CN112069649A - Electric vehicle EPS system reliability evaluation method based on Model Driven Architecture (MDA) - Google Patents
Electric vehicle EPS system reliability evaluation method based on Model Driven Architecture (MDA) Download PDFInfo
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Abstract
The invention belongs to the field of electric automobiles, in particular to a method for evaluating reliability of an electric automobile EPS system based on a Model Driven Architecture (MDA), which aims at the problem that the reliability of the existing EPS system is only researched on a single part, and provides the following scheme, comprising the following steps: the method comprises the following steps: combining MDA and FIA, and analyzing and evaluating the reliability of the EPS system; step two: starting from an EPS system model level, building a system architecture model A-EPS and a system reliability model R-EPS by comprehensively adopting AADL and EMA languages; step three: researching and improving the mapping rule from the MDA model to the FTA model, and the fourth step: and (5) realizing the conversion from the R-EPS model to the EPS fault tree model according to the rule in the step three. Compared with the prior art, the invention comprehensively considers the dependency relationship among all the components of the EPS system and the safety problems of the soft and hard components in the operation of the system, realizes the analysis of the comprehensive reliability of the EPS system and provides a theoretical basis for the actual engineering.
Description
Technical Field
The invention relates to the field of electric automobiles, in particular to a method for evaluating reliability of an electric automobile EPS system based on a Model Driven Architecture (MDA).
Background
At present, the reliability research of the automobile EPS system at home and abroad mainly focuses on two aspects of EPS system core component modeling and control technology and mechanical fault diagnosis. The EPS system core component modeling and control technology mainly refers to mathematical equation description of an EPS system core component, and the control technology refers to an EPS system control method and a control strategy. The mechanical fault diagnosis mainly comprises the following steps: fault diagnosis and isolation of components, component optimization design and fault-tolerant control technology, and fault statistics and prediction. The method only aims at the single part of the EPS system, and a more effective reliability analysis method is needed to be adopted, starting from the system level, construct the reliability model of the EPS system and carry out comprehensive reliability evaluation.
The document, "yaowangxijust, model-based automobile electric power steering fault diagnosis system research [ D ]. combined fertilizer industry university, 2019, 4" proposes a model-based structural analysis method (structural analysis, SA), which analyzes a failure mode and a failure influence of an EPS system component by failure mode influence and hazard analysis (FMECA), determines a system key component by qualitative analysis, and establishes an EPS system fault model, but the method is a system fault model established under FMECA qualitative analysis, and has fuzzy uncertainty and does not comprehensively consider the influence of software and hardware on system reliability. Therefore, starting from a system model level, comprehensively considering all components of the EPS system and the dependency relationship among the components, and comprehensively considering the safety problems of the soft and hard components in the operation of the system, so as to realize the analysis of the comprehensive reliability of the EPS system.
Disclosure of Invention
Based on the problem that the existing EPS system reliability research only researches a single part, the invention provides a Model Driven Architecture (MDA) -based electric vehicle EPS system reliability evaluation method.
The invention provides a Model Driven Architecture (MDA) -based electric vehicle EPS system reliability evaluation method, which comprises the following steps:
the method comprises the following steps: combining MDA and FIA, and analyzing and evaluating the reliability of the EPS system;
step two: starting from an EPS system model level, building a system architecture model A-EPS and a system reliability model R-EPS by comprehensively adopting AADL and EMA languages;
step three: researching and improving a mapping rule from an MDA model to an FTA model;
step four: according to the rule in the third step, the conversion from the R-EPS model to the EPS fault tree model is realized;
step five: the failure rate of the EPS system is predicted through FIA quantitative analysis, the EPS reliability key components are determined through FTA qualitative analysis, rationalization suggestions are given, and theoretical basis is provided for engineering practice.
Preferably, in the first step: the model driven structure Method (MDA) can be researched from a system model level, the function and non-function attributes (real-time performance, safety and the like) of a system are verified and analyzed, the system development period is greatly shortened, the development cost is reduced, and the model driven structure Method (MDA) is an important means for ensuring the reliability of the system, the Architecture Analysis and Design Language (AADL) is a structure design analysis language with cooperation of software and hardware proposed by SAE and is an important architecture description language in the MDA method, wherein the AADL comprises a core language and an extended attachment (BehaviorAnanenx, Errorodelalnenx), the AADL core language can model a complex system architecture model, an error model attachment (Errorodelalnenx, FMA) can be used for evaluating the reliability of the system and describes the fault information during the operation of the system, including the fault of an AADL component, the fault state transition, the fault propagation and the like, and the AADL reliability model can be used for constructing the AADL reliability model of the system by the AADL core language and the EMA sublanguage, in order to make the system reliability model have more accurate verification and analysis, the AADL reliability model is often converted into various forms of Petri net finite state machines such as GSPN, SAGSPN and the like, Fault Trees (FTA) and other formal models, and the reliability of the system is analyzed and evaluated through strict mathematical theoretical derivation and formal tools, so as to verify and optimize the architecture design of the system model.
In the formal model, a fault tree analysis (FIA) has the characteristics of simplicity, high efficiency, strong logicality and the like, qualitative and quantitative analysis can be performed on the reliability of the system, and the weakest link of the system can be obtained by tracking through FTA (fiber to the infrastructure) qualitative analysis; and the probability of the overall failure of the system can be solved through FTA quantitative analysis.
Preferably, the AADL reliability model in the second step includes an AADL architecture model (a-EPS) and an AADL Error Model (EMA);
according to the working principle and components of the EPS system, the EPS system in the AADL model architecture (a-EPS) of the system is composed of a control unit (process EPS _ control), a sensor (Speed _ sensor), an Electric motor (Electric motor), an Electromagnetic clutch (Electromagnetic _ clutch), and a Steering mechanism (Steering _ mechanism); the AADL Error Model (EMA) describes information related to the reliability of the component, including fault type, fault event, fault state transition, fault distribution and the like; combining the AADL architecture model and the AADL error model, an EPS reliability model (R-EPS) can be constructed.
Preferably, the mapping rule in step three is:
definition 1: the basic failure tree FTA ═ (TE, IE, BE, G). The TE is a TopEvent top event which is positioned at the top end of the fault tree and represents the result of the joint occurrence of all events;
IE is an IntermediateEvent intermediate event between a top event and a bottom event; TE and IE are both represented by rectangular symbols;
a BottomEvent bottom event, including a basic event, indicates that the event does not need to BE continuously ascertained, has a known failure mode and is generally represented by a circular symbol;
and G is a Gate comprising an OR Gate ORGate and an AND Gate ANDGate. Or gate: when at least one input event occurs, an output event occurs; and gate: meaning that an output event will only occur if all input events occur.
Definition 2: according to the EMA basic element, the EMA can be expressed as (ES, EE, T, OD);
ES: the set of all error states, ES ═ ES1, ES2, …, esm };
EE: a set of all error events, EE ═ EE1, EE2, …, eem };
OD: fault distribution and probability of occurrence of an OccurecunceDistribution error event;
t: the set of all transitions between error states, the transfer function T (esi, eej) ═ esk.
Comparing the basic elements of the EMA model and the FTA model to obtain the corresponding relation of the elements in the two models, wherein the conversion rule is as follows:
rule1, EMA (EE) -FTA (BE), converting the error event in the error model into the bottom event in the fault tree;
rule2, EMA (EE ^ OD) > FTA (BE), wherein the fault distribution type and probability of the error event in the error model are converted into the bottom event probability in the fault tree;
rule3 EMA (ES) > FTA (ME) \ (TE), the error state in the error model is converted to an intermediate event or a top event in the fault tree;
rule4 EMA (T) -FTA (G), the connection arc of the error model is converted into a logic gate in the fault tree, wherein the conversion Rule of the logic gate has two types:
(1) the composite error behavior in the EMA describes composite fault behavior, and expresses the relationship between an error event and state transition. and means that several error events occur to cause a state transition, or means that any one event occurrence will cause a state transition. For this purpose, the and gate of EMA can be converted into FTA, or into the OR gate of FTA;
(2) when correlation exists between EEi and EEj, namely fault event j is caused by occurrence of fault event i, the correlation can be converted into AND gate; if there is no correlation between EEi and EEj, i.e. the occurrence of fault event i does not cause the occurrence of fault event j, it can be converted into an or gate, which expresses the interdependence between different components.
Preferably, in the fourth step: after an EPS system reliability model (R-EPS) is constructed, the R-EPS system is instantiated, the instantiation content comprises an EPS system architecture instance and an error model instance, the error model instance is converted based on the mapping rules of the three steps, the error model instance is generated through software OSATE plug-in Runfaulttreeaysis, a. fta file is generated, and finally the. fta file is analyzed through an OpenFTA tool to generate an EPS fault tree model.
Preferably, in the fifth step, TE is first set as a top event of the fault tree, and X ═ X1,X2,....XnThe method comprises the steps of conducting quantitative analysis on an R-EPS system FTA according to a structural function to obtain probability of a top event, and then conducting qualitative analysis on the R-EPS system FTA, wherein contribution of a fault tree bottom event or a minimal cut set to the top event can be determined, system weak links can be designed according to the improved system scheme, and the system weak links can be divided into probability importance, critical importance and structural importance
The beneficial effects of the invention are as follows:
the invention provides a Model Driven Architecture (MDA) -based reliability assessment method for an EPS system of an electric vehicle, which comprises the steps of analyzing to obtain common fault events and failure probabilities of the EPS system according to fault tracking records of an electric vehicle of an autonomous brand in a development and test period and failure characteristic parameter derivation theories of parts of components, establishing a system architecture model A-EPS by using an AADL language, establishing a system reliability model R-EPS by using an EMA sub-language on the basis, improving conversion rules from the EMA model to an FTA model to generate a system FTA model, and finally obtaining the following conclusions through quantitative and qualitative analysis of the FTA.
1. The FTA quantitative analysis shows that the probability of the system top event is 0.274, the actual test result is 0.266, the theoretical and actual error is 3%, and the accuracy is high.
2. From the viewpoint of the failure probability of the element itself, three important events causing the failure of the EPS are: normally closing a relay contact, outputting a constant value by a torque sensor and failing an electromagnetic clutch; most of the faults with the smallest influence belong to software type faults, and key events of the system are found and reasonable suggestions are given.
3. In terms of the position of the element in the system, events such as sampling resistance failure, feedback current signal processing failure, feedback current circuit fault and the like have the highest importance degree and are positioned at key parts of the system; and secondly, finding weak links of system software and hardware architecture for constant low output of the motor driving chip, open circuit of the input circuit of the motor driving chip and the like, and finally for corner signal processing failure, torque signal processing failure and the like, and providing theoretical reference for EPS system developers.
The method system can be used for checking weak links of the system in the early stage of system development, and provides a theoretical basis for system improvement and element health management.
Compared with the prior art, the invention comprehensively considers the dependency relationship among all the components of the EPS system and the safety problems of the soft and hard components in the operation of the system, realizes the analysis of the comprehensive reliability of the EPS system and provides a theoretical basis for the actual engineering.
Drawings
Fig. 1 is a flowchart of a method for evaluating reliability of an electric vehicle EPS system based on a Model Driven Architecture (MDA) according to the present invention;
fig. 2 is a schematic diagram of an EPS system structure and a working principle of an electric vehicle EPS system reliability evaluation method based on a Model Driven Architecture (MDA) according to the present invention;
FIG. 3 is an architecture diagram of an EPS system AADL model of an electric vehicle EPS system reliability assessment method based on a Model Driven Architecture (MDA) according to the present invention;
FIG. 4 is a schematic diagram illustrating a part of attribute definitions of an A-EPS of an electric vehicle EPS system reliability evaluation method based on a Model Driven Architecture (MDA) according to the present invention;
FIG. 5 is a schematic diagram of a subsystem universal error model library of a method for evaluating reliability of an electric vehicle EPS system based on a Model Driven Architecture (MDA) according to the present invention;
FIG. 6 is a schematic diagram of a reliability model of an EPS _ control of an electric vehicle EPS system reliability evaluation method based on a Model Driven Architecture (MDA) according to the present invention;
fig. 7 is a schematic diagram of an EPS system FTA model of an electric vehicle EPS system reliability evaluation method based on a Model Driven Architecture (MDA) according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments.
In this embodiment, referring to fig. 1 to 7, a method for evaluating reliability of an electric vehicle EPS system based on a Model Driven Architecture (MDA) includes the following steps:
the method comprises the following steps: combining MDA and FIA, and analyzing and evaluating the reliability of the EPS system;
step two: starting from an EPS system model level, building a system architecture model A-EPS and a system reliability model R-EPS by comprehensively adopting AADL and EMA languages;
step three: researching and improving the mapping rule of the MDA model to the FTA model,
step four: according to the rule in the third step, the conversion from the R-EPS model to the EPS fault tree model is realized;
step five: the failure rate of the EPS system is predicted through FIA quantitative analysis, the EPS reliability key components are determined through FTA qualitative analysis, rationalization suggestions are given, and theoretical basis is provided for engineering practice.
Wherein, in the step one: the model driven structure Method (MDA) can be researched from a system model level, the function and non-function attributes (real-time performance, safety and the like) of a system are verified and analyzed, the system development period is greatly shortened, the development cost is reduced, and the method is an important means for ensuring the reliability of the system, the Architecture Analysis and Design Language (AADL) is a software and hardware cooperative structural design analysis language proposed by SAE and is an important architecture description language in the MDA method, wherein the AADL comprises a core language and an extended accessory (Behavior Annex, Error model Annex), the AADL core language can model a complex system architecture model, the Error model accessory (Errormodelannex, FMA) can be used for system reliability evaluation, fault information during system operation can be described, the fault information comprises AADL component fault, fault state transition, fault propagation and the like, and the AADL reliability model of the system can be constructed by utilizing the AADL core language and EMA sublingual, in order to make the system reliability model have more accurate verification and analysis, the AADL reliability model is often converted into various forms of Petri net finite state machines such as GSPN, SAGSPN, etc., Fault Trees (FTA), etc., and the reliability of the system is analyzed and evaluated through strict mathematical theory derivation and formal tools, so as to verify and optimize the architecture design of the system model.
In the formal model, a fault tree analysis (FIA) has the characteristics of simplicity, high efficiency, strong logicality and the like, qualitative and quantitative analysis can be performed on the reliability of the system, and the weakest link of the system can be obtained by tracking through FTA (fiber to the infrastructure) qualitative analysis; and the probability of the overall failure of the system can be solved through FTA quantitative analysis.
The AADL reliability model in the step two comprises an AADL architecture model (A-EPS) and an AADL Error Model (EMA);
according to the working principle and components of the EPS system, the EPS system in an AADL model architecture (A-EPS) of the system is composed of a Control unit (process EPS _ Control), a sensor (Speed _ sensor), a motor (Electric _ motor), an Electromagnetic clutch (Electromagnetic _ clutch) and a Steering mechanism (Steering _ mechanism), wherein the process EPS _ Control comprises Signal _ Processing, Control _ decision and Start up three threads (subtasks), and the thread Start up is responsible for carrying out self-check on the EPS system and sending out a Control command when the system is normal, and the Control thread Control _ decision carries out decision Control; the thread Signal _ Processing is responsible for carrying out data Processing on the acquired speed (speed _ Signal), Torque (Torque _ Signal) and corner Signal (Angle _ Signal), and submitting the processed data to the thread Control _ decision for decision Control, and finally submitting target speed, Torque and corner Signal quantities to the motor to Control the effective operation of the motor, the process EPS _ Control is bound to the MCU, and all sensors, the motor, the electromagnetic clutch and the steering mechanism are connected through a CAN bus, wherein part of attributes of the A-EPS are defined as shown in FIG. 4;
according to the software part parameters of the experimental sample car, the I/O port, the task type, the bus attribute, the relevant attribute of the processor and the like can be described in detail by utilizing the standard attribute set and the custom attribute set. The thread precision is periodic and the period is 30ms, the calculation cut-off time is defaulted to be equal to the period, the calculation execution time is 3ms, the processor adopts NXPSTM32L431RCT6, the thread exchange execution time is 2-3ns, and the scheduling strategy adopts EDF; the bus adopts a high-speed CAN, a Carrier Sense Multiple Access (CSMA) protocol is adopted, and a hardware part adopts a VHDL description language; in addition, the priority and scheduling policy of the thread, the processing rate and priority range of the processor, the bandwidth of the bus, etc. can be described in detail.
An AADL Error Model (EMA) describes information related to component reliability, including information of fault types, fault events, fault states, fault state transition, fault distribution and the like, fault information of each element needs to be described for building an EPS system EMA model, a detailed subsystem universal error model library is provided in FIG. 5, and in the diagram, a mark (1) defines the fault types, including equipment fault (close _ fault), signal processing fault (Speed _ signal _ fault), circuit short-circuit fault (IO _ module fault), output value fault (MCU _ key _ high) and the like; the fault type can be provided by the EMA standard set, and can also be customized, such as Signal _ processing _ failure. The mark (2) defines a fault event (Speed and the like) and a fault state (failed), the mark (3) defines a fault transition, the mark (4) defines a fault distribution type which can be divided into Poisson probability distribution and Fixed probability distribution, in addition, the severity level, the possibility, the hazard and the like can be defined, and the marks (2), (3) and (4) jointly define a fault behavior model; an EPS reliability model (R-EPS) can be constructed by combining the AADL architecture model and the AADL error model, and fig. 6 shows a reliability model of EPS system process control.
Wherein, the mapping rule in the third step is as follows:
definition 1: the basic fault tree FTA is (TE, IE, BE, G), TE is a Top event of TopEvent, is positioned at the top end of the fault tree and represents the result of the joint action of all events;
IE is an IntermediateEvent intermediate event between a top event and a bottom event; TE and IE are both represented by rectangular symbols;
a BottomEvent bottom event, including a basic event, indicates that the event does not need to BE continuously ascertained, has a known failure mode and is generally represented by a circular symbol;
gate, including or Gate and Gate, or Gate: when at least one input event occurs, an output event occurs; and gate: meaning that an output event will only occur if all input events occur.
Definition 2: according to the EMA basic element, the EMA can be expressed as (ES, EE, T, OD);
ES: the set of all error states, ES ═ ES1, ES2, …, esm };
EE: a set of all error events, EE ═ EE1, EE2, …, eem };
OD: fault distribution and probability of occurrence of an OccurecunceDistribution error event;
t: the set of all transitions between error states, the transfer function T (esi, eej) ═ esk.
Comparing the basic elements of the EMA model and the FTA model to obtain the corresponding relation of the elements in the two models, wherein the conversion rule is as follows:
rule1, EMA (EE) -FTA (BE), converting the error event in the error model into the bottom event in the fault tree;
rule2, EMA (EE ^ OD) > FTA (BE), wherein the fault distribution type and probability of the error event in the error model are converted into the bottom event probability in the fault tree;
rule3 EMA (ES) > FTA (ME) \ (TE), the error state in the error model is converted to an intermediate event or a top event in the fault tree;
rule4 EMA (T) -FTA (G), the connection arc of the error model is converted into a logic gate in the fault tree, wherein the conversion Rule of the logic gate has two types:
(1) the composite error behavior in the EMA describes composite fault behavior, and expresses the relationship between an error event and state transition. and means that several error events occur to cause a state transition, or means that any one event occurrence will cause a state transition. For this purpose, the AND gate of the EMA neutral-and-pass can be converted into FTA, or can be converted into the OR gate of FTA;
(2) when correlation exists between EEi and EEj, namely fault event j is caused by occurrence of fault event i, the correlation can be converted into AND gate; if there is no correlation between EEi and EEj, i.e. the occurrence of fault event i does not cause the occurrence of fault event j, it can be converted into an or gate, which expresses the interdependence between different components.
Wherein, in the fourth step: after an EPS system reliability model (R-EPS) is constructed, the R-EPS system is instantiated, the instantiation content comprises an EPS system architecture instance and an error model instance, the error model instance is converted based on the mapping rules of the three steps, the error model instance is generated through software OSATE plug-in Runfaulttreeaysis, a FTA file is generated, finally, the FTA file is analyzed through an OpenFTA tool, an EPS fault tree model is generated, and the EPS system FTA model is finally generated, wherein the name and the number of each intermediate event are shown in the table 1.
TABLE 1 intermediate event name and numbering
As shown in fig. 7, the use of an and gate between the basic events X26 and X27 indicates that the correlation between "open circuit of the motor driver chip input circuit" and "constant low output of the motor driver chip" is high, and the occurrence of the former causes the occurrence of the latter, and the use of an and gate between the basic events X19 and X23 indicates that the sensor does not have a signal when "no signal output from the torque sensor", "no signal processing from the vehicle speed", "no failure in the rotation angle signal processing", "no signal processing from the torque signal processing", and "no signal output from the vehicle speed sensor" occur.
In the fifth step, TE is first set as the top event of the fault tree, and X ═ X1,X2,....XnThe method is characterized in that the method is a set of n mutually independent bottom events of a fault tree, and quantitative analysis is carried out on the FTA of the R-EPS system according to a structural function, wherein the structural function is expressed as follows:
in the formula, n is the number of all bottom events of the fault tree, xiIs a state indicating whether the bottom event occurs or not; 0 indicates that the ith bottom event does not occur; 1 indicates that the ith bottom event occurred.
The structure function of the basic logic gate is as follows:
A. and gate:
B. or gate:
probability calculation of logic gates:
A. and gate: fs(t)＝E[φ(X)]＝F1(t).F2(t)...Fn(t) (5)
B. Or gate: fs(t)＝1-[1-F1(t)].[1-F2(t)]...[1-Fn(t)] (6)
According to an EPS system FTA model and EPS fault bottom events and the probability thereof in table 1, the probability of each intermediate event can be calculated layer by layer, the probability of the top event is finally calculated, the probability of the top event is calculated to be 0.274, compared with fault statistical data, EPS fault early warning accounts for 26.6% of the total number of faults, the error of actual test and model analysis data is 3%, and the accuracy is high.
Then, performing FTA qualitative analysis on the R-EPS system, wherein the contribution of a fault tree bottom event or a minimum cut set to a top event can be determined, and the system weak link can be designed according to the improved system scheme and can be divided into probability importance, critical importance and structural importance;
1) basic event probability importance analysis
Expressing the degree of the change of the unreliability of the ith element to the change of the unreliability of the system, the function g of the probability of occurrence of the event is a multiple linear function, and is applied to the independent variable Fi(t) obtaining the probability importance coefficient of the basic event by calculating the partial derivative, wherein the mathematical formula is as follows:
Fi(t) -component unreliability
Fs(t) -system unreliability,
the probability importance of each elementary event can be found using equation (7), as shown in table 2.
TABLE 2 basic event probability importance
Table 2 shows the priority of the probability of the basic event, from which it can be seen that the event X7, i.e., the relay contact is normally closed to cause the greatest effect, followed by X5, i.e., the torque sensor outputs a constant value, and X3, i.e., the electromagnetic clutch itself fails, so the system engineer should pay more attention to these key components during design to reduce the failure probability as much as possible, and design a fault-tolerant system as necessary to improve the reliability of the whole system, and the event with the least effect is X19-X23, which mostly belongs to software type failures (vehicle speed signal processing failure, corner signal processing failure, and torque signal processing failure).
2) Critical importance analysis
The critical importance, also called key importance, is considered from the perspective of system safety, and the importance of the basic event is represented by the ratio of the relative change rate of the occurrence probability of the basic event to the relative change rate of the occurrence probability of the top event, which is an importance criterion for measuring each basic event by comprehensively considering the sensitivity and the occurrence probability itself, and the formula is as follows:
the relationship between the probability importance and the critical importance is:
the critical importance of each elementary event can be derived according to equations (8) and (9), as shown in table 3:
TABLE 3 Critical importance of elementary events
From table 3, the three events with the highest critical importance are X7-normally closed relay contact, X5-constant torque sensor output, and X3-failure of electromagnetic clutch, respectively, which are similar to the analysis of probability importance result of basic event.
(3) Structural importance analysis
The structural importance degree expresses the importance degree of the element component in the system, the importance degree is irrelevant to the failure probability of the element component of the system, only the influence degree of each basic event on the occurrence of the top event is analyzed structurally, and the mathematical expression is as follows:
n is the number of components included in the system
The structural importance of the EPS system is found by analysis to be I (X18) ═ I (X17) ═ I (X16) ═ I (X15) ═ I (X11) ═ I (X10) ═ I (X9) ═ I (X8) ═ I (X7) ═ I (X6) ═ I (X5) ═ I (X4) ═ I (X3) ═ I (X2) ═ I (X1) > I (X27) ═ I (X26) ═ I (X25) ═ I (X24) ═ I (X14) ═ I (X13) > -I (X23) ═ I (X22) ═ I (X21) ═ I (X20) ═ I (X19) ═ I (X8672)
The EPS-based data acquisition system is characterized in that events such as sampling resistance failure, feedback current signal processing failure, feedback current circuit failure and the like have the highest importance degree in the EPS structure, are located at key parts of the system, are output constant low for a motor driving chip, input circuits of the motor driving chip are open-circuited and the like, and are finally corner signal processing failure, torque signal processing failure and the like.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and equivalent substitutions or changes according to the technical solution and the inventive concept of the present invention should be covered by the scope of the present invention.
Claims (6)
1. A reliability evaluation method for an electric vehicle EPS system based on a Model Driven Architecture (MDA) is characterized by comprising the following steps:
the method comprises the following steps: combining MDA and FIA, and analyzing and evaluating the reliability of the EPS system;
step two: starting from an EPS system model level, building a system architecture model A-EPS and a system reliability model R-EPS by comprehensively adopting AADL and EMA languages;
step three: researching and improving a mapping rule from an MDA model to an FTA model;
step four: according to the rule in the third step, the conversion from the R-EPS model to the EPS fault tree model is realized;
step five: the failure rate of the EPS system is predicted through FIA quantitative analysis, the EPS reliability key components are determined through FTA qualitative analysis, rationalization suggestions are given, and theoretical basis is provided for engineering practice.
2. The method for evaluating reliability of the EPS system of the electric vehicle based on the Model Driven Architecture (MDA) of claim 1, wherein the first step comprises: the model driven structure Method (MDA) can be researched from a system model level, the functional and non-functional attributes (real-time performance, safety and the like) of a system are verified and analyzed, the development period of the system is greatly shortened, the development cost is reduced, and the model driven structure Method (MDA) is an important means for ensuring the reliability of the system, the Architecture Analysis and Design Language (AADL) is a software and hardware cooperation structure design analysis language provided by SAE and is an important architecture description language in the MDA method, wherein the AADL comprises a core language and an extended attachment (BehaviorAnanenx, Errorodelenx), the AADL core language can model a complex system architecture model, an error model attachment (Errorodelenx, FMA) can be used for evaluating the reliability of the system, fault information during the operation of the system is described, the fault information comprises the failure of an AADL component, the transition of a fault state, the fault propagation and the like, and the AADL reliability model of the system can be constructed by utilizing the AADL core language and the EMA sublingual, in order to make the system reliability model have more accurate verification and analysis, the AADL reliability model is often converted into Petri net finite state machine, Fault Tree (FTA) and other formal models in various forms such as GSPN, SAGSPN and the like, the reliability of the system is analyzed and evaluated through strict mathematical theory derivation and formal tools, thereby verifying and optimizing the architecture design of the system model,
in the formal model, a fault tree analysis (FIA) has the characteristics of simplicity, high efficiency, strong logicality and the like, qualitative and quantitative analysis can be performed on the reliability of the system, and the weakest link of the system can be obtained by tracking through FTA (fiber to the Home) qualitative analysis; and the probability of the overall failure of the system can be solved through FTA quantitative analysis.
3. The method for evaluating reliability of an electric vehicle EPS system based on a Model Driven Architecture (MDA) of claim 1, wherein the AADL reliability model in the second step comprises an AADL architecture model (A-EPS) and an AADL Error Model (EMA);
according to the working principle and components of the EPS system, the EPS system in the AADL model architecture (a-EPS) of the system is composed of a control unit (process EPS _ control), a sensor (Speed _ sensor), an Electric motor (Electric motor), an Electromagnetic clutch (Electromagnetic _ clutch) and a Steering mechanism (Steering _ mechanism); the AADL Error Model (EMA) describes information about the reliability of the component; the method comprises the following steps of (1) including information such as fault types, fault events, fault states, fault state transition, fault distribution and the like; combining the AADL architecture model and the AADL error model, an EPS reliability model (R-EPS) can be constructed.
4. The method for evaluating reliability of an electric vehicle EPS system based on Model Driven Architecture (MDA) of claim 1, wherein the mapping rule in the third step is:
definition 1: basic failure tree FTA = (TE, IE, BE, G),
the TE is a TopEvent top event which is positioned at the top end of the fault tree and represents the result of the joint occurrence of all events;
IE is an IntermediateEvent intermediate event between a top event and a bottom event; TE and IE are both represented by rectangular symbols;
a BottomEvent bottom event, including a basic event, indicates that the event does not need to BE continuously ascertained, has a known failure mode and is generally represented by a circular symbol;
gate, comprising an or Gate ORGate and an and Gate andsgate,
or gate: when at least one input event occurs, an output event occurs; and gate: meaning that only when all input events occur, an output event will occur,
definition 2: according to the EMA basic element, can be expressed as EMA = (ES, EE, T, OD);
ES: set of all error states, ES = { ES1, ES2, …, esm };
EE: set of all error events, EE = { EE1, EE2, …, eem };
OD: fault distribution and probability of occurrence of an OccurecunceDistribution error event;
t: the set of all transitions between error states, the transfer function T (esi, eej) = esk,
comparing the basic elements of the EMA model and the FTA model to obtain the corresponding relationship of the elements in the two models, wherein the conversion rule is as follows:
rule1, EMA (EE) -FTA (BE), converting the error event in the error model into the bottom event in the fault tree;
rule2, EMA (EE ^ OD) > FTA (BE), wherein the fault distribution type and probability of the fault event in the fault model are converted into the bottom event probability in the fault tree;
rule3 EMA (ES) > FTA (ME) \ (TE), the error state in the error model is converted to an intermediate event or a top event in the fault tree;
rule4 EMA (T) -FTA (G), the connection arc of the error model is converted into a logic gate in the fault tree, wherein the conversion Rule of the logic gate has two types:
(1) the composite error behavior in the EMA describes composite fault behavior, expresses the relation between error events and state transitions,
and means that several error events occur to cause a state transition, or means that any one event occurrence will cause a state transition,
for this purpose, the AND gate of the EMA neutral-and-pass can be converted into FTA, or can be converted into the OR gate of FTA;
(2) when correlation exists between EEi and EEj, namely fault event j is caused by occurrence of fault event i, the correlation can be converted into AND gate; if there is no correlation between EEi and EEj, i.e. the occurrence of fault event i does not cause the occurrence of fault event j, it can be converted into an or gate, which expresses the interdependence between different components.
5. The method for evaluating reliability of an electric vehicle EPS system based on Model Driven Architecture (MDA) of claim 1, wherein in the fourth step: after an EPS system reliability model (R-EPS) is constructed, the R-EPS system is instantiated, the instantiation content comprises an EPS system architecture instance and an error model instance, the error model instance is converted based on the mapping rules of the three steps, the error model instance is generated through software OSATE plug-in Runfaulttreeaysis, a fta file is generated, and finally the fta file is analyzed through an OpenFTA tool to generate an EPS fault tree model.
6. The method for evaluating reliability of an EPS system of an electric vehicle based on a Model Driven Architecture (MDA) of claim 1, wherein in the fifth step, TE is first set as a top event of a fault tree,
the method is characterized in that n independent bottom events of a fault tree are collected, quantitative analysis is conducted on an R-EPS system FTA according to a structural function to obtain probability of a top event, then qualitative analysis is conducted on the R-EPS system FTA, and contribution of the fault tree bottom events or a minimal cut set to the top event can be determined, so that system weak links can be improved, and system scheme design can be divided into probability importance, critical importance and structural importance.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113743510A (en) * | 2021-09-07 | 2021-12-03 | 电子科技大学 | Gear transmission system FMMEA analysis method based on fault tree analysis |
| CN113960992A (en) * | 2021-12-17 | 2022-01-21 | 西安电子科技大学 | Improved FMECA mechanical product fault tree automatic construction system and method |
| WO2023276569A1 (en) * | 2021-06-28 | 2023-01-05 | 株式会社デンソー | Mobile object control system and program |
| CN115577990A (en) * | 2022-12-08 | 2023-01-06 | 中国人民解放军国防科技大学 | Method for establishing equipment system task reliability model |
| CN115796589A (en) * | 2022-12-05 | 2023-03-14 | 三亚学院 | Method for analyzing risk and hidden danger sensitivity of three-electric-system of pure electric vehicle |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101901186A (en) * | 2010-07-08 | 2010-12-01 | 西北工业大学 | Embedded system reliability analysis and evaluation method |
| WO2011148891A1 (en) * | 2010-05-24 | 2011-12-01 | 日本電気株式会社 | Method and system for analyzing static fault tree from system model |
| CN106874200A (en) * | 2017-02-14 | 2017-06-20 | 南京航空航天大学 | Embedded software reliability modeling and appraisal procedure based on AADL |
| CN109948197A (en) * | 2019-02-28 | 2019-06-28 | 北方民族大学 | System optimization analysis method based on AADL V1 |
| CN111209177A (en) * | 2020-01-06 | 2020-05-29 | 华东师范大学 | Hardware system reliability verification system |
-
2020
- 2020-07-21 CN CN202010706496.XA patent/CN112069649B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011148891A1 (en) * | 2010-05-24 | 2011-12-01 | 日本電気株式会社 | Method and system for analyzing static fault tree from system model |
| CN101901186A (en) * | 2010-07-08 | 2010-12-01 | 西北工业大学 | Embedded system reliability analysis and evaluation method |
| CN106874200A (en) * | 2017-02-14 | 2017-06-20 | 南京航空航天大学 | Embedded software reliability modeling and appraisal procedure based on AADL |
| CN109948197A (en) * | 2019-02-28 | 2019-06-28 | 北方民族大学 | System optimization analysis method based on AADL V1 |
| CN111209177A (en) * | 2020-01-06 | 2020-05-29 | 华东师范大学 | Hardware system reliability verification system |
Non-Patent Citations (7)
| Title |
|---|
| QUAN ZHANG,ET AL: "Some Improvements on The Rules for Exchanging Between Error Model Annex and AADL to Fault Tree", 《2013 INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND APPLICATIONS》, pages 338 - 342 * |
| 张晓策: "基于动态故障树的嵌入式系统故障模型研究", 《中国优秀硕士学位论文全文数据库信息科技辑》, no. 3, pages 137 - 73 * |
| 张晓策等: "一种AADL故障模型到动态故障树的转换方法", 《计算机技术与发展》, vol. 27, no. 11, pages 110 - 114 * |
| 李东民等: "AADL模型可靠性分析方法", 《计算机科学》, vol. 44, no. 6, pages 182 - 188 * |
| 董云卫等: "AADL模型可靠性分析评估工具", 《软件学报》, vol. 22, no. 06, pages 1252 - 1266 * |
| 陈奇等: "基于模型的汽车电动助力转向系统故障诊断", 《汽车工程》, vol. 41, no. 7, pages 839 - 850 * |
| 马铮: "基于MDA的电动汽车整车控制系统可靠性建模与评估", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》, no. 4, pages 1 - 58 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2023276569A1 (en) * | 2021-06-28 | 2023-01-05 | 株式会社デンソー | Mobile object control system and program |
| CN113743510A (en) * | 2021-09-07 | 2021-12-03 | 电子科技大学 | Gear transmission system FMMEA analysis method based on fault tree analysis |
| CN113960992A (en) * | 2021-12-17 | 2022-01-21 | 西安电子科技大学 | Improved FMECA mechanical product fault tree automatic construction system and method |
| CN115796589A (en) * | 2022-12-05 | 2023-03-14 | 三亚学院 | Method for analyzing risk and hidden danger sensitivity of three-electric-system of pure electric vehicle |
| CN115796589B (en) * | 2022-12-05 | 2023-09-29 | 三亚学院 | Sensitivity analysis method for risk hidden danger of three-electric system of pure electric vehicle |
| CN115577990A (en) * | 2022-12-08 | 2023-01-06 | 中国人民解放军国防科技大学 | Method for establishing equipment system task reliability model |
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