WO2020211845A1 - Safety tree model-based electric vehicle safety design optimization method - Google Patents

Abstract

A safety tree model-based electric vehicle safety design optimization method, comprising: S1. constructing a safety tree, the safety tree comprising multiple low-level events, intermediate-level events, top-level events, and logical causalities between and safety importance levels of the low-level events, the intermediate-level events, and the top-level events; S2. sorting the low-level events by the safety importance levels on the basis of the safety tree; S3. performing a fault reconstruction analysis with respect to a high-probability branch in the safety tree on the basis of the safety importance levels of the low-level events, and reducing the probability of the low-level events in the branch on the basis of the analysis result. The technical solution, by means of constructing and updating the safety tree, by mining and analyzing sample data fed back by different electric vehicles, discovers some potential safety hazards of typical problems and irrationalities in design and production, and, by reconstructing these problems, continually improve an electric vehicle designing and manufacturing process.

Description

An optimization method for safety design of electric vehicles based on safety tree model Technical field

The invention relates to a transportation tool, and more specifically, to an electric vehicle safety design optimization method based on a safety tree model.

Background technique

With the rapid development of the world economy and the importance of environmental protection awareness, the penetration rate of automobiles has become higher and higher, and the requirements for automobile exhaust emissions have also become higher. Energy-saving, safe, and pollution-free electric vehicles are the future development trend. However, electric vehicles generally have electrical systems as high as hundreds of volts, which exceeds the safe voltage range of DC. If reasonable design and protection are not carried out, high voltage safety problems such as electric shocks may be caused. In addition, electric vehicles include multiple components such as steering systems, braking systems, and safety control systems, and each component includes multiple components. The failure or malfunction of any component may cause the entire vehicle to lose control or malfunction, thereby causing the driver or passenger to encounter danger. However, there is still a lack of methods for the safety management and control of electric vehicles that can combine systematic and effective theoretical analysis and engineering experience; and methods to quantitatively describe the safety status of the entire vehicle and accurately reflect the safety characteristics of each system.

Summary of the invention

The technical problem to be solved by the present invention is to provide an electric vehicle safety design optimization method based on a safety tree model in view of the above-mentioned defects of the prior art.

The technical solution adopted by the present invention to solve its technical problems is: constructing an electric vehicle safety design optimization method based on a safety tree model, including:

S1. Construct a security tree, the security tree including multiple bottom-level events, middle-level events, top-level events, and the logical causality and safety importance between the bottom-level events, the middle-level events, and the top-level events;

S2. Sort the security importance of each underlying event based on the security tree;

S3. Perform a fault reconstruction analysis on the branch with a high probability of occurrence in the safety tree based on the safety importance of the underlying event, and reduce the occurrence probability of the underlying event in the branch based on the analysis result.

In the safety tree model-based electric vehicle safety design optimization method of the present invention, the step S3 further includes:

S31. Determine the priority of the top-level event in the security tree based on the security importance of the bottom-level event;

S32. For top-level events with high priority, look for corresponding bottom-level events according to branches with high occurrence probability in the security tree;

S33. Redesign the underlying event based on the electric vehicle operation theory and the fault logic relationship.

In the safety tree model-based electric vehicle safety design optimization method of the present invention, the step S3 further includes:

S34. Evaluate the rationality and impact of the redesign;

S35. Update the security tree based on the evaluation.

In the safety tree model-based electric vehicle safety design optimization method of the present invention, the step S1 further includes:

S11. Collect vehicle safety failure data of electric vehicles;

S12. Map the vehicle safety failure data into different safety event groups, and separately count the frequency data of each safety event group;

S13. Use a joint analysis method to classify the safety failure data of the entire vehicle in each safety event group to construct a safety tree.

In the safety tree model-based electric vehicle safety design optimization method of the present invention, the step S13 further includes:

S131. Divide the safety failure data of the vehicle into at least a first failure category, a second failure category, a third failure category, and a fourth failure category;

S132. Use different analysis methods to analyze the vehicle safety failure data of the first failure category, the second failure category, the third failure category, and the fourth failure category to determine the electric vehicle The hierarchical relationship between the safety fault data determines the logical causality and safety importance among the bottom-level event, the middle-level event, and the top-level event, and the bottom-level event, the middle-level event, and the top-level event;

S133. Establish fault causality layer by layer until all the vehicle safety fault data is traversed to complete the construction of the safety tree of the electric vehicle.

In the safety tree model-based electric vehicle safety design optimization method of the present invention, the step S2 further includes

S21. Through the collection and statistics of middle-level events, analyze the parameter deviation of the middle-level events, and convert the original frequency data of the middle-level events into standardized frequency data of intermediate events at all levels;

S22. Obtain the occurrence probability of each underlying event through the analysis and statistics of the logical causality and the result of the intermediate event;

S23. Obtain the occurrence probability of each top-level event based on the collection of the security tree and the middle-level event and the statistics of the frequency of the middle-level event;

S24. Based on the probability of each bottom-level event to each intermediate event and the occurrence probability of each top-level event, calculate the probability of each bottom-level event affecting the top-level event;

S25. Sort the safety importance of each bottom-level event based on the impact probability of each bottom-level event on each top-level event.

In the safety tree model-based electric vehicle safety design optimization method of the present invention, the step S21 includes:

S211. Collect the fault data of the intermediate event of the electric vehicle and perform statistical decoupling, analyze the existing parameter deviation for the dynamic change of the operating parameter of the electric vehicle; compare the parameter deviation and the sudden change in the fault data Sending a failure alarm event as the original frequency data of the middle-level event;

S212. For the working environment corresponding to the original frequency data of the intermediate events at all levels, convert the original frequency data into standardized intermediate event frequency data at all levels.

In the method for optimizing the safety design of electric vehicles based on the safety tree model of the present invention, the step S22 includes: collecting statistics on the frequency data of standardized intermediate events at all levels in the field application, testing, and inspection scenarios, and calculating the corresponding The probability of the underlying event.

In the safety tree model-based electric vehicle safety design optimization method of the present invention, in the step S23, the occurrence probability of the top-level event is calculated through the occurrence frequency statistics and distribution of intermediate events and the risk value of each intermediate event And/or in the step S24, the Bayes algorithm is used to calculate the probability of each bottom-level event affecting the top-level event.

Another technical solution adopted by the present invention to solve its technical problems is to construct a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the safety tree model-based electric vehicle safety is realized. Design optimization methods.

The implementation of the safety tree model-based electric vehicle safety design optimization method and computer-readable storage medium of the present invention can construct and update the safety tree, and by mining and analyzing the sample data fed back by different electric vehicles, some typical problems can be found Potential safety hazards and irrationality of design and production, through the reconstruction of these problems to continuously improve the design and manufacturing process of electric vehicles.

Description of the drawings

The present invention will be further described below in conjunction with the accompanying drawings and embodiments. In the accompanying drawings:

FIG. 1 is a schematic flowchart of a method for optimizing safety design of electric vehicles based on a safety tree model according to a first preferred embodiment of the present invention;

FIG. 2 is a schematic flowchart of a method for optimizing safety design of electric vehicles based on a safety tree model according to a second preferred embodiment of the present invention;

3 is a schematic diagram of the classification of the entire vehicle safety failure data of the safety tree model-based optimization method for the safety design of electric vehicles according to a preferred embodiment of the present invention;

4a-4c are schematic diagrams of partial safety trees of the safety tree model-based optimization method for safety design of electric vehicles according to a preferred embodiment of the present invention.

detailed description

In order to make the objectives, technical solutions, and advantages of the present invention clearer, the following further describes the present invention in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, but not to limit the present invention.

The present invention relates to an electric vehicle safety design optimization method based on a safety tree model, including: S1. Building a safety tree, the safety tree including a plurality of bottom-level events, middle-level events, top-level events, and the bottom-level events, the middle-level events Level events, the logical causality between the top-level events and the degree of safety importance; S2. Sort the safety importance of each bottom-level event based on the safety tree; S3. Sort the safety based on the safety importance of the bottom-level event A branch with a high probability of occurrence in the tree performs fault reconstruction analysis, and based on the analysis result, the probability of occurrence of the underlying event in the branch is reduced. The implementation of the safety tree model-based electric vehicle safety design optimization method of the present invention can construct and update the safety tree. By mining and analyzing the sample data fed back by different electric vehicles, the safety hazards of certain typical problems and the design and production problems can be found. Unreasonable, through the reconstruction of these problems to continuously improve the design and manufacturing process of electric vehicles.

In the present invention, the safety tree of electric vehicles is a systematic method to comprehensively solve the safety problems of electric vehicles. It is constructed by establishing a related logic system through top-level events, bottom-level events, related logic and data, and constructing through vehicle safety requirements analysis and vehicle system The safety event model establishes a tree diagram, which is a description of the logical relationship between different levels of events in the vehicle, and graphical representation and qualitative description of multiple subsystems or components such as the braking system, steering system, and body parts. The safety tree focuses on real events, tracking and penetrating the system to set barriers, and modular and open system design. In the present invention, the safety importance of the safety tree is the main measure for quantitative analysis and evaluation of the importance of the impact of the bottom-level events on the top-level events, which reflects the weight of the impact of each bottom-level event on the safety of the entire vehicle. In the present invention, the safety importance of the safety tree includes the probability of each bottom-level event, the differentiation of each intermediate event, and the risk degree factor of each top-level event, and is the magnitude of the impact of each bottom-level event on each top-level event Quantitative evaluation. The importance of safety represents the safety weight of each underlying event of an electric vehicle. In the present invention, bottom-level events can be understood as basic faults, and top-level events can be understood as surface faults. There is a direct causal relationship or an indirect causal relationship between the bottom-level events and the top-level events. Between the bottom-level events and the top-level events, there may be intermediate events. In the present invention, the importance of safety gives statistical characteristics to each underlying event, which is a quantitative description of system safety and a tool for quantitative analysis of the safety of electric vehicle systems.

Fig. 1 is a schematic flow chart of an electric vehicle safety design optimization method based on a safety tree model according to a first preferred embodiment of the present invention. As shown in Figure 1, in step S1, a security tree is constructed. The safety tree includes multiple bottom-level events, middle-level events, top-level events, and logical causality and safety importance between the bottom-level events, the middle-level events, and the top-level events. In a preferred embodiment of the present invention, any known method can be used to construct the security tree, or an existing security tree can be used.

The method of constructing a security tree according to a preferred embodiment of the present invention is described below. Those skilled in the art know that in other preferred embodiments of the present invention, other methods may be used to construct the security tree. The present invention is not limited by the specific construction method here.

In a preferred embodiment of the present invention, the step of constructing a safety tree includes: collecting vehicle safety failure data of electric vehicles; mapping the vehicle safety failure data to different safety event groups, and calculating each Safety event group frequency data; using a joint analysis method to classify the vehicle safety failure data in each safety event group to construct a safety tree.

In a preferred embodiment of the present invention, the step of collecting safety failure data of the entire vehicle of the electric vehicle may further include transmitting data in the entire vehicle controller, safety controller, and driving recorder of the electric vehicle through the CAN bus. To the platform database; then obtain the vehicle safety failure data of the electric vehicle from the data. For example, the vehicle safety failure data can be mapped into multiple subsystems or components such as brake systems, steering systems, and body parts, so that the vehicle safety failure data can be included in different groups according to the principle of mapping classification. Among them, and count the batches of each security event group.

As shown in Figure 3, in a preferred embodiment of the present invention, the vehicle safety failure data can be mapped to structural safety events, electrical safety events, functional logic safety events, collision safety events, thermal safety events, and explosion-proof events. Safety incidents, operation and maintenance safety incidents, environmental safety incidents and life cycle safety incidents. In addition, according to data classification, analysis and calculation, the basic event probability can be obtained as structural safety events 30%, electrical safety events 10%, functional logic safety events 20%, collision safety events 5%, thermal safety events 5%, 8% of explosion-proof safety incidents, 9% of operation and maintenance safety incidents, 8% of environmental safety incidents, and 5% of life-cycle safety incidents. The above-mentioned inductive analysis process can use various methods known in the art, or use known methods to calculate the probability of each safety event group accounting for all safety failures, or use the respective measurement and collection experience data of electric vehicle manufacturers.

In a preferred embodiment of the present invention, the step of using a joint analysis method to classify the safety failure data of the entire vehicle in each safety event group to construct a safety tree further includes: dividing the safety failure data of the entire vehicle at least Divided into the first fault category, the second fault category, the third fault category and the fourth fault category; using different analysis methods to analyze the first fault category, the second fault category, the third fault category and the The vehicle safety failure data of the fourth failure category to determine the hierarchical relationship between the electric vehicle safety failure data to determine the bottom-level event, the middle-level event, and the top-level event, as well as the bottom-level event and the middle-level event , Logical causality and safety importance between the top-level events; establishing fault causality layer by layer until all the vehicle safety fault data is traversed to complete the construction of the safety tree of the electric vehicle. Wherein, the first fault category is a fault with a clear mechanism or a verifiable mechanism, the second fault category is a fault with an unclear mechanism but an empirical verification basis, and the third fault category is a fault with an unclear mechanism but supported by operating data Failure; the fourth type of failure category is a clear mechanism but complex system structure. For example, the vehicle safety failure data of the first failure category is divided into top-level events, middle-level events, and bottom-level events according to the mechanism; Bayesian inference method is used to analyze the failure of the vehicle safety failure data of the second failure category Factor correlation, so that the vehicle safety failure data of the second failure category is divided into top-level events, middle-level events, and bottom-level events based on the analysis results; machine learning method is used to analyze the vehicle safety failure data of the third failure category Based on the analysis results, the vehicle safety failure data of the third fault category is divided into top-level events, middle-level events, and bottom-level events; the interpretation structure method is used to analyze the vehicle safety of the fourth fault category Based on the correlation of the failure factors of the failure data, the vehicle safety failure data of the fourth failure category is divided into top-level events, middle-level events, and bottom-level events based on the analysis result.

In a preferred embodiment of the present invention, the step of using a joint analysis method to classify the safety failure data of the entire vehicle in each safety event group to construct a safety tree further includes: aiming at a top-level event and all its corresponding The bottom-level event, according to its multi-level causality, establishes the "IF...THEN..." rule layer by layer to describe the causal relationship between events, until all "top-level events-bottom-level events" pairs are traversed; based on the top-level events, the bottom-level Events and the causal relationship between them and the experienced middle-level events generate a rule set expressing the logical relationship between the top-level event and the bottom-level event; based on the rule set, the top-level event, the bottom-level event, and the Middle-level events, and the security tree module constructs the security tree; verifies the rule set to remove logical relationship errors or event errors.

Figures 4a-4c are schematic diagrams of part of the security tree of the preferred embodiment of the present invention. As shown in Figure 4a-4c, three intermediate events can be subdivided under structural safety events, namely, braking safety events, driving safety events, and steering safety events. We can build a safety tree for each event. Let's take the brake safety incident as an example. As shown in Figure 4b, taking the braking safety event as the top-level event, we found that it actually has a causal relationship with multiple intermediate security events and multiple bottom-level security events. For the first category, events with clear mechanism or verifiable failure, such as brake valve damage X14, pipeline joint damage X16, hydraulic controller abnormal X21, hydraulic oil insufficient X24, hydraulic motor abnormal X22, you can directly obtain their information Causality. At this time, it can be directly determined based on the mechanism that the brake valve is damaged X14, the pipe joint is damaged X16, the hydraulic controller is abnormal X21, the hydraulic oil is insufficient X24, and the hydraulic motor is abnormal X22 are the underlying events, and the "IF...THEN..." rule is adopted. The causal relationship between the described events is that if the brake valve is damaged X14, the pipe joint is damaged X16, the hydraulic controller is abnormal X21, the hydraulic oil is insufficient X24, and the hydraulic motor is abnormal X22, then a brake safety event occurs.

For the second category, the failure of the mechanism is not clear but has an empirical verification basis, the Bayesian inference method is used to analyze the correlation of the failure factor of the safety failure data of the second failure category, and the second fault category is classified based on the analysis result. The vehicle safety failure data is divided into top-level events, middle-level events, and bottom-level events. As shown in Figure 4c, taking the braking safety event as the top-level event, we can use Bayesian algorithm to find that the steering safety event is the first middle-level event, and the second middle-level event turns to operating mechanism failure and steering. Cause and effect correlation between engine failure and steering actuator failure. The failures of the steering operating mechanism are directly causally related to multiple underlying events such as abnormal steering wheel tightening, steering tube bearing damage, steering column spline wear, spline tightness, fixed screw sliding teeth, and insufficient spline lubricant. Steering gear failures are directly causally related to multiple underlying events: insufficient steering gear lubricating oil X6, steering gear spline damage X7, steering gear wear damage X8, steering gear tightening screws loose X9, and steering gear flooding X10. Steering actuator failures are directly causally related to multiple underlying events, steering knuckle arm damage X11, steering ball joint damage X12, steering angle deformation/break X13, steering stabilizer bar break X14, and steering interference X15.

For the third category, for failures whose mechanism is not clear but supported by operating data, machine learning methods can be used to analyze the correlation of the failure factor of the vehicle safety failure data of the third failure category, so as to classify the third failure category based on the analysis results. The vehicle safety failure data is divided into top-level events, middle-level events, and bottom-level events. As shown in Figure 4b, taking the brake safety event as the top-level event, we can find through the similar state comparison method that the parking brake failure can actually be regarded as the first-level intermediate event, and its sum is the first-level intermediate event. The service brake failure of the incident is causally related to the abnormal brake pressure of the second-level intermediate incident. The abnormal brake pressure has a causal relationship with multiple bottom layer events, brake oil seal damage X6, brake oil leakage X5, and brake bottom plate deformation X8. At the same time, the parking brake failure is directly related to multiple underlying events handle damage X8, friction sheet wear X1, brake cylinder jam X2, brake spring damage X3, and drive shaft damage X12.

For the fourth category, the mechanism is clear but the system structure is complex; the interpretation structure method is used to analyze the failure factor correlation of the vehicle safety failure data of the fourth failure category, and the fourth failure category is based on the analysis result. Safety fault data is divided into top-level events, middle-level events, and bottom-level events. As shown in Figure 4b, taking the brake safety event as the top-level event, we can find through the interpretation structure method that the service brake failure can actually be used as the first-level intermediate event, and it is related to multiple bottom-level events. Wear X1, brake cylinder jamming X2, brake spring damage X3, bracket bearing damage X4 are directly causal, and at the same time, there is a causal relationship with the abnormal brake pressure of the second layer of intermediate events. The abnormal brake pressure has a causal relationship with the brake oil seal damage X6 and brake oil leakage X5 in the underlying event.

Therefore, those skilled in the art can construct the entire safety tree of the electric vehicle and/or part of the safety tree according to the above teachings. In a preferred embodiment of the present invention, after the safety tree is constructed, the rule set is verified to remove the logical relationship. Error or event error. For the "IF...THEN..." rule set describing the security tree, find errors in the logical relationship of events and common event relationship errors.

The safety tree of the present invention is a comprehensive, open, and full-cycle safety system based on data-driven, probabilistic calculation and safety importance analysis. It is a system model used to evaluate the safety status of vehicles and is a quantitative analysis system safety system. A powerful tool for sex. The safety tree system can be designed for different safety fault classifications, breaking through the limitation of individual safety analysis for each system component, and can better reflect the safety status of electric vehicles. The safety tree is set up for the fault data in the safety field. The correlation between the safety fault data at each level is not only based on logical deduction, but also determined by the statistical characteristics and data of the fault event. The safety tree model focuses on the actual occurrence of failure events, tracking and penetrating the system setting barriers according to design ideas or systems, and modular and open system design. Based on the new fault data, the safety tree can be updated in real time, forming a virtuous circle and continuous optimization. The safety tree application is oriented to the actual design, production, operation and maintenance process, which is more in line with the requirements of engineering practice.

In step S2, the security importance of each bottom-level event is sorted based on the security tree. In a preferred embodiment of the present invention, the step S2 may further include S21. Through the collection and statistics of the intermediate event, analyze the parameter deviation of the intermediate event, and convert the original frequency data of the intermediate event It is the standardized frequency data of intermediate events at all levels; S22. Obtain the occurrence probability of each bottom-level event through the analysis and statistics of the logical causality and the result of the intermediate event; S23. Collection based on the safety tree and the intermediate-level event Statistics with the frequency data of the intermediate events to obtain the occurrence probability of each top-level event; S24. Based on the probability of each bottom-level event to each intermediate event and the occurrence probability of each top-level event, calculate the probability of each bottom-level event affecting the top-level event; S25 . Based on the probability of each bottom-level event's impact on each top-level event, sort the safety importance of each bottom-level event.

Preferably, in the step S21, through the collection and statistics of the intermediate event, the parameter deviation of the intermediate event is analyzed, and the original frequency data of the intermediate event is converted into a standardized intermediate event frequency of each level data. In a preferred embodiment of the present invention, the intermediate event fault data of the electric vehicle can be collected for statistical decoupling, and the dynamic changes of operating parameters can be analyzed for possible parameter deviations. Parameter deviations and sudden failure alarms constitute the original data of intermediate events at all levels, and the frequency data is finally converted; for the working environment corresponding to the original frequency data of intermediate events at all levels, the original frequency data is converted into standardized intermediate event frequency data at all levels. Those skilled in the art know that any method known in the art can be used to count the occurrence frequency of each intermediate event and perform standardized corrections. Preferably, in the step S22, the standardized intermediate event frequency data at various levels in the field application, test, and inspection scenarios are counted, and the occurrence probability of each underlying event is calculated respectively. Preferably, in the step S22, the occurrence probability of the top-level event is calculated based on the occurrence frequency statistics and distribution of the intermediate events and the risk value of each intermediate event; preferably, in the step S24, based on each bottom-level event pair The probability of each intermediate event and the occurrence probability of each top-level event can be calculated by Bayesian calculation to obtain the probability of each bottom-level event affecting the top-level event; those skilled in the art know that in addition to the following calculation methods, those skilled in the art also According to the actual situation, other calculation formulas can be used for calculation. The present invention is not limited by the specific calculation method here.

In a preferred embodiment of the present invention, the importance of the bottom-level event is equal to the partial derivative of the probability of occurrence of the top-level event with respect to the probability of occurrence of the bottom-level event after the standardized correction. In a further preferred embodiment of the present invention, the security importance of the underlying event can be calculated based on the following formula:

Wherein, I _G (i) is an important security of the underlying events of X _i; q _i is the probability of occurrence of the underlying event is a normalized correction; G is the top event occurrence probability, which is about q _1, q ₂ ,...q _i ,...,q _N cut sets.

In a further preferred embodiment of the present invention, a structure function and a minimum cut set set can be constructed based on the occurrence probability of the underlying event after standardization and correction, and the structural safety importance of the underlying event can be calculated according to the safety tree safety importance formula. For example, assume that the underlying i-th event, the occurrence probability of each event of the underlying X _i, Building Structure Function

Then create the minimum cut set set as {X ₁ }, {X ₂ }, {X ₃ },……,{X _i }. Safety importance formula based on safety tree

The safety importance of the safety tree structure can be calculated

In step S3, a fault reconstruction analysis is performed on a branch with a high probability of occurrence in the safety tree based on the safety importance of the underlying event, and the occurrence probability of the underlying event in the branch is reduced based on the analysis result. In the preferred embodiment of the present invention, after the safety tree of the electric vehicle is established, the safety performance of the entire vehicle can be statically thoroughly analyzed. The importance and critical importance of the safety tree-based fault structure calculated on the basis of probability are the digital support basis for the vehicle safety system. The basic failure importance and the probability of failure are combined to form a comprehensive and objective evaluation of each branch in the safety tree. This evaluation result is the basis for the design of the overall vehicle safety system. The safety status of the entire vehicle is obtained through real-time calculations such as integration, integration, and correction based on the safety tree model, branch structure, and importance of the safety tree. It is an important indicator for the evaluation of the entire vehicle safety system. Perform fault reconstruction analysis on branches with higher importance of the safety tree structure and/or higher probability of occurrence at the top level, trace the source to find the root cause of the basic failure of the safety fault, and eliminate the hidden danger of safety failure from a deeper level. In the actual operation of electric vehicles, the safety performance of the entire vehicle is constantly changing over time. Real-time, accurate and digital assessment of the safety of the entire vehicle is necessary. The safety status of the entire vehicle refers to the overall safety tree model of the entire vehicle, and the integration of various safety fault states to calculate the important parameters of the entire vehicle that are indicative and unified for the safety of the entire vehicle. This is based on the safety tree model for the safety of the entire vehicle. Real-time quantitative description of the situation.

Preferably, first determine the priority of surface safety faults based on the evaluation results, select the most preferred branch to find the corresponding basic fault events; then find the core issues based on the electric vehicle operation theory and the fault logic relationship, and propose a redesign method for the core issues ; Next, to evaluate the rationality and impact of the redesign plan, it is necessary to comprehensively consider the data support of the basic failure structure importance and the critical importance of the basic failure probability, and implement redesign, maintenance, and transformation. For example, relevant parts can be renewed. Design, maintenance and replacement, regular troubleshooting and inspection, improving the operating environment, etc.

The implementation of the safety tree model-based electric vehicle safety design optimization method of the present invention can construct and update the safety tree. By mining and analyzing the sample data fed back by different electric vehicles, the safety hazards of certain typical problems and the design and production problems can be found. Unreasonable, through the reconstruction of these problems to continuously improve the design and manufacturing process of electric vehicles.

Fig. 2 is a schematic flow chart of an electric vehicle safety design optimization method based on a safety tree model according to a second preferred embodiment of the present invention. As shown in FIG. 2, in step S1, a security tree is constructed, the security tree includes a plurality of bottom-level events, middle-level events, top-level events, and the relationship between the bottom-level events, the middle-level events, and the top-level events. Logical causality and safety importance. In this embodiment, the construction of the security tree can refer to the embodiment shown in FIG. 1, which will not be repeated here. In step S2, the security importance of each bottom-level event is sorted based on the security tree. Similarly, the specific operation of the step S2 can also refer to the embodiment shown in FIG. 1, which will not be repeated here.

In step S3, the priority of the top-level event in the security tree is determined based on the security importance of the bottom-level event. Take the security tree in Figure 3-Figure 4c as an example for description as follows. Referring to Figure 3, it can be seen that the highest priority of the top-level events is the structural safety event, because according to the safety tree, its occurrence probability is 30%.

In step S4, for the top-level events with high priority, the corresponding bottom-level events are searched for according to the branches with high occurrence probability in the security tree. According to Figure 4a-4b, it can be seen that the most likely intermediate event to cause structural safety incidents is the braking safety incident, and its occurrence probability is 15%. Therefore, we choose the branch of braking safety events to find the corresponding bottom-level events. According to Figure 4b, we can conclude that the lowest level events that are most likely to cause brake safety incidents are the abnormality of the hydraulic sensor and the oil leakage of the brake tubing. The probability is as high as 1%.

In step S5, the underlying event is redesigned based on the electric vehicle operation theory and the fault logic relationship. Continuing to take Figure 4b as an example, we found that the two bottom-layer events, the hydraulic sensor abnormality and the brake oil pipe leakage, have the highest probability. Based on the known electric vehicle operation theory and fault logic relationship, we know that higher quality hydraulic sensors and brake tubing can be used, and by increasing the number of maintenance, the two bottom layers of hydraulic sensor abnormality and brake tubing oil leakage can be reduced. The probability of occurrence of the event. Then we can redesign the two underlying events of hydraulic sensor abnormality and brake oil leakage by regularly replacing the hydraulic sensor and brake tubing. Here, the above operations can be performed on each underlying event with a high probability of occurrence, for example, above 0.5%.

In step S6, we can further evaluate the rationality and impact of the redesign. For example, by re-running the electric vehicle, detecting the probability of occurrence of the two bottom-level events of the redesigned hydraulic sensor abnormality and the brake oil pipe leakage, and assessing whether the redesign is reasonable and how it affects the safety status of the entire electric vehicle. Here, any method known in the art can be used for evaluation.

In step S7, we can update the security tree based on the evaluation. The method for constructing the security tree can be referred to as shown in step S1, which will not be repeated here.

The implementation of the safety tree model-based electric vehicle safety design optimization method of the present invention can construct and update the safety tree. By mining and analyzing the sample data fed back by different electric vehicles, the safety hazards of certain typical problems and the design and production problems can be found. Unreasonable, through the reconstruction of these problems to continuously improve the design and manufacturing process of electric vehicles.

Another technical solution adopted by the present invention to solve its technical problems is to construct a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the safety tree model-based electric vehicle safety is realized. Design optimization methods.

Therefore, the present invention can be implemented by hardware, software or a combination of software and hardware. The present invention can be implemented in a centralized manner in at least one computer system, or implemented in a decentralized manner by different parts distributed in several interconnected computer systems. Any computer system or other equipment that can implement the method of the present invention is applicable. The combination of commonly used software and hardware can be a general computer system with a computer program installed, and the computer system is controlled by installing and executing the program to make it run according to the method of the present invention.

The present invention can also be implemented by a computer program product. The program contains all the features capable of implementing the method of the present invention. When it is installed in a computer system, the method of the present invention can be implemented. The computer program in this document refers to any expression of a set of instructions that can be written in any programming language, code, or symbol. The instruction set enables the system to have information processing capabilities to directly implement specific functions, or to perform After one or two steps, a specific function is realized: a) conversion into other languages, codes or symbols; b) reproduction in a different format.

Therefore, the present invention also relates to a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the method for constructing a safety tree for an electric vehicle is realized.

The present invention also relates to an electric vehicle, including a processor, and a computer program stored in the processor, and when the program is executed by the processor, the safety tree construction method of the electric vehicle is realized.

The implementation of the safety tree model-based electric vehicle safety design optimization method and computer-readable storage medium of the present invention can construct and update the safety tree, and by mining and analyzing the sample data fed back by different electric vehicles, some typical problems can be found Potential safety hazards and irrationality of design and production, through the reconstruction of these problems to continuously improve the design and manufacturing process of electric vehicles.

The above descriptions are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modification, equivalent replacement and improvement made within the spirit and principle of the present invention shall be included in the protection of the present invention. Within range.

Claims (8)

1. An electric vehicle safety design optimization method based on a safety tree model is characterized in that it includes:

   S1. Construct a security tree, the security tree including multiple bottom-level events, middle-level events, top-level events, and the logical causality and safety importance between the bottom-level events, the middle-level events, and the top-level events;

   S2. Sort the security importance of each underlying event based on the security tree;

   S3. Perform fault reconstruction analysis on the branch with a high probability of occurrence in the security tree based on the safety importance of the underlying event, and reduce the occurrence probability of the underlying event in the branch based on the analysis result;

   The step S1 further includes:

   S11. Collect vehicle safety failure data of electric vehicles;

   S12. Map the vehicle safety failure data into different safety event groups, and separately count the frequency data of each safety event group;

   S13. Use a joint analysis method to classify the safety failure data of the entire vehicle in each safety event group to construct a safety tree;

   The step S2 further includes

   S21. Through the collection and statistics of middle-level events, analyze the parameter deviation of the middle-level events, and convert the original frequency data of the middle-level events into standardized frequency data of intermediate events at all levels;

   S22. Obtain the occurrence probability of each underlying event through the analysis and statistics of the logical causality and the result of the intermediate event;

   S23. Obtain the occurrence probability of each top-level event based on the collection of the security tree and the middle-level event and the statistics of the frequency of the middle-level event;

   S24. Based on the probability of each bottom-level event to each intermediate event and the occurrence probability of each top-level event, calculate the probability of each bottom-level event affecting the top-level event;

   S25. Sort the safety importance of each bottom-level event based on the impact probability of each bottom-level event on each top-level event.
2. The method for optimizing the safety design of electric vehicles based on the safety tree model according to claim 1, wherein the step S3 further comprises:

   S31. Determine the priority of the top-level event in the security tree based on the security importance of the bottom-level event;

   S32. For top-level events with high priority, look for corresponding bottom-level events according to branches with high occurrence probability in the security tree;

   S33. Redesign the underlying event based on the electric vehicle operation theory and the fault logic relationship.
3. The method for optimizing the safety design of electric vehicles based on the safety tree model according to claim 2, wherein the step S3 further comprises:

   S34. Evaluate the rationality and impact of the redesign;

   S35. Update the security tree based on the evaluation.
4. The method for optimizing safety design of electric vehicles based on a safety tree model according to claim 1, wherein the step S13 further comprises:

   S131. Divide the safety failure data of the vehicle into at least a first failure category, a second failure category, a third failure category, and a fourth failure category;

   S132. Use different analysis methods to analyze the vehicle safety failure data of the first failure category, the second failure category, the third failure category, and the fourth failure category to determine the electric vehicle The hierarchical relationship between the safety fault data determines the logical causality and safety importance among the bottom-level event, the middle-level event, and the top-level event, and the bottom-level event, the middle-level event, and the top-level event;

   S133. Establish fault causality layer by layer until all the vehicle safety fault data is traversed to complete the construction of the safety tree of the electric vehicle.
5. The method for optimizing safety design of electric vehicles based on a safety tree model according to claim 1, wherein the step S21 comprises:

   S211. Collect the fault data of the intermediate event of the electric vehicle and perform statistical decoupling, analyze the existing parameter deviation for the dynamic change of the operating parameter of the electric vehicle; compare the parameter deviation and the sudden change in the fault data Sending a failure alarm event as the original frequency data of the middle-level event;

   S212. For the working environment corresponding to the original frequency data of the intermediate events at all levels, convert the original frequency data into standardized intermediate event frequency data at all levels.
6. The method for optimizing the safety design of electric vehicles based on the safety tree model according to claim 5, wherein said step S22 comprises: counting the frequency data of standardized intermediate events at all levels in field application, testing, and inspection scenarios, and Calculate the probability of occurrence of each underlying event respectively.
7. The method for optimizing the safety design of electric vehicles based on the safety tree model according to claim 1, characterized in that, in the step S23, the top layer is calculated by the frequency statistics and distribution of intermediate events and the risk value of each intermediate event. The occurrence probability of the event; and/or in the step S24, the Bayes algorithm is used to calculate the probability of each bottom-level event affecting the top-level event.
8. A computer-readable storage medium with a computer program stored thereon, wherein the program is executed by a processor to realize the safety tree model-based electric vehicle according to any one of claims 1-7 Safety design optimization method.

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