A hybrid power system health management capability verification and assessment method

Abstract

A hybrid power system health management capability verification and assessment method belongs to the field of health management system verification and system evaluation. The platform can collect data from different sensors and information systems in real time, such as operating status, temperature, vibration, etc., and process and analyze them immediately. This real-time performance helps to quickly identify signs of failure, take preventive measures in a timely manner, and reduce the risk of failure and maintenance costs. Simulation testing can not only reduce development and verification costs, but also avoid damage to equipment and improve the safety of overall testing. Not only can verification be performed during the system design phase, but the health status of equipment can also be continuously monitored during the production, operation and maintenance phases to predict and diagnose faults. By combining historical data with real-time data, the platform can provide effective support for the management of the entire life cycle of equipment, extend the service life of the system, and optimize operation and maintenance decisions.

Description

Technical Field

[0001] The present invention belongs to the field of verification and system evaluation of health management systems. Specifically, by building a universal health management hardware verification system and a health management software verification system, a standard mathematical measurement indicator system is used to strictly evaluate the prediction and health management performance of the health management system, thereby achieving quantitative performance verification and evaluation of the health management system. Background Art

[0002] The increasing complexity, informatization, and integration of health management systems are placing higher demands on system reliability, safety, and economic efficiency. Currently, engines primarily utilize preventive maintenance methods. However, facing the demands of the new generation of engines for enhanced fault warning and diagnostic capabilities, improved engine health management, and enhanced decision-making capabilities, existing technologies are no longer adequate for future developments. Research into fault warning and health management technologies is urgently needed.

[0003] A hybrid electric vehicle (HEV) is a vehicle powertrain that combines a traditional internal combustion engine (usually a gasoline engine) with an electric motor. When the vehicle is starting or traveling at low speeds, it is typically driven by the electric motor, which saves fuel and reduces exhaust emissions. The electric motor is powered by the vehicle's battery. At high speeds or during sudden acceleration, the internal combustion engine and electric motor work together. The internal combustion engine can provide greater power, while the electric motor can compensate for the power lag of the internal combustion engine during acceleration, improving overall acceleration performance. When decelerating or braking, hybrid vehicles are typically equipped with a regenerative braking system. This system converts the kinetic energy generated during braking into electrical energy and stores it in the battery. This not only improves energy efficiency but also extends the life of the braking system.

[0004] Validation and system evaluation of health management systems are crucial steps in the development and deployment of health management systems. Once a health management system is operational, further modifications require significant human, material, and financial resources. The lack of systematic, targeted research and application of health management system validation and evaluation technologies has hindered their further development. Therefore, research on universal health management system validation and evaluation technologies is needed to support health management system validation.

[0005] The present invention proposes a hybrid power system health management capability verification and evaluation method, which supports access to real-time simulation models and health management systems, can provide typical fault data sets and a semi-physical simulation experimental test environment for the health management system, and has typical fault injection and simulation capabilities, health management model verification capabilities, and health management system evaluation capabilities. Summary of the Invention

[0006] The primary purpose of this invention is to enable efficient, accurate, and cost-effective verification of system health management solutions by integrating multiple technologies. Running models of real hardware in a virtual simulation environment avoids the high risk and cost of direct testing on actual equipment, reducing equipment wear and tear during testing. This ensures the accuracy, reliability, and availability of the health management system across all phases and application scenarios, enabling more efficient and secure equipment management and maintenance.

[0007] A hybrid power system health management capability verification and assessment method includes the following steps:

[0008] Step 1. System Modeling: In this phase, the objectives and scope of fault diagnosis are defined, and corresponding mathematical, physical, or simulation models are established. These models provide the basis for fault injection, ensuring the accuracy and controllability of the fault injection process.

[0009] Step 2. Fault Diagnosis Algorithm Selection and Verification: A fault diagnosis algorithm suitable for the system characteristics was selected, which could be a data-driven, model-driven, or hybrid approach. Algorithm parameters were then adjusted to suit the actual application environment. The algorithm was then tested and verified using simulation platforms and hardware-in-the-loop experiments.

[0010] Step 3. Fault injection: Fault simulation and testing of the hybrid system are achieved by constructing a fault injection test set, simulating fault behavior, monitoring system response, and automating injection diagnosis to optimize the system model and improve the efficiency and standardization of fault diagnosis.

[0011] Step 4. Diagnostic Testing: The possible fault types are defined and the key nodes or components for fault injection are identified. Then, real-time fault diagnosis testing is performed to verify the performance of the diagnostic system and record the results of fault injection and diagnosis in detail.

[0012] Beneficial effects of the present invention:

[0013] (1) The platform can collect data from different sensors and information systems in real time, such as operating status, temperature, vibration, etc., and process and analyze them immediately. This real-time nature helps to quickly identify signs of failure, take preventive measures in a timely manner, and reduce the risk of failure and maintenance costs.

[0014] (2) Simulation testing not only reduces development and verification costs, but also avoids damage to equipment and improves overall testing safety. It not only enables verification during the system design phase, but also allows for continuous monitoring of equipment health during production, operation, and maintenance, enabling fault prediction and diagnosis. By combining historical and real-time data, the platform can effectively support the full lifecycle management of equipment, extending system service life and optimizing operational and maintenance decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is the overall architectural design diagram of the present invention;

[0016] Figure 2 is a diagram of a hybrid system of the present invention;

[0017] Figure 3 is a fault diagnosis algorithm diagram of the present invention;

[0018] Figure 4 is a fault injection flow chart of the present invention;

[0019] Figure 5 It is a system hardware architecture diagram of the present invention;

[0020] Figure 6 It is a functional diagram of the verification platform of the present invention. DETAILED DESCRIPTION

[0021] The specific implementation of the present invention is further described below in conjunction with the accompanying drawings and technical solutions.

[0022] The overall architecture design diagram of the present invention is as follows: Figure 1 ,First, the hybrid system is studied, and then a high-precision simulation model is established, its health management model is designed, and then appropriate indicators are selected to evaluate the health management system. Figure 2 , decomposing the system from feasibility study to component-level testing to ensure that the system design meets the requirements and operates reliably.

[0023] Step 1: According to the needs of the specific hybrid power system, clarify the object, diagnosis scope, and verification target of the fault diagnosis. Model the hybrid power system to be tested to obtain a hybrid power system model, and use the hybrid power system model to form a simulation platform or an in-the-loop test platform to ensure the accuracy and controllability of fault injection. The operation of the hybrid power system depends on a series of precise models working together. The hybrid power system model includes the vehicle dynamics model, motor control model, motor model, battery control model, battery model, generator control model, generator model, engine-generator coupling device model, engine controller model, engine model, power coupling device model, and reduction gearbox model;

[0024] First, the vehicle dynamics model calculates the required power and torque based on the driver's input and vehicle state. Next, the motor control model adjusts the motor's operating state based on the power and torque, while the motor model is responsible for actually generating the power. The battery control model manages the battery's charging and discharging to ensure a stable power supply to the motor and generator, while the battery model describes the battery's physical characteristics and behavior. When the battery is low, the generator control model activates the generator to supplement the battery or provide power directly, while the generator model describes the generator's physical characteristics and power generation behavior. The engine-generator coupling model manages the mechanical connection between the engine and generator, while the engine controller model controls the engine's operation based on demand. The engine model describes the engine's physical characteristics and operating behavior. The power coupling model transfers the power generated by the engine, generator, and motor to the reduction gearbox, while the reduction gearbox model converts high-speed, low-torque power into low-speed, high-torque power suitable for driving the wheels. The tandem operation of these models ensures that the hybrid system intelligently distributes and converts power sources under varying driving conditions, achieving optimal fuel efficiency and performance.

[0025] Step 2: Construct a classifier-free guided diffusion model based on data drive, model drive or hybrid drive. Adjust and optimize the parameters of the classifier-free guided diffusion model to adapt to the actual application environment and system. In actual work, experimental data faces the problem of data imbalance, that is, the number of fault data is less than that of healthy data. The present invention uses a method of generating fault data using a classifier-free guided diffusion model to solve the data imbalance problem. Figure 3 .

[0026] The classifier-free guided diffusion model uses real fault data to simulate the data generation process, gradually adding noise and performing denoising to generate synthetic fault data similar to the real fault data. This synthetic fault data is integrated with the real fault data to further identify all fault types (e.g., mechanical, electrical, sensor, and software faults in the powertrain, transmission, and auxiliary systems).

[0027] The classifier-free guided diffusion model adds a multi-head self-attention mechanism and skip connections to the diffusion model to enhance feature extraction capabilities. This allows the model to handle time steps and label embeddings for generating hybrid powertrain fault data. The diffusion model comprises an encoder, an intermediate feature extractor, and a decoder. The model takes into account data imbalance and optimizes the performance of generated data by adjusting key hyperparameters.

[0028] Furthermore, synthetic fault data is injected into the simulation platform to test and verify the classifier-free guided diffusion model. Injecting synthetic fault data into the in-the-loop test platform, combined with hardware-in-the-loop experiments or hardware-in-the-loop experiments, allows for physical fault injection and diagnosis to verify the performance of the classifier-free guided diffusion model in real-world scenarios.

[0029] Step 3: All fault types are combined into a fault injection test set. Relevant faults are called from the fault injection test set and injected into the simulation platform or in-loop test platform to simulate the fault behavior of the hybrid power system and its related equipment. The injection of different fault types is achieved through the following methods:

[0030] Method 1: Short-circuit or open-circuit some models in the simulation platform or the loop test platform;

[0031] Method 2: Change the component parameters of some models in the simulation platform or the loop test platform;

[0032] Furthermore, testing is performed after each fault injection. During testing, the hybrid system's response is monitored and relevant data is recorded for analysis. Based on the analysis results, the hybrid system model, test cases, and injection method after the fault injection are adjusted to better simulate real-world conditions. This ensures that the parameters during the fault injection process are controllable, and that both the injection time and the fault intensity can be quantitatively controlled.

[0033] Furthermore, automated injection and automated diagnosis technologies are used to implement the fault injection process, reduce manual intervention, quantify fault injection and diagnosis results, provide a basis for establishing the standardization and replicability of the fault diagnosis system, and improve the efficiency and standardization level of the verification process.

[0034] Step 4 Fault injection flow chart is as follows Figure 4 . Connect the health management system to the simulation platform or the in-loop test platform, and perform fault injection according to the method in step 3. After the fault injection, perform a real-time test of fault diagnosis to verify whether the health management system can detect, identify and locate the fault in time when the fault occurs. Record the detailed results of each fault injection and fault diagnosis, including data such as fault type, injection parameters, and diagnostic delay. Quantitatively measure various evaluation indicators of the health management system and form a verification report containing fault injection methods, diagnostic results, comparative analysis, optimization suggestions, etc., as the basis for the final evaluation of the diagnostic system performance. The evaluation indicators selected for the verification evaluation system are as follows:

[0035] Table 1 Evaluation indicators

[0036] Evaluation indicators Introduction Fault isolation rate The proportion of all detected faults that can be successfully isolated and identified Fault detection time The time interval from when a fault occurs to when it is recognized by the detection system Fault isolation time The time required from the time a fault is detected to the time the source of the fault is successfully identified and isolated Fault detection rate The proportion of all faults that can be successfully detected by the detection system Prediction accuracy The proportion of correct predictions among all prediction results

[0037] The corresponding calculation formula is:

[0038] 1. Fault Isolation Rate (FIR)

[0039] Definition: The ratio of the number of faults that the system can correctly isolate to specific faulty components to the total number of actual faults that occur.

[0040] Calculation formula: Standard: FIR ≥ 90%

[0041] 2. Mean Time to Detect (MTTD)

[0042] Definition: The average time required for a system to detect a fault from the beginning to its confirmation.

[0043] Calculation formula: Standard: MTTD ≤ 5 minutes

[0044] 3. Mean Time to Isolation (MTTI)

[0045] Definition: The average time required for a system to isolate and confirm the faulty component.

[0046] Calculation formula: Standard: MTTI ≤ 10 minutes

[0047] 4. Fault Detection Rate (FDR)

[0048] Definition: The ratio of the number of faults that a system can correctly detect to the total number of faults that actually occur.

[0049] Calculation formula: Standard: FDR ≥ 95%

[0050] 5. Prediction Accuracy (PA)

[0051] Definition: The proportion of correct predictions among all predictions.

[0052] Calculation formula: Standard: PA ≥ 90%.

[0053] Developing appropriate requirements indicators for health management systems can cover the entire lifecycle of health management system design, integration, physical and semi-physical verification, and requirements changes. This can improve implementation efficiency, reduce equipment costs throughout the entire lifecycle, and improve economic benefits. Comprehensive evaluation of health management systems requires integrating algorithm performance, system design and implementation, and full lifecycle management to ensure system effectiveness and cost-effectiveness. Establish a comprehensive testing plan covering functional testing, performance testing, and safety testing to ensure the system's normal operation under various circumstances. By developing comprehensive requirements indicators, we can ensure that the system operates efficiently in all aspects of design, integration, verification, and requirements change management, thereby achieving the goal of reducing lifecycle costs and improving economic benefits.

[0054] While the embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the specific embodiments described above. The embodiments described above are merely illustrative and instructive, and are not restrictive. A person skilled in the art, informed by this specification and without departing from the scope of the claims, may devise various alternative embodiments, all of which fall within the scope of protection of the present invention.

Claims

1. A hybrid power system health management capability verification and assessment method, characterized in that: Here are the steps: Step 1: Based on the specific requirements of the hybrid power system, the object, scope of diagnosis, and verification goal of fault diagnosis are clarified; the hybrid power system to be tested is modeled to obtain a hybrid power system model, and a simulation platform or an in-the-loop test platform is constructed using the hybrid power system model to ensure the accuracy and controllability of fault injection; the hybrid power system model includes a vehicle dynamics model, a motor control model, a motor model, a battery control model, a battery model, a generator control model, a generator model, an engine-generator coupling device model, an engine controller model, an engine model, a power coupling device model, and a reduction gearbox model; Step 2: construct a classifier-free guided diffusion model based on data-driven, model-driven or hybrid-driven methods; Adjust and optimize the parameters of the non-classifier guided diffusion model to adapt to the actual application environment and system; A method for generating fault data using a classifier-free guided diffusion model to address the data imbalance problem; The classifier-free guided diffusion model uses real fault data to simulate the data generation process, gradually adds noise and performs denoising to generate synthetic fault data similar to the real fault data; the synthetic fault data is integrated with the real fault data to further obtain all fault types; The classifier-free guided diffusion model adds a multi-head self-attention mechanism and skip connections to the diffusion model to enhance feature extraction capabilities, enabling the classifier-free guided diffusion model to process time steps and label embeddings to generate hybrid power system fault data. The diffusion model includes an encoder, an intermediate feature extractor, and a decoder. The classifier-free guided diffusion model takes into account data imbalance and optimizes the performance of generated data by adjusting key hyperparameters. Step 3: All fault types are combined into a fault injection test set. Relevant faults are called from the fault injection test set and injected into the simulation platform or in-loop test platform to simulate the fault behavior of the hybrid power system and its related equipment. The injection of different fault types is achieved through the following methods: Method 1: Short-circuit or open-circuit some models in the simulation platform or the loop test platform; Method 2: Change the component parameters of some models in the simulation platform or the loop test platform; Step 4: Connect the health management system to the simulation platform or in-loop test platform and perform fault injection according to the method in step 3. After the fault injection, perform real-time fault diagnosis testing to verify whether the health management system can detect, identify and locate the fault in a timely manner when it occurs. Record the detailed results of each fault injection and fault diagnosis, including fault type, injection parameters, and diagnostic delay data; quantitatively measure various evaluation indicators of the health management system, and form a verification report that includes the fault injection method, diagnostic results, comparative analysis, and optimization suggestions as the basis for the final evaluation of the diagnostic system performance.

2. A hybrid power system health management capability verification and evaluation method according to claim 1, characterized in that: In step 1, the vehicle dynamics model first calculates the required power and torque based on the driver's operation and vehicle status. Next, the motor control model adjusts the motor's operating state based on the power and torque, while the motor model is responsible for actually generating power. The battery control model manages the charging and discharging of the battery to ensure a stable power supply to the motor and generator, while the battery model describes the physical characteristics and behavior of the battery. When the battery is low on power, the generator control model activates the generator to supplement the power or directly supply power, while the generator model describes the physical characteristics and power generation behavior of the generator. The engine-generator coupling model is responsible for the mechanical connection between the engine and generator, while the engine controller model controls the operation of the engine according to demand; the engine model describes the physical characteristics and operating behavior of the engine; the power coupling model transfers the power generated by the engine, generator and electric motor to the reduction gearbox, while the reduction gearbox model converts high-speed, low-torque power into low-speed, high-torque power suitable for driving the wheels; the series connection of all models ensures that the hybrid system intelligently distributes and converts power sources under different driving conditions to achieve optimal fuel efficiency and performance.

3. A hybrid power system health management capability verification and evaluation method according to claim 1, characterized in that: In step 2, synthetic fault data is injected into the simulation platform to test and verify the classifier-free guided diffusion model; synthetic fault data is injected into the in-the-loop test platform, and physical fault injection and diagnosis are performed in combination with hardware-in-the-loop experiments or semi-physical experiments to verify the performance of the classifier-free guided diffusion model in actual scenarios.

4. A hybrid power system health management capability verification and assessment method according to claim 1, characterized in that: In step 3, a test is performed after each fault injection; during the test, the response of the hybrid system is monitored and relevant data is recorded for analysis; based on the analysis results, the hybrid system model, test cases, and injection method after the fault injection are adjusted to better simulate real-world conditions; To ensure that the parameters in the fault injection process are controllable, the injection time and fault intensity can be quantitatively controlled.

5. A hybrid power system health management capability verification and assessment method according to claim 1, characterized in that: In step 3, automated injection and automated diagnosis technologies are used to implement the fault injection process, reduce manual intervention, and quantify fault injection and diagnosis results.

6. A hybrid power system health management capability verification and assessment method according to claim 1, characterized in that: In step 4, the evaluation indicators include: fault isolation rate, fault detection time, fault isolation time, fault detection rate, and prediction accuracy; the calculation process is as follows: Fault isolation rate: Standard: FIR ≥ 90%; Mean time to detect failure: Standard: MTTD ≤ 5 minutes; Mean time to isolate fault: Standard: MTTI ≤ 10 minutes; Fault detection rate: Criteria: FDR ≥ 95%; Prediction accuracy: Standard: PA ≥ 90%.

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