supports an 800V high voltage battery pack architecture that simulates up to 300 series cells.

Description

BMS hardware in-loop test method and test system Technical Field The disclosure relates to the technical field of testing, in particular to a testing method and a testing system for BMS hardware in-loop testing. Background The battery management system is critical to the safety, reliability and performance of the electric vehicle. HIL (HARDWARE IN THE Loop, hardware-in-Loop) testing has become the core means of BMS (Battery MANAGEMENT SYSTEM) verification. The HIL test system of the conventional BMS generally relies on pre-written fixed test cases, lacks dynamic generation capability for boundary conditions and extreme scenes, and the test cases require a lot of manual intervention, which makes it difficult to achieve full coverage and rapid iteration. Some current improvements attempt to solve the problem of low automation and intelligent degree of the HIL test system through special equipment, but often face new challenges such as high cost, complex system and the like. For example, some high-end HIL systems use dedicated battery simulator modules, but the cost per channel increases significantly, with poor economies in large-scale battery pack testing. In addition, although the HIL test scheme based on the cloud platform can improve the resource utilization rate, the HIL test scheme is limited by the requirements of data safety and real-time property, and challenges are still faced in industrial application. Therefore, how to improve the test effect of the test system for the ring test of the BMS hardware is a technical problem that needs to be solved currently. Disclosure of Invention In view of this, the embodiment of the disclosure provides a testing method and a testing system for testing BMS hardware in a ring, so as to solve the technical problem in the prior art that the testing effect of the testing system for testing BMS hardware in a ring is poor. In order to achieve the above purpose, the technical scheme adopted in the present disclosure is as follows: According to a first aspect of the embodiment of the disclosure, a testing method for BMS hardware in-loop testing is provided, which comprises the steps of generating a test case set covering normal, boundary and fault scenes by adopting a multi-objective optimization algorithm according to a testing target, scheduling and executing the test case set, operating an electrothermal coupling battery model calibrated by experimental data and injecting faults into a BMS to be tested, simultaneously recording response data of the BMS in real time, dynamically adjusting priority and execution sequence of subsequent test cases according to a real-time testing result, analyzing the response data, evaluating BMS performance and generating a testing report. In some embodiments, before the test case set covering the normal, boundary and fault scenes is generated by adopting the multi-objective optimization algorithm according to the test objective, the test method further comprises configuring the battery pack parameters, the communication protocol and the test objective according to the specification of the BMS to be tested. In some embodiments, a multi-objective optimization algorithm is adopted to generate a test case set covering normal, boundary and fault scenes according to a test objective, and the method comprises the steps of analyzing BMS control logic, identifying critical conditions and generating corresponding boundary test scenes, and adding the boundary test scenes into the test case set. In some embodiments, the multi-objective optimization algorithm comprises a genetic algorithm or a particle swarm optimization algorithm, and the objectives of the multi-objective optimization algorithm optimization comprise test scenario coverage, test time, and computing resource utilization. In some embodiments, injecting faults into the BMS under test includes injecting at least one of hard faults, soft faults, progressive faults, intermittent faults, and intelligent triggering faults based on model states, wherein the hard faults include open faults, short circuit faults, abnormal connection impedance, and the soft faults include sensor accuracy drift, performance decay, communication delay, packet loss, and protocol errors. In some embodiments, the fault injection to the BMS to be tested further comprises triggering a plurality of associated faults sequentially or simultaneously according to preset logic to simulate a composite fault scene, and/or configuring the faults according to fault configuration data input by a user through a man-machine interaction interface, wherein the fault configuration data comprises fault types, severity and occurrence time. In some embodiments, the test method further includes automatically triggering and executing a complete test flow from test case generation to report generation after the developer submits the code by integrating with an external CI/CD tool, implementing an automated regression test. In some embodime