CN109920082B - Fault diagnosis and early warning method for hybrid electric vehicle - Google Patents
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Abstract
The invention discloses a fault diagnosis and early warning method for a hybrid electric vehicle, which comprises the following steps: according to the neural network model, fault categories for monitoring signals are given, and the fault categories comprise: the coding layer for monitoring a plurality of monitoring signals is constructed into a first neural network model, and the monitoring signals are analyzed in the first neural network to obtain a first intermediate vector group for representing the logical relation of the monitoring signals; inputting the first intermediate vector group into a fuzzy controller to obtain a vector group representing the fault category; and the vector group representing the fault category is output as a fault diagnosis answer, and the fault category signal is obtained through a network algorithm, so that the driver is early warned according to the fault category, and the driving accident rate is effectively reduced.
Description
Technical Field
The invention relates to the field of automobile maintenance, in particular to a fault diagnosis and early warning method for a hybrid electric automobile.
Background
The system of the hybrid electric vehicle mainly comprises a battery, a battery controller, a motor controller, an engine, a main controller HCU, accessory parts and the like. The system not only plays a role in transmitting power, but also plays a role in monitoring a hardware system and processing information. When the hybrid electric vehicle runs in each mode, monitoring of speed, torque, temperature, current, voltage, relays and a cooling system exists, and when the parameters exceed a calibration range, the system can report faults, and the faults are displayed. Conventional automobiles only display simple parameters of the vehicle, such as the rotation speed, the vehicle speed, the oil pressure, the temperature, etc., in an instrument panel, and when an abnormality occurs in the vehicle, these visual displays can inform the driver. Since the hybrid vehicle is more complicated than the conventional vehicle, the conventional vehicle failure display method is no longer suitable for the hybrid vehicle. The fault display of the conventional hybrid electric vehicle is realized by setting the working range of each hybrid special part and setting a threshold value, once the system fails to operate, the fault display can be realized only by a single signal lamp or an alarm mode, and the fault display of which part has a problem cannot be known in detail.
Disclosure of Invention
The invention designs and develops a fault diagnosis and early warning method for a hybrid electric vehicle, which adopts a neural network algorithm to obtain a fault category signal and carries out early warning on a driver according to the fault category, thereby effectively reducing the driving accident rate.
The invention also aims to provide an ignition fault adjusting method, and a scheme for correcting the spark advance angle in the ignition fault is provided when the secondary fault occurs, so that the secondary fault can be eliminated, and the stable starting of the automobile is realized.
The technical scheme provided by the invention is as follows:
a fault diagnosis and early warning method for a hybrid electric vehicle comprises the following steps:
the method comprises the steps that signals are input into main components of a hybrid power system, power source torque response signals and whole vehicle CAN bus communication signals are monitored to obtain a plurality of monitoring signals;
comparing the monitoring signal with the normal working state signal and then classifying to obtain a representative system running state identifier;
determining a matched fault type according to the system operation state identifier;
if not, giving fault categories for the monitoring signals according to the neural network model, wherein the fault categories comprise:
the coding layer for monitoring a plurality of monitoring signals is constructed into a first neural network model, and the monitoring signals are analyzed in the first neural network to obtain a first intermediate vector group for representing the logical relation of the monitoring signals;
inputting the first intermediate vector group into a fuzzy controller to obtain a vector group representing the fault category;
and outputting the vector group representing the fault category as a fault diagnosis answer.
Preferably, the CAN bus communication circuit includes:
at least one CAN controller;
the CAN transceiver has receiving and sending functions and CAN complete message filtering;
the controller nodes are connected with the CAN controller and CAN independently complete corresponding data processing and realize the communication function with the CAN bus communication circuit;
the fault detection sensors are connected with the automobile parts to be detected and communicated with the controller node, can read detection signals of the automobile parts and transmit the detected signals to the controller node;
the main control module is connected with the CAN bus communication circuit, CAN monitor the working state of a system, process characteristic detection signals and confirm the fault of the hybrid electric vehicle;
the master control module CAN control one or more of the nodes to complete control, data acquisition of the sensor or monitor the state of the CAN bus communication circuit.
Preferably, the CAN controller uses an STC15W204S chip.
Preferably, the failure detection sensor includes: temperature sensor, gas sensor, ampere meter, voltmeter and fuel gauge.
Preferably, the first neural network is a three-layer BP neural network model, and the input layer variables are sequentially normalized to determine an input layer vector X ═ { X ] of the three-layer neural network1,x2,x3,x4}; wherein x is1Is the engine speed coefficient, x2Is the temperature coefficient of the coolant, x3Is the coefficient of throttle opening, x4Is a vehicle speed coefficient; the input layer vector is mapped to an intermediate layer, the intermediate layer vector being Y ═ Y1,y2,y3…ymM is the number of nodes; the output layer vector is O ═ O1,o2,o3In which o is1To coefficient of ignition advance, o2Is the idle stability factor, o3Is the superheat factor.
Preferably, the input layer variables are normalized according to the following formula:
wherein x isiFor parameters in the input layer vector, QiWhere i is 1,2,3,4 are the measurement parameters η, T respectivelyd、α、vd(ii) a Eta is engine speed, TdFor coolant temperature, α is throttle opening, vdIs the vehicle speed; qimaxAnd QiminRespectively, a maximum value and a minimum value in the corresponding measured parameter.
Preferably, the calculation formula of the number of intermediate layer nodes is as follows:
wherein m is the number of intermediate layer nodes, n is the number of input layer nodes, and p is the number of output layer nodes.
Preferably, the fuzzy controller comprises:
comparing the ignition advance angle coefficient with a preset ignition advance angle coefficient to obtain an ignition advance angle deviation signal, comparing the idle speed stability coefficient with a preset idle speed stability coefficient to obtain an idle speed stability coefficient deviation signal, and comparing the overheating coefficient with a preset overheating signal to obtain an overheating deviation signal;
carrying out differential calculation on the ignition advance angle deviation signal to obtain an ignition advance angle change rate signal, carrying out differential calculation on the idle speed stability coefficient deviation signal to obtain an idle speed stability coefficient change rate signal, and carrying out differential calculation on the overheat deviation signal to obtain an overheat deviation change rate signal;
and amplifying the fire advance angle change rate signal, the idle speed stability coefficient change rate signal and the overheating deviation change rate signal, inputting the amplified signals into a fuzzy controller, and outputting the amplified signals as fault grades.
Preferably, an ignition fault adjustment process is further included, which includes:
if the output fault grade is I2First, the ratio is calculatedComparing coefficients:
wherein o is1To coefficient of ignition advance, o2In order to be the idle stability factor,
is the average value of the ignition advance angle coefficients in the period,
the average value of the idle speed stability coefficient in the period is;
calculating the optimal ignition advance angle:
wherein G is the optimal ignition advance angle G0And g' is an initial ignition advance angle and a corrected value of the ignition advance angle.
Preferably, the ignition timing correction value is obtained by the following equation:
if it is
And Td≥90℃，
Wherein, KrIs a temperature coefficient, TdK is a rotation speed proportionality coefficient for the temperature of the cooling liquid,
if it is
And Td≤60℃，
Wherein, KrIs a temperature coefficient, TdK is the rotation rate proportionality coefficient for the coolant temperature.
The beneficial effects of the invention
The invention designs and develops a fault diagnosis and early warning method for a hybrid electric vehicle, which adopts a neural network algorithm to obtain a fault category signal, wherein the fault is divided into four grades, namely zero-grade fault, which indicates that a system is normal, and the first-grade fault indicates that the system can continue to operate, and the second-grade fault indicates that the system needs to be repaired, and the third-grade fault indicates that the system needs to be stopped emergently.
Drawings
Fig. 1 is a schematic structural diagram of a fault diagnosis and early warning method for a hybrid electric vehicle according to the invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
As shown in fig. 1, the method for diagnosing and warning the fault of the hybrid electric vehicle provided by the invention comprises the following steps:
step S110, inputting signals to main components of a hybrid power system, monitoring power source torque response signals and whole vehicle CAN bus communication signals to obtain a plurality of monitoring signals;
step S120, comparing the monitoring signal with the normal working state signal and then classifying to obtain a representative system running state identifier;
step S130, according to the system running state identifier, determining the matched fault type, such as that a wiper cannot be started, the oil pressure is too high during acceleration, understeer or oversteer occurs due to the swing of a steering wheel, and the like, detecting and matching the fault according to a finished automobile CAN bus, and directly diagnosing a result and feeding back the result to a driver;
if no matched fault category exists in the system, giving a fault category aiming at the monitoring signal according to the neural network model, wherein the fault category comprises the following steps:
step S141, constructing a coding layer for monitoring a plurality of monitoring signals into a first neural network model, and analyzing the monitoring signals in the first neural network to obtain a first intermediate vector for representing the logical relationship of the plurality of monitoring signals;
step S142, inputting the first intermediate vector group into a fuzzy controller to obtain a vector group representing the fault type;
step S150, and outputting the vector group representing the fault type as the fault diagnosis answer
In another embodiment, a CAN bus communication circuit includes:
at least one CAN controller; the CAN transceiver has receiving and sending functions and CAN complete message filtering; the controller nodes are connected with the CAN controller, and each node CAN independently complete corresponding data processing and realize the communication function with the CAN bus communication circuit; the fault detection sensors are connected with the automobile parts to be detected and communicated with the controller node, can read detection signals of the automobile parts and transmit the detected signals to the controller node; the main control module is connected with the CAN bus communication circuit, CAN monitor the working state of the system, process characteristic detection signals and confirm the fault of the hybrid electric vehicle; the master control module CAN control one or more of the nodes to complete control, data acquisition of the sensor or monitor the state of the CAN bus communication circuit. Preferably, the CAN controller uses STC15W204S chip.
In another embodiment, the fault detection sensor 240 includes: temperature sensor, gas sensor, ampere meter, voltmeter and fuel gauge.
The specific working process of the ignition fault diagnosis and early warning is taken as an example for further explanation
Step one, establishing a BP neural network model:
the BP network architecture adopted by the invention consists of three layersThe first layer is an input layer, n nodes are provided in total, n detection signals representing the working state of the equipment are corresponded, and the signal parameters are given by a data preprocessing module; the second layer is a hidden layer with m nodes,
wherein m is the number of intermediate layer nodes, n is the number of input layer nodes, and p is the number of output layer nodes; the third layer is an output layer which has p nodes and is determined by the response actually required to be output by the system
The mathematical model of the network is:
inputting a layer vector: x ═ x1,x2,…,xn)T
Intermediate layer vector: y ═ y1,y2,…,ym)T
Outputting a layer vector: z is (z)1,z2,…,zp)T
In the invention, the number of nodes of an input layer is n-4, and the number of nodes of an output layer is p-3; the number m of hidden layer nodes is estimated by the following formula:
4 parameters, x, input according to the sampling period1Is the engine speed coefficient, x2Is the temperature coefficient of the coolant, x3Is the coefficient of throttle opening, x4Is a vehicle speed coefficient;
the data acquired by the sensors belong to different physical quantities, and the dimensions of the data are different. Therefore, the data needs to be normalized to a number between 0-1 before it is input into the neural network.
Specifically, the engine speed coefficient x is obtained by normalizing η engine speed1：
Wherein eta isminAnd ηmaxRespectively the minimum rotational speed and the maximum rotational speed within the sampling period.
Likewise, for the coolant temperature TdNormalized to obtain the temperature coefficient x of the coolant2：
Wherein, TdminAnd TdmaxRespectively, a minimum value and a maximum value of the coolant temperature.
Similarly, the throttle opening α is normalized to obtain a throttle opening coefficient x3：
Wherein alpha isminAnd alphamaxThe minimum value and the maximum value of the throttle opening degree are respectively.
Wherein v isdminAnd vdmaxRespectively, a minimum value and a maximum value of the vehicle speed.
The 3 parameters of the output signal are respectively expressed as: o1To coefficient of ignition advance, o2Is the idle stability factor, o3Is the superheat factor.
Step two: carrying out BP neural network training:
after the BP neural network node model is established, the training of the BP neural network can be carried out. Obtaining training samples according to empirical data of the product, and giving a connection weight w between an input node i and a hidden layer node jijConnection weight w between hidden layer node j and output layer node kjkThreshold value theta of hidden layer node jjThreshold value w of node k of output layerij、wjk、θj、θkAre all random numbers between-1 and 1.
Continuously correcting w in the training processijAnd wjkUntil the system error is less than or equal to the expected error, the training process of the neural network is completed.
As shown in table 1, a set of training samples is given, along with the values of the nodes in the training process.
TABLE 1 training Process node values
Step three, the first intermediate vector group O is set as { O ═ O1,o2,o3In which o is1To coefficient of ignition advance, o2Is the idle stability factor, o3Inputting the coefficient into fuzzy controller to obtain vector group representing fault category
Advance the ignition angle coefficient o1Coefficient of advance angle of ignition
Comparing to obtain ignition advance angle deviation signal and comparing idle speed stability coefficient o2And a preset idle speed stability coefficient
Comparing to obtain idle speed stability coefficient deviation signal and comparing the overheating coefficient o3And a preset overheat signal
Comparing to obtain an overheating deviation signal;
carrying out differential calculation on the ignition advance angle deviation signal to obtain an ignition advance angle change rate signal, carrying out differential calculation on the idle speed stability coefficient deviation signal to obtain an idle speed stability coefficient change rate signal, and carrying out differential calculation on the overheat deviation signal to obtain an overheat deviation change rate signal;
advancing the fire by an angular rate of change signal e1Idle stability coefficient rate of change signal e2And rate of change of superheat e3The signals are amplified and input into a fuzzy controller, and the output is a fault grade gamma ═ I0,I1,I2,I3In which I0Zero order fault, indicating system is normal, I1Indicating continued operation for a first order fault, I2For secondary failure, indicating need of repair, I3A three-level failure indicates that an emergency stop is required.
Wherein e is1、e2、e3Respectively has a practical variation range of [ -1,1 [ -1 [ ]]，[-1,1]，[-1,1]；E1、E2、E3The discrete domains of (a) are all { -6, -5, -4, -3, -2, -1, 0, 1,2,3,4, 5, 6}, and the discrete domain of Γ is {0, 1,2, 3}
The quantization factor k1＝6/1，k2＝6/1，k3＝6/1；
Defining fuzzy subsets and membership functions:
the spark advance rate signal is divided into 7 fuzzy states: PB (positive big), PM (positive middle), PS (positive small), ZR (zero), NS (negative small), NM (negative middle) and NB (negative big), and the ignition advance angle change rate signal e is obtained by combining experience1As shown in table 2.
TABLE 2 spark advance Angle Rate Signal e1Table of membership functions
Idle stability factor rate of change signal e2There are 7 fuzzy states: PB (positive large), PM (middle small), PS (positive small), ZR (zero), NS (negative small)) NM (negative middle), NB (negative big), and the idle stability coefficient change rate signal e is obtained by combining experience2Is shown in Table 3
TABLE 3 Idle stability coefficient Rate of Change Signal e2Table of membership functions
| e2 | -6 | -5 | -4 | -3 | -2 | -1 | 0 | +1 | +2 | +3 | +4 | +5 | +6 |
| PB | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2 | 0.8 | 1.0 |
| PM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.4 | 0.7 | 1.0 | 0.6 | 0.1 |
| PS | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.8 | 1.0 | 0.7 | 0.7 | 0 | 0 |
| ZR | 0 | 0 | 0 | 0 | 0.2 | 0.8 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 |
| NB | 0 | 0 | 0.3 | 0.7 | 1.0 | 0.8 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 |
| NM | 0.2 | 0.5 | 1.0 | 0.7 | 0.1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| NS | 1.0 | 0.7 | 0.6 | 0.2 | 0 | 0 | 0 | 0.2 | 0 | 0 | 0 | 0 | 0 |
Rate of change of superheat e3Seven fuzzy states are divided: PB (positive large), ZR (zero), NB (negative large), in combination with empirical superheat deviation rate of change e3Table 4, as shown in table 4.
TABLE 4 rate of change e of superheat deviation3To a function table of subordinates
| e3 | -6 | -5 | -4 | -3 | -2 | -1 | 0 | +1 | +2 | +3 | +4 | +5 | +6 |
| PB | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2 | 0.8 | 1.0 |
| ZR | 0 | 0 | 0 | 0 | 0.2 | 0.8 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 |
| NS | 1.0 | 0.7 | 0.6 | 0.2 | 0 | 0 | 0 | 0.2 | 0 | 0 | 0 | 0 | 0 |
The fuzzy reasoning process has to execute complex matrix operation, the calculated amount is very large, the on-line reasoning is difficult to meet the real-time requirement of a control system, the fuzzy reasoning method is adopted to carry out the fuzzy reasoning operation, the fuzzy reasoning decision adopts a three-input single-output mode to summarize the preliminary control rule of the fuzzy controller through experience, the fuzzy controller carries out defuzzification on the output signal according to the obtained fuzzy value to obtain the fault grade gamma, and a fuzzy control query table is solved.
Table 5 is a fuzzy control rule table
In another embodiment, an ignition fault adjustment process is also included, comprising:
if the output fault grade is I2First, a comparison coefficient is calculated:
wherein o is1To coefficient of ignition advance, o2In order to be the idle stability factor,
is the average value of the ignition advance angle coefficients in the period,
the average value of the idle speed stability coefficient in the period is;
calculating the optimal ignition advance angle:
wherein G is the optimal ignition advance angle G0And g' is an initial ignition advance angle and a corrected value of the ignition advance angle.
The ignition timing correction value is obtained by:
if it is
And Td≥90℃，
Wherein, KrIs a temperature coefficient of 35.6, TdFor the coolant temperature, k is the rotation rate proportionality coefficient, which has a value of 2800
If it is
And Td≤60℃，
KrIs a temperature coefficient of 35.6, TdFor coolant temperature, k is the rotation rate proportionality coefficient, which has a value of 2800.
The invention designs and develops a fault diagnosis and early warning method for a hybrid electric vehicle, which adopts a neural network algorithm to obtain a fault category signal, and carries out early warning on a driver according to the fault category, thereby effectively reducing the driving accident rate, providing an ignition fault adjusting method, and providing a correction scheme of the spark advance angle in the ignition fault during the secondary fault, so that the secondary fault can be eliminated, and the stable starting of the vehicle can be realized.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (3)
1. A fault diagnosis and early warning method for a hybrid electric vehicle is characterized by comprising the following steps:
the method comprises the steps that signals are input into main components of a hybrid power system, power source torque response signals and whole vehicle CAN bus communication signals are monitored to obtain a plurality of monitoring signals;
comparing the monitoring signal with the normal working state signal and then classifying to obtain a representative system running state identifier;
determining a matched fault type according to the system operation state identifier;
if the matched fault category does not exist, giving a fault category aiming at the monitoring signal according to a neural network model, wherein the fault category comprises the following steps:
the coding layer for monitoring a plurality of monitoring signals is constructed into a first neural network model, and the monitoring signals are analyzed in the first neural network to obtain a first intermediate vector group for representing the logical relation of the monitoring signals;
inputting the first intermediate vector group into a fuzzy controller to obtain a vector group representing the fault category;
and the vector group representing the fault category is output as a fault diagnosis answer;
the CAN bus communication circuit includes:
at least one CAN controller;
the CAN transceiver has receiving and sending functions and CAN complete message filtering;
the controller nodes are connected with the CAN controller and CAN independently complete corresponding data processing and realize the communication function with the CAN bus communication circuit;
the fault detection sensors are connected with the automobile parts to be detected and communicated with the controller node, can read detection signals of the automobile parts and transmit the detected signals to the controller node;
the main control module is connected with the CAN bus communication circuit, CAN monitor the working state of a system, process characteristic detection signals and confirm the fault of the hybrid electric vehicle;
the master control module CAN control one or more of the nodes to complete control, data acquisition of a sensor or monitor the state of the CAN bus communication circuit;
the CAN controller adopts an STC15W204S chip;
the failure detection sensor includes: the device comprises a temperature sensor, a gas sensor, an ammeter, a voltmeter and a fuel gauge;
the first neural network is a three-layer BP neural network model, input layer variables are normalized in sequence, and input layer vectors X ═ X of the three-layer neural network are determined1,x2,x3,x4}; wherein x is1Is the engine speed coefficient, x2Is the temperature coefficient of the coolant, x3Is the coefficient of throttle opening, x4Is a vehicle speed coefficient; the input layer vector is mapped to the middle layer, and the middle layer vector is Y ═ Y1,y2,y3…ymM is the number of nodes; the output layer vector is O ═ O1,o2,o3In which o is1To coefficient of ignition advance, o2Is the idle stability factor, o3Is the superheat coefficient;
the working process of the fuzzy controller comprises the following steps:
comparing the ignition advance angle coefficient with a preset ignition advance angle coefficient to obtain an ignition advance angle deviation signal, comparing the idle speed stability coefficient with a preset idle speed stability coefficient to obtain an idle speed stability coefficient deviation signal, and comparing the overheating coefficient with a preset overheating signal to obtain an overheating deviation signal;
carrying out differential calculation on the ignition advance angle deviation signal to obtain an ignition advance angle change rate signal, carrying out differential calculation on the idle speed stability coefficient deviation signal to obtain an idle speed stability coefficient change rate signal, and carrying out differential calculation on the overheat deviation signal to obtain an overheat deviation change rate signal;
amplifying the fire advance angle change rate signal, the idle speed stability coefficient change rate signal and the overheating deviation change rate signal, inputting the amplified signals into a fuzzy controller, and outputting the amplified signals as fault grades;
also included is an ignition fault adjustment process, comprising:
if the output fault grade is I2First, a comparison coefficient is calculated:
wherein o is1To coefficient of ignition advance, o2In order to be the idle stability factor,
is the average value of the ignition advance angle coefficients in the period,
the average value of the idle speed stability coefficient in the period is;
calculating the optimal ignition advance angle:
wherein G is the optimal ignition advance angle G0And g' is an initial ignition advance angle and a corrected value of the ignition advance angle.
2. The hybrid electric vehicle fault diagnosis and early warning method according to claim 1, characterized in that the input layer variables are normalized according to the following formula:
wherein x isiFor parameters in the input layer vector, QiWhere i is 1,2,3,4 are the measurement parameters η, T respectivelyd、α、vd(ii) a Eta is engine speed, TdFor coolant temperature, α is throttle opening, vdIs the vehicle speed; qimaxAnd QiminRespectively, a maximum value and a minimum value in the corresponding measured parameter.
3. The hybrid electric vehicle fault diagnosis and early warning method according to claim 2, wherein the calculation formula of the number of intermediate layer nodes is as follows:
wherein m is the number of intermediate layer nodes, n is the number of input layer nodes, and p is the number of output layer nodes.
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