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FAULT MODELLING FOR HIERARCHICAL FAULT DIAGNOSIS AND PROGNOSIS Jiyu Zhang Giorgio Rizzoni Department of Mechanical and Aerospace Department of Mechanical and Aerospace Engineering and Engineering and Center for Automotive Research Center for Automotive Research The Ohio State University The Ohio State University Columbus, OH 43210 Columbus, OH 43210
[email protected] [email protected] Qadeer Ahmed Center for Automotive Research The Ohio State University Columbus, OH 43210
[email protected] ABSTRACT Fault modeling ,which is the determination of the effects of a fault on a system, is an effective way for conducting failure analysis and fault diagnosis for complex system. One of the major challenges of fault modeling in complex systems is the 36 pt 0.5 in ability to model the effects of component-level faults on the 12.7 mm system. This paper develops a simulation-based methodology for failure analysis through modeling component-level fault effect on the system level, with application to electric vehicle powertrains. To investigate how a component fault such as short circuit in a power switch or open circuit in a motor winding affects the vehicle system, this paper develops a detailed simulator which allows us to see system and subsystem failure behaviors by incorporating fault models in the system. This fault modeling process provides useful knowledge for designing a reliable and robust fault diagnosis and prognosis procedures for electrified powertrains.
and fault diagnosis, which can effectively improve reliability of complex systems. In this paper, we develop fault models for electrified powertrains, with special focus on the electric traction system consisting of the energy storage, electro-mechanical energy 36 pt conversion and all the associated electrical and electronic 0.5 in 12.7 mm systems for a battery-powered electric vehicle. Input (driver command)
System
Output (velocity, torque, power, etc.)
Vehicle
Level 1 Subsystem Faults
u Subsystem
EV Powertrain
Level 2 Component Faults
INTRODUCTION The reliability and safety issues relating complex systems have attracted a great deal of attention among researchers worldwide. Complex engineering systems are characterized by a high degree of nonlinearity, noise and disturbances, and uncertainties in model parameters, which can result in system performance degradation or loss of function [1]. If one is able to characterize the failure modes and effects and their criticality at the component level, the question then arises as to how will these component-level faults affect the subsystem within which these components are contained, and in turn, how will these subsystems affect the overall system. This process is called fault modeling and is conceptually depicted in Fig. 1. Fault modeling is an important process in conducting failure analysis
Components Level 3
Components Faults
FIGURE 1 FAULT PROPAGATION FROM COMPONENT LEVEL TO SYSTEM LEVEL
The electric traction system depicted in Fig. 2, and which is the subject of this paper, is a key element of any electrified vehicle. The electric traction system contains a high voltage battery, a power converter, an electric motor and motor controller, and associated high-voltage electrical circuits. Failure of one component in any one of these subsystems can 1 72 pt 1 in 25.4 mm
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BACKGROUND Electric vehicles, due to their locally zero emissions and simpler control systems when compared to hybrid electric vehicles, have seen increasing market penetration. Today, virtually every major automotive manufacture produces one or more battery electric vehicle models. Further, all hybrid electric vehicles also use the same basic subsystems as a battery electric vehicle. The reliability and fault-tolerant operation of these systems are therefore of interest to the industry. In particular, electric drive system may be subject to extreme conditions such as high temperature, rapid speed variation and frequent start/stops. The ability to identify incipient faults caused by premature aging of components, and to provide corrective measures when appropriate, is therefore very valuable. Thus, it is imperative to develop accurate fault diagnosis and appropriate fault tolerant control methods to guarantee the continuity of operation in case one of the components fails. The purpose of this paper is to provide a systematic way to analyze various kinds of faults and to investigate the effects of these faults on vehicle performance, enabling the design of a reliable and effective fault diagnosis and fault tolerant control system.
lead to reduced performance and eventually to the complete shutdown of the whole vehicle system. Therefore, substantial efforts should be devoted in developing a reliable and highly efficient fault diagnosis and prognosis system for key components in automobile electrified powertrains so that appropriate corrective actions (fault tolerant control) can be taken before a failure occurs.
Electric Traction System Hazard Analysis Hazard analysis is a comprehensive approach, based on top-down and bottom-up fault and failure analysis methods, that explores all possible failure modes of a system, conducting an analysis of the effects of each fault. In [7], the authors present a comprehensive hazard analysis for electrified powertrains using Failure Modes and Effects Analysis (FMEA). 36 pt We briefly summarize the results of this work. Figure 3 0.5 in presents a typical electric vehicle drive system containing a DC 12.7 mm battery, one or more DC capacitors, a three-phase inverter, which typically consists of power transistors (MOSFETs, IGBTs) and anti-parallel (or flywheel) diodes; a permanent magnet synchronous machine as well as a controller. Table 1 summarizes the potential failure modes and their effects of all the components in PMSM drive:
FIGURE 2 BLOCK DIAGRAM OF ELECTRIC VEHICLE POWERTRAIN
The principal subsystems in an electric drivetrain are the electric machine, the associated drive (inverter and controller), 36 pt the energy storage system, and the high-voltage electrical 0.5 in circuits. Among various types of electric machines, 12.7 mm permanent magnet synchronous machine (PMSM) are the most widely used in the automotive industry due to their high efficiency, high energy density and wide speed range. This paper focuses on fault modeling and analysis for an electric vehicle equipped with a PMSM. The literature does not lack publications on PMSM fault analysis, Bianchi et al. (1996) study various kinds of faults that may occur in a voltage fed inverter system for PMSM drive [2]. Welchko et al. (2002) explore the response of a PMSM drive to a single-phase open circuit fault [3] and to asymmetric and symmetric short circuit faults [4]. Estima and Cardos (2008) present performance analysis of a PMSM drive in the presence of inverter faults, including power switch open-circuit fault and single-phase open circuit fault on the motor side [5]. Sun et al. present simulation results under various types of faulty conditions for PMSM drive based using Matlab/Simulink simulations [6]. However, little work has been done on the failure analysis of electric traction system applied on the complex EV system. Further, none of these works addresses the system-level effects of component faults. In an earlier paper [7], we summarize important issues concerning fault diagnosis and prognosis of electrified powertrain systems. In this paper, we focus on understanding the system level effects of component-level faults using simulation tools.
T1
T3
T5
C1 Ia a
Vdc
Ib b
Ic
T6 T2
T4
C2
c PMSM
Three phase Inverter
Motor Controller
FIGURE 3 PMSM DRIVE SYSTEM
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TABLE 1. FAILURE MODES AND EFFECTS FOR A PMSM TRACTION SYSTEM
Items/Fault Distribution
Battery faults
36 pt 0.5 in 12.7 mm
Failure Modes
Failure Effects
Aging
Cell swelling; Separation of electrolyte; Higher heat dissipation
Electrolyte leakage
Human contact with the electrolyte; Short circuit of adjacent electronic systems
Cell swelling
Battery capacity loss
Cell thermal runaway
Increased internal cell temperature; Increased internal pressure; Cell venting; Ignition of cell vent gases; Ejection of cell windings; Thermal runaway of adjacent cells
Switch off failure of transistors
Catastrophic failure
Switch on failure of transistors
Increased torque pulsations, reduced mean torque and efficiency, increased copper losses
Inverter faults
DC capacitor fault
Short circuit and open circuit (Catastrophic failure)
Air-gap eccentricities: static and dynamic
In this paper we focus on a few specific faults to illustrate the methodology: a single phase winding open circuit fault; an IGBT short circuit fault in the PMSM inverter; and a current sensor fault. These are among the most common faults reported in the literature. ELECTRIC VEHICLE SIMULATOR The fault modeling process consists of a set of simulation tools that enable us to simulate fault conditions in specific components such as electronic power switches, electric machine faults, or individual battery cell faults, with a good degree of fidelity. A hierarchical simulation of the system determines the effects of such component faults on the system level. Figure 4 presents the layout of the simulator of a battery electric vehicle in which a detailed model of the electric traction system consisting of a battery pack, an inverter, and a permanent-magnet motor is included. The simulator consists of block diagrams of the driver, a supervisory controller, the EV powertrain and the vehicle. First, 36 pt The supervisory controller receives the acceleration or 0.5 in deceleration commands from the driver, which is represented in 12.7 mm the form of accelerator or brake pedal position and convert it into a torque request from the EV powertrain. When the vehicle is accelerating, the total torque request is the torque request from the electric motor. On the other hand, if the vehicle is decelerating, the electric machine can be used as a generator to recover the braking torque into electrical energy to charge the battery. The EV powertrain block contains a detailed model of the battery, of the three phase inverter, and of the electric machine, as well as a model of the motor controller, This detailed model calculates the actual torque that the motor is producing according to the torque requested by the driver. Moreover, this model enables us to inject fault models for the purpose of analyzing fault effects from the component level to the system level. Finally, the vehicle block models the vehicle dynamics based on the electric machine torque production and road loads. The driver model emulates the behavior of a human driver by tracking velocity changes and taking the desired accelerating and braking actions to track a desired velocity profile. . Figure 5 defines all the inputs and outputs of the simulator blocks.
Increased ripple voltage, which accelerates failure of power transistors
Degradation (ESR increase)
Electric Machine faults
Winding asymmetry such as open phase Winding short circuit Demagnetization
Reduced Torque
Damage in rolling element bearing
Vibration and noise; flaking or spalling; increase in wearing
Increase in bearing wear; stator windings exposure to harmful vibration; rotorto-stator rub damaging the stator core and stator windings
Catastrophic failure Reduced Torque
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ª «cos T « 2« cos(T 3« « «cos(T ¬
f abc
º » sin T » 2 S ) » f dq sin(T » 3 2 » S)» sin(T 3 ¼
2 S) 3 2 S) 3
fDE
ª¬ fD f E º¼
f abc
> fa
freedom to this model to independently represent the EM dynamics. Electric Motor Field Oriented Control In this paper, rotor field oriented control (FOC) is applied in the electric motor drive model. FOC makes the stator field vector orthogonal with respect to the rotor field vector. This is achieved by driving the d axis current to be zero so that the electromagnetic torque is only proportional to iq . The
(3)
T
(4)
fb f c @
T
ª¬ f d f q º¼
f dq
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advantages of FOC is that we can achieve maximum torquecurrent ratio to reduce copper losses and achieve greater efficiency [9].
(5)
Figure 8 gives a schematic representation of rotor field oriented control scheme. The electric drive system receives a a reference torque Te* coming from the driver demand. The reference
T
(6)
where, f can represent the machine current, voltage or flux linkage. Electric Machine
torque goes to a constant gain, by which iq * is obtained,
principle
The electric model of the PMSM is represented by the voltage and electromagnetic torque equations in the rotor reference frame [8]:
vq
36 pt 0.5 in 12.7 mm
where,
rs iq
Lq
diq dt Ld
PZm Ld id
did dt
vd
Rs id
Te
1.5P ª¬Omiq
id * is set to be zero according to the basic
based on Eq. (9).
Model
PZm Om
PZm Lqiq
( Lq
Ld )id iq º¼
field
oriented
coordinates, magnets,
(7)
(8)
Te
Va
V dc
DC voltage source
Vb
Inverter
PMSM
Vc
iq and id represent the stator ia ib
Lq and Ld represent stator inductance in d-q
ic Hysteresis VCurrent VE D Controller
represents the rotational speeds of the rotor and
represents the electromagnetic torque. The machine
Te*
equations are derived based on the following assumptions [9]: 1) 2) 3) 4) 5)
id * are
12.7 mm
(9)
Om represents the magnetic flux induced by rotor
Zm
and
transformation and are sent into a current hysteresis controller, comparing them with three phase currents measured from the electric machine outputs and convert the error signals into the switching signals for the inverter using the Pulse Width Modulation (PWM)Techniques. The PWM signals are used to control the switching timing of the six switches in the inverter 36 pt in order to get the desired three phase AC voltages. 0.5 in
currents in the rotor reference frame, P represents the number of pole pairs,
iq *
control.
ia * , ib * , ic* using the inverse Park's
transformed into
vq and vd are the stator voltages in the rotor reference
frame, Rs is the stator resistance,
of
Saturation is neglected. The induced back EMFs in stator windings are sinusoidal. Hysteresis and eddy current losses are negligible. There are no field current dynamics. There is no cage on the rotor.
T*e oiq
*
id
*
0
abc
Position Sensor
dq
Te
Further, we lump the inertia of the electric machine and drveline with the (much larger) inertia of the vehicle, hence the inertial dynamics of the machine are inherently incorporated in the vehicle longitudinal dynamics. If appropriate and necessary, it would be straightforward to add a mechanical degree of
FIGURE 8 BLOCK DIAGRAM OF PMSM FOC SCHEME
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Inverter Module T1 C1
The gate signals in the three-phase inverter have eight possible combinations. Table 2 shows the values of the output phase voltages corresponding to each state, where, 1 stands for the on state of the upper switch (T1, T3, T5) while 0 represents the on state of the lower switch (T2, T4, T6).
Va Vdc/2 Vdc/2 -Vdc/2 -Vdc/2 -Vdc/2 Vdc/2 0 0
Vb -Vdc/2 Vdc/2 Vdc/2 Vdc/2 -Vdc/2 -Vdc/2 0 0
a
then
ia
id cos T iq sin T
iD
becomes
Vc -Vdc/2 -Vdc/2 -Vdc/2 Vdc/2 Vdc/2 Vdc/2 0 0
ia , then id cos T
zero.
vq are established.
vE
vd sin T
Switch Signals 100 110 010 011 001 101 000 111
Ld
Vb 0 Vdc/2 Vdc/2 0 -Vdc/2 -Vdc/2 0 0
Vc 0 -Vdc/2 -Vdc/2 0 Vdc/2 Vdc/2 0 0
When a short circuit happens to one of the switches, the other switch on the same phase leg must be immediately turned off by protection circuit in case of serious short circuit in the DC battery. The terminal voltage of the phase becomes permanently Vdc/2 or -Vdc/2, depending on whether the faulty switch is the upper or the lower leg of the inverter, except when the rest of the normal phases are switched to the same side as the faulty switch, in which case the output three phase voltages become zero [6] . Figure 10 shows a schematic diagram when a short circuit fault occurs in a power transistor in phase A. Table 3 shows the switching table when the upper switch of phase a becomes short circuit.
Since
vq cos T Rs iq cos T
Va 0 0 0 0 0 0 0 0
Short circuit fault of one of the transistors Short circuit faults are considered to be the most serious 36 pt type of fault. A short circuit can result in rapidly increasing 0.5 in 12.7 mm current, leading to heat generation that would lead to serious damage to the electrical and electronic devices in a very short period. For electric vehicles equipped with high voltage systems, short circuit faults are directly related to safety issues. Therefore, electric vehicles should be equipped with a reliable and robust short-circuit detection and protection system.
vE and vd
diq did sin T Lq cos T dt dt Ze Lq iq sin T Ze Ld id cos T Ze Om cos T Rs id sin T
T6
TABLE 3. SWITCHING TABLE OF THE INVETER DUE TO OPEN CIRCUIT FAULT IN PHASE A
(2003) proposed a modified dq-frame machine model which is capable of dealing with this asymmetric faulty conditions [3].
and
T4
FIGURE 9 SCHEMATIC DIAGRAM OF PMSM DRIVE SINGLE PHASE OPEN CIRCUIT FAULT
iq sin T . Welcho
In this paper, a voltage equation only relating
PMSM Ic c
C2 T2
Single Phase Open Circuit Fault If one of the phases becomes open, then the electric machine is driven only by the other two phases. This results in rotational asymmetry on the stator and rotor, making the classic dq transformation technique not suitable any more. Thus, the machine model has to be modified. Figure 9 shows a schematic diagram when a single phase becomes open circuit. Table 3 presents the terminal voltages corresponding to the different states under phase a open circuit. If phase a becomes open circuit,
Ib b
FAULT MODELING IN ELECTRIC TRACTION SYSTEM One approach of doing fault modeling is to isolate the fault behavior of the system from the normal system in the form of a fault model, as well as combining the fault behaviors with the nominal behavior for fault analysis [10]. In this paper, we build fault models for three specific types of faults: a single phase open circuit fault created by open circuit in one of the machine phases; a single phase short circuit fault resulting from a short circuit in a power switch; and a current sensor fault. 36 pt 0.5 in 12.7 mm
T5
Ia
Vd
TABLE 2. SWITCHING TABLE OF THE INVETER
Switch Signals 100 110 010 011 001 101 000 111
T3
(10)
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Accelerator
T3
100
T5 Accelerator Position (%)
T1 C1 Ia a
Vdc
Ib PMSM
b
Ic
T6 T2
c
T4
80 60 40 20 0
C2
0
0.5
Vb -Vdc/2 -Vdc/2 -Vdc/2 0 -Vdc/2 -Vdc/2 Vdc/2 0
Velocity (km/h)
Vc -Vdc/2 Vdc/2 Vdc/2 0 -Vdc/2 Vdc/2 -Vdc/2 0
2.5
3
3.5
4
4.5
3
3.5
4
4.5
5
0
0
0.5
1
1.5
2
2.5 Time (sec)
FIGURE 11 ACCELERATOR PEDAL COMMAND AND ELECTRIC VEHICLE (SYSTEM) VELOCITY PROFILE FOR THE SHORT ACCELERATION TEST IN HEALTHY CONDITION, SINGLE PHASE OPEN CIRCUIT FAULT AND TRANSISTOR SHORT CIRCUIT FAULT
Next, we analyze the sub-system (Electric Drive system) of EVSIM to locate the actual cause of the system degraded behavior. Figure 12 shows the torque demand from the electric machine and the actual torque provided by the electric machine in healthy and faulty conditions. It can be seen that in the presence of single phase open circuit fault, the electric machine is unable to deliver the requested torque, and undergo significant high frequency oscillations in the delivered torque; 36 pt In the presence of transistor short circuit fault, the oscillations 0.5 in 12.7 mm in the electric machine torque is even greater, with the magnitude up to +/-200Nm, yet the mean value of the delivered torque is around zero. This can explain what we have found in the vehicle velocity profile that the vehicle can hardly accelerate with transistor short circuit fault.
SIMULATION RESULTS The fault modeling simulations are conducted in a scenario that helps effectively identify and diagnose the faults in the system, sub-systems and components. In this simulation exercise, it is assumed that only one fault will occur at a time and the fault is present in the system from the start of the simulation. Electric Machine and Power Converter Faults Figure 11 shows the accelerator pedal profile command for a short acceleration test in EVSIM, and the resulting response of the electric vehicle. It can be seen that the pedal is pressed up to 60% of its maximum value till 1.6s and then it is released. The velocity profiles depict the electric vehicle behavior in healthy condition and in the presence of the faults discussed above i.e. single phase open circuit fault and transistor short circuit fault. It can be seen that electric vehicle with single phase open circuit fault is accelerating slowly and is unable to meet the driver's demand. In the presence of transistor short circuit, electric vehicle can hardly move at the beginning and experience some speed fluctuations when it starts accelerating. These results correspond to the system level response to the faults, but the real cause of fault is yet to be diagnosed.
Desired Torque from Electric Machine
Torque (Nm)
200 100 Single Phase Open Circuit Fault Transistor Short Circuit Fault No Fault
0 -100
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
3.5
4
4.5
Electric Machine Torque 200
Torque (Nm)
36 pt 0.5 in 12.7 mm
Va Vdc/2 Vdc/2 Vdc/2 0 Vdc/2 Vdc/2 Vdc/2 0
2
10
TABLE 3. SWITCHING TABLE OF THE INVETER DUE TO SHORT CIRCUIT FAULT OF THE UPPER TRANSISTOR IN PHASE A
Switching Signals 000 001 010 011 100 101 110 111
1.5
Vehicle Speed Single Phase Open Circuit Fault Transistor Short Circuit Fault No Fault
15
FIGURE 10 SCHEMATIC DIAGRAM OF POWER TRANSISTOR SHORT CIRCUIT
1
100 0 -100 -200 0
0.5
1
1.5
2
2.5 Time (sec)
3
FIGURE 12 TORQUE REQUEST FROM THE ELECTRIC MACHINE AND THE TORQUE SUPPLIED BY THE ELECTRIC MACHINE IN NO FAULT, SINGLE PHASE OPEN CIRCUIT FAULT AND TRANSISTOR SHORT CIRCUIT FAULT
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Next, we analyze the electric machine response to the same faults. Figure 13 shows the motor phase currents i.e. (Ia, Ib, Ic) in response to no fault (red), transistor short circuit fault (blue) and single phase open circuit fault (black). All faults are applied to Phase a. In the presence of the transistor short circuit fault, the phase a current can only have positive sequence while the currents in the other two phases can only have negative sequences. The magnitude of the current in the faulty phase is zero in the beginning, but then rise to the value of almost twice as in the normal case when the power request becomes higher. The sharply increased current can bring serious damage to the power semiconductors. In the presence of the open circuit fault in the a winding, the response of Ia clearly shows that there is no current supply in that phase.
Battery Current 35
Current (A)
30 25 20 15 10 5 0 0
0.5
1
Ia(A) Ib(A) Ic(A)
400 200 0 -200 -400 0
400 200 0 -200 -400 0
0.5
0.5
1
1
1
1.5
1.5
1.5
2
2
2
2.5 Time (sec)
2.5 Time (sec)
3
2.5 Time (sec)
3
3.5
3.5
4
4
4
3.5
4
4.5
4
4.5
Volatge (V)
363 362
0.5
1
Single Phase Open Circuit Fault Transistor Short Circuit Fault No Fault 1.5 2 2.5 3 3.5 Time (sec)
FIGURE 14 BATTERY VOLTAGE AND CURRENT RESPONSE IN THE PRESENCE OF NO FAULT, SINGLE PHASE OPEN CIRCUIT FAULT AND TRANSISTOR SHORT CIRCUIT FAULT
4.5
Sensor Faults In automotive applications, electric vehicle propulsion control is highly dependent on the availability and accuracy of sensor measurements. Therefore, sensor faults are of critical importance, as they affect the behavior of the controllers to which they supply information [12]. If a fault occurs in the current or speed sensor, the controller will miss or receive the false signals, and may not able to send out the correct control 36 pt action to the rest of the drive system, which may result in 0.5 in undesired EV performance. We begin by modeling a sensor 12.7 mm fault as a bias fault:
4.5
No Fault Single Phase Open Circuit Fault Transistor Short Circuit Fault
3.5
3
364
360 0
3
2.5 Time (sec)
Battery Voltage
Phase Currents
0.5
2
365
361
400 200 0 -200 -400 0
1.5
4.5
36 pt 0.5 in 12.7 mm FIGURE 13 PHASE CURRENTS (IA, IB, IC) RESPONSE IN THE
PRESENCE OF NO FAULT, SINGLE PHASE OPEN CIRCUIT FAULT AND TRANSISTOR SHORT CIRCUIT FAULT.
I s e n s I e s t 'I 'I Gc Ie s
Finally, we examine how the electric machine and inverter faults affect the battery response. This helps us understand how a component fault can affect the other components of the electric drive system. Figure 14 shows battery current and voltage in both healthy and faulty conditions. In the case of single-phase open circuit fault, the battery current undergoes significant higher degree of oscillations, with the average value decreased. The oscillation becomes more evident in higher current region. For the transistor short circuit fault, the oscillation is also remarkable, with lower frequency. The fluctuations in the battery voltage and current can result in faster degradation of the battery pack, which should be avoided in real operation.
(10) (11)
Figures 15 and 16 show vehicle speed and electric drive torque response when Gc = +25% in the current sensor in phase a. It can be seen that a current sensor fault doesn't affect the vehicle response as much as the catastrophic failure such as a open circuit in the motor winding or short circuit in the transistor, though an oscillation in the torque is still evident(and would clearly be perceived as a drivability problem), and the vehicle performance is somewhat reduced.
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Measured Three Phase Currents with no Fault
Three Phase Currents [A]
60 40 20 0
0
0.5
1
1.5
2
2.5 3 time [s] Vehicle Speed
3.5
4
Velocity [kph]
20 Current Sensor Bias +25% No Fault
15 10 5 0
0
0.5
1
1.5
2
2.5 Time [sec]
3
3.5
4
-400
0
0.5
1
1.5
2
2.5 3 3.5 4 4.5 Time [s] Measured Three Phase Currents with Current Sensor Gain +25%
300
Measured Ia Measured Ib Measured Ic
200 100 0 -100 -200 -300
0
0.5
1
1.5
2
2.5 Time [s]
3
3.5
4
4.5
Error between Measured Iabc and Estimated Iabc Phase a Error Phase b Error Phase c Error
40
100
20 Current Error [A]
EM Torque [Nm]
-200
60
Desired Torque from Electric Machine
0
0
0.5
1
1.5
2
2.5 3 Time [sec] Electric Machine Torque
200 EM Torque [Nm]
0
FIGURE 17 PHASE CURRENTS (IA, IB, IC) RESPONSE IN HEALTHY CONDITON AND IN THE PRESENCE OF CURRENT SENSOR GAIN +25% FAULT
200
-100
Measured Ia Measured Ib Measured Ic
200
4.5
FIGURE 15 ACCELERATOR PEDAL COMMAND AND ELECTRIC VEHICLE (SYSTEM) VELOCITY PROFILE FOR THE MILD ACCELERATION TEST IN HEALTHY CONDITION AND IN THE PRESENCE OF CURRENT SENSOR GAIN +25% FAULT
36 pt 0.5 in 12.7 mm
400
4.5 Three Phase Currents [A]
Acceleratior Position (%)
Acclerator 80
3.5
4
4.5
0
36 pt 0.5 in 12.7 mm
-20
Current Sensor Bias +25% No Fault
-40
100 -60
0
-100
0
0.5
1
1.5
2
2.5 Time [sec]
3
3.5
4
0
0.5
1
1.5
2
2.5 3 time [sec]
3.5
4
4.5
5
FIGURE 18 ERROR BETWEEN THE MEASURED THREE PHASE CURRENTS AND ESTIMATED THREE PHASE CURRENTS IN THE PRESENCE OF CURRENT SENSOR GAIN +25% FAULT
4.5
FIGURE 16 TORQUE REQUEST FROM THE ELECTRIC MACHINE AND THE TORQUE SUPPLIED BY THE ELECTRIC MACHINE IN HEALTHY CONDITION AND IN THE PRESENCE OF CURRENT SENSOR GAIN +25% FAULT
Next, let's examine the battery response. Figure 19 shows the battery current and voltage response. This fault is not catastrophic in nature, as the two faults described earlier, and has more subtle effects on system-level performance. Comparable effects could be attributed to other sensor faults and to software faults. A complete analysis of realistic software, sensor and hardware faults is the subject of this ongoing study. In this paper we have simply illustrated the capabilities of a system/component level simulation tool in supporting fault analysis and diagnosis.
Figure 17 shows the measured three phase currents in the healthy and faulty cases. As can be seen, if the phase a current sensor gain increases 25%, the magnitude of the measured phase current is a bit higher than the measured currents in the other two phases, compared to the healthy case where the magnitude of the three phase currents is identical. If we look at the difference between the measured three phase currents and those estimated from the model, which is depicted in Figure 18, we can see the error is notable.
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REFERENCES [1] Cui, X., Ma, J., and Zeng, S. "The Fault Modeling Methodology of Actuator System Based on Modelica". 2011 9th International Conference on Reliability, Maintanability and Safety, 12-15 June, 2011.
Battery Current
Battery Current [A]
40 30 20 10 0
Current Sensor Gain +25% No Fault 0
0.5
1
1.5
2
2.5 Time [sec]
3
3.5
4
[2] Bianchi, N., Bolognani, S., and Zigliotto, M., 1996. 27th Annual IEEE Power Electronics Specialists Conference, 23-27 June, 1996.
4.5
Battery Voltage
Battery Voltage [V]
365 364
[3] Welchko, B.A., Jahns, T.M.., and Hiti, S., 2002. "IPM Synchronous Machine Drive Response to a Single-Phase Open Circuit Fault". IEEE Transactions On Power Electronics, Vol. 17, No. 5, September, 2002.
Current Sensor Gain +25% No Fault
363 362 361
0
0.5
1
1.5
2
2.5 Time [sec]
3
3.5
4
[4] Welchko, B.A., Jahns, T.M., Soong, W.L., and Nagashinma, J.M., 2003. "IPM Synchronous Machine Drive Response to Symmetrical and Asymmetrical Short Circuit Faults". IEEE Transactions on Power Energy Conversion, Vol. 18, No. 2, June, 2003.
4.5
FIGURE 19 BATTERY VOLTAGE AND CURRENT RESPONSE IN HEALTHY CONDITON AND IN THE PRESENCE OF CURRENT SENSOR GAIN +25% FAULT
[5] Estima, J.O., and Cardoso, A.J.M., 2008. "Performance Analysis of Permanent Magnet Synchronous Motor Drives Under Inverter Fault Conditions". Proceedings of the 2008 International Conference on Electrical Machines, pp.1-6.
Conclusion Reliability and safety issues related to complex systems such as an electric vehicles is a common concern in engineering fields, which stimulates the necessity for developing a reliable and robust set of fault diagnosis and prognosis procedures. The fundamental requirements for designing fault diagnosis and prognosis is the ability to conduct a thorough and accurate failure analysis for the system and all the subsystems. 36 pt Therefore, it is important to identify all the failure modes and 0.5 in effects and their criticality at the component level and how the 12.7 mm faults in a component can affect the system. This can be achieved by means of fault modeling.
[6] Sun, T., Lee, S.H., Hong, J.P., 2007. "Fault Analysis and Simulation for Interior Permanent Magnet Synchronous Motor Using Simulink@MATLAB". Proceeding of International Conference on Electrical Machines and Systems 2007, 8-11 Oct., Seoul, Korea. 36 pt
[7] Rizzoni, G., Zhang, J., Cordoba-Arenas, A. and Onori, 0.5 in S.(2012). "Diagnostics and Prognostics Needs and 12.7 mm Requirements for Electrified Vehicles Powertrains". 7th IFAC Symposium on Advances in Automotive Control, Sep.4-7, 2013
This paper develops simulation tools for fault modeling in an electrified powertrain. Through comparison of the vehicle behavior under the normal mode and faulty mode, the effects of the component fault on the system can be clearly seen and well understood. We have investigated the effects of electric machine and power converter faults (a open circuit in machine winding and short circuit in a transistor) as well as sensor faults (a current sensor bias fault). It is shown that if there is an open circuit or short circuit fault in the inverter and the electric machine, the results are catastrophic: the electric drive system would undergo significant torque and power oscillations, which can result in undesired vibration and drivability issues, and eventually lead the electric drive to shut down. The battery pack would suffer from greater voltage and current fluctuations, which accelerates battery aging. In particular, in the presence of a transistor short circuit fault, the rapidly increased currents can bring severe damage to the power semiconductors. In the case of current sensor faults, the effects are less catastrophic, but drivability would still be affected due to the significant torque oscillations. In future work will focus on understanding the vehicle-system level implications of these faults, develop diagnostic algorithms, and explore opportunities for faulttolerant control and limp-home mode operation.
[8] Krause, P. C., Wasynczulk, O. and Pekarek, S., "Electromechanical Motion Devices, 2nd Edition". IEEE Press Series on Power Engineering, pp. 185-211. [9] Tiwari, A.N., 2011. "Controller Design and Simulation of PMSM". International Journal of Engineering Science & Technology, Vol. 3 Issue 4, pp3357. [10] Joshi, A., Miller, S., Whalen, M. and Heimdahl, M.P. A Proposal for Model-Based Safety Analysis. Proceedings of 24th DASC, Nov 2005 [11] Wang, X. and Na, R., 2009. "Simulation of PMSM fieldoriented control based on SVPWM". IEEE Vehicle Power and Propulsion Conference, Harbin, 2009. [12] Tabbache, B., Benbouzid, M., Kheloui, A. and Bourgeot, J.M., 2012. "DSP-Based Sensor Fault Detection and Post Fault-Tolerant Control of an Induction Motor-Based Electric Vehicle". International Journal of Vehicular Technology, Vol.2011
10 72 pt 1 in 25.4 mm
