energies Article
Model-Based Temperature Sensor Fault Detection and Fault-Tolerant Control of Urea-Selective Catalyst Reduction Control Systems Jie Hu 1,2, *, Junliang Wang 1,2 , Jiawei Zeng 1,2 and Xianglin Zhong 3 1 2 3
*
Wuhan University of Technology, Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan 430070, China;
[email protected] (J.W.);
[email protected] (J.Z.) Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan 430070, China China Automotive Technology and Research Center Co., Ltd., Tianjin 300300, China;
[email protected] Correspondence:
[email protected]; Tel.: +86-130-7123-7418
Received: 5 May 2018; Accepted: 3 July 2018; Published: 9 July 2018
Abstract: This paper aims at investigating the fault diagnosis of the selective catalyst reduction (SCR) outlet temperature sensors and fault-tolerant control methods of the SCR system, and three typical faults of downstream temperature sensors were modeled and analyzed to present influences of different faults on the SCR system performances (such as nitrogen oxides (NOx ) emission and conversion efficiency, NH3 slip, urea dosage and ammonia coverage estimation). A temperature model was established to estimate the SCR outlet temperature, and diagnostics were developed based on the differences between model estimates and sensor measurements. Once a downstream temperature sensor fault was detected, the fault-tolerant control will be enabled, and the output of the sensor may be substituted with the estimates of the model. Thus, SCR performances shall be maintained within the acceptable ranges. Moreover, a 0-D SCR model was also established to validate the capability of diagnostics and fault-tolerant control strategy over the European transient cycle (ETC). Keywords: diesel engine; urea-selective catalyst reduction (SCR); temperature sensor; fault diagnosis; fault-tolerant control
1. Introduction Diesel engines are popular due to their high thermal efficiency, power and durability; however, the diesel exhaust emissions cause heavy environmental pollution, whose major exhaust pollutants include nitrogen oxides (NOx ), particulate matter (PM), unburned hydrocarbons (HC), and carbon monoxide (CO). Because of the high air-fuel ratio and heterogeneous combustion of air-fuel mixture in the diesel cylinder, NOx and PM levels are relatively higher than those in the gasoline engine exhaust [1–5]. Selective catalyst reduction (SCR) systems have been extensively investigated for the removal of NOx [6], where an aqueous urea solution is injected into the upstream tailpipe of the SCR catalyst to be decomposed into NH3 and then restored on a catalyst surface for reaction with NOx in the exhaust gas, generating nitrogen (N2 ) and water vapor as the main products. Obviously, the urea dosing is a significant control object in the whole system; its excess injection will lead to high urea consumption and undesirable –NH3 slip, which is also regarded as a pollutant, while on the other hand, it is entirely possible that NOx emissions will exceed the regulation limit if the urea is underdosed [7]. With the increasing stringency of the emission legislations, it is crucial to develop effective control strategies conform to the legislation limits. SCR control strategies mainly focus on two modes Energies 2018, 11, 1800; doi:10.3390/en11071800
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(namely open-loop and closed-loop), which have been studied extensively in recent years. For example, the open-loop control strategy is sufficient to satisfy Euro-4 and Euro-5 emission legislation requirement with a NOx conversion efficiency of 60–80% under slowly changing operating conditions [8]; however, some corrections are needed when using the open-loop approaches in rapidly changing conditions, such as corrections for the changes of exhaust temperature, which require a lot of calibration efforts to obtain the specific correction values [9]. On the other hand, it is difficult to achieve high NOx reduction and low NH3 slip [10]. Thus, more efficient control techniques are necessary for such operating conditions, which include adaptive control [11,12], state estimation [13–16] and model predictive control [17], etc. A model-based closed-loop strategy along with a PI controller was designed in [9] to control the ammonia storage at a proper level, which has been validated under Euro-6 WHTC test cycle. Both simulation and experimental results showed that the control strategy was effective. Devarakonda et al. [13] designed a model-based estimator via simulation and a control system based on NH3 sensor feedback. A NOx sensor feedback-based control strategy was also developed for reference, and their outcomes showed that the performance of the NOx -based control strategy is slightly better than the NH3 -based one. As the structure of the SCR control system is becoming increasingly complicated, some sensors are required to provide corresponding signals for the control system. A SCR system is typically equipped with a downstream NOx sensor and upstream and downstream temperature sensors, respectively. Signals from the former are generally used to calculate the corresponding urea dosage, ammonia coverage ratio and NOx conversion efficiency, and signals from the latter also play a significant role in the calculation of the SCR chemical process; for instance, calculating the catalyst temperature and ammonia coverage ratio as they cannot be measured directly. Obviously, most of the advanced control strategies are based on sensor outputs and their performances are highly dependent on the accuracy of the outputs; moreover, SCR system typically works in poor environment with high temperature, erosion and vibration. All these unstable factors are harmful to the stability of sensors, which make them shall be prone to malfunction; what’s worse, faults from various sensors may be multiple, which may lead to a deterioration of SCR performances. Hence, it is significant to detect faults timely and regulate the control strategy to accommodate fault conditions, and restrain the performances degradation within acceptable ranges. A number of studies have been conducted on fault diagnosis of SCR sensors and related components. Chen et al. [18] developed a model-based diagnostic method for the injection system and outlet NOx sensor. The simulation results indicated that it succeeded in fault detection and isolation with good robustness and sensitivity. Wang et al. [19] put forward an integrated on-board diagnosis and fault-tolerant control method for the SCR urea injection system. The methodology was based on the estimation of the injected urea flow by a linear parameter varying (LPV) model, which presented good performances in vehicle tests. Sun et al. [20] found a relationship between injector pulse amplitude modulated (PAM) commands and line pressures in their study, and a fault diagnosis algorithm was proposed based on power spectrum and time domain analysis. The diagnostic method was verified in a urea SCR system, which was shown to be effective. An OBD system for the SCR was developed by Matsumoto et al. [21], and it was based on a NH3 slip model with simplified descriptions of NOx or NH3 reaction formulae. The test results showed that it achieved good diagnostic performance which is required in Euro-6c. Unfortunately, few papers can be found on temperature sensors fault diagnosis and fault-tolerant control of SCR systems. Pezzini et al. [22] proposed a model-based fault diagnosis algorithm on the basis of a reduced lean NOx trap (LNT) model. Diagnosis of the NOx sensor faults, temperature sensor faults, and thermal damage faults were investigated by simulation. Wang et al. [23] provided a method for on-board diagnosis of SCR temperature sensors, which was based on the differences between estimated temperature and real temperature measurements. However, further studies (such as the influence of faults and fault-tolerant control) were not discussed in the aforementioned papers. Temperature sensor faults occurring in various forms may influence SCR performances to various
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degrees. In this study, typical faults of the outlet temperature sensors were discussed firstly and then influences of different faults on SCR performances were investigated. Moreover, a model-based fault detection approach for SCR-out temperature sensors was developed based on the differences between model estimates and sensor measurements, where a high fidelity SCR temperature model was used to estimate the SCR-out temperature. In addition, a fault-tolerant control strategy was proposed, and validated over an ETC test cycle based on a 0-D SCR model. 2. Selective Catalyst Reduction Modeling and Control Strategy 2.1. Selective Catalyst Reduction Modeling Principle of Selective Catalyst Reduction The working processes of a urea-SCR system are described in the following steps [24]: (1) (2) (3) (4) (5)
Urea is injected into the exhaust tailpipe. What left after completing evaporation of urea solution are solid substances under the corresponding thermal decomposition temperature. The above solid substances are decomposed into gaseous NH3 . Gaseous NH3 is adsorbed into catalyst along with desorption of NH3 . N2 and H2 O are generated based on the reaction between NH3 and NOx . The corresponding chemical reactions are given by the following equations: Urea evaporation : NH2 − CO − NH2 (liquid) → NH2 − CO − NH2∗ (solid) + nH2 O
(1)
Urea decomposition : NH2 − CO − NH2∗ (solid) + H2 O → 2NH3 + CO2
(2)
NH3 adsorption and desorption : NH3 + Sfree ↔ NH3∗
(3)
Primary NOx removal reactions: 4NH3∗ + 4NO + O2 → 4N2 + 6H2 O
(4)
2NH3∗ + NO + NO2 → 2N2 + 3H2 O
(5)
4NH3∗ + 3NO2 → 3.5N2 + 6H2 O
(6)
Equation (4) is often called as the “standard” SCR since its relatively fast reaction rate in a conventional catalyst (V2 O5 -WO3 /TiO2 ) and high percentage of NO in NOx emissions (up to 90% in typical diesel exhaust). Equation (5) is known as the “fast” SCR reaction whose reaction rate is approximately 10 times faster than that of the standard SCR reaction. Equation (6) is the “slow” SCR whose reaction rate in V2 O5 -WO3 /TiO2 is very low [25]. Meanwhile, oxidization of NH3 to N2 with O2 is also taken into account in the model, whose reaction is expressed by [26]: 4NH3∗ + 3O2 → 2N2 + 6H2 O (7) In view of the complexity of chemical reactions in the SCR catalyst convertor, several assumptions and simplifications are made as follows [27,28]: (1) (2) (3) (4) (5)
The components of exhaust gasses are considered as ideal gases, which are homogeneous and incompressible. Effects of variations in the water and oxygen concentrations of exhaust gases are negligible. The catalyst convertor may be discretized into several uniform cells along its flow axis. All variables are homogeneous in the radial direction and only vary along the axis of convertor. The reaction rate of adsorption/desorption is much slower than that of any other reaction.
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(6)
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Only adsorbed NH3 is involved in NOx reduction processes. According to the Arrhenius law, rates of main reactions mentioned above can be defined by: s NH3 adsorption : Rads = Cs Sc αprob
RT CNH3 (1 − θ) 2πMNH3
NH3 desorption : Rdes = Cs kdes e− NOx reduction : Rscr = Cs RTkscr e− NH3 oxidation : Rox = Cs kox e
Edes RT
Escr RT
(8)
θ
(9)
θCNOx
(10)
ox − ERT
θ
(11)
where Cs is the concentration of active atoms with respect to converter volume, and Sc is the area of 1 mol active surface atoms. αprob is the sticking probability. MNH3 is the NH3 molar mass. R is the universal gas constant. T is the catalyst temperature of the catalyst convertor, which is a crucial parameter throughout reactions and will be discussed in detail later. θ is the ammonia surface coverage, and Ex and Kx represent the activation energies and pre-exponential factor of the reactions (x = ads, des, red, oxi). Defining: cell a = pREG , a2 = NεV amb 1 q c Edes RT − RT (12) a3 = Cs Sc αprob 2πM , a = C k e s 4 des NH3 E E scr ox a5 = Cs RTkscr e− RT , a6 = Cs kox e− RT where Ncell is the number of cells of the SCR model, and one cell was used in this study. Based on the mass conservation law and the continuous stirred tank reactor (CSTR) approach, the SCR model can be given by [27]: . C. NOx = a2 nNOx ,in − CNOx ( a1 a2 mEG T + a5 θ) CNH3 = a2 nNH3 ,in + a4 θ − CNH3 [ a1 a2 mEG T + a3 (1 − θ)] . θ = a3 (1 − θ)CNH3 − a4 θ − a5 CNOx θ − a6 θ /Cs
(13)
Since the main reactions in the catalyst convertor are all temperature-dependent, the catalyst temperature remains a significant parameter throughout the whole NOx removal processes. The following formula is adopted here to calculate the catalyst temperature T: T = 0.3Tin + 0.7Tout
(14)
where Tin and Tout represent the inlet and outlet temperatures of the SCR cell, respectively. The upstream temperature of SCR changes dramatically along with the variation of engine operating conditions. Thus, to reduce impacts of any frequent upstream temperature fluctuation on the accuracy of model prediction, the weighing factors of upstream and downstream temperatures are assigned to 0.3 and 0.7, respectively. 2.2. Control Strategy The SCR control strategy used here is the ammonia-coverage-ratio-based closed-loop control strategy [11,27,29], which is shown in Figure 1.
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Figure 1. Schematic of the ammonia-coverage-ratio-based closed-loop control strategy. Figure 1. Schematic of the ammonia-coverage-ratio-based closed-loop control strategy.
A feedforward controller is adopted to calculate the basic urea dosage based on the known A feedforward controller is adopted calculate the basic urea dosage based on the known parameters, which can be described by the to following equation: parameters, which can be described by the following equation: 𝑚𝑚exh 𝜐𝜐𝜐𝜐NOx 𝑀𝑀urea ηmax 𝐴𝐴𝐴𝐴𝐴𝐴 𝑚𝑚adblue = (15) 𝑀𝑀exh 𝐶𝐶adblue mexh υVNO x Murea ηmax ANR madblue = (15) where madblue is the urea dosage. mexh is the exhaustMmass flow rate. υ is the stoichiometric coefficient exh Cadblue of urea decomposition (υ = 0.5). 𝑉𝑉NOx is the engine-out NOx concentration. ηmax is the maximum NOx where madblueefficiency. is the ureaANR dosage. exhaust mass flow rate. υ is the stoichiometric coefficient exh isofthe conversion is themratio NH 3 to NOx concentrations. Cadblue is the urea mass fraction of in urea decomposition (υ = 0.5). V is the engine-out ηmax is exhaust the maximum x concentration. NO x Mexh represent theNO Adblue, which is 32.5%. Murea and molar masses of urea and gas, NO conversion efficiency. ANR is the ratio of NH to NO concentrations. C is the urea mass x x 3 adblue respectively. fractionThe in Adblue, is 32.5%. Mexh represent masses urea and exhaust urea and by ammoniawhich coverage ratio isM estimated means of EKF,the andmolar the state spaceofmodel of EKF for gas, respectively. a nonlinear system is as follows: The ammonia coverage ratio is estimated by means of EKF, and the state space model of EKF for x(k ) =f [ x(k − 1), u (k )] + w(k ) a nonlinear system is as follows: (16) = z (k ) h[ x(k )] + v(k ) ( = finput [ x (k −vector. 1), u(kw(k) )] + w k) process noise with zero-mean where x(k) is the state vector. u(k)x (isk )the is(the (16) z(k) =state h[ x (function. k )] + v(k)z(k) is the measurement vector. v(k) is the Gaussian noise. f(x, u) is the nonlinear measurement noise with zero-mean Gaussian noise, and h(x) is the nonlinear measurement function. where x(k) the vector state vector. is the input vector. The is state may beu(k) estimated in two steps:w(k) is the process noise with zero-mean Gaussian noise. fPrediction (x, u) is the nonlinear state function. z(k) is the measurement vector. v(k) is the measurement Step: noise with zero-mean Gaussian noise, and h(x) is the nonlinear measurement function. x(k |k −= 1) f [ x(k − 1|k − 1), u (k )] (17) The state vector may be estimated in two steps: Prediction Step: P(k |= k − 1) F (k ) P(k − 1|k − 1) F (k )T + Q(k ) (18) x (k|k − 1) = f [ x (k − 1|k − 1), u(k )] (17) where P is the error covariance matrix. F is the Jacobian matrix of the nonlinear state function (f), and T (18) Q is the covariance of w(k).P(k |k − 1) = F (k ) P(k − 1|k − 1) F (k ) + Q(k ) Updating Step: the state vector x(k) and the error covariance matrix P are updated in accordance where P is the error covariance matrix. F is the Jacobian matrix of the nonlinear state function (f ), with the difference between predicted and measured z(k). and Q is the covariance of w(k). −1 T Updating Step: the stateKvector (19) + R(k )P are updated in accordance (k ) = P(x(k) k |k −and 1) H (the k )T error H (k ) Pcovariance (k |k − 1) H (k )matrix with the difference between predicted and measured z(k).
{
}
x(k |= k ) x(k | k − 1) h+ K (k ) z (k ) − h [ x(k | k − 1), u (k )] i−1 K ( k ) = P ( k | k − 1) H ( k ) T H ( k ) P ( k | k − 1) H ( k ) T + R ( k ) P ( k |k ) = [ I − K (k ) H (k )] P(k|k − 1)
(20) (19) (21)
x (k|k ) = x (k|k − 1) + K (k ){z(k) − h[ x (k|k − 1measurement ), u(k)]} (20) where K is the Kalman gain. H is the Jacobian matrix of the nonlinear function (h). R is the covariance of v(k), and I is the P(identity k|k) = [matrix. I − K (k) H (k )] P(k|k − 1) (21) The four-state EKF prediction model was derived here based on Equation (15): where K is the Kalman gain. H is the Jacobian matrix of the nonlinear measurement function (h). R is the covariance of v(k), and I is the identity matrix.
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k − 1|k − 1) θ( θ(k |k − 1) θ(k − 1|k − 1) x(k= |k − 1) CNO x (k |k −= 1) CNO x (k − 1|k − 1) + ∆t C NO x (k − 1|k − 1) The four-state EKF prediction model was Equation (15): on derived here based CNH3 (k |k − 1) CNH3 (k − 1|k − 1) CNH3 (k − 1|k − 1)
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(22)
. θ( k − 1| k − 1) θ( k | k − 1) θ( k − 1| k − 1) . EKF. where Δt is the updating the x (k|step k − 1)size = for = CNOx (k − 1|k − 1) + ∆t CNOx (k − 1|k − 1) (22) CNO x ( k | k − 1) . Then, measurementsCof the downstream NO x concentration and NH 3 slip are selected as the ( k | k − 1 ) C ( k − 1 | k − 1 ) NH3 NH3 CNH (k − 1|k − 1)
3
observer vector here. The EKF measurement model is expressed as: where ∆t is the step size for updating the EKF. CNO x (k ) Then, measurements of the downstream and NH3 slip are selected as (23) the = z (k ) hNO = [ x(kx)]concentration observer vector here. The EKF measurement model is expressed CNH3 (k ) as: Meanwhile, a PID feedback controller is designedCNO given by Equation (24). The input of the PID x (k) z(k ) = h[ x (k)] = (23) controller is derived from the difference between estimated CNH3 (k) and reference ammonia coverage ratio (see Figure 2). Thus, the controller is capable of regulating the urea dosage and maintaining the NOx Meanwhile, a 3PID controller is designed emissions and NH slipfeedback within the reasonable ranges: given by Equation (24). The input of the PID controller is derived from the difference between estimated and reference ammonia coverage ratio 𝑡𝑡 𝑑𝑑�𝑒𝑒NH3 � (see Figure 2). Thus, the controller the+urea and maintaining the NO (24) x 𝑢𝑢ureais=capable 𝐾𝐾𝑃𝑃 𝑒𝑒NH3 of + regulating 𝐾𝐾𝐼𝐼 � 𝑒𝑒NH3 𝑑𝑑𝑑𝑑 𝐾𝐾𝐷𝐷 dosage 𝑑𝑑𝑑𝑑 0 emissions and NH3 slip within the reasonable ranges: where uurea is the corrected urea dosage. Kp, KZI and KD represent the proportional, integral and t d eNH3 derivative factors, respectively. is the difference and desired ammonia 𝑁𝑁𝐻𝐻K 3 P eNH + K I uurea 𝑒𝑒= eNHbetween dt + K Dthe estimated (24) 3 3 dt 0 coverage ratios. The reference for the ammonia coverage ratio was derived by the following methods [30,31]: where uurea is the corrected urea dosage. Kp, KI and KD represent the proportional, integral and derivative factors, is the differencethe between the estimated and desired ammonia coverage ratios. (1) In respectively. a specific testeNH cycle, maximizing NOx conversion efficiency and maintaining the ammonia 3 The reference for the the ammonia ratio was derived by the following methods slip within limits (25 coverage ppm at peak, 10 ppm on average) by regulating the[30,31]: urea dosage. (2) Calculating the ammonia coverage ratio based on Equations (25) and (26). Meanwhile, (1) In a specific test cycle, maximizing the NOx conversion efficiency and maintaining the ammonia calculating the catalyst temperature by Equation (14). slip within the limits (25 ppm at peak, 10 ppm on average) by regulating the urea dosage. (3) Then the function relationship between ammonia coverage ratio and catalyst temperature can (2) be Calculating thefitting ammonia ratio based on Equations (25) and (26). Meanwhile, calculating derived by and coverage interpolation. the catalyst temperature by Equation (14). by temperature fluctuation, limiting the ammonia (4) To avoid substantial ammonia slip caused (3) coverage Then the function relationship between ammonia coverage ratio and catalyst temperature can be ratio at low temperatures. derived by fitting and interpolation. N NH3 ,storage (4) To avoid substantial ammonia slip caused fluctuation, limiting the ammonia (25) θ= by temperature Θ coverage ratio at low temperatures. "
#
NNH3 ,storage 2madblue mexh θ =⋅ Cadblue ∫R h 2m M·Curea Θ −m M exh ⋅ VDeNOx + VNH3 i dt adblue adblue = − exh · V +V dt
(
= N NH3 storage NNH3 ,storage
Murea
Mexh
DeNOx
)
NH3
(25) (26) (26)
where N NNH3 storage isismoles the amount of NOx reduction. VNH3 is the VDeNO is where molesof ofammonia ammonia storage. storage. V NH3 ,storage DeNOx is the amount of NOx reduction. V NH3 is the outlet moles ofof maximum ammonia storage. outlet NH NH33 concentration, concentration, and and ΘΘis is moles maximum ammonia storage. x
Figure 2. 2. Ammonia Ammonia coverage coverage ratio ratio reference reference at at different different catalyst catalyst temperatures. temperatures. Figure
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2.3. Parameter Identification and Model Validation The rate of reactions in the SCR catalyst primarily depends on the velocity and catalyst temperature. To ensure the accuracy of parameter identification, typical operating conditions should be considered in the identification experiment. Thus, the engine speed at 900 r/min, 1500 r/min, and 2500 r/min, and the exhaust temperature at 200 ◦ C, 350 ◦ C and 500 ◦ C are covered. The urea injection follows the rule of insufficient dosage (ANR = 0.75), moderate dosage (ANR = 1) and over dosage (ANR = 1.25), respectively. Parameter identification of the SCR model is performed by using the Isqnonlin function in the Matlab/Simulink, which is based on the nonlinear least square method. The SCR model needs to be discretized before it is processed further, and the discretized model is expressed as:
CNOx (k +1)−CNOx (k ) = a2 nNOx ,in (k) − CNOx (k)( a1 a2 mEG (k) T (k) + a5 θ(k)) Ts CNH3 (k +1)−CNH3 (k ) = a2 nNH3 ,in (k) + a4 θ(k) − CNH3 (k)[ a1 a2 mEG (k) T (k) + a3 (1 − θ(k))] Ts θ(k +1)−θ(k ) 1 = Cs a3 (1 − θ(k))CNH3 (k) − a4 q(k) − a5 CNOx (k)θ(k) − a6 θ(k) Ts
(27)
where Ts is the sampling time, and k is the sampling point. Since the amount of parameters need to be identified is large, the values of some parameters (namely αprob , kdes , Edes , kox and Eox ) were adopted from [27] to reduce the difficulty of identification. The identified parameters are shown in Table 1. Table 1. Result of parameter identification. Parameters
Identified Value
Unit
Sc
13,300 1.11 × 10−3 4.5 0.514 15.2 10 28,471 3.34 × 106 1.16 × 105
m2 /mol 1 mol/m3 1/s J/mol m2 /s J/mol 1/s J/mol
αprob Cs kdes Edes kscr Escr kox Eox
The data for validating the SCR model was collected from an engine test bench over an ETC cycle, whose layout diagram is shown in Figure 3 and which is made up of an inline 6-cyclinder YC6J-42 diesel engine equipped in the AVL PUMA OPEN test bench, a 13.5 L V2 O5 -WO3 /TiO2 SCR catalyst convertor, two control units for engine operation and urea dosing, respectively, and measuring equipment of interest. The details of the engine specification and measuring equipment are listed in Tables 2 and 3, respectively. Table 2. Engine information. Features
Parameters
Engine model Displacement Rated power Maximum torque Idle speed
Inline 6-cylinder, YC6J-42 6.6 L 132 kW 660 N·m (1200–1700 r/min) 650 ± 50 r/min
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Table 3. Measuring and monitoring equipment.
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Equipment Application Table 3. Measuring and monitoring equipment. AC electrical dynamometer Equipment AVL PUMA Dynamometer dynamometer OPEN test bench AC electrical control system Dynamometer control Throttle actuator system AVL PUMA Thermal Throttle actuator measurement system Thermal measurement SEMTECH-EFM2 system AVLSEMTECH-EFM2 DiGas 4000 light (Port 1) AVL DiGas 4000 light AVL DiGas 4000 light(Port (Port1)2) AVL DiGas 4000 light (Port 2) NOx sensor NOx sensor LDS6 LDS6 Temperature sensor Temperature sensor
OPEN test bench
Measuring engine speed and torque Application Controlling speed or torque dynamometer to change Measuring engineofspeed and torque the engine load Controlling speed or torque of dynamometer to change Regulating the operating of engine the enginecondition load Monitoring thermodynamic parameters such as cooling Regulating the operating condition of engine water temperature, intake and exhaust pressure Monitoring thermodynamic parameters such as cooling Measuring intake exhaustand flow rate pressure water temperature, exhaust MeasuringMeasuring NOx concentration at upstream exhaust flow rate pipe Measuring NO x concentration at upstream pipe Measuring NOx concentration at downstream pipe Measuring NOx concentration at downstream pipe Measuring NOx concentration at downstream pipe Measuring NOx concentration at downstream pipe Measuring NH3 slip Measuring NH3 slip Measuring temperature Measuringupstream upstreamand anddownstream downstream temperature
Figure 3. 3. Schematic Schematic diagram diagram of of our our experimental experimental setup. setup. Figure
NO NOxx and and NH NH33 emissions emissionsas asprimary primarySCR SCRperformances performanceswere wereused usedto tovalidate validatethe theSCR SCRmodel. model. Figure simulation andand experimental results overover the ETC cycle.cycle. The Figure 44shows showscomparisons comparisonsbetween between simulation experimental results the ETC simulation and experimental SCR-out NO x emissions are relatively in a good agreement with a mean The simulation and experimental SCR-out NOx emissions are relatively in a good agreement with absolute error (MAE) 85.15 of ppm, and the and accumulated NOx emissions in the whole cycle arecycle 1.92 a mean absolute errorof (MAE) 85.15 ppm, the accumulated NOx emissions in the whole g/kWh 1.85 g/kWh, respectively. The estimated NH 3 slip are also acceptable with a MAE of 0.98 are 1.92and g/kWh and 1.85 g/kWh, respectively. The estimated NH3 slip are also acceptable with ppm. The trends of NO x emission and NH3 slip are correctly estimated even though disagreements a MAE of 0.98 ppm. The trends of NOx emission and NH3 slip are correctly estimated even though are found in certain operating points,operating and the reasons may be explainedmay by the disagreements are found in certain points, for andthe thedisagreements reasons for the disagreements be two factors below: explained by the two factors below: (1) Several assumptions were induced for model simplification. (1) Several assumptions were induced for model simplification. (2) The operating conditions fluctuate drastically during certain periods of time, which makes it (2) The operating conditions fluctuate drastically during certain periods of time, which makes it difficult to predict the corresponding NH3 and NOx emissions accurately. difficult to predict the corresponding NH3 and NOx emissions accurately.
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NOx Concentration[ppm]
1800
C_NOx-Us C_NOx-Exp C_NOx-Sim
1600 1400 1200 1000 800 600 400 200 0 0
200
400
600
800 1000 Time [s]
1200
1400
1600
1800
(a)
7
C_NH3-Exp C_NH3-Sim
NH3 Slip[ppm]
6 5 4 3 2 1 0 0
200
400
600
800 1000 Time [s]
1200
1400
1600
1800
(b) Figure 4. 4. Comparison Comparison of of simulation simulation and and experiment experimentresults. results.(a) (a)NO NOx concentration; concentration; and and (b) (b) NH NH3 slip. slip. Figure x 3
3. Fault Detection and Fault-Tolerant Control 3. Fault Detection and Fault-Tolerant Control 3.1. Description Description of of Faults Faults 3.1. SCR temperature temperature sensors sensors work work in in harsh harsh environment environment including including high high temperature temperature and and corrosion corrosion SCR from exhaust exhaust gas gas which which may may lead lead to to structural structural damages damages or or changes changes in in chemical chemical or or physical physical property property from of measuring parts. Thus, the errors of sensor measurements exist on different degrees when the of measuring parts. Thus, the errors of sensor measurements exist on different degrees when the sensors malfunction. The fault outputs of a temperature sensor are complicated and can be expressed sensors malfunction. The fault outputs of a temperature sensor are complicated and can be expressed in several several different different forms forms [32–34], [32–34], in in aa perspective perspective of of simulation, simulation, its its typical typical faults faults may may be be primarily primarily in simplified into the following three forms: simplified into the following three forms: (1) outputs of the sensorsensor are fixed at maximum) primarily (1) Stuck StuckFaults: Faults:thethe outputs of temperature the temperature are(normally fixed (normally at maximum) due to open circuits, which can which be given primarily due to open circuits, canby: be given by: 𝑦𝑦𝑜𝑜𝑜𝑜𝑜𝑜 yout (t(𝑡𝑡) ) ==C 𝐶𝐶
(28) (28)
where C is a constant. where C is a constant. (2) Drift Faults: the outputs decrease or increase linearly from their respective normal states [35], (2) Drift Faults: the outputs decrease or increase linearly from their respective normal states [35], which can be described as: which can be described as: (29) 𝑦𝑦𝑜𝑜𝑜𝑜𝑜𝑜 α𝑡𝑡𝑡𝑡 yout (29) (t(𝑡𝑡) ) ==αty (t(𝑡𝑡) ) in𝑖𝑖𝑖𝑖 where αα is is the the drift drift slope. slope. where (3) Gain Faults: the outputs are multiples of normal outputs, which can be given by: 𝑦𝑦𝑜𝑜𝑜𝑜𝑜𝑜 (𝑡𝑡) = β𝑦𝑦𝑖𝑖𝑖𝑖 (𝑡𝑡)
(30)
Energies 2018, 11, 1800
10 of 17
(3) Gain Faults: the outputs are multiples of normal outputs, which can be given by: yout (t) = βyin (t)
Energies 2018, 11, x
(30) 10 of 17
where where β β is is the the proportionality proportionality coefficient. coefficient. Those faults are schematically Those faults are schematically presented presented in in Figure Figure 55 where where the the output output signals signals are are normal normal during during the first fifty seconds and then a fault is induced at the 50th second. the first fifty seconds and then a fault is induced at the 50th second. 1000
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Figure 5. Schematic diagram of fault samples. (a) Normal data; (b) stuck fault; (c) drift fault; and (d) Figure 5. Schematic diagram of fault samples. (a) Normal data; (b) stuck fault; (c) drift fault; and (d) gain fault. fault. gain
3.2. Fault Detection and Fault-Tolerant Control 3.2. Fault Detection and Fault-Tolerant Control The methodology of fault detection is developed based on a temperature model which is used The methodology of fault detection is developed based on a temperature model which is used to to estimate the SCR-out gas temperature, where two primary forms of heat transfer (namely estimate the SCR-out gas temperature, where two primary forms of heat transfer (namely convection convection between catalyst and exhaust gas flow and radiation between catalyst and ambient) are between catalyst and exhaust gas flow and radiation between catalyst and ambient) are considered. considered. The corresponding exchanged heat can be expressed by: The corresponding exchanged heat can be expressed by: 𝑄𝑄c = 𝑐𝑐p,exh 𝑚𝑚exh (𝑇𝑇out − 𝑇𝑇in ) (31) Qc = cp,exh mexh ( Tout − Tin ) (31) 4 4 εRad σSB 𝐴𝐴Rad (𝑇𝑇out − 𝑇𝑇amb ) (32) 𝑄𝑄r = 4 4 εRad σSB ARad𝑁𝑁cell Tout − Tamb Qr = (32) N By applying the energy conservation law, thecellcatalyst convertor has the following energy By applying equilibrium [27,36]:the energy conservation law, the catalyst convertor has the following energy equilibrium [27,36]: 4 4 ) 𝑐𝑐pc ∗ 𝑚𝑚c 𝑑𝑑𝑑𝑑out εRad σSB 𝐴𝐴Rad (𝑇𝑇out − 𝑇𝑇amb (33) = 𝑐𝑐p,exh 𝑚𝑚exh (𝑇𝑇out − 𝑇𝑇in ) − 4 4 𝑁𝑁 𝑑𝑑𝑑𝑑 𝑁𝑁 cpc ∗ mcell εRad σSB ARad cell Tout − Tamb c dTout =c mexh ( Tout − Tin ) − (33) Ncell heat dt of thep,exh Ncell where cpc is the specific catalyst convertor. mc is the mass of the catalyst convertor. cp,exh is the specific heat at constant pressure of exhaust gas. mexh is the mass flow rate of exhaust gas. εRad is where cpc is the specific heat of the catalyst convertor. mc is the mass of the catalyst convertor. cp,exh is the emissivity of the catalytic convertor. σSB is the radiation constant. ARad is the radiating surface area the specific heat at constant pressure of exhaust gas. mexh is the mass flow rate of exhaust gas. εRad is of the convertor. Tamb is the ambient temperature. the emissivity of the catalytic convertor. σSB is the radiation constant. ARad is the radiating surface Defining: area of the convertor. Tamb is the ambient temperature. 𝑁𝑁cell 𝑐𝑐p,exh 𝑏𝑏1 = (34) 𝑐𝑐p,c 𝑚𝑚c Then, Equation (33) is rewritten as:
𝑏𝑏2 =
εRad σSB 𝐴𝐴Rad 𝑁𝑁cell 𝑐𝑐p,𝐜𝐜 𝑚𝑚𝑐𝑐
(35)
Energies 2018, 11, 1800
11 of 17
Defining: Ncell cp,exh cp,c mc
(34)
εRad σSB ARad Ncell cp,c mc
(35)
b1 = b2 = Energies 2018, Equation 11, x Then,
11 of 17
(33) is rewritten as: dT𝑑𝑑𝑑𝑑 out 4 4 out Tout ==b1𝑏𝑏m𝑚𝑚 ) )−−b𝑏𝑏 4 4 − Tamb out −−Tin 2 (𝑇𝑇 exh ( T(𝑇𝑇 𝑇𝑇 − 𝑇𝑇 dt𝑑𝑑𝑑𝑑 1 exh out in 2 out amb )
(36) (36)
Identification of of parameter parameter bb11 and and bb22was wasperformed performedwith withthe thesame samemethod methodas as aforementioned, aforementioned, Identification −13 , respectively. The temperature model was validated −13 whose results are 0.0712 and 8.03 × 10 whose results are 0.0712 and 8.03 × 10 , respectively. The temperature model was validated over an over cycle, an ETC cycle, and the simulated and measured temperatures are agreement in a good agreement ETC and the simulated and measured temperatures are in a good (MAE: 4.66(MAE: K) as 4.66 K)in asFigure shown6.inThus, Figure Thus, the SCR-out temperature can bepredicted reasonably by the shown the6.SCR-out temperature can be reasonably by predicted the temperature temperature model. model. 20 15
660 610 560 T_Ds-Exp T_Ds-Sim T_Us-Exp
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Figure 6. Comparison of simulation and experimental results. (a) Temperature vs. time; and (b) Figure 6. Comparison of simulation and experimental results. (a) Temperature vs. time; and (b) absolute error. absolute error.
Temperature sensor sensor measurements measurements will will be be compared compared with with the the estimates, estimates, and and the the temperature temperature Temperature residuals R can be expressed as: residuals RTT can be expressed as: m e R T = Tout − Tout (37) 𝑚𝑚 e (37) 𝑅𝑅𝑇𝑇 = 𝑇𝑇out − 𝑇𝑇out m is the measurement of the outlet temperature sensor, and T e is the estimate of outlet where Tout out e m where 𝑇𝑇 the measurement of the model. outlet temperature sensor, and 𝑇𝑇out is the estimate of outlet out temperatureisbased on the temperature temperature based on the temperature model. When the residual is too large out of the threshold for a period of time, a fault will be detected by When the system residual[23]: is too large out of the threshold for a period of time, a fault will be detected the diagnostic ( by the diagnostic system [23]: | R T | > R∗T (38) ∗ * t|Re T>| >t RT (38) * where the R∗T is the temperature residual threshold. te > t The te is the duration of temperature residual above the threshold, and the t* is the time threshold. ∗ where the 𝑅𝑅 the temperature residual threshold. 𝑡𝑡𝑒𝑒 iswith the duration of temperature residual 𝑇𝑇 istemperature Once any sensor malfunctions, itsThe outputs errors will be transferred to the ∗ above the threshold, and the 𝑡𝑡 is the time threshold. control system to estimate catalyst temperature, which may have negative impact on SCR performances any sensor malfunctions, outputs with errors be transferred to the (see Once Section 4).temperature Thus, an effective fault-tolerant its control strategy, which will is illustrated in Figure 7, control system to estimate catalyst temperature, which may have negative impact on SCR is developed based on the temperature model to reduce the impacts of faults on SCR performances. performances 4). Thus, an effective fault-tolerant control strategy, which illustrated in While any fault(see of aSection downstream temperature sensor is detected, the sensor outputs willisbe substituted Figure is developed based on the temperature model to reduce the impacts of faults on SCR with the7,estimates. performances. While any fault of a downstream temperature sensor is detected, the sensor outputs will be substituted with the estimates.
Once any temperature sensor malfunctions, its outputs with errors will be transferred to the control system to estimate catalyst temperature, which may have negative impact on SCR performances (see Section 4). Thus, an effective fault-tolerant control strategy, which is illustrated in Figure 7, is developed based on the temperature model to reduce the impacts of faults on SCR performances. While any fault of a downstream temperature sensor is detected, the sensor outputs Energies 2018, 11, 1800 12 of 17 will be substituted with the estimates.
Energies 2018, 11, x Energies 2018, 11, x
12 of 17 12 of 17
Figure 7. Fault-tolerant control strategy of SCR system.
4. Results and Discussion
Figure 7. Fault-tolerant control strategy of SCR system. Figure 7. Fault-tolerant control strategy of SCR system.
4. Results and Discussion
Results and Discussion To 4. investigate the influences of the faults on SCR performances, the aforementioned typical faults To investigate the influences of the faults on SCR performances, the aforementioned typical To investigate the sensors influences of the faults onin SCR performances, theThe aforementioned typical of downstream temperature were simulated Matlab/Simulink. parameter C in the stuck faults of downstream temperature sensors were simulated in Matlab/Simulink. The parameter C in faults of downstream temperature sensors were simulated in Matlab/Simulink. The parameter in fault fault was to 1000. Theset parameter α in the gainαfault to was 0.7. set Thetoparameter β in theC theset stuck fault was to 1000. The parameter in thewas gainset fault 0.7. The parameter β drift in the stuck fault was set to 1000. The parameter α in the gain fault was set to 0.7. The parameter β in was set to 0.2.fault Thewas faults were towere the SCR model during the whole cycle.ETC The simulation the− drift set to −0.2.induced The faults induced to the SCR model during ETC the whole cycle. the drift fault was set to −0.2. The faults were induced to the SCR model during the whole ETC cycle. The simulation results of SCR-out NO x concentration, NO x conversion efficiency and ammonia slip results of SCR-out NOresults NO x concentration, x conversion efficiency and ammonia slip are demonstrated The simulation of SCR-out NO x concentration, NOx conversion efficiency and ammonia slip are 8–10. demonstrated in Figures 8–10. Comparing with thewhose normal condition whose NOconversion x emission and in Figures Comparing with the normal condition and efficiency x emission are demonstrated in Figures 8–10. Comparing with the normalNO condition whose NOx emission and conversion efficiency are 1.90 g/kWh and 77.20%, respectively. The NOx emissions present clear are 1.90conversion g/kWh and 77.20%, The77.20%, NOx emissions increasing efficiency arerespectively. 1.90 g/kWh and respectively.present The NOclear x emissions presenttrends clear under increasing trends under some fault conditions; for example, especially under the drift fault, it reaches increasing trends under some fault especially conditions; for example, especially under the drift fault, to it reaches some fault example, under the drift fault, itand reaches 4.60 g/kWh, up toconditions; 4.60 g/kWh, for which exceeds the Euro-5 emission limit (2 g/kWh), the NOup x conversion up to 4.60 g/kWh, which exceeds the Euro-5 emission limit (2 g/kWh), and the NO x conversion which exceeds the Euro-5 emission limit (2 g/kWh), and the NO conversion decreased to 44.30%. decreased to 44.30%. x
NOxConcentration[ppm] Concentration[ppm] NOx
decreased to 44.30%. 1600 1600 1400 1400 1200 1200 1000 1000 800 800 600 600 400 400 200 200 0 0 0 0
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Figure 8. OutletNO NOx concentration vs.vs. time. Figure 8. Outlet time. Figure 8. Outlet NOxx concentration concentration vs. time.
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Figure 9. NOx conversion efficiency vs. time.
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Figure 9. NOx conversion efficiency vs. time. Figure 9. NOx conversion efficiency vs. time.
Gain Gain Normal Normal
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352018, 11, 1800 Energies
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Figure 10. NH3 slip vs. time. Figure 10. NH3 slip vs. time.
Figure 10. NH3 slip vs. time.
In addition, under the gain fault conditions, the NOx conversion efficiency decreases to 62.57% In addition, under the gain fault conditions, the NOx conversion efficiency decreases to 62.57% and NOxand emission increases to 3.02 g/kWh, which also exceeds the Euro-5 emission standard. Since NOx emission 3.02 conditions, g/kWh, whichthe alsoNO exceeds the Euro-5 emission standard. Since In addition, under increases the gaintofault x conversion efficiency decreases to 62.57% the outputs of the faulty temperature sensors are smaller (e.g., under outputs of the faulty temperature sensors are smaller (e.g., under thethe gaingain and and drift drift fault) fault) than than and NOthe x emission increases to 3.02 g/kWh, which also exceeds the Euro-5 emission standard. Since the those of those the normal ones, they will lead to erroneous predictions of the catalyst temperature of the normal ones, they will lead to erroneous predictions of the catalyst temperature and and outputs of the faulty temperature sensors are smaller (e.g., under the gain and drift fault) than those coverage ratio (Figure is notedthat, that, the the control used herehere is based on the on the ammoniaammonia coverage ratio (Figure 11).11). It isIt noted controlstrategy strategy used is based of the normal ones, they will and leadthe toestimated erroneous predictions of ratio the catalyst temperature ammonia coverage ammonia coverage increases along with with the and decline ammoniaammonia coverage ratio, ratio, and the estimated ammonia coverage ratio increases along the decline coverage ratio (Figure 11). It isThus, noted that, control used is based on the ammonia of the catalyst temperature. when thethe fault outputsstrategy are smaller thanhere the normal outputs, it will of the catalyst temperature. Thus, when the fault outputs are smaller than the normal outputs, it will lead to theand decline urea dosageammonia and NH3 slip, however, the increase of NO x emission. it of the coverage ratio, theofestimated coverage ratio increases along with Moreover, the decline lead to the decline ofthe urea dosage and NH3 slip, however, the increase of NOx emission. Moreover, it is found that NO x conversion efficiency under the drift fault shows a significant decline in the catalyst temperature. Thus, when the fault outputs are smaller than the normal outputs, it will lead is foundlater thatpart the of NO x conversion under the drift significant decline the cycle, and theefficiency corresponding NH 3 slip andfault urea shows dosage adecline markedly. This in the to the decline of urea dosage and NH3 slip, however, the increase of NOx emission. Moreover, it is later part of the because cycle, and the corresponding NH3 slip and ureaand dosage declineofmarkedly. primarily the absolute value of the temperature residual the deviation ammonia This found that the NO efficiency under the drift fault shows a significant decline in the later x conversion coverage ratio estimations theof real values (Figure 11c)residual become increasingly larger overoftime. primarily because the absolutefrom value the temperature and the deviation ammonia part of the cycle, and the corresponding NH urea dosage decline markedly. This primarily 3 slip and SCR performances each condition are presented in Table 4. coverageDetailed ratio estimations fromdata the of real values (Figure 11c) become increasingly larger over time. because theThe absolute value of theratio temperature and the discrepancies deviation ofwith ammonia ratio ammonia coverage estimationsresidual show substantial the realcoverage values Detailed SCR performances data of each condition are presented in Table 4. under fault conditions. For instance, under the stuck fault, the estimates are much smaller (see Figure estimations from the real values (Figure 11c) become increasingly larger over time. Detailed SCR The11a) ammonia coverage ratio showwhich substantial with the real values duedata to the outputs of estimations temperature sensor are4. largerdiscrepancies than the real temperature. performances offault each condition are presented in Table under fault conditions. For instance, under the stuck fault, the estimates are much smaller (see Figure The ammonia coverage ratio estimations show substantial discrepancies with the real values under 11a) due to the fault outputs of temperature sensor which are larger thefaults. real temperature. Table 4. Selective catalyst reduction (SCR) performances underthan different fault conditions. For instance, under the stuck fault, the estimates are much smaller (see Figure 11a) Mean NOx Conversion Mean NH3 Slip Mean Urea due to theFault fault outputs temperature Type NOxof Emission (g/kWh) sensor which are larger than the real temperature. Efficiency (ppm) Dosage (g/h) Table 4. Selective catalyst reduction (SCR) performances under different faults. No fault 1.92 77.20% 2.32 671.26 Table 4. Selective catalyst reduction (SCR) performances under different faults. Mean NO x Conversion Mean NH3 Slip Mean Urea Stuck fault 0.53 93.62% 13.02 944.95 Fault Type NOx Emission (g/kWh) Gain fault 3.02 62.57% 1.74 548.66 Efficiency (ppm) Dosage (g/h) NO1.92 Mean NO77.20% Mean NH3 Slip Dosage 4.60 1.072.32Mean Urea 337.86 x Emission x44.30% No faultDrift fault 671.26
(g/kWh)
Ammonia Coverage Ratio
0.53 1.92 3.02 0.53 4.60 3.02
1
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Ammonia Coverage Ratio
1.2 1 0.8
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77.20% 62.57% 93.62% 0.7 62.57%44.30%0.6 Estimated 44.30%
4.60
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Fault Type
Stuck fault Gain faultNo fault Stuck 1.2 fault Drift fault Gain fault
13.02 1.74 1.07
2.32 13.02 1.74 1.07
0.5
(g/h)
Rea l
944.95
671.26 548.66 944.95 337.86 548.66 Estimated 337.86
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0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 0 0
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0.7 0.7 0.6 0.6 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 0 0
14 of 14 of 17 17 14 of 17 Estimated Estimated
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(c) (d) (c) (d) Figure 11. Ammonia coverage ratio vs. time. (a) Stuck fault; (b) gain fault; (c) drift fault; and (d) Figure 11. coverageratio ratiovs. vs.time. time. Stuck fault; (b) gain (c) fault; drift and fault; (d) Figure 11. Ammonia Ammonia coverage (a)(a) Stuck fault; (b) gain fault;fault; (c) drift (d)and normal. normal. normal.
Moreover, stuck fault, thethe mean andand peak NH3NH slips are up 13.02 ppm and Moreover,under underthe the stuck fault, mean peak 3 slips aretoup to 13.02 ppm30.90 and ppm, 30.90 Moreover, under the stuck fault, the mean and peak NH 3 slips are up to 13.02 ppm and 30.90 respectively, which exceed the Euro-5 emission limits (10 ppm onppm average, 25 ppm25 at ppm peak).atThis is ppm, respectively, which exceed the Euro-5 emission limits (10 on average, peak). ppm, respectively, which exceed the Euro-5 emission limits (10 ppm onNH average, 25 ppm at peak).in aThis fault fault outputs larger than the normal outputs. increase iswhere a faultthe where the faultare outputs are larger than the normalThe outputs. Thetends NH3toslip tends to 3 slip This isa acase, faultalthough where the are larger than the normal outputs. The 3 slip tends to such thefault NOx outputs conversion remains relatively high. TheNH urea dosage the increase in such a case, although the NOefficiency x conversion efficiency remains relatively high. Theisurea increase in such a case, although the NO x conversion efficiency remains relatively high. The urea primary in the SCR system, which influences the SCR performance. shown dosage control is the object primary control object in the directly SCR system, which directly influencesAsthe SCR dosage is 12, thetheprimary control object in the SCR system, which directly influencesleads the SCR in Figure urea upthe to 944.95 stuck fault, which g/h obviously the performance. Asmean shown indosage Figureis12, mean g/h ureaunder dosage is up to 944.95 under stucktofault, performance. As shown in Figure 12, the mean urea dosage is up to 944.95 g/h under stuck fault, over-consumption of urea solution. which obviously leads to the over-consumption of urea solution. which obviously leads to the over-consumption of urea solution. Stuck Stuck Drift Drift
Urea Dosage[g/h] Urea Dosage[g/h]
3000 3000 2700 2700 2400 2400 2100 2100 1800 1800 1500 1500 1200 1200 900 900 600 600 300 300 0 0 0 0
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Figure12. 12.Urea Ureadosage dosagevs. vs.time. time. Figure Figure 12. Urea dosage vs. time.
Overall,faults faults of SCR-out temperature sensors negatively impact SCR performances; thus, Overall, sensors negatively impact SCR SCR performances; thus, timely Overall, faults of of SCR-out SCR-outtemperature temperature sensors negatively impact performances; thus, timely fault detection and fault-tolerant control shall be essential in such cases. To validate the fault detection and fault-tolerant control control shall beshall essential in such cases. validate proposed timely fault detection and fault-tolerant be essential in suchTocases. To the validate the proposed fault detection system and fault-tolerant control strategy, a fault is induced during the test fault detection system and fault-tolerant control control strategy, a fault aisfault induced during the test proposed fault detection system and fault-tolerant strategy, is induced during thecycle. test cycle. For instance, a gain fault (α = 0.7) is injected in the downstream temperature sensor at the 380th For instance, a gain fault (α = 0.7) is injected in the downstream temperature sensor at the 380th second cycle. Forover instance, a gain fault = 0.7)in is injected in the downstream sensor at the 380th second an ETC cycle as (α shown Figure 13a, after which the temperature NOx emission grows obviously. over anover ETCan cycle ascycle shown Figurein13a, after13a, which thewhich NO obviously. Meanwhile, x emission second ETC asinshown Figure after the NOxgrows emission grows obviously. Meanwhile, from Figure 13b, the temperature residual exceeds the threshold immediately. After the from Figurefrom 13b, the temperature exceeds the threshold the fault isAfter detected Meanwhile, Figure 13b, the residual temperature residual exceeds immediately. the thresholdAfter immediately. the fault isdiagnosis detectedsystem, by the diagnosis system, control the fault-tolerant control take second, effect atand the the 410th second, and by the the fault-tolerant take effect at the 410th NO x emission fault is detected by the diagnosis system, the fault-tolerant control take effect at the 410th second, and the NO x emission declines to the normal level. declines to the normal level. the NOx emission declines to the normal level.
15 of 17 15 of 17 15 of 17 Gain Nor Gainmal Nor mal
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0 370 370
410 430 Time [s] 430 410 (a) Time [s]
450 450
Temperature Residual Temperature Residual
NOx Concentration [ppm] NOx Concentration [ppm]
Energies 2018, 11, 1800 Energies 2018, 11, x Energies 2018, 11, x 200 200 160 160 120 120 80 80 40 40 0 0 360 360
Gai n Gai n Threshold Threshold
380 380
400 420 Time [s] 420 400 (b) Time [s]
440 440
(a) (b) Figure (a) (a) NONO x concentration; and (b) Figure 13. 13. Fault Faultdetection detectionand andfault-tolerant fault-tolerantcontrol controlperformances. performances. x concentration; and Figure 13. Fault detection and fault-tolerant control performances. (a) NOx concentration; and (b) temperature residual. (b) temperature residual. temperature residual.
900 800 900 700 800 600 700 500 600 400 500 300 400 200 300 100 200 1000 0 0 0
7
Fault-tolerant Normal Fault-tolerant NH3NH Slip[ppm] 3 Slip[ppm]
Concentration[ppm] NOxNOx Concentration[ppm]
SCR performances under fault-tolerant control over the whole ETC cycle are shown in Figure 14. SCR performances performances under under fault-tolerant fault-tolerant control control over over the the whole whole ETC cycle cycle are areshown shownin inFigure Figure14. 14. SCR The NO x emissions under fault-tolerant control and normal strategyETC are 1.94 g/kWh and 1.92 g/kWh, The NO emissions under fault-tolerant control and normal strategy are 1.94 g/kWh and 1.92 g/kWh, x The NOx emissions under control and normal strategy arefault-tolerant 1.94 g/kWh and 1.92and g/kWh, respectively. The mean NHfault-tolerant 3 slips are also similar, with 2.31 ppm under control 2.32 respectively. The Themean meanNH NH slips are also similar, with 2.31 ppm under fault-tolerant control and 3 respectively. 3 slips are also similar, with 2.31 ppm under fault-tolerant control and 2.32 ppm under the normal strategy. Since the good performance of the temperature model, the SCR 2.32 ppm under the normal strategy. Since the good performance ofthe thetemperature temperaturemodel, model, the the SCR ppm under the normal strategy. Since theare good performances under fault-tolerant control veryperformance similar with of them of normal conditions afterSCR the performances under fault-tolerant control are very similar with them of normal conditions after the performances under fault-tolerant control are very similar with them of normal conditions after the measured temperature is replaced by the estimate. measured temperature temperature is is replaced replaced by measured by the the estimate. estimate.
Normal
67
Fault-tolerant Normal Fault-tolerant
56
Normal
45 34 23 12 01
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(a) (b) Figure 14. SCR performances under fault-tolerant control. (a) NOx concentration; and (b) NH3 slip. Figure 14. 14. SCR under fault-tolerant fault-tolerant control. control. (a) (a) NO NOx concentration; Figure SCR performances performances under concentration; and and (b) (b) NH NH3 slip. slip. x
3
5. Conclusions 5. Conclusions 5. Conclusions The main conclusions of this study can be summarized as follows: The main conclusions of this study can be summarized as follows: TheTemperature main conclusions of this study can beare summarized as follows: due to their harsh working (1) sensors of SCR systems prone to malfunction (1) Temperature Temperature sensors oftypical SCR systems systems are prone to to malfunction due to their their harsh working (1) sensors SCR prone malfunction to harsh conditions. In this study, threeof faults ofare downstream temperaturedue sensor of the SCRworking catalyst conditions. In this study, three typical faults of downstream temperature sensor of the SCR catalyst conditions. In this study, three typical faults of downstream temperature sensor of the SCR catalyst were analyzed and modeled, and the influence of different temperature sensor faults on the SCR were analyzed analyzed and modeled, and and theon influence of different different temperature sensor faults faults on the the SCR SCR were and modeled, the influence of sensor on performance was investigated based a 0-D SCR model. temperature The results showed that temperature performance was investigated based on a 0-D SCR model. The results showed that temperature sensor performance was investigated based on a 0-D SCR model. The results showed that temperature sensor faults occurring in various forms influence SCR performances to various degrees. In a case faults occurring in various forms influence SCR performances to various degrees. In NO a case the sensor faults in smaller various formsthe influence SCR performances to various degrees. Inthat a case that the fault occurring outputs are than normal outputs (e.g., the drift fault), the x emission faultthe outputs are smaller than the normal outputs (e.g., the drift fault), thethe NO to be that fault outputs smaller thancondition. the normal outputs (e.g., thethat drift fault), the NOtends x are emission x emission tends to be larger than are it under normal However, in a case fault outputs larger larger than it under normal condition. However, in a case that the fault outputs are larger than the tends to be larger than it under normal condition. However, in a case that the fault outputs are larger than the normal outputs (e.g., the stuck fault), the NH3 slip tends to be larger than it under normal normal outputs (e.g., the stuck fault), the NH slip tends to be larger than it under normal condition. than the normal outputs (e.g., the stuck fault), the NH 3 slip tends to be larger than it under normal 3 condition. (2) A model-based fault detection system and aa fault-tolerant fault-tolerant control control strategy strategy were were developed, developed, condition. (2) A model-based fault detection system and and the diagnosis algorithm was validated over the ETC cycle by simulation. The results indicated (2) A model-based fault detection system and a fault-tolerant control strategy were developed, and the diagnosis algorithm was validated over the ETC cycle by simulation. The results indicated that the the fault fault detection detection system is capable capable of detecting detecting typical faults of temperature temperature sensors after their and the diagnosis algorithm was validated over the ETC cycle by of simulation. Thesensors results after indicated that system is of typical faults their occurrences, and the fault-tolerant control strategy proposed can take effect for regulating the SCR that the fault detection system is capable of detecting typical faults of temperature sensors after their occurrences, and the fault-tolerant control strategy proposed can take effect for regulating the SCR system under fault conditions and maintaining SCR performances within their reasonable ranges. occurrences, and the fault-tolerant control strategy proposed can take effect for regulating the SCR system under fault conditions and maintaining SCR performances within their reasonable ranges. system under faultthis conditions and maintaining SCR performances within their reasonable ranges. In summary, summary, this study presents presents clear illustration illustration of how how the the SCR SCR performances performances are influenced influenced In study aa clear of are In summary, this study presents a clear illustration of how the SCR performances are influenced by three typical faults of the downstream temperature sensor, and it may contribute to the recognition by three typical faults of the downstream temperature sensor, and it may contribute to the recognition by the three typical faults of the downstream sensor, and it may contribute the recognition of fault propagation paths and rulestemperature in the SCR system. Furthermore, a fault to detection system of the fault propagation paths and rules in the SCR system. Furthermore, a fault detection system and a fault-tolerant control strategy are designed for the SCR control system. Simulation results on a and a fault-tolerant control strategy are designed for the SCR control system. Simulation results on a
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of the fault propagation paths and rules in the SCR system. Furthermore, a fault detection system and a fault-tolerant control strategy are designed for the SCR control system. Simulation results on a 0-D SCR model proved their effectiveness, which indicates their enormous potential of practical application on vehicles. Further researches will focus on the experimental validation of the fault detection system and fault-tolerant control strategy; moreover, fault diagnosis of other sensors (such as NOx sensors and NH3 sensors) will be also investigated. Author Contributions: All authors have contributed equally to the design of the research, data collection and analysis and the final preparation of the manuscript. Funding: This research was funded by [National Key R&D Program of China] grant number [2017YFC0211203], [National Natural Science Foundation] grant number [51406140], [National Engineering Laboratory for Mobile Source Emission Control Technology] grant number [NELMS2017A08], [Natural Science Foundation of Hubei Province] grant number [2018CFB592] and [111 Project] grant number [B17034]. Acknowledgments: The authors would like to gratefully acknowledge the Hubei Key Laboratory of Advanced Technology for Automotive Components (Wuhan University of Technology). Conflicts of Interest: The authors declare no potential conflict of interest.
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