A vehicle power system reliability prediction method fusing failure rate correction and closed-loop GO method
By integrating failure rate correction and closed-loop GO method, a component failure rate model is established and a closed-loop feedback mechanism is introduced, which solves the problem that traditional methods fail to consider component time-varying and closed-loop feedback, and improves the accuracy and safety of vehicle power system reliability assessment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA NORTH VEHICLE RES INST
- Filing Date
- 2026-03-16
- Publication Date
- 2026-06-19
AI Technical Summary
Traditional vehicle powertrain reliability assessment methods fail to fully consider the time-varying nature of component failure rates and the closed-loop feedback logic within the system, resulting in discrepancies between assessment results and actual operating conditions.
By integrating failure rate correction and closed-loop GO method, a component failure rate correction model is established to correct component failure rate in real time. A closed-loop feedback mechanism is introduced into the GO model to construct a vehicle power system model with closed loop and calculate system reliability.
This improves the accuracy of vehicle powertrain reliability assessment, reduces the deviation between assessment results and actual operating conditions, and enhances vehicle operational safety and maintenance economy.
Smart Images
Figure CN122242001A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of vehicle engineering and reliability engineering technology, and specifically to a method for predicting the reliability of vehicle powertrain systems that integrates failure rate correction and closed-loop GO method. Background Technology
[0002] The vehicle powertrain system is a core component of a vehicle, and its reliability directly affects the safety and availability of the entire vehicle. Traditional reliability assessment methods are typically based on the assumption of a constant failure rate. For example, the IEEE Standard Framework for Reliability Prediction of Hardware (IEEE Std 1413-2010) provides the theoretical foundation and methodological guidance for predicting the reliability of hardware systems. In addition, commonly used modeling methods include Failure Mode, Effects and Criticality Analysis (FMECA) and Reliability Block Diagram (RBD). However, in actual operation, the failure rate of components is affected by various dynamic parameters such as temperature and vibration, exhibiting time-varying characteristics. Traditional methods fail to fully consider these factors; furthermore, traditional methods cannot characterize the dynamic dependencies and feedback mechanisms between components within the system, leading to discrepancies between the assessment results and actual operating conditions.
[0003] To overcome the above shortcomings, the goal-oriented methodology (GO) has been proposed as a success-oriented reliability analysis method and has been applied in the reliability modeling of complex systems. However, the current application of the GO method in the vehicle field is mostly limited to static models or open-loop systems, ignoring some closed-loop feedback logic in the system. Regarding the reliability analysis of closed-loop systems, Li Jingkui et al.'s paper, "Reliability Analysis of Aircraft Fuel Closed-Loop Control System Based on GO-Markov" (Acta Ordnance et al., 2022, 43(06):1447-1455), uses Markov state transition process theory to combine the states of the closed-loop, deriving the formula for calculating the state probability of the closed-loop links, thus solving the problem that the general GO method cannot calculate the reliability of systems containing closed-loops. However, this method is mainly used to analyze the steady-state probability of the system after long-term operation, and it is difficult to accurately reflect the reliability changes of the system within a specific task time. Moreover, for systems containing multiple closed-loops and complex logical relationships, the Markov method requires the construction of a large number of state combinations, resulting in high computational complexity, which is not conducive to the rapid calculation of the reliability of complex systems. Summary of the Invention
[0004] To address the shortcomings of existing technologies, this invention develops a vehicle powertrain reliability prediction method that integrates failure rate correction and closed-loop GO method. The purpose of this invention is to achieve accurate assessment of vehicle powertrain reliability by establishing a component failure rate correction model and considering the closed-loop feedback logic present in the vehicle powertrain.
[0005] To achieve the above objectives, the vehicle powertrain reliability prediction method integrating failure rate correction and closed-loop GO method described in this invention includes the following steps: S1. Establish a failure rate correction model for the components included in the vehicle powertrain system, and correct the failure rate of the components in real time: Collect the mean time between failures (MTBF) for each component and convert it into a basic failure rate. =1 / MTBF; Screen and collect real-time dynamic parameters that significantly affect the failure rate of each component, including one or more of the following: state of charge, temperature, voltage, vibration, pressure, rotational speed, noise, heartbeat signal, and pressure difference. Establish a function exp[ for the factors affecting component failure rate. The basic failure rate of each component is dynamically corrected; where Let n be the influence function of the i-th dynamic parameter, and n be the number of parameters; the corrected failure rate. The calculation formula is: S2. Calculate the reliability of each component based on the corrected failure rate of each component; S3. Establish a GO model of the vehicle powertrain system with closed loops: First, by analyzing the working principle of the system, the physical components, control units and logical relationships are mapped to different types of GO operators; then, based on the actual path of energy flow or control flow, the operators are connected through signal flow; and a closed loop is introduced at the location where state feedback exists to construct a GO model of the vehicle powertrain system with closed loops. S4. Assign the reliability of each component calculated in step S2 to the corresponding operator in the GO model; starting from the signal source operator, calculate the reliability of each signal flow in sequence according to the signal flow direction; the reliability of the final system output signal flow is the overall reliability of the vehicle power system.
[0006] Specifically, the reliability of each component at time t in step S2. .
[0007] Specifically, in step S4, the process of calculating the reliability of the vehicle powertrain system is as follows: S4.1 Setting the Calculation Step Size Initialize the current time. And initialize the reliability of all signal streams; S4.2 For each time step Perform the following calculations, where T is the total simulation duration: S4.2.1. Following the direction of signal flow in the GO model, starting from the signal source operator, calculate the reliability of each operator at the current time t. S4.2.2. When the calculation reaches the closed-loop feedback loop, call... The feedback signal value at time t is used as the output of the feedback loop and participates in the calculation at time t. S4.2.3. After completing the calculation of the reliability of all GO models at the current time t, store the reliability values of the signal flow that need to be fed back; The reliability of the system output signal stream at time T in S4.3 is the reliability of the vehicle power system at that time.
[0008] The advantages and positive effects of this invention are as follows: This invention integrates failure rate correction with the closed-loop Go (Go) method, quantifying the dynamic impact of parameters such as temperature and vibration on component failure behavior, thus reducing evaluation bias caused by neglecting the time-varying nature of component failure rates. Simultaneously, it introduces a closed-loop mechanism based on historical states, overcoming the shortcomings of traditional Go methods in static analysis that do not consider the inherent closed-loop control logic and feedback dependencies within the system. Applying the method described in this invention has significant engineering value for improving the accuracy of reliability assessments of vehicle powertrain systems and the economy of vehicle maintenance. Attached Figure Description
[0009] Figure 1 This is a flowchart of the method of the present invention.
[0010] Figure 2 These are the eight standard GO operators used in this invention.
[0011] Figure 3 This is a schematic diagram of the GO model with closed-loop feedback.
[0012] Figure 4 This is a structural diagram of the functional modules of the vehicle power system according to an embodiment of the present invention.
[0013] Figure 5 This is a reliability information diagram of each component after failure rate correction in the embodiment.
[0014] Figure 6 This is a GO diagram of a vehicle powertrain system with a closed loop according to an embodiment of the present invention.
[0015] Figure 7 This is a reliability information diagram of the vehicle powertrain system according to an embodiment of the present invention. Detailed Implementation
[0016] To make the technical problems solved by this invention, the technical solutions adopted, and the technical effects achieved clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the scope of protection of the invention.
[0017] like Figure 1 As shown, the vehicle powertrain reliability prediction method based on the fusion of failure rate correction and closed-loop GO method described in this invention includes the following steps: S1. First, establish a component failure rate correction model to correct the component failure rate in real time; S2. Calculate the reliability of each component based on the corrected failure rate; S3. Establish a GO model of the vehicle power system containing a closed loop based on the system structure and functional logic of the vehicle power system. S4. Input the component reliability into the GO model and calculate the reliability of the vehicle power system by combining the GO method calculation rules.
[0018] The process of correcting component failure rate in step S1 is as follows: S1.1 Obtain the mean time between failures (MTBF) of a component using data, reliability standards, or manuals provided by the component manufacturer, and convert it into a basic failure rate. : S1.2 Parameters that significantly affect component failure rates are screened and collected, including: temperature, vibration, voltage, state of charge, pressure, rotational speed, noise, oil condition, pressure difference, and heartbeat signal; then, the basic failure rate of each component is dynamically corrected by constructing an influencing factor function to obtain the corrected failure rate of each component. : in Let be the influence function of the i-th dynamic parameter.
[0019] The process of calculating component reliability in step S2 is as follows: Based on the corrected failure rate of each component obtained in step S1 The reliability of each component after operating time t is calculated using a reliability function: The process of establishing the GO model of the vehicle powertrain system containing the closed loop in step S3 is as follows: First, we analyze the components and working principle of the vehicle's powertrain system. Figure 2The eight standard GO operators used in this invention are as follows: Operator 1 is a two-state unit, Operator 2 is an OR gate, Operator 4 is a multi-signal generator, Operator 5 is a signal generator, Operator 6 is a signal-conducting unit, Operator 10 is an AND gate, Operator 12 is a path separator, and Operator 14 is a linear combination generator. During modeling, the physical components, control units, and logical functions in the vehicle powertrain system are mapped to corresponding GO operators according to their functional characteristics. The operators are then connected based on the actual paths of energy flow, signal flow, or control flow to establish the GO model of the vehicle powertrain system.
[0020] In actual operation, the state of some controllers or execution units in the system will be affected by feedback information from other components. Therefore, a closed loop is added at the location where state feedback exists to characterize the dynamic dependencies and closed-loop control logic within the system, and finally to construct a complete GO model of the vehicle power system with closed loop.
[0021] Step S4 calculates the reliability of the vehicle powertrain system, and its core characteristics are: Assign the reliability of each component calculated in step S2 to the corresponding operator in the GO model; starting from the signal source operator, calculate the reliability of each signal flow in sequence according to the signal flow direction; finally, the reliability of the system output signal flow is the overall reliability of the vehicle power system.
[0022] To address the closed-loop feedback structure in the system, a time-delayed feedback mechanism based on historical states is employed: since the input signal of the feedback path originates from the component that emitted the feedback signal at the previous historical moment... The state output is stored, and the historical signal is used for the GO operation at the current time t, thus avoiding the problem of logical loops.
[0023] by Figure 3 Taking the GO model with closed-loop feedback as an example, the specific processing flow is as follows: (1) Set step size Initialize the reliability of all components and signal flows.
[0024] (2) At time t, according to the signal flow sequence of the GO model, the reliability of each component is determined. Substitute the values and calculate the reliability of each signal stream in sequence. For signal flows containing closed-loop feedback, it is necessary to call... The reliability of the feedback signal at any given time is used in the calculation.
[0025] (3) The specific process is as follows: Calculate the reliability of the feedback signal at time t-1 And store it.
[0026] Calculate the output reliability of signal stream 1: Calculate the output reliability of signal stream 2: Calculate the output reliability of signal stream 3: Calculate the output reliability of a system with a closed loop: .
[0027] The embodiment takes the power system of a certain type of hybrid vehicle as an example. This power system mainly includes: a fuel supply controller, an engine controller, a generator controller, a drive motor controller, a dust extraction controller, a water pump controller, a fan controller, a low-pressure oil pump, a high-pressure oil pump, a pressure relief valve, a coarse oil filter 1, a coarse oil filter 2, a fine oil filter, an engine, a generator, a left drive motor, a right drive motor, a dust extraction motor, a water pump, a fan, a high-voltage bus, a high-voltage battery, a high-voltage capacitor, and a high-voltage power distribution device, such as... Figure 4 As shown.
[0028] S1. First, based on the component manual, manufacturer data, and bench tests, obtain the Mean Time Between Failures (MTBF) for each component and convert it into a basic failure rate. : Taking the high-voltage capacitor (number 15) as an example, its basic failure rate is .
[0029] High-voltage capacitors are mainly affected by temperature (T), voltage (V), and vibration (T). The following influencing factor function is used for correction: The failure rate of the high-voltage capacitor after correction at time t is: The failure rates of other components are similarly adjusted according to their corresponding typical influencing factor functions (see Table 1).
[0030] The characterization parameters and typical influencing factor functions corresponding to each component are shown in Table 1.
[0031] Table 1 S2. Calculate the reliability R(t) of each component after operating time t using the reliability function, and obtain the reliability information diagram of each component after the vehicle power system has been operating for 3000 hours, as shown below. Figure 5 As shown.
[0032] S3, Vehicle powertrain system GO model construction.
[0033] Step 1: The vehicle powertrain is the core assembly of the entire vehicle, primarily responsible for energy supply, conversion, distribution, and power output. This system can be broken down into a series of independent components such as the engine, high-voltage battery, generator, and drive motor. Its success criterion is defined as: under specified operating conditions, the system can stably and reliably complete the vehicle's driving tasks.
[0034] Step 2: Identify the system components and determine the operator types.
[0035] Based on the definition of system functions and boundaries, all key components in the vehicle powertrain system are identified, and according to the standardized modeling rules of the GO method, each component is mapped to a corresponding type of GO operator according to its functional attributes: The oil supply controller and each motor controller serve as control signal sources and are defined as "signal generator" operators; Energy conversion and transmission components such as coarse oil filter, fine oil filter, high-pressure oil pump, pressure relief valve, generator, high-voltage capacitor, and high-voltage battery are defined as "two-state unit" operators; Components such as engines and drive motors that are controlled by a controller are defined as "signal-conducting units" operators; The high-voltage busbar is defined as the "linear combination generator" operator; High-voltage power distribution equipment and low-pressure oil pumps are defined as “path separator” operators.
[0036] Assign a unique identifier to each operator, in the format of "type number-instance number", for example, the fuel supply controller is identified as "5-1", where "5" represents the operator type and "1" represents the operator number.
[0037] Step 3: Create the vehicle powertrain system GO diagram.
[0038] Establish the main signal flow path based on the energy flow direction: (1) The oil supply controller outputs a command signal to the low-pressure oil pump; (2) The low-pressure oil pump outputs a fuel flow signal, which is transmitted to the high-pressure oil pump through the coarse oil filter and the fine oil filter in sequence; (3) The high-pressure oil pump delivers fuel to the engine; (4) The engine outputs mechanical energy to drive the generator to operate; (5) The generator generates electrical energy and transmits it to the high-voltage bus, where it is combined with the electrical energy output from the high-voltage battery and high-voltage capacitor and then input into the high-voltage power distribution device. (6) The high-voltage power distribution device distributes electrical energy to each motor; (7) The motor controller drives the corresponding motor to work.
[0039] During the analysis, it was found that the pressure relief valve opens to release oil when the system pressure is too high, directly changing the hydraulic load of the low-pressure oil pump and forming a mechanical-hydraulic feedback, which is represented by the dashed line 8b; the working state of the generator needs to be dynamically adjusted according to the real-time voltage of the high-voltage bus, which is represented by the dashed line 14b. Through the above process, a closed-loop GO diagram of the vehicle powertrain system that accurately reflects the system structure, function, and control logic is finally constructed, such as... Figure 6 As shown.
[0040] S4. Following the GO algorithm rules, starting from the source operator, calculate the reliability of each signal flow sequentially along the signal flow direction. .
[0041] Specifically, with Figure 6 Taking the GO diagram of a vehicle powertrain system with a closed loop as an example, and setting the step size Δt = 1h, the reliability calculation formulas for each signal flow in the system are as follows: The reliability calculation formula for signal flow 1 is as follows: The reliability calculation formula for signal flow 2 is as follows: The reliability calculation formula for signal flow 3 is as follows: The reliability calculation formula for signal stream 4 is as follows: The reliability calculation formula for signal stream 5 is as follows: The reliability calculation formula for signal stream 6 is as follows: The reliability calculation formula for signal stream 7 is as follows: The reliability calculation formula for signal stream 8 is as follows: The reliability calculation formula for signal stream 8b is as follows: The reliability calculation formula for signal stream 9 is as follows: The reliability calculation formula for signal stream 10 is as follows: The reliability calculation formula for signal flow 11 is as follows: The reliability calculation formula for signal stream 12 is as follows: The reliability calculation formula for signal flow 13 is as follows: The reliability calculation formula for signal flow 14 is as follows: The reliability calculation formula for signal stream 14b is as follows: The reliability calculation formula for signal flow 15 is as follows: The reliability calculation formula for signal stream 16 is as follows: The reliability calculation formula for signal flow 17 is as follows: The reliability calculation formula for signal stream 18 is as follows: The reliability calculation formula for signal stream 19 is as follows: The reliability calculation formula for signal stream 20 is as follows: The reliability calculation formula for signal flow 21 is as follows: The reliability calculation formula for signal flow 22 is as follows: The reliability calculation formula for signal flow 23 is as follows: The reliability calculation formula for signal stream 24 is as follows: The reliability calculation formula for signal flow 25 is as follows: The reliability calculation formula for signal stream 26 is as follows: The reliability calculation formula for signal flow 27 is as follows: The reliability calculation formula for signal stream 28 is as follows: The reliability calculation formula for signal flow 29 is as follows: signal flow The reliability of the vehicle is the same as the reliability of the vehicle's power system.
[0042] Substitute the reliability of each component in the system The reliability of the vehicle's powertrain system can then be determined, such as... Figure 7 As shown, the reliability of the vehicle powertrain system after 3000 hours of operation is: = 0.9995439 This invention, by comprehensively considering the real-time changes in component failure rates and the closed-loop feedback mechanism within the system, effectively reduces the deviation between reliability analysis results and actual operating conditions, thereby more objectively and accurately characterizing the reliability of vehicle powertrain systems. This method helps improve the operational safety and maintainability of vehicle powertrain systems and provides a scientific basis and engineering reference for system design optimization and manufacturing processes.
[0043] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this invention, and these modifications or substitutions should all be covered within the scope of protection of this invention. Therefore, the scope of protection of this invention should be determined by the scope of the claims.
Claims
1. A method for predicting the reliability of a vehicle powertrain system that integrates failure rate correction and closed-loop GO method, characterized in that, Includes the following steps: S1. Establish a failure rate correction model for the components included in the vehicle powertrain system, and correct the failure rate of the components in real time: Collect the mean time between failures (MTBF) for each component and convert it into a basic failure rate. =1 / MTBF; Screen and collect real-time dynamic parameters that significantly affect the failure rate of each component, including one or more of the following: state of charge, temperature, voltage, vibration, pressure, rotational speed, noise, heartbeat signal, and pressure difference. Establish a function exp[ for the factors affecting component failure rate. The basic failure rate of each component is dynamically corrected; where Let n be the influence function of the i-th dynamic parameter, where n is the number of parameters; Corrected failure rate The calculation formula is: S2. Calculate the reliability of each component based on the corrected failure rate of each component; S3. Establish a GO model of the vehicle powertrain system with closed loops: First, by analyzing the working principle of the system, the physical components, control units and logical relationships are mapped to different types of GO operators; then, based on the actual path of energy flow or control flow, the operators are connected through signal flow; and a closed loop is introduced at the location where state feedback exists to construct a GO model of the vehicle powertrain system with closed loops. S4. Assign the reliability of each component calculated in step S2 to the corresponding operator in the GO model; starting from the signal source operator, calculate the reliability of each signal flow in sequence according to the signal flow direction; the reliability of the final system output signal flow is the overall reliability of the vehicle power system.
2. The vehicle powertrain reliability prediction method based on the fusion of failure rate correction and closed-loop GO method according to claim 1, characterized in that, Reliability of each component at time t in step S2 .
3. The vehicle powertrain reliability prediction method based on the fusion of failure rate correction and closed-loop GO method according to claim 1, characterized in that, In step S4, the method for calculating the reliability of the vehicle powertrain system includes the following steps: S4.1 Setting the Calculation Step Size Initialize the current time. And initialize the reliability of all signal streams; S4.2 For each time step Perform the following calculations, where T is the total simulation duration: S4.2.
1. Following the direction of signal flow in the GO model, starting from the signal source operator, calculate the reliability of each operator at the current time t. S4.2.
2. When the calculation reaches the closed-loop feedback loop, call... The feedback signal value at time t is used as the output of the feedback loop and participates in the calculation at time t. S4.2.
3. After completing the calculation of the reliability of all GO models at the current time t, store the reliability values of the signal flow that need to be fed back; The reliability of the system output signal stream at time T in S4.3 is the reliability of the vehicle power system at that time.