Description:FORM 2
THE PATENTS ACT, 1970
(39 OF 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
[See section 10, Rule 13]

TITLE OF INVENTION
System for Monitoring Catalytic Converter and Method Thereof

APPLICANT
TVS MOTOR COMPANY LIMITED, an Indian company, having its address at “Chaitanya”, No.12 Khader Nawaz Khan Road, Nungambakkam, Chennai 600 006, Tamil Nadu, India.

PREAMBLE TO THE DESCRIPTION
The following specification particularly describes the invention and the manner in which it is to be performed.

FIELD OF THE INVENTION
[001] The present invention relates to a system and a method for monitoring age of a catalytic converter disposed in an exhaust manifold of an engine.

BACKGROUND OF THE INVENTION
[002] In recent past, stringent emission norms on vehicles employing an internal combustion engine make after-treatment of exhaust gases a mandated practice. One such technique employed for after-treatment of exhaust gases is by routing the exhaust gases through a catalytic converter. The catalytic converter converts toxic gases such as CO, NO and unburnt hydrocarbons present in the exhaust gases into nontoxic gases by using precious metals such as Rh, Pd, Ce etc. The catalytic converter can simultaneously oxidize carbon monoxide and hydrocarbons to carbon dioxide and water vapor, while reducing NOx gases to nitrogen dioxide (NO2). The ceramic substrate of the catalytic converter has the capability to store oxygen on the surface, thus the catalytic converter is capable of supplying oxygen into the reaction and store the oxygen back in the next phase of the reaction. However, the catalytic converter ages with time and thus conversion efficiency decreases over time.
[003] In order to monitor the after-treatment of exhaust gases, the vehicles are typically equipped with On-Board Diagnostic (OBD) systems. However, these systems only concern about after-treatment of exhaust gases and fail to monitor the conversion efficiency of the catalytic converter. Also, in recent past, the norms for the OBD systems are revised making it important to monitor health of the catalytic converter as well.
[004] In known art, a phase-shift method of monitoring the catalytic converter is employed. In the phase-shift method, a phase-shift is measured between input signals received from a lambda sensor positioned at an inlet (pre-lambda sensor) of the catalytic converter and from a lambda sensor positioned at an outlet (post-lambda sensor) of the catalytic converter. The phase shift between these two input signals is determined, in order to monitor health of the catalytic converter. However, in the phase-shift method, the phase shift determined between the two input signals from the pre-lambda sensor and the post-lambda sensor is not regular in all operating points of the engine, consequently rendering inaccurate diagnosis or health of the catalytic converter, which is undesirable.
[005] Further, there are many diagnostic model-based approaches that determine oxygen levels in the catalytic converter, for determining aging of the catalytic converter. These methods involve neural network based modeling of reaction kinetic structure, nonlinear partial differential equation based model using finite discrete method, etc. Such model based approaches require a lot of computations and may not be efficient to run on an Engine Control Unit (ECU) of the vehicle.
[006] In view of the above, there is a need for a system and a method for monitoring a catalytic converter, which addresses one or more limitations stated above.

SUMMARY OF THE INVENTION
[007] In one aspect, a system for monitoring a catalytic converter disposed in an exhaust manifold of an engine is disclosed. The system comprises a first sensor disposed in an upstream side of the catalytic converter. The first sensor is configured to generate a first signal corresponding to oxygen content in an exhaust gas entering the catalytic converter. A second sensor is disposed in a downstream side of the catalytic converter. The second sensor is configured to generate a second signal corresponding to the oxygen content in the exhaust gas discharged from the catalytic converter. One or more sensors are coupled to the engine. The one or more sensors are configured to generate one or more engine parameters. A cloud computing unit is communicably coupled to the first sensor, the second sensor and the one or more sensors. The cloud computing unit is configured to receive the first signal from the first sensor, the second signal from the second sensor and the one or more engine parameters from the one or more sensors. Thereafter, a condition of the catalytic converter is determined by the cloud computing unit based on the first signal, the second signal and the one or more engine parameters. The cloud computing unit is configured to determine the condition of the catalytic converter based on a numerical computation technique. The numerical computation technique is adapted to estimate a gain factor of the catalytic converter based on the first signal, the second signal and the one or more engine parameters.
[008] In an embodiment, the numerical computation technique is a recursive least square technique with a Kalman filter module.
[009] In an embodiment, the cloud computing unit is configured to classify the condition of the catalytic converter as one of a fresh condition, an intermediate condition and an aged condition, based on the gain factor using a trained classifier unit.
[010] In an embodiment, the cloud computing unit in communication with an Electronic Control Unit (ECU) is configured to generate an indication signal upon determining the aged condition of the catalytic converter.
[011] In an embodiment, an alerting device is communicably coupled to the cloud computing unit and adapted to indicate the aged condition of the catalytic converter. The alerting device is configured to receive an indication signal from the cloud computing unit for indicating the aged condition of the catalytic converter.
[012] In an embodiment, the one or more engine parameters comprises a throttle position of a throttle body of the engine and an engine speed of the engine.
[013] In an embodiment, the first signal and the second signal are input to a numerical computation device employing a recursive least square technique for computing the gain factor, and the one or more engine parameters are input to a trained classifier unit for classifying the catalytic converter.
[014] In an embodiment, the ECU is communicably coupled to the first sensor, the second sensor and the cloud computing unit. The ECU is configured for pre-processing the first signal from the first sensor and the second signal from the second sensor, wherein the pre-processing digitalizes the first signal and the second signal prior to input to the cloud computing unit.
[015] In another aspect, a method for monitoring the catalytic converter disposed in the exhaust manifold of the engine. The method comprises generating by the first sensor the first signal corresponding to oxygen content in the exhaust gas entering the catalytic converter, the first sensor being disposed in the upstream side of the catalytic converter. Thereafter, the second signal corresponding to the oxygen content in the exhaust gas discharged from the catalytic converter is generated by the second sensor, the second sensor being disposed in the downstream side of the catalytic converter. Subsequently, the one or more sensors are coupled to the engine for generating the one or more engine parameters. The cloud computing unit then receives the first signal from the first sensor, the second signal from the second sensor and the one or more engine parameters from the one or more sensors from the ECU communicably coupled to the engine. The cloud computing unit thereafter determines the condition of the catalytic converter based on the first signal, the second signal and the one or more engine parameters, wherein the cloud computing unit is configured to determine condition of the catalytic converter based on the numerical computation technique, the numerical computation technique being adapted to estimate the gain factor of the catalytic converter based on the first signal and the second signal.

BRIEF DESCRIPTION OF THE DRAWINGS
[016] Reference will be made to embodiments of the invention, examples of which may be illustrated in accompanying figures. These figures are intended to be illustrative, not limiting. Although the invention is generally described in context of these embodiments, it should be understood that it is not intended to limit the scope of the invention to these particular embodiments.
Figure 1 is a block diagram of a system for monitoring a catalytic converter, in accordance with an exemplary embodiment of the present invention.
Figure 2 is a block diagram of a cloud platform of the system, in accordance with an exemplary embodiment of the present invention.
Figure 3 is a flow diagram of a diagnosis of the catalytic converter by the cloud platform, in accordance with an exemplary embodiment of the present invention.
Figure 4 is a schematic view of a decision tree for classifying the catalytic converter, in accordance with an exemplary embodiment of the present invention.
Figure 5 is a graphical representation of time versus a gain factor for the catalytic converter, in accordance with an exemplary embodiment of the present invention.
Figure 6 is a flow diagram of a method of monitoring the catalytic converter, in accordance with an exemplary embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION
[017] Various features and embodiments of the present invention here will be discernible from the following further description thereof, set out hereunder.
[018] Figure 1 illustrates a system 100 for monitoring a catalytic converter 102 in accordance with an exemplary embodiment of the present invention. The catalytic converter 102 is disposed in an exhaust manifold of an engine 104. Accordingly, the catalytic converter 102 receives exhaust gases discharged from the engine 104 for after-treatment. The system 100 provides a cloud based platform for monitoring health of the catalytic converter 102, thereby reducing computation burden on a control unit. The system 100 also provides a real time diagnosis of the catalytic converter 102, thereby ensuring accurate monitoring of health during operation of the engine 104. In an embodiment, the catalytic converter 102 is a honeycomb ceramic block (not shown) with a tubular reactor (not shown), that maximizes an exposed surface area (not shown). Exhaust gases from the engine 104 pass through the honeycomb ceramic block and undergoes oxidation or reduction reactions. In another embodiment, the catalytic converter 102 is a three-way converter which is effective when the engine 104 is operated within a narrow band of air-fuel ratios near a stoichiometric point of the engine 104, such that composition of the exhaust gas oscillates between a rich composition (excess fuel) and a lean composition (excess oxygen). In another embodiment, the catalytic Converter 102 operate in a closed-loop system including one or more lambda or oxygen sensors (depicted as 106, 108) for regulating the air-to-fuel ratio of the engine 104. The catalytic converter 102 can simultaneously oxidize carbon monoxide and hydrocarbons to carbon dioxide and water vapor, while reducing NOx to nitrogen dioxide (NO2). The honeycomb ceramic block of the catalytic converter 102 has the capability to store oxygen on the surface, thus enable supply oxygen into the reaction and store the oxygen back during the next phase of the after-treatment reaction.
[019] The system 100 comprises a first sensor 106 disposed in an upstream side 102a of the catalytic converter 102. The first sensor 106 is configured to monitor oxygen content in the exhaust gas entering the catalytic converter 102. Accordingly, the first sensor 106 is configured to generate a first signal corresponding to the oxygen content of the exhaust gas entering the catalytic converter 102. In an embodiment, the first sensor 106 is a lambda sensor.
[020] The system 100 also comprises a second sensor 108 disposed in a downstream side 102b of the catalytic converter 102. The second sensor 108 is configured to monitor oxygen content in the exhaust gas discharged from the catalytic converter 102. Accordingly, the second sensor 108 is configured to generate a second signal corresponding to the oxygen content of the exhaust has discharged from the catalytic converter 102. In an embodiment, the second sensor 108 is also a lambda sensor. The system 100, based on the first signal from the first sensor 106 and the second signal from the second sensor 108 determines the oxygen storage capacity of the catalytic converter 102.
[021] The system 100 further comprises one or more sensors 110 communicably coupled to the engine 104. The one or more sensors 110 are adapted to generate one or more engine parameters during operation of the engine 104. In an embodiment, the one or more engine parameters are indicative of operating conditions of the engine 104. The data pertaining to the one or more engine parameters along with the first signal from the first sensor 106 and the second signal from the second sensor 108 enables the system 100 to determine age of the catalytic converter 102. Accordingly, the system 100 is capable of classifying the catalytic converter 102 into a fresh condition, an intermediate condition and an aged condition.
[022] In an embodiment, the one or more sensors 110 comprises a throttle position sensor 110a of a throttle body (not shown) of the engine 104 and an engine speed sensor 110b.
[023] In an embodiment, the throttle position sensor 110a is located in the throttle body of the engine 104. The throttle position sensor 110a is configured to monitor a degree of opening of the throttle body. Based on the degree of opening of the throttle body, the throttle position sensor 110a is configured to generate a throttle position signal.
[024] In an embodiment, engine speed sensor 110b is a Rotation Per Minute (RPM) sensor, mounted adjacent to a crankshaft (not shown) of the engine 104. The engine speed sensor 110b is configured to monitor the speed of rotation of the crankshaft. Based on the speed of rotation of the crankshaft, the engine speed sensor 110b is configured to generate an engine speed signal.
[025] Further, the system 100 comprises a cloud computing unit 112 adapted to monitor health of the catalytic converter 102. The cloud computing unit 112 is communicably coupled to the first sensor 106, the second sensor 108, and the one or more sensors 110. Accordingly, the cloud computing unit 112 receives the first signal from the first sensor 106, the second signal from the second sensor 108, the throttle position signal, and the engine speed signal. In an embodiment, the one or more engine parameters, i.e. the engine speed signal and the throttle position signal may be transmitted to a trained classifier unit (not shown) of the cloud computing unit 112, for classifying age of the catalytic converter 102.
[026] In an embodiment, the cloud computing unit 112 is communicably coupled to an Electronic Control Unit (ECU) 116 of a vehicle (not shown). The ECU 116 may be adapted to monitor the operating conditions of the engine 104. As such, the ECU 116 may receive the one or more engine parameters from the one or more sensors 110 for controlling operation of the engine 104. Accordingly, the cloud computing unit 112 may be receive the one or more engine parameters from the ECU 116 rather than the one or more sensors 110.
[027] A key phenomenon of the catalytic converter 102 is oxygen storage capacity on its surface, quantified as Oxygen Storage Capacity (OSC). As mentioned earlier, the catalytic converter 102 undergoes cycles of oxidation and reduction reactions during after-treatment of the exhaust gases. These reactions vary the amount of oxygen stored on surface of the catalytic converter 102. When the catalytic converter 102 undergoes reduction reactions, during lean conditions, to reduce Nitrogen Oxides, oxygen (O2) is released as a by-product of the reaction. On the other hand, during rich conditions, when hydrocarbons and carbon monoxide are oxidized, oxygen is consumed for the reaction. The oxygen released during the reduction reaction, however, is not always same as the oxygen consumed during oxidation. If more oxygen is released than required, the excess oxygen is stored on surface of the catalytic converter 102. Similarly, if more oxygen is required than released, the excess oxygen is supplied from the surface of the catalytic converter 102. Therefore, the ability of the catalytic converter 102 to effectively store and release oxygen forms the backbone of efficient functioning. A new and efficiently working catalytic converter 102 stores and releases oxygen at a rate such that the catalytic converter 102 behaves as time varying emissions capacitor between the first sensor 106 and the second sensor 108. Conversely, the catalytic converter 102 behaves as a free-flowing wire after going through the effects of age. Therefore, oxygen is stored and released from the catalytic converter 102 so that the second sensor 108 measures a heavily attenuated response. With increased use of the catalytic converter 102, number of active sites for storing oxygen are reduced, resulting in a decline of oxygen storage capacity of the catalytic converter 102. As a result, release and storage of oxygen is not able to match the rate to ensure attenuated signal response at the second sensor 108. Thus, indicating a decline in health of the catalytic converter 102, and reduction in conversion capacity of the catalytic converter 102. A metric to quantify the oxygen storage capacity is therefore, a good estimate to know the health and conversion efficiency of the catalytic converter 102. This metric is an online adaptive gain factor (K) that relates to the post catalyst narrowband oxygen sensor voltage to deviations in feed gas fuel-air ratio about unity. The gain factor K encapsulates and combines behavior of the catalytic converter 102 including its oxygen storage capacity into one metric. In an embodiment, the catalytic converter 102 in the fresh condition should have near zero or consistently low gain factor K value and the catalytic converter 102 in the aged condition should have a higher gain factor K value.
[028] Referring to Figure 2 in conjunction with Figure 1, a block diagram of a cloud platform in the cloud computing unit 112 for monitoring age of the catalytic converter 102 is depicted providing an end-to end remote diagnostic system. In an embodiment, the cloud platform considered for monitoring the catalytic converter 102 is MATLAB. The cloud computing unit 112 is configured to determine age or monitor the catalytic converter 102 using a numerical computation technique. As depicted, the cloud computing unit 112 is in communication with a cloud storage device 118 (depicted as ‘cloud storage’ in Figure 2) for storing the information received and processed by the cloud computing unit 112. As an example, the cloud storage device 118 is configured to store the data pertaining to the first signal, the second signal, and the one or more parameters that are received by the cloud computing unit 112. In an embodiment, the cloud storage device 118 may be an integral unit within the cloud computing unit 112 or may be a distinct device in communication with the cloud computing unit 112. The cloud storage device 118 may be in communication with a load balancer unit 120 for streamlining the data received from the first sensor 106, the second sensor 108, and the one or more sensors 110 as per requirement. The data from the load balancer unit 120 and/or the data from the cloud storage device 118 is transmitted to a numerical computation device 122 through a serverless function for computation of a gain factor K. In an embodiment, the gain factor K computed by the numerical computation device 122 is a variation of value in the first signal with respect to the second signal. The gain value is indicative of the extend of oxygen storage in the catalytic converter 102, consequently, depicting age of the catalytic converter 102. The mathematical relation between the rate of change of the second signal being equal to the product of the gain factor K and the first signal is modeled in the discrete form in the numerical computation device 122. Based on the mathematical relation, an estimated second signal is determined by the numerical computation device 122 and the error between the estimated second signal and the measured second signal from the second sensor 108 is reduced by employing a recursive least square technique in the numerical computation device 122 and while reducing the error, the value of gain factor K is determined.
[029] Referring to Figure 3 in conjunction with Figure 2, a flow diagram 300 of diagnosis of the catalytic converter 102 is depicted. As depicted, at step 302, the first signal from the first sensor 106 and the second signal from the second sensor 108 are generated. The first signal and the second signal are then input to the numerical computation device 122 at step 304, wherein the numerical computation device 122 is adapted to employ a recursive least square technique for computing the gain factor K at step 306. In an embodiment, the recursive least square technique recursively determines coefficients that minimize a weighted linear least squares cost function relating to the first and the second signals, adapting based on a total error computed from the beginning of a data set. In an embodiment, in the recursive least square technique, the coefficients are updated at each iteration when new data is input, thereby reducing computation and convergence time required for determining the gain factor K.
[030] Upon determining the gain factor K, the step 308 is implemented. At step 308, the gain factor K is sent to the classifier unit for classification at step 310 in presence of the one or more engine parameters.
[031] In an embodiment, the recursive least square technique uses a Kalman filter with reduced required throughput in the cloud computing unit 112. The Kalman filter along with the recursive least square technique consider the total square errors rather than mean square errors during the numerical computation. Further, new data sample of the measured input, that is, current data sample of the first signal and measured output, that is, current data sample of the second signal will be received by the numerical computation device 122. Also, the estimated current data sample of the second signal is determined by the mathematical model in the numerical computation device 122. Further, an estimation error will be calculated by subtracting the estimated output from the measured output. The estimation error will be fed to a Kalman gain update block (not shown) and then a pseudo inverse matrix will be updated jointly along with the gain factor K. The gain factor K will be recursively updated since the measured data is continuously streaming. Thus, the numerical computation device 122 updates online the gain factor K, and the gain factor K is transmitted to the classifier unit for classification of the catalytic converter 102. In an embodiment, for a predetermined time, the values of the gain factor K are transmitted to the classifier unit along with one or more engine parameters.
[032] Referring to Figure 4 in conjunction with Figures 1-3, a schematic view of a decision tree within the trained classifier unit for classifying the catalytic converter 102 is depicted. In the present embodiment, Figure 4 depicts a binary search tree method for classification of the catalytic converter 102. During the training of the classifier unit, the weights of the decision tree for values of K, engine RPM, and the TPS are set to the values shown in Figure 4 to classify the catalytic converter 102 as different age conditions, where ‘K’ is the gain factor, RPM is the engine rotational speed and ‘TP’ is the throttle position of the throttle body of the engine 104. As depicted, based on the gain factor K determined using the numerical computation device 122, age of the catalytic converter 102 is determined as one of the fresh condition, the intermediate condition, and the aged condition by the trained classifier unit of the numerical computation device 122. From Figure 4, it is evident that a gain factor K of less than 0.256 at an engine RPM of 2216.5 rpm, the catalytic converter 102 is classified as intermediate condition and if K is greater than 0.256 and RPM is also greater than 2216, the catalytic converter 102 is classified as of aged condition. Similarly, when K is greater than 0.256 at a TPS value greater than 8.93, then the catalytic converter 102 is classified as of fresh condition. As such, in real time, when a sample of the engine parameters engine RPM, TPS value are received and the gain factor K is received, the classifier unit classifies the catalytic converter 102 into the one of the conditions.
[033] In an embodiment, the system 100 comprises an alerting device 114 (shown in Figures 1 and 2) communicably coupled to the ECU 116. The cloud computing unit 112 upon determining an aged condition of the catalytic converter 102 is adapted to enable the ECU 116 to send an indication signal to the alerting device 114. The alerting device 114 upon receiving the alerting signal from the ECU 116 is adapted to alert a user of the vehicle regarding the aged/failure condition of the catalytic converter 102. In an embodiment, the alerting device 114 may be configured within an instrument cluster (not shown) of the vehicle, which may be configured to provide a visual alert or an audible alert or a tactile alert to the user.
[034] Referring to Figure 5, a graphical representation of the gain factor K versus time is depicted for the catalytic converter 102. As depicted, the gain factor K of the catalytic converter 102 will convert to a different value based on the age of the catalytic converter 102. The gain value ‘K’ is typically varying for a predetermined period of time and then settles down to a constant distinct gain factor K. As such, the gain factor K is not passed to the trained classifier unit until a distinct value is obtained.
[035] Further, as depicted in Figure 5, the gain factor K values for three differently aged catalytic converters 102 termed as ‘Fresh catalyst’ (referenced as ‘506’), ‘Intermediate catalyst’ (referenced as ‘504’) and ‘Aged catalyst’ (referenced as ‘502’) is depicted. As shown, the gain factor K increases as age of the catalytic converter 102 increases. The gain factor K for fresh catalyst is constantly low. As such, in spite of the exhaust feed gas reaching the upstream sensor 106, the emissions in the second sensor 108 are very low. As the age of the catalytic converter 102 increases, an increase in gain factor K is observed. This is due to deterioration of the catalytic converter 102, due to which the emissions reaching the second sensor 108 increase. Thus, showing that a lower gain indicates higher oxygen storage capacity in the catalytic converter 102. The gain factors are then used with engine parameters like RPM and throttle position to classify the catalysts into three categories that is, the fresh condition (such as 1000km aged), the intermediate condition (such as 20000km aged) and the aged condition (such as 65000km aged). Thus, inclusion of the one or more engine parameters with gain factor K makes the classification of the catalytic converter 102 robust.
[036] Figure 6 illustrates a flow diagram of a method 600 for monitoring the catalytic converter 102. At step 602, the first sensor 106 generates the first signal corresponding to the oxygen content in the exhaust gas entering the catalytic converter 102. Thereafter, at step 604 the second sensor 108 generates the second signal corresponding to the oxygen content in the exhaust gas discharged from the catalytic converter 102. Subsequently, at step 606, the one or more sensors 110, i.e. the throttle body sensor 110a and the engine speed sensor 110b generate one or more engine parameters based on operation of the engine 104. The first signal, the second signal, and the one or more engine parameters may be pre-processed for digitalizing the signals prior to input to the cloud computing unit 112.
[037] Thereafter, the method 600 moves to step 608, wherein the cloud computing unit 112 receives the first signal and the second signal. The first signal and the second signal are routed to the numerical computation device 122 for determining the gain factor K as already described in description pertaining to Figures 3-6. Upon determining the gain factor K, the method moves to step 610.
[038] At step 610, the cloud computing unit 112 receives the one or more engine parameters. Particularly, the one or more engine parameters are input to the classifier unit in the numerical computation device 122. Based on the one or more engine parameters and the gain factor K determined, the cloud computing unit 112 is adapted to classify the catalytic converter 102 as one of the fresh condition, the intermediate condition, and the aged condition. If the catalytic converter 102 is classified as the aged condition, the cloud computing unit 112 provides the indication signal to the alerting device 114 through the ECU 116 for alerting the user of the vehicle.
[039] The claimed invention as disclosed above is not routine, conventional or well understood in the art, as the claimed aspects enable the following solutions to the existing problems in conventional technologies. Specifically, the aspect of employing the cloud computing unit for monitoring the catalytic converter reduces computation burden on the ECU of the vehicle. Also, the system recursively takes the measured data and update the gain factor K. Thus, a real-time diagnosis is possible through the system. Further, the system is applicable for all the operating points of the engine as the system operates on a dynamic drive cycle. Additionally, the system takes into account the first signal and the second signal along with the one or more engine parameters, making the system more robust. Further, the accuracy of monitoring of age of the catalytic converter by the system is enhanced vis-à-vis the conventional systems as the system operates as a gray box model in the cloud computing unit. Moreover, the system is capable of quantifying age or state of health of the catalytic converter. Further, the method in the present invention is capable of diagnosing the catalytic converter at every moment during operation of the vehicle or in real-time. As such, a specific diagnosis cycle for diagnosing the catalytic converter is not necessary. Additionally, online diagnosis of the catalytic converter is carried out by estimating the model parameters.

Reference numerals

100 System
102 Catalytic converter
102a Upstream side of catalytic converter
102b Downstream side of catalytic converter
104 Engine
106 First sensor
108 Second sensor
110 One or more sensors
110a Throttle position sensor
110b Engine speed sensor
112 Cloud computing unit
114 Alerting device
116 ECU
118 Cloud storage device
120 Load balancer unit
122 Numerical computation device
K Gain factor
, Claims:WE CLAIM:
1. A system (100) for monitoring a catalytic converter (102) disposed in an exhaust manifold of an engine (104), the system (100) comprising:
a first sensor (106) disposed in an upstream side of the catalytic converter (102), the first sensor (106) being configured to generate a first signal corresponding to oxygen content in an exhaust gas entering the catalytic converter (102);
a second sensor (108) disposed in a downstream side of the catalytic converter (102), the second sensor (108) being configured to generate a second signal corresponding to the oxygen content in the exhaust gas discharged from the catalytic converter (102);
one or more sensors (110) coupled to the engine (104), the one or more sensors (110) being configured to generate one or more engine parameters; and
a cloud computing unit (112) communicably coupled to the first sensor (106), the second sensor (108), and the one or more sensors (110), the cloud computing unit (112) being configured to:
receive, the first signal from the first sensor (106), the second signal from the second sensor (108), and the one or more engine parameters from the one or more sensors (110); and
determine, a condition of the catalytic converter (102) based on the first signal, the second signal, and the one or more engine parameters, wherein the cloud computing unit (112) being configured to determine condition of the catalytic converter (102) based on a numerical computation technique, the numerical computation technique being adapted to estimate a gain factor (K) of the catalytic converter (102) based on the first signal, the second signal, and the one or more engine parameters.

2. The system (100) as claimed in claim 1, wherein the numerical computation technique being a recursive least square technique with a Kalman filter module.

3. The system (100) as claimed in claim 1, wherein the cloud computing unit (112) being configured to classify the condition of the catalytic converter (102) as one of a fresh condition, an intermediate condition, and an aged condition, based on the gain factor (K) using a classifier unit.

4. The system (100) as claimed in claim 3, wherein the cloud computing unit (112) in communication with an Electronic Control Unit (ECU) (116) being configured to generate an indication signal upon determining the aged condition of the catalytic converter (102).

5. The system (100) as claimed in claim 4 comprising an alerting device (114) communicably coupled to the ECU (116) and adapted to indicate the aged condition of the catalytic converter (102), the alerting device (114) being configured to receive an indication signal from the cloud computing unit (112) for indicating the aged condition of the catalytic converter (102).

6. The system (100) as claimed in claim 1, wherein the one or more engine parameters comprises:
a throttle position of a throttle body of the engine (104); and
an engine speed of the engine (104).

7. The system (100) as claimed in claim 1, wherein the first signal and the second signal being input to a numerical computation device (122) employing a recursive least square technique for computing the gain factor (K), and the one or more engine parameters being input to a trained classifier unit for classifying the catalytic converter (102).

8. The system (100) as claimed in claim 1 comprises an ECU (116) communicably coupled to the first sensor (106), the second sensor (108) and the cloud computing unit (112), the ECU (116) being configured for pre-processing the first signal from the first sensor (106) and the second signal from the second sensor (108), wherein the pre-processing digitalizes the first signal and the second signal prior to input to the cloud computing unit (112).

9. A method (600) for monitoring a catalytic converter (102) disposed in an exhaust manifold of an engine (104), the method (600) comprising:
generating (602), by a first sensor (106), a first signal corresponding to oxygen content in an exhaust gas entering the catalytic converter (102), the first sensor (106) being disposed in an upstream side of the catalytic converter (102);
generating (604), by a second sensor (108), a second signal corresponding to the oxygen content in the exhaust gas discharged from the catalytic converter (102), the second sensor (108) being disposed in a downstream side of the catalytic converter (102);
generating (606), by one or more sensors (110) coupled to the engine (104), one or more engine parameters; and
receiving (608), by a cloud computing unit (112), the first signal from the first sensor (106), the second signal from the second sensor (108), and the one or more engine parameters from the one or more sensors (110) from an ECU (116) communicably coupled to the engine (104); and
determining (610), by the cloud computing unit (112), a condition of the catalytic converter (102) based on the first signal, the second signal, and the one or more engine parameters, wherein the cloud computing unit (112) being configured to determine a condition of the catalytic converter (102) based on a numerical computation technique, the numerical computation technique being adapted to estimate a gain factor (K) of the catalytic converter (102) based on the first signal and the second signal.

10. The method (600) as claimed in claim 9, wherein the numerical computation technique being a recursive least square technique with a Kalman filter module.

11. The method (600) as claimed in claim 9 comprising, classifying, by the cloud computing unit (112), the condition of the catalytic converter (102) as one of a fresh condition, an intermediate condition, and an aged condition, based on the gain factor (K).

12. The method (600) as claimed in claim 11 comprising, generating, by the cloud computing unit (112), an indication signal upon determining the aged condition of the catalytic converter (102).

13. The method (600) as claimed in claim 12 comprising providing, by the cloud computing unit (112), the indication signal to an alerting device (114), the alerting device (114) being configured to indicate the aged condition of the catalytic converter (102) upon receiving the indication signal.

14. The method (600) as claimed in claim 9, wherein the first signal and the second signal being input to a numerical computation device (122) employing a recursive least square technique for computing the gain factor (K), and the one or more engine parameters being input to a trained classifier unit for classifying the catalytic converter (102).

15. The method (600) as claimed in claim 9 comprising, pre-processing, by an ECU (116) communicably coupled to the first sensor (106), the second sensor (108) and the cloud computing unit (112), the ECU (116) being configured for pre-processing the first signal from the first sensor (106) and the second signal from the second sensor (108), wherein the pre-processing digitalizes the first signal and the second signal prior to input to the cloud computing unit (112).

Dated this 13th day of October 2022

TVS MOTOR COMPANY LIMITED
By their Agent & Attorney

(Nikhil Ranjan)
of Khaitan & Co
Reg No IN/PA-1471