DESC:FIELD OF THE INVENTION
The present subject matter is related, in general to on-board diagnostic systems, and more particularly, but not exclusively to a method and a system for on-board real-time determination of lambda sensor ageing using a plurality of sensors and controller on a vehicle.
BACKGROUND OF THE INVENTION
Lambda sensor plays a crucial role in controlling closed loop fuel-injection system and consequently, the emission of harmful exhaust gases into the atmosphere by maintaining stoichiometric ratio which ensures proper the health of the catalytic converter. Lambda sensors performance in the vehicle deteriorates because of lead posing or accumulation of particles/oil on its sensing surface. OBD-II norms require monitoring the lambda sensor performance periodically and alert the user of the vehicle, to control eth emissions from the vehicle.
Conventional systems utilize one additional lambda sensor as a reference for comparing the performance of aged lambda sensor. However, if the reference lambda sensor is also aged due to any particular reason, then conventional systems provide incorrect diagnosis. Moreover, conventional systems are costlier as one extra lambda sensor is necessary for diagnosing the age of the lambda sensor under observation.
In an embodiment, the lambda sensor age diagnosis is performed at higher engine RPM, however aged lambda sensors typically fail to respond at higher RPM, and such the lambda sensor provides incorrect output voltage at higher RPM. Thus, the results are not reliable. Further, few conventional systems are limited to diagnosis of symmetry of the lambda sensor instead of the age of the lambda sensor. Hence, the age of the sensor is undetermined.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.
SUMMARY
The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
According to embodiments illustrated herein, there is provided a method for determining ageing of a lambda sensor for a vehicle. The method is implemented by an electronic control unit (ECU). The method comprises receiving data from a plurality of sensors. In an embodiment, the plurality of sensors comprises a throttle position sensor, an engine crank angle sensor, a crank shaft speed sensor, a lambda sensor, a MAP sensor, and an engine temperature sensor. In an embodiment, based on the received data a number of a drive cycle, a riding zone, a rider’s behaviour, and a most recurrent drive cycle is identified. The method comprises determining in real-time an optimal engine operating point, for running diagnostics on the lambda sensor, based on the received data, In an embodiment, the determination is performed at a pre-defined frequency. The method comprises generating in real-time an adaptive perturbation signal, wherein the adaptive perturbation signal controls an injection time of a lean and rich air-fuel mixture. In an embodiment, a fuel injection pulse time period is changed based on an amplitude of the adaptive perturbation signals. The method comprises determining in real-time response values of the lambda sensor in response to the generated adaptive perturbation signal. The method comprises comparing in real-time the response values with a precalculated value of a threshold perturbation. In an embodiment, the precalculated value of the threshold perturbation is a maximum point after which the lambda sensor is diagnosed as aged. The method comprises displaying in real-time the percentage of deterioration of the lambda sensor on an instrument cluster or on an OBD-II diagnostics tool.
According to embodiments illustrated herein, there is provided a system to determine ageing of a lambda sensor for a vehicle. The system comprises a plurality of sensors, and an Electronic control Unit (ECU) communicatively coupled with an instrument cluster. In an embodiment, the plurality of sensors comprises a throttle position sensor, an engine crank angle sensor, a crank shaft speed sensor, a lambda sensor, a MAP sensor, and an engine temperature sensor. The ECU is configured to receive data from the plurality of sensors, wherein based on the received data a number of a drive cycle, a riding zone, a rider’s behaviour, and a most recurrent drive cycle is identified. The ECU is configured to determine in real-time an optimal engine operating point, for running diagnostics on the lambda sensor, based on the received data. In an embodiment, the determination is performed at a pre-defined frequency. The ECU is configured to generate in real-time an adaptive perturbation signal. In an embodiment, the adaptive perturbation signal controls the injection time of a lean and rich mixture. In an embodiment, a fuel injection pulse time period is changed based on an amplitude of the adaptive perturbation signals. The ECU is configured to determine in real-time response values of the lambda sensor for the generated adaptive perturbation signal. The ECU is configured to compare in real-time the response values with a precalculated value of a threshold perturbation. In an embodiment, the precalculated value of the threshold perturbation is a maximum point after which the lambda sensor is diagnosed as aged. The ECU is configured to display in real-time the percentage of deterioration of the lambda sensor on the instrument cluster or on an OBD-II diagnostics tool.
BRIEF DESCRIPTION OF THE DRAWINGS
The present invention will become more fully understood from the detailed description given herein below and the accompanying drawings which are given by way of illustration only, and thus are not limitative of the present invention, and wherein
Figure 1 shows a side elevational view of a vehicle, such as a motorcycle incorporating the invention.
Figure 2 illustrates a block diagram overview for determining ageing of the lambda sensor, in accordance with some embodiments of the present disclosure.
Figure 3 illustrates a block diagram of a signal flow across a plurality of components of the system for determining ageing of the lambda sensor, in accordance with some embodiments of the present disclosure.
Figure 4 illustrates a block diagram overview for virtual lambda sensor using neural networks utilized for determining ageing of the lambda sensor, in accordance with some embodiments of the present disclosure.
Figures 5A and 5B depict a flowchart illustrating a method performed by an electronic control unit (ECU) for determining ageing of the lambda sensor, in accordance with some embodiments of the present disclosure.
Figure 6 depicts a flowchart illustrating a method performed by an electronic control unit (ECU) to perform the lambda sensor diagnostics, in accordance with some embodiments of the present disclosure.
Figure 7 depicts a flowchart illustrating a method performed by an electronic control unit (ECU) for determining in real-time, by the ECU, an optimal engine operating point, for running diagnostics on the lambda sensor, in accordance with some embodiments of the present disclosure.
Figure 8 depicts the generated adaptive perturbation signal, in accordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE DRAWINGS
The present disclosure may be best understood with reference to the detailed figures and description set forth herein. Various embodiments are discussed below with reference to the figures. However, those skilled in the art will readily appreciate that the detailed descriptions given herein with respect to the figures are simply for explanatory purposes as the methods and systems may extend beyond the described embodiments. For example, the teachings presented and the needs of a particular application may yield multiple alternative and suitable approaches to implement the functionality of any detail described herein. Therefore, any approach may extend beyond the particular implementation choices in the following embodiments described and shown.
References to “one embodiment,” “at least one embodiment,” “an embodiment,” “one example,” “an example,” “for example,” and so on indicate that the embodiment(s) or example(s) may include a particular feature, structure, characteristic, property, element, or limitation but that not every embodiment or example necessarily includes that particular feature, structure, characteristic, property, element, or limitation. Further, repeated use of the phrase “in an embodiment” does not necessarily refer to the same embodiment.
The present invention now will be described more fully hereinafter with different embodiments. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather those embodiments are provided so that this disclosure will be thorough and complete, and fully convey the scope of the invention to those skilled in the art.
The present invention is illustrated with a motorcycle type vehicle. However, a person skilled in the art would appreciate that the present invention is not limited to a motorcycle type vehicle and certain features, aspects and advantages of embodiments of the present invention can be used with other types of two wheelers such as scooter type vehicle, step thru, and the like. In an embodiment, the scooter type vehicle comprises a low floorboard type vehicle and the term scooter as used herein should not be inferred to restrict the maximum speed, the displacement amount or the like of the vehicle.
The object of the present subject matter is to provide an age diagnosis method to satisfy the requirements of government’s upcoming norm for automotive industry, OBD-II, for on board diagnosis. Another aspect of the present subject matter determines the age of the catalytic converter based on the diagnosed age of the lambda sensor. Disclosed herein is an onboard diagnosis system that provides information about real-time age of the lambda sensor with existing elements/data and employing it with the indigenously designed algorithm.
Figure 1 shows a side elevational view of a vehicle 100, such as a motorcycle incorporating the invention.
With reference to Figure 1, 100 denotes a vehicle 100, such as a motorcycle, 102 denotes a front wheel, 103 denotes a rear wheel, 104 denotes a front fork, 105 denotes a seat, 106 denotes a rear fork, 107 denotes a leg shield made of resin or metal, 108 denotes a headlight, 109 denotes a tail light, 110 denotes an aesthetic covering, 111 denotes a battery fitted inside the aesthetic covering, 112 denotes a fuel tank, and 113 denotes a handle bar. In an embodiment, a main frame extends along a center of a body of the vehicle from a front portion of the vehicle 100 and extending in a rearwardly direction. The main frame is made up of a metallic pipe.
In an embodiment, the vehicle 100 may be a scooter type vehicle and may have main frame that extends along a center of the body of the vehicle from a front portion of the vehicle and extending in a rearwardly direction. The main frame is made up of a metallic pipe and the main frame is provided under the floorboard for a scooter type vehicle. A swing type power unit is coupled to the rear end of the main frame for a scooter type vehicle. A rear wheel is supported on one side of the rear end of the swing type power unit. In an embodiment, the swing type power unit is suspended in the rear of a body frame for a scooter type vehicle.
The center of the body for a scooter type vehicle forms a low floorboard for functioning as a part for putting feet and a under cowl which is located below a rider's seat and covers at least a part of the engine. In an embodiment, the under cowl is made up of metal or resin. The under cowl is hinged to the seat. Further, a utility box opens from the rear end to hinged portion. In an embodiment, the utility box is provided under the seat extending longitudinally of a vehicle body and the inside of the utility box has a large capacity so that a large article, such as a helmet can be housed. Additionally, in a scooter type vehicle, side covers both on left and right sides, cover the utility box 12 and other parts of the vehicle, thereby providing a good appearance to the vehicle.
Figure 2 illustrates a block diagram overview for determining ageing of the lambda sensor, in accordance with some embodiments of the present disclosure.
The vehicle 100 comprises an electronic control unit (ECU) 216, a plurality of inputs received from a plurality of sensors and an output. The plurality of sensors comprises an engine crank speed sensor 202, a throttle position sensor, a lambda sensor 206, a virtual lambda sensor 208, manifold air pressure sensor and temperature sensor 210, engine temperature sensor 212 and a wheel speed sensor 214. The data captured by each of the plurality of sensors os provided as input to the ECU 216. Further, the output comprises age diagnosis of the lambda sensor 218 and the fuel injection duration based on the perturbation signal 220.
The ECU 216 comprises suitable logic, circuitry, interfaces, and/or code that is configured to determine the optimal engine operating point, generate the adaptive perturbation signal, and determine ageing of a lambda sensor for a vehicle. The ECU 216 may be implemented based on a number of processor technologies known in the art. Examples of the ECU 216 include, but not limited to, an X86-based processor, a Reduced Instruction Set Computing (RISC) processor, an Application-Specific Integrated Circuit (ASIC) processor, a Complex Instruction Set Computing (CIBC) processor, and/or other processor.
The ECU 216 is configured to implement a method for determining ageing of a lambda sensor for a vehicle. The method comprising steps of: receiving, by an Electronic control Unit (ECU), data from a plurality of sensors. In an embodiment, the plurality of sensors comprises a throttle position sensor, an engine crank angle sensor, a crank shaft speed sensor, a lambda sensor, a MAP sensor, and an engine temperature sensor. In an embodiment, based on the received data a number of a drive cycle, a riding zone, a rider’s behaviour, and a most recurrent drive cycle is identified. In an embodiment, data received from the plurality of sensors is passed through one or more filter blocks and the one or more filter blocks is configured to perform signal conditioning, and noise removal.`
The instrument cluster is an interactive part of the vehicle 100 configured to display information associated with the vehicle 100. In an embodiment, the information comprises vehicle speed, current drive mode, menu options, fault codes, instructions for operation, user details, connected phone details, expected driving range of the vehicle 100, battery State of Charge (SOC), Lambda sensor ageing state, lambda sensor ageing percentage and the like. Apart from above mentioned details the instrument cluster also performs functions like controlling display, navigation maps, music control, display incoming call and message details, and the like. In an embodiment, the instrument cluster comprises a plurality of embedded LEDs to provide an indication/alert to the user.
The method further comprises determining in real-time, by the ECU, an optimal engine operating point, for running diagnostics on the lambda sensor, based on the received data, wherein the determination is performed at a pre-defined frequency. In an embodiment, determining the optimal engine operating point comprises: receiving a number of a drive cycle; receiving the pre-defined frequency at which the age of the lambda sensor is to be determined; determining if a predefined condition is satisfied and then receive data from the plurality of sensors. In an embodiment, the predefined condition comprises the number of the drive cycle being a multiple of the pre-defined frequency.
In an embodiment, determining the optimal engine operating point further comprises plotting a mesh grid comprising a plurality of operating points associated with the drive cycle; identifying a most frequent operating point from the plurality of operating points. In an embodiment, the most frequent operating point corresponds to the optimal engine operating point at which the lambda age diagnosis is to be performed.
Further, the ECU validates if one or more conditions are satisfied at the most frequent operating point. In an embodiment, determining the optimal engine operating point the one or more conditions comprises the vehicle is neither accelerating nor decelerating, the vehicle is neither in an idling condition or in a cruising condition but in a window where an engine speed and an intake manifold pressure are relatively constant.
The method further comprises generating in real-time, by the ECU, an adaptive perturbation signal. In an embodiment, the adaptive perturbation signal controls an injection time of a lean and rich air-fuel mixture, and a fuel injection pulse time period is changed based on an amplitude of the adaptive perturbation signals.
In an embodiment, generating the adaptive perturbation signal comprises: defining an initial step size of perturbation reference signal with a predefined percent of base fuel pulse width, wherein a longitudinal jerk of the engine is below a maximum permissible longitudinal jerk of the engine; and iteratively increasing perturbation of the reference signal by a pre-defined percentage when output voltage of the lambda sensor fails to switch from 800 mV to below 450 mV, wherein the perturbation of the reference signal is increased iteratively until switching is not observed in the lambda sensor value.
In an embodiment, the adaptive perturbation signal is a cyclic reference signal comprising at least one of a sine wave, a saw tooth wave, a triangular wave, a square wave, trapezoidal wave, or any custom waveform. Further, generation of the adaptive perturbation signals and identifying optimal operating point is developed and run on an OBD-II compliant ECU. In an embodiment, the ECU is configured to calculate a longitudinal jerk of the engine using the wheel speed sensor and then compare the calculated longitudinal jerk with a maximum threshold to ensure safety of the rider and the vehicle.
The method further comprises determining in real-time, by the ECU, response values of the lambda sensor in response to the generated adaptive perturbation signal. The method further comprises comparing in real-time, by the ECU, the response values with a precalculated value of a threshold perturbation, wherein the precalculated value of the threshold perturbation is a maximum point after which the lambda sensor is diagnosed as aged. The method further comprises displaying in real-time, by the ECU, the percentage of deterioration of the lambda sensor on an instrument cluster or on an OBD-II diagnostics tool.
Further, after determining the ageing of the lambda sensor, the ECU is configured to determine the ageing percentage of the lambda sensor. The method further comprises estimating, by the ECU, a virtual value associated with the lambda sensor based on an engine speed, a throttle value, an ion current, a manifold air pressure, an fuel injection time, and an exhaust temperature using a neural network model.
The method further comprise comparing, by the ECU, the response values with the virtual value; determining, by the ECU, a percentage of error based on comparison of the response values with the virtual value associated with the lambda sensor; and determining, by the ECU, a percentage of deterioration of the lambda sensor based on the percentage of error, wherein the percentage of deterioration is indicative of an ageing of the lambda sensor.
In an embodiment, if the percentage of deterioration is greater than a predefined threshold then the lambda sensor is aged completely, and if the percentage of deterioration is lesser than the predefined threshold then the lambda sensor is partially aged.
Figure 2 represents the overview of the inputs and outputs of the proposed system as a block diagram. Here, the inputs to the system are provided by the sensors: engine crank speed sensor, throttle position sensor, lambda sensor, virtual lambda sensor which is implemented with the help of neural networks model (shown in Fig. 4), manifold air pressure and temperature sensor, engine temperature sensor and wheel speed sensor. These sensors provide the required inputs to the ECU. In an embodiment, the main algorithm for the lambda sensor age diagnosis would be executed by the ECU. Based on the inputs provided by the sensors, the algorithm determines the age of the lambda sensor and the fuel injection duration based on the perturbation signal as an output.
In an embodiment, by implementing the claimed invention, the onboard diagnosis system provides the information about real-time age of the lambda sensor with already existing elements/data and employing it with the indigenously designed algorithm. The system would use data relating to throttle position, engine crank angle, crank shaft speed, lambda sensor value, TMAP and engine temperature. Further, a neural network-based model is implemented to obtain a virtual value of the lambda sensor.
A proposed mechanism for generating adaptive perturbation signals and identifying optimal operating point is developed and would run on an OBD-II compliant ECU at an appropriate engine operating point. Such appropriate engine operating point is identified with the help of data obtained from the sensors mentioned above. The proposed mechanism is implemented to identify particular drive cycle, riding zone, rider’s behaviour and most recurrent drive cycle.
By combining and processing all the mentioned information, an optimal operating point is recognized to execute the lambda sensor age diagnosis algorithm. Further, performance of the lambda sensor is observed at various perturbation signals once the diagnosis is initiated. By observing the response of the lambda sensor with respect to the adaptive perturbation signal, the age of the lambda sensor is determined by comparing it with precalculated value of the threshold perturbation. Threshold perturbation is the maximum point after which the sensor would be diagnosed as aged. The value which is obtained from the lambda sensor is also compared with the virtual lambda sensor value obtained from neural networks. By observing the percentage of error between both the values, the percentage of deterioration of the lambda sensor is determined. The claimed system would be implemented on OBD-II compliant ECU. It would contain input and output connections for reading the sensor data and would execute the algorithm to determine the age of the lambda sensor.
A system is disclosed herein to determine ageing of a lambda sensor for a vehicle, the system comprising: a plurality of sensors comprising a throttle position sensor, an engine crank angle sensor, a crank shaft speed sensor, a lambda sensor, a MAP sensor, and an engine temperature sensor. The system further comprises an Electronic control Unit (ECU) communicatively coupled with an instrument cluster, wherein the ECU is configured to: receive data from the plurality of sensors, wherein based on the received data a number of a drive cycle, a riding zone, a rider’s behaviour, and a most recurrent drive cycle is identified.
The ECU is configured to determine in real-time an optimal engine operating point, for running diagnostics on the lambda sensor, based on the received data, wherein the determination is performed at a pre-defined frequency. The ECU is configured to generate in real-time an adaptive perturbation signal. In an embodiment, the adaptive perturbation signal controls the injection time of a lean and rich mixture, and a fuel injection pulse time period is changed based on an amplitude of the adaptive perturbation signals. The ECU is configured to determine in real-time response values of the lambda sensor for the generated adaptive perturbation signal. The ECU is configured to compare in real-time the response values with a precalculated value of a threshold perturbation, wherein the precalculated value of the threshold perturbation is a maximum point after which the lambda sensor is diagnosed as aged. The ECU is configured to display in real-time the percentage of deterioration of the lambda sensor on an instrument cluster or on an OBD-II diagnostics tool.
The ECU is further configured to estimate a virtual value associated with the lambda sensor based on an engine speed, a throttle value, an ion current, a manifold air pressure, an fuel injection time, and an exhaust temperature using a neural network model. The ECU is configured to compare the response values with the virtual lambda sensor value and determine a percentage of error based on comparison of the response values with the virtual value associated with the lambda sensor. The ECU is configured to determine a percentage of deterioration of the lambda sensor based on the percentage of error. In an embodiment, the percentage of deterioration is indicative of an ageing of the lambda sensor.
Figure 3 illustrates a block diagram of a signal flow across a plurality of components of the system for determining ageing of the lambda sensor, in accordance with some embodiments of the present disclosure.
All the sensor data is passed through the filter blocks which would employ hardware as well as software filters for signal conditioning. The lambda sensor who’s age is to be determined is an upstream narrow band lambda sensor. The sensors implemented for the data acquisition in the proposed methods are typically available on a vehicle except virtual lambda sensor. The virtual lambda sensor is realized by implementing a neural networks model as described in figure 4. The inputs which are utilized for deriving the estimated value of the lambda sensors are engine speed, throttle, ion current, Manifold Air Pressure (MAP), fuel injection time, exhaust temperature and these values are derived from the respective sensors. These inputs are given to the neural networks model which would execute the algorithm for predicting the lambda value based on this data.
Figure 4 illustrates a block diagram overview for virtual lambda sensor using neural networks utilized for determining ageing of the lambda sensor, in accordance with some embodiments of the present disclosure.
Engine crank speed sensor is used to obtain information regarding the engine crank speed as well as crank angle, which would benefit in the procedure of identifying the appropriate operating point for the diagnosis. For the identification of the appropriate operating point, data regarding the throttle position, manifold air pressure and temperature, engine temperature is also utilized in the process. The same can be observed in figure 3 as the input blocks are utilized to determine the suitable operating point in a drive cycle. And this process would execute inside the ECU. The wheel speed sensor is used in the calculation of the longitudinal jerk, which is compared with the maximum threshold of the same considering the safety of the rider and the vehicle which is an extremely important parameter. Depending upon the determined value of permissible jerk, the maximum allowed percentage of perturbation value is estimated. Using this value, suitable and adaptive perturbation signals are generated after receiving the signals for the execution of the diagnosis from the ECU. These perturbation signals are further used for observing the performance of the lambda sensor and determining the age of the same with the help of precalculated perturbation threshold. Perturbation signals would also determine the duration of injection fuel pulse which might cause a small amount of jerk while driving the vehicle. The filtered values received from observed lambda, which is obtained from actual lambda sensor and estimated lambda, which is obtained from the neural networks model [as shown in Figure 4], are compared to give the percentage deterioration of the of the lambda sensor.
A person with ordinary skills in the art will appreciate that the systems, modules, and sub-modules have been illustrated and explained to serve as examples and should not be considered limiting in any manner. It will be further appreciated that the variants of the above disclosed system elements, modules, and other features and functions, or alternatives thereof, may be combined to create other different systems or applications.
Figures 5A and 5B depict a flowchart illustrating a method performed by an electronic control unit (ECU) for determining ageing of the lambda sensor, in accordance with some embodiments of the present disclosure.
The claimed method and system for the real-time age diagnosis of the lambda sensor is an indigenous method which incorporates various modules namely, sensor inputs, neural networks model for the prediction of the precise value of the lambda sensor, advanced mechanism for determining maximum allowed jerk value and a mechanism for generating perturbation signals considering the jerk as well as a technique for identifying the suitable operating point for executing the real-time diagnosis.
The first step which is to gather the data regarding engine crank speed and angle, throttle position, lambda sensor value, pressure and temperature of manifold air, engine temperature and wheel speed is acquired from the respective sensors which are mentioned in the input blocks figure 2 and figure 3 respectively. After collecting the data from sensors, the data is passed through the software and hardware filters in order to reduce the noise by performing signal conditioning. After obtaining the data from the filters, it is processed to identify the most suitable operating point for the diagnosis. Figure 8 describes the technique for the same. The technique disclosed in Figure 8 is executed at a certain frequency rather than executing it at every drive cycle.
Referring to figure 7, x is the number of the drive cycle, which is currently running in the vehicle. n is the frequency at which the algorithm would be executed. Hence, if x is a multiple of n, this algorithm would be executed otherwise it would wait until the particular drive cycle at which the desired frequency of the drive cycle is achieved. Once the desired frequency is achieved, the data which is obtained from the sensors is used to identify the various operating points of in a particular drive cycle. With the help of the data obtained regarding the various operating points, a mesh grid (with respect to the throttle and crankshaft speed data) is populated.
From this plot, an operating point with maximum occurrence and time period is identified. The diagnosis for the age determination of the lambda sensor would be performed at that particular operating point. Once the suitable operating point is identified, the ECU is configured to generate the adaptive perturbation signals is executed. A perturbation signal can be any cyclic reference signal like sine wave, saw tooth wave, triangular wave, square wave or any custom waveform, etc. In the current example, trapezoidal wave is considered as a reference perturbation signal. Figure 8 depicts the generated adaptive perturbation signal, in accordance with some embodiments of the present disclosure.
Referring to figure 6, once the desired operating point is achieved as mentioned in figure 7, adaptive perturbation signal generation would start. The fuel injection pulse time period would change according to the amplitude of the perturbation signals. Hence, higher the perturbation signal amplitude, higher is the fuel injection time period and resultantly the air-fuel mixture would become rich. Vice versa for lean air-fuel mixture. Hence, the amplitude of the reference perturbation signal can cause a longitudinal jerk in the vehicle, which may affect driving experience. Therefore, a wheel speed sensor/IMU/accelerometer is used to determine the maximum allowable jerk considering the rider’s safety. This method makes sure that the rider does not feel considerable amount of interruption while riding the vehicle as well as it also assures the safety of the rider as well as vehicle.
Limiting the jerk to a safe value can also be achieved by capturing data either from an IMU or a wheel speed sensor or an accelerometer. The data which is coming from the wheel speed sensor is double differentiated in order to approximately calculate the longitudinal jerk value. And in case of IMU or accelerometer, the output data would be differentiated once to get the longitudinal jerk data. If the calculated value of the jerk is greater than the maximum allowable jerk value, the method will be immediately terminated and further processes will not be performed. If the jerk is below the precalculated allowable value, the perturbation signal would be generated with an initial step size of predefined percent of base fuel pulse.
Typically, the lambda value is always at 800 mV i.e. in rich condition. As a perturbation signal is given to the fuel injection pulse, it will make air-fuel mixture to go from rich state to lean state, hence the output voltage of the lambda sensor should switch from 800 mV to below 450 mV. If that switching of the voltage is not observed, then the percentage of perturbation is increased by an amount x%. Hence the amplitude of the next perturbation signal would increase by x%. By increasing the amplitude of the reference perturbation signal, the fuel injection pulse time period would also increase, causing some more variations in the composition of the air-fuel mixture. The value of x is increased in every iteration as long as no switching is observed in the lambda sensor value. Because of the characteristic of changing amplitude of the perturbation signal according to the system’s requirement, it has been called as an adaptive reference perturbation signal. The percentage perturbation which is currently being used in the cycle is compared with the threshold perturbation signal to determine whether the sensor is aged or not. This threshold value of the perturbation signal would be precalculated and stored in the ECU in advance.
Once the lambda sensor switching is detected as mentioned in the above procedure, the obtained value of the lambda sensor is compared with the estimated value of the lambda sensor which is obtained from the neural networks model. If the observed lambda value is reasonably equal to the estimated lambda value, then the sensor is working fine and there is no partial deteriorated or ageing. If the observed lambda value is not equal to the estimated lambda value, then the system would calculate the error between the estimated lambda and observed lambda. From this calculated value, the percentage deterioration of the lambda sensor is described. If the perturbation percent of the current reference perturbation signal for this cycle is greater than the threshold perturbation signal, then it is concluded that the lambda sensor is completely aged and needs to be replaced. Otherwise, if it is lesser than the threshold value, the lambda sensor is not completely aged. There is some partial deterioration of the sensor, and it still can be used or replaced according to the user’s desire. This would be performed in block.
Figure 6 depicts a flowchart illustrating a method performed by an electronic control unit (ECU) to perform the lambda sensor diagnostics, in accordance with some embodiments of the present disclosure.
At step 602, the operating point is determined by executing the steps mentioned in figure 7. Step 604, the ECU is configured to define an initial step size of perturbation reference signal with a predefined percent of base fuel pulse width. In an embodiment, a longitudinal jerk of the engine is below a maximum permissible longitudinal jerk of the engine. At step 606, x==0 is set. Further, at step 608 the perturbation is increase by x%. Further, at step 610 x is incremented. At step 612, the ECU is configured to check if the lambda value switched below 450mv. If yes then method proceeds to next step 614, else iteratively perturbation is increased by x%. Further, at step 614, if percentage perturbation is less than threshold perturbation then the lambda sensor is not aged, else it is determined at step 618 that the sensor is aged.
Figure 7 depicts a flowchart illustrating a method performed by an electronic control unit (ECU) for determining in real-time, by the ECU, an optimal engine operating point, for running diagnostics on the lambda sensor, in accordance with some embodiments of the present disclosure.
At step 702, receiving a number of a drive cycle i.e. x. Further, at step 704, receiving the pre-defined frequency ‘n’ at which the age of the lambda sensor is to be determined. At step 706, determining if a predefined condition is satisfied and then receive data from the plurality of sensors. In an embodiment, the predefined condition comprises the number of the drive cycle being a multiple of the pre-defined frequency.
At step 708, receiving, by an Electronic control Unit (ECU), data from a plurality of sensors. In an embodiment, the plurality of sensors comprises a throttle position sensor, an engine crank angle sensor, a crank shaft speed sensor, a lambda sensor, a MAP sensor, and an engine temperature sensor. In an embodiment, based on the received data a number of a drive cycle, a riding zone, a rider’s behaviour, and a most recurrent drive cycle is identified;
At step 710, the ECU is configured to plot a mesh grid comprising a plurality of operating points associated with the drive cycle. At step 712, the ECU is configured to identify a most frequent operating point from the plurality of operating points. In an embodiment, the most frequent operating point corresponds to the optimal engine operating point at which the lambda age diagnosis is to be performed. At step 714, the ECU is configured to validate if one or more conditions are satisfied at the most frequent operating point. In an embodiment, the one or more conditions comprises the vehicle is neither accelerating nor decelerating, the vehicle is neither in an idling condition or in a cruising condition but in a window where an engine speed and an intake manifold pressure are relatively constant. Further, at 716, the ECU is configured for running diagnostics on the lambda sensor at the determined optimal engine operating point.
The terms “an embodiment”, “embodiment”, “embodiments”, “the embodiment”, “the embodiments”, “one or more embodiments”, “some embodiments”, and “one embodiment” mean “one or more (but not all) embodiments of the invention(s)” unless expressly specified otherwise. The terms “including”, “comprising”, “having” and variations thereof mean “including but not limited to”, unless expressly specified otherwise. The terms “a”, “an” and “the” mean “one or more”, unless expressly specified otherwise.
Advantages
The disclosed invention determines whether the lambda sensor is aged or not as well as by what percentage the sensor is aged can also be determined with the help of estimated value obtained from neural networks’ virtual lambda sensor. The proposed diagnosis is executed at most frequent operating point. Because of which, it is made sure that the disclosed method gets adequate time to complete the execution as well as the time to run the diagnosis can be controlled by the system.
Further, driver’s safety is also guaranteed by limiting the jerk value to the safety limits and hence limiting the percentage of perturbation signal. As the claimed method do not require any additional components or elements, it is also cost effective. Further, the claimed solution is a novel adaptive approach which is implemented by designing an indigenous algorithm for determining the age of the lambda sensor. The claimed solution also utilizes neural networks for the virtual lambda sensor value estimation which is a very unique approach.
The claimed invention requires the system behavior modelling, identification of different drive cycles and operating points as well as selecting the most appropriate one for the proposed diagnosis which is not obvious. The claimed invention is cost effective as no other extra elements are required for the implementation of the proposed solution. The claimed invention identifies a particular operating point in real-time at which the results of the diagnosis are most reliable, and the diagnosis is executed onboard at that operating point instead of executing it at any random drive cycle where the results might not be reliable. Also, it is taken care of that the rider feels least amount of disruption because of the presence of perturbation signals.
Rather than just depending on the diagnosis of the symmetry of the lambda sensor, the claimed invention of diagnosis also determines the age of the lambda sensor in real-time. The claimed invention not just determines whether the lambda sensors is aged or not but also diagnoses the percentage by which the sensor is deteriorated using the neural network model. Further, the claimed invention does not depend on performance of any other lambda sensor for comparing with as a reference, thus reduced failure points in the system.
Additionally, the claimed invention can be applied to 2W, 3W and 4W industry as well as any other industry where a narrow band sensor is implemented for determining the concentration of gases. In an embodiment, the claimed invention may be implemented for satisfying the OBD-II norms. Additionally, the claimed technology would be extremely beneficial for intelligent mobility in transport.
In light of the above-mentioned advantages and the technical advancements provided by the disclosed method and system, the claimed steps as discussed above are not routine, conventional, or well understood in the art, as the claimed steps enable the following solutions to the existing problems in conventional technologies. Further, the claimed steps clearly bring an improvement in the functioning of the device itself as the claimed steps provide a technical solution to a technical problem.
A description of an embodiment with several components in communication with a other does not imply that all such components are required, On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention,
Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter and is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the embodiments of the present invention are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.
While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.
The present disclosure may be realized in hardware, or a combination of hardware and software. The present disclosure may be realized in a centralized fashion, in at least one computer system, or in a distributed fashion, where different elements may be spread across several interconnected computer systems, a computer system or other apparatus adapted for carrying out the methods described herein may be suited. A combination of hardware and software may be a general-purpose computer system with a computer program that, when loaded and executed, may control the computer system such that it carries out the methods described herein. The present disclosure may be realized in hardware that comprises a portion of an integrated circuit that also performs other functions.
A person with ordinary skills in the art will appreciate that the systems, modules, and sub-modules have been illustrated and explained to serve as examples and should not be considered limiting in any manner. It will be further appreciated that the variants of the above disclosed system elements, modules, and other features and functions, or alternatives thereof, may be combined to create other different systems or applications.
Those skilled in the art will appreciate that any of the aforementioned steps and/or system modules may be suitably replaced, reordered, or removed, and additional steps and/or system modules may be inserted, depending on the needs of a particular application. In addition, the systems of the aforementioned embodiments may be implemented using a wide variety of suitable processes and system modules, and are not limited to any particular computer hardware, software, middleware, firmware, microcode, and the like. The claims can encompass embodiments for hardware and software, or a combination thereof.
While the present disclosure has been described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the scope of the present disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from its scope. Therefore, it is intended that the present disclosure not be limited to the particular embodiment disclosed, but that the present disclosure will include all embodiments falling within the scope of the appended claims.
,CLAIMS:Claims
I / We claim

1. A method for determining ageing of a lambda sensor for a vehicle, the method comprising steps of:
receiving (502), by an Electronic control Unit (ECU), data from a plurality of sensors, wherein the plurality of sensors comprises a throttle position sensor, an engine crank angle sensor, a crank shaft speed sensor, a lambda sensor, a MAP sensor, and an engine temperature sensor, wherein based on the received data a number of a drive cycle, a riding zone, a rider’s behaviour, and a most recurrent drive cycle being identified;
determining (503) in real-time, by the ECU, an optimal engine operating point, for running diagnostics on the lambda sensor, based on the received data, wherein the determination is performed at a pre-defined frequency;
Generating (508) in real-time, by the ECU, an adaptive perturbation signal, wherein the adaptive perturbation signal controls an injection time of a lean and rich air-fuel mixture, and wherein a fuel injection pulse time period being changed based on an amplitude of the adaptive perturbation signals
determining (510)in real-time, by the ECU, response values of the lambda sensor in response to the generated adaptive perturbation signal;
comparing (512) in real-time, by the ECU, the response values with a precalculated value of a threshold perturbation, wherein the precalculated value of the threshold perturbation is a maximum point after which the lambda sensor is diagnosed as aged; and
displaying (532, 534) in real-time, by the ECU, the percentage of deterioration of the lambda sensor on one or more of an instrument cluster and an OBD-II diagnostics tool.

2. The method as claimed in claim 1, comprising
estimating, by the ECU, a virtual value associated with the lambda sensor based on an engine speed, a throttle value, an ion current, a manifold air pressure, an fuel injection time, and an exhaust temperature using a neural network model;
comparing (520), by the ECU, the response values with the virtual value;
computing (526), by the ECU, a percentage of error based on comparison of the response values with the virtual value associated with the lambda sensor; and
determining (528), by the ECU, a percentage of deterioration of the lambda sensor based on the percentage of error, wherein the percentage of deterioration being indicative of an ageing of the lambda sensor.
3. The method as claimed in claim 1, wherein when the percentage of deterioration being greater than a predefined threshold then the lambda sensor being aged completely, and when the percentage of deterioration being lesser than the predefined threshold then the lambda sensor being partially aged.
4. The method as claimed in claim 1, wherein the adaptive perturbation signal being a cyclic reference signal comprising at least one of a sine wave, a saw tooth wave, a triangular wave, a square wave, trapezoidal wave, or any custom waveform.
5. The method as claimed in claim 5, comprising calculating a longitudinal jerk of the engine using the wheel speed sensor.
6. The method as claimed in claim 5, comparing the calculated longitudinal jerk with a maximum threshold to ensure safety of the rider and the vehicle.
7. The method as claimed in claim 1, wherein the data received from the plurality of sensors is passed through one or more filter blocks, wherein the one or more filter blocks being configured to perform signal conditioning, and noise removal.

8. The method as claimed in claim 1, wherein generating the adaptive perturbation signal comprises:
defining (604) an initial step size of perturbation reference signal with a predefined percent of base fuel pulse width, wherein a longitudinal jerk of the engine being below a maximum permissible longitudinal jerk of the engine; and
iteratively increasing (606) perturbation of the reference signal by a pre-defined percentage when output voltage of the lambda sensor fails to switch from 800 mV to below 450 mV, wherein the perturbation of the reference signal being increased iteratively until no switching being observed in the lambda sensor value.
9. The method as claimed in claim 1, wherein determining the optimal engine operating point comprises:
receiving (702) a number of a drive cycle;
receiving (704) the pre-defined frequency at which the age of the lambda sensor being determined
determining (706) whether a predefined condition being satisfied and then receive data from the plurality of sensors, wherein the predefined condition comprises the number of the drive cycle being a multiple of the pre-defined frequency;
plotting (710) a mesh grid comprising a plurality of operating points associated with the drive cycle;
identifying (712) a most frequent operating point from the plurality of operating points, wherein the most frequent operating point corresponds to the optimal engine operating point at which the lambda age diagnosis being performed;
validating (714) when one or more conditions being satisfied at the most frequent operating point, wherein the one or more conditions comprises the vehicle being neither accelerating nor decelerating, the vehicle being neither in an idling condition or in a cruising condition but in a window wherein an engine speed and an intake manifold pressure being relatively constant.

10. The method as claimed in claim 1, wherein generation of the adaptive perturbation signals and identifying optimal operating point being developed and run on an OBD-II compliant ECU.
11. A system to determine ageing of a lambda sensor for a vehicle, the system comprising:
a plurality of sensors comprising a throttle position sensor, an engine crank angle sensor, a crank shaft speed sensor, a lambda sensor, a MAP sensor, and an engine temperature sensor,
an Electronic control Unit (ECU) communicatively coupled with an instrument cluster, wherein the ECU being configured to:
receive data from the plurality of sensors, wherein based on the received data a number of a drive cycle, a riding zone, a rider’s behaviour, and a most recurrent drive cycle being identified;
determine in real-time an optimal engine operating point, for running diagnostics on the lambda sensor, based on the received data, wherein the determination being performed at a pre-defined frequency;
generate in real-time an adaptive perturbation signal, wherein the adaptive perturbation signal controls the injection time of a lean and rich mixture, and wherein a fuel injection pulse time period is changed based on an amplitude of the adaptive perturbation signals;
determine in real-time response values of the lambda sensor for the generated adaptive perturbation signal;
compare in real-time the response values with a precalculated value of a threshold perturbation, wherein the precalculated value of the threshold perturbation being a maximum point after which the lambda sensor being diagnosed as aged;
display in real-time the percentage of deterioration of the lambda sensor on one or more of an instrument cluster and an OBD-II diagnostics tool.
12. The system as claimed in claim 11, wherein the ECU is configured to:
estimate a virtual value associated with the lambda sensor based on an engine speed, a throttle value, an ion current, a manifold air pressure, an fuel injection time, and an exhaust temperature using a neural network model;
compare the response values with the virtual lambda sensor value;
determine a percentage of error based on comparison of the response values with the virtual value associated with the lambda sensor; and
determine a percentage of deterioration of the lambda sensor based on the percentage of error, wherein the percentage of deterioration is indicative of an ageing of the lambda sensor.