WO2020211844A1

WO2020211844A1 - Electric vehicle security control method based on security tree probabilities and security importance, and electric vehicle

Info

Publication number: WO2020211844A1
Application number: PCT/CN2020/085368
Authority: WO
Inventors: 张伟
Original assignee: 深圳市德塔防爆电动汽车有限公司
Priority date: 2019-04-19
Filing date: 2020-04-17
Publication date: 2020-10-22
Also published as: CN110084500B; CN110084500A

Abstract

An electric vehicle security control method based on security tree probabilities and security importance. The method comprises: constructing a security tree; analyzing a parameter deviation existing for intermediate events, and converting original frequency data of the intermediate events into standardized intermediate event frequency data at all levels; performing analysis and statistics collection on the basis of the logical causality and the results of the intermediate events to obtain the occurrence probability of each bottom event; collecting statistics to obtain the occurrence probability of each top event on the basis of the security tree, the acquisition of the intermediate events, and the intermediate event frequency data; obtaining the impact probabilities of the bottom events on the top events; sequencing the security importance of the bottom events on the bass of the impact probabilities of the bottom events on the top events; and performing security control on the electric vehicle on the basis of the security importance of the security tree. According to the method, security tree probabilities and security importance can be calculated, and the system can effectively analyze, quantitatively describe, and accurately reflect the security state of an electric vehicle.

Description

Electric vehicle safety control method and electric vehicle based on safety tree probability and safety importance

Technical field

The invention relates to a transportation tool, and more specifically, to an electric vehicle safety control method and an electric vehicle based on safety tree probability and safety importance.

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 control method based on the safety tree probability and safety importance in view of the above-mentioned defects of the prior art. By constructing the safety tree of the electric vehicle, the safety tree probability and safety importance are analyzed. The system effectively analyzes, quantitatively describes, and accurately reflects the safety status of electric vehicles.

The technical solution adopted by the present invention to solve its technical problems is: constructing an electric vehicle safety control method based on safety tree probability calculation and safety importance, including:

S1. The security tree includes multiple bottom-level events, middle-level events, top-level events, and logical causality and safety importance among the bottom-level events, the middle-level events, and the top-level events;

S2. Analyze the parameter deviations of the intermediate events through the collection and statistics of the intermediate events, and convert the original frequency data of the intermediate events into standardized intermediate event frequency data at all levels;

S3. Obtain the occurrence probability of each underlying event through the analysis and statistics of the logical causality and the result of the intermediate event;

S4. 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;

S5. 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;

S6. Sort the safety importance of each bottom-level event based on the impact probability of each bottom-level event on each top-level event;

S7. Perform safety control on the electric vehicle based on the safety importance of the safety tree.

In the electric vehicle safety control method based on the safety tree probability and safety importance of the present invention, the step S2 includes:

S21. Collect the fault data of the intermediate events of the electric vehicle and perform statistical decoupling, analyze the existing parameter deviations for the dynamic changes of the operating parameters of the electric vehicle; compare the parameter deviations and the sudden changes in the fault data Sending a failure alarm event as the original frequency data of the middle-level event;

S22. According to the working environment corresponding to the original frequency data of the intermediate events at all levels, the original frequency data is converted into standardized intermediate event frequency data at all levels.

In the electric vehicle safety control method based on the safety tree probability and safety importance of the present invention, in the step S3, the frequency data of standardized intermediate events at all levels in the field application, test, and inspection scenarios are counted, and Calculate the probability of occurrence of each underlying event respectively.

In the electric vehicle safety control method based on the safety tree probability and safety importance of the present invention, in the step S4, the top-level event is calculated through the frequency statistics and distribution of intermediate events and the risk value of each intermediate event The probability of occurrence.

In the electric vehicle safety control method based on the safety tree probability and safety importance of the present invention, in the step S5, the Bayes algorithm is used to calculate the influence probability of each bottom event on the top event.

In the electric vehicle safety control method based on the safety tree probability and safety importance of the present invention, in the step S7, the contribution of each bottom-level event to the occurrence probability of each top-level event is calculated to evaluate the contribution of each bottom-level event to each The magnitude of the impact of top-level events, in turn, provides a quantitative basis for the design and production, process improvement, and operation and maintenance of electric vehicles.

In the electric vehicle safety control method based on the safety tree probability and safety importance 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 electric vehicle safety control method based on the safety tree probability and safety importance 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. Analyze the safety failure data of the entire vehicle of the first fault category, the second fault category, the third fault category, and the fourth fault category using different analysis methods to determine the entire vehicle The hierarchical relationship between safety failure data;

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. 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-based probability and safety importance are realized. Safety control method for electric vehicles.

Another technical solution adopted by the present invention to solve its technical problem is to construct an electric vehicle, including a processor, a computer program stored in the processor, and when the program is executed by the processor, the security tree-based Probability and safety importance of electric vehicle safety control methods.

The implementation of the safety tree probability and safety importance-based electric vehicle safety control method, computer readable storage medium, and electric vehicle of the present invention can analyze the safety tree probability and safety importance by constructing a safety tree of the electric vehicle, and the system can effectively analyze, Quantitatively describe and accurately reflect the safety status 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 flowchart of an electric vehicle safety control method based on safety tree probability and safety importance in a preferred embodiment of the present invention;

2 is a schematic diagram of the classification of the entire vehicle safety failure data of the electric vehicle safety control method based on the safety tree probability and the safety importance of the electric vehicle according to the preferred embodiment of the present invention;

3a-3c are schematic diagrams of part of the safety tree of the safety control method for electric vehicles based on safety tree probability and safety importance in a preferred embodiment of the present invention;

4a-b are the probability lists and safety importance results of the bottom-level electrical safety events of the electric vehicle safety control method based on the safety tree probability and safety importance of the electric vehicle according to the 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 control method based on safety tree probability and safety importance, including: S1. Building a safety tree, the safety tree including multiple bottom-level events, middle-level events, top-level events, and the bottom-level events, The logical causality and safety importance between the middle-level event and the top-level event; S2. Through the collection and statistics of the middle-level event, analyze the parameter deviation of the middle-level event, and compare the middle-level event The original frequency data is converted into standardized intermediate event frequency data at all levels; S3. The probability of occurrence of each underlying event is obtained through the analysis and statistics of the logical causality and the result of the intermediate event; S4. Based on the safety tree and the The collection of middle-level events and the statistics of the frequency data of the middle-level events obtain the probability of each top-level event; S5. Based on the probability of each bottom-level event versus each mid-level event, and the probability of each top-level event, calculate each bottom-level event versus top-level event S6. Sort the safety importance of each bottom event based on the probability of each bottom event's impact on each top event; S7. Perform safety control on the electric vehicle based on the safety importance of the safety tree. The safety control method for electric vehicles based on the safety tree probability and safety importance of the present invention can analyze the safety tree probability and safety importance by constructing the safety tree of the electric vehicle, and the system can effectively analyze, quantitatively describe, and accurately reflect the safety state of the electric vehicle .

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 flowchart of an electric vehicle safety control method based on safety tree probability and safety importance in a 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 the 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 2, 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, explosion-proof 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 is used to determine the hierarchical relationship between the vehicle safety failure data; the failure causal relationship is established layer by layer until all the vehicle safety failure data are traversed to complete the electric vehicle The security tree is constructed. 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 The failure of the fourth 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 3a-3c are schematic diagrams of part of the security tree of the preferred embodiment of the present invention. As shown in Figure 3a-3c, 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. We then take the brake safety incident as an example. As shown in Figure 3b, 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 is the bottom event, 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 3c, taking the braking safety event as the top-level event, we can find the steering safety event as the first middle-level event through the Bayesian algorithm, respectively, and the second middle-level event steering operating mechanism failure, 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 3b, 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 3b, 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 the step S2, 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 standardized intermediate event frequency data at all levels. 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.

In step S3, count the standardized intermediate event frequency data at all levels in the field application, test, and inspection scenarios, and calculate the occurrence probability of each underlying event respectively.

In step S4, in the step S4, the occurrence probability of the top-level event is calculated through the occurrence frequency statistics and distribution of the intermediate events, and the risk value of each intermediate event;

In step S5, based on the probability of each bottom-level event to each intermediate event and the occurrence probability of each top-level event, the probability of each bottom-level event affecting the top-level event can be obtained through Bayesian calculation; those skilled in the art know that, except for the following In addition to the calculation method, those skilled in the art can also use other calculation formulas for calculation according to the actual situation. 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 formula of the safety tree structure can be calculated

In step S7, the contribution of each bottom-level event to the occurrence probability of each top-level event is calculated to evaluate the impact of each bottom-level event on each top-level event, thereby providing a quantitative basis for the design, production, process improvement, and operation and maintenance of electric vehicles. 4a-b are the probability lists and safety importance results of the bottom-level electrical safety events of the electric vehicle safety control method based on the safety tree probability and safety importance of the electric vehicle according to the preferred embodiment of the present invention. For example, as shown in Figure 4a-4b, the safety importance calculated for X2 power limit abnormality, X29 current safety fault, and X33 insulation resistance fault are all 0.1122, indicating that the electrical faults on the top layer have the greatest impact. After obtaining such parameters, those skilled in the art can perform safety control of the electric vehicle by, for example, using high-quality accessories, high-frequency maintenance, or other control safety control methods known in the art. For another example, after an electrical fault occurs, those skilled in the art can first check the underlying events in these aspects based on the above-mentioned safety importance, so as to save maintenance time as much as possible.

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 probability calculation and safety importance-based electric vehicle safety control method, computer-readable storage medium and electric vehicle of the present invention can analyze the safety tree probability and safety importance degree by constructing the safety tree of the electric vehicle, and effectively analyze the system , Quantitatively describe and accurately reflect the safety status of electric vehicles.

Although the present invention is described through specific embodiments, those skilled in the art should understand that various changes and equivalent substitutions can be made to the present invention without departing from the scope of the present invention. In addition, various modifications can be made to the present invention for specific situations or materials without departing from the scope of the present invention. Therefore, the present invention is not limited to the disclosed specific embodiments, but should include all embodiments falling within the scope of the claims of the present invention.

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

An electric vehicle safety control method based on safety tree probability and safety importance 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. Analyze the parameter deviations of the intermediate events through the collection and statistics of the intermediate events, and convert the original frequency data of the intermediate events into standardized intermediate event frequency data at all levels;

S3. Obtain the occurrence probability of each underlying event through the analysis and statistics of the logical causality and the result of the intermediate event;

S4. 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;

S5. 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;

S6. Sort the safety importance of each bottom-level event based on the impact probability of each bottom-level event on each top-level event;

S7. Perform safety control on the electric vehicle based on the safety importance of the safety tree;

The step S2 includes:

S21. Collect the fault data of the intermediate events of the electric vehicle and perform statistical decoupling, analyze the existing parameter deviations for the dynamic changes of the operating parameters of the electric vehicle; compare the parameter deviations and the sudden changes in the fault data Sending a failure alarm event as the original frequency data of the middle-level event;

S22. For the working environment corresponding to the original frequency data of intermediate events at all levels, convert the original frequency data into standardized intermediate event frequency data at all levels; in the step S3, statistics are used in field application, testing, and inspection scenarios The standardized frequency data of intermediate events at all levels, and the corresponding probability of occurrence of each bottom event are respectively calculated; in step S4, through the frequency statistics and distribution of intermediate events, and the risk value of each intermediate event, the Probability of occurrence.
The safety control method for electric vehicles based on safety tree probability and safety importance according to claim 1, characterized in that, in said step S5, Bayesian algorithm is used to calculate the influence probability of each bottom-level event on said top-level event .
The safety control method for electric vehicles based on safety tree probability and safety importance according to claim 1, characterized in that, in said step S7, the contribution of each bottom-level event to the occurrence probability of each top-level event is calculated to evaluate each The magnitude of the impact of the bottom-level events on each top-level event provides a quantitative basis for the design, production, process improvement, and operation and maintenance of electric vehicles.
The safety control method for electric vehicles based on safety tree probability and safety importance according to claim 1, wherein the step S1 further comprises:

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 safety control method for electric vehicles based on safety tree probability and safety importance according to claim 4, wherein said 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. Analyze the safety failure data of the entire vehicle of the first fault category, the second fault category, the third fault category, and the fourth fault category using different analysis methods to determine the entire vehicle The hierarchical relationship between safety failure data;

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.
A computer-readable storage medium with a computer program stored thereon, wherein the program, when executed by a processor, implements the safety tree based probability and safety importance according to any one of claims 1 to 5 Degree of safety control method for electric vehicles.
An electric vehicle, characterized by comprising a processor, a computer program stored in the processor, and when the program is executed by the processor, the security tree based on any one of claims 1 to 5 is realized Probability and safety importance of electric vehicle safety control methods.