FORM 2
THE PATENTS ACT, 1970
[39 OF 1970]
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
[See section 10 and Rule 13]
TITLE: “A CONTROL SYSTEM AND A METHOD OF CONTROLLING A VEHICLE”
Name and Address of the Applicant: TATA MOTORS LIMITED, an Indian company having its registered office at Bombay House, 24 Homi Mody Street, Hutatma Chowk, Mumbai 400 001, Maharashtra
Nationality: Indian
The following specification particularly describes the invention and the manner in which it is to be performed.

TECHNICAL FIELD
[001] The present disclosure relates in general to automobiles. Particularly, but not exclusively, the present disclosure relates to control system and method for estimating moving mass of the vehicle and controlling the vehicle.
BACKGROUND
[002] Generally vehicles are controlled based on various parameters. Vehicle mass plays a vital important role in controlling the vehicle. For example, vehicle mass has to be accurately estimated for commercial vehicles as they carry heavy load. Also, in autonomous or semi-autonomous vehicles where automatic controls are applied, the vehicle mass information is used for controlling throttle and gear to achieve vehicle performance and fuel economy. Vehicle’s unladen mass can be obtained from vehicle specification. However, there is a challenge in obtaining vehicle moving mass (mass when the vehicle is loaded).
[003] In existing vehicles, specific sensor is used to estimate the moving mass of the vehicle. However, there is a significant challenge in integrating the sensor with the electronics of the vehicle. As electronics of different vehicle makes are different, the sensor compatibility with the electronics of the vehicle is an issue. Also, the additional sensor adds cost and complexity to the vehicle system. Further, considering the conditions in which the vehicle is driven, the sensor needs to be replaced often.
[004] Hence, there is a need to estimate the moving mass of the vehicle using existing information in the vehicle.
[005] The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgment or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
SUMMARY

[006] Additional features and advantages are realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure.
[007] Disclosed herein is a method of controlling a vehicle using vehicle mass estimate. The method comprises obtaining by an Electronic Control Unit (ECU) of the vehicle one or more parameters of the vehicle. The one or more parameters may comprises at least one of, an engine Rotations Per Minute (RMP) value, a velocity value, a torque value, a clutch switch value and a brake switch value. Further, the method comprises determining a vehicle mass estimate based on the one or more parameters of the vehicle using a mathematical model. The mathematical model is used to first predict a state space model using the one or more parameters and a noise parameter. Further, the statistical model is used to update the state space model based on recursive estimation of the noise parameter until the noise parameter is within a threshold range. Thereafter, the vehicle is controlled based on the vehicle mass estimate.
[008] Further, the present disclosure discloses a control system. The control system includes a communication network, a memory and an Electronic Control Unit (ECU). The communication network may receive one or more parameters of the vehicle from one or more existing sensors in the vehicle, in real-time. The one or more parameters may comprise at least one of, an engine Rotations Per Minute (RMP) value, a velocity value, a torque value, a clutch switch value and a brake switch value. The ECU obtains the one or more parameters from the communication network and determines a vehicle mass estimate using a statistical model. The mathematical model is used to first predict a state space model using the one or more parameters and a noise parameter. Further, the statistical model is used to update the state space model based on recursive estimation of the noise parameter until the noise parameter is within a threshold range. Thereafter, the ECU controls the vehicle based on the vehicle mass estimate.
[009] 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 may become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
[0010] The novel features and characteristic of the disclosure are set forth in the appended claims. The disclosure itself, however, as well as a preferred mode of use, further objectives, and advantages thereof, may best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. One or more embodiments are now described, by way of example only, with reference to the accompanying figures wherein like reference numerals represent like elements and in which:
[0011] Fig. 1 shows an exemplary block diagram of a control system for estimating mass of a vehicle, in accordance with some embodiments of the present disclosure;
[0012] Fig. 2 shows a detailed block diagram of an ECU for estimating mass of a vehicle, in accordance with some embodiments of the present disclosure;
[0013] Fig. 3 shows a flowchart illustrating method steps for controlling a vehicle using vehicle mass estimate, in accordance with some embodiments of the present disclosure; and
[0014] Fig. 4 illustrates forces acting on a vehicle, in accordance with some embodiments of the present disclosure.
[0015] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it may be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes, which may be substantially represented in computer readable medium and executed by a computer or processor, whether or not such computer or processor is explicitly shown.
DETAILED DESCRIPTION

[0016] In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
[0017] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and may be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the scope of the disclosure.
[0018] The terms “comprises”, “includes” “comprising”, “including” or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” or “includes…a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.
[0019] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.
[0020] Fig. 1 shows an exemplary block diagram of a control system (100) for estimating mass of a vehicle. In an embodiment, the control system (100) estimates moving mass of the vehicle. The moving mass may be the mass of the vehicle carrying a load. For example, a commercial vehicle carrying load such as sand, furniture, steel and the like. Another example includes a vehicle carrying passengers. Th control system (100) comprises a communication network (102) and an

Electronic Control Unit (ECU) (103). The control system (100) may be integrated with vehicle electronics such as one or more existing sensors (101a, 101b, 101c, 101d) in the vehicle. The one or more existing sensors (101a, 101b, 101c, 101d) may be the sensors that are provided by vehicle Original Equipment Manager (OEM). The one or more existing sensors (101a, 101b, 101c, 101d) may include but not limited to, an engine Rotations Per Minute (RPM) sensor, a torque sensor, a speed sensor, a clutch pedal sensor, a brake pedal sensor, and the like. Fig.1 discloses four sensors (101a, 101b, 101c, 101d) for illustration purposes only and should not be considered as a limitation.
[0021] In an embodiment, communication network (102) enables communication between different electronic components of the vehicle. The electronic modules includes at least one of the ECU (103), a Transmission Control Unit (TCU) (not shown), a Body Control Unit (BCU) (not shown), an Anti-Braking System (ABS) (not shown), one or more actuators (not shown). For example, the communication network (102) enables communication between the ECU (103) and the one or more sensors (101a, 101b, 101c, 101d) and the one or more actuators. The communication network (102) may use protocols such as Controller Area Network (CAN), CAN Flexible Data rate (CAN-FD), Local Interconnect Network (LIN), Ethernet, Flex Ray and the like. The communication network (102) may comprise a single wire, a twisted pair cable, a fiber optic cable, and the like for transmitting signals between the electronic components.
[0022] In an embodiment, the ECU (103) is configured to receive one or more parameters of the vehicle via the communication network (102). The one or more parameters of the vehicle may include at least one of, an engine RPM value, a torque value, a velocity value, a clutch switch value and a brake switch value. The ECU (103) processes the one or more parameters and estimates a moving mass of the vehicle. The ECU (103) uses a statistical model to estimate the moving mass of the vehicle, unlike conventional techniques where specific sensors, such as a road grade sensor, a load sensor are used to estimate moving mass of the vehicle. The estimated moving mass of the vehicle is then used to control the vehicle. For example, throttle valve may be controlled based on the moving mass to save fuel. In case of an autonomous vehicle, steering, acceleration and brake of the vehicle may be controlled using the moving mass of the vehicle. Also, torque and gear shift may be controlled using the moving mass of the vehicle.

[0023] Fig. 2 shows a detailed block diagram of the ECU (103). The ECU (103) may include Central Processing Unit (“CPU” or “processor”) (203) and a memory (202) storing instructions executable by the processor (203). The processor (203) may include at least one data processor for executing program components for executing user or system-generated requests. The memory
(202) may be communicatively coupled to the processor (203). The ECU (103) further includes an Input/ Output (I/O) interface (201). The I/O interface (201) may be coupled with the processor
(203) through which an input signal or/and an output signal may be communicated.
[0024] In some embodiments, ECU (103) comprises modules (209). The modules (209) may be stored within the memory (202). In an example, the modules (209) are communicatively coupled to the processor (203) configured in the computing system (102), may also be present outside the memory (202) as shown in Fig. 2 and implemented as hardware. As used herein, the term modules (209) may refer to an application specific integrated circuit (ASIC), a Field Programmable Gate Array (FPGA ), an electronic circuit, a processor (203) (shared, dedicated, or group), and memory (202) that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. In some other embodiments, the modules (209) may be implemented using at least one of ASICs and FPGAs.
[0025] In an embodiment, the I/O interface (201) may enable communication between the ECU (103) and the electronic components. The I/O interface (201) may include at least one of, a CAN port, an Ethernet port, a Flex Ray port, and the like.
[0026] In one implementation, the modules (209) may include, for example, am acceleration estimator (205), a gear estimator (206), a prediction module (207), a recursive mass estimator (208) and other modules (209). It may be appreciated that such aforementioned modules (209) may be represented as a single module or a combination of different modules (209).
[0027] In an embodiment the acceleration estimator (205) is used to estimate acceleration value of the vehicle using the one or more parameters. The ECU (103) estimates the acceleration value using velocity value of the vehicle. In an embodiment, the speed sensor (e.g., 101a) may provide

velocity value of the vehicle. The velocity value captured at different time instances may be used to estimate the acceleration value of the vehicle.
[0028] In an embodiment, the gear estimator (206) is used to estimate gear of the vehicle using the one or more parameters. The gear of the vehicle is estimated using parameters such as gear ratio, the engine RPM, a dynamic load radius, the velocity value and rear axel ratio. In an embodiment, the one or more parameters may be obtained from the one or more sensors (101a, 101b, 101c, 101d) and/ or a specification of the vehicle.
[0029] In an embodiment, a state space matrix may be generated using a state space model. The state space matrix may comprise a state variable and one or more measurement variables. The one or more measurement variables may be the one or more parameters measured using the one or more sensors (101a, 101b, 101c, 101d) and the state variable may be some function of the mass of the vehicle. The mass of the vehicle may be represented as the state variable as the value of the mass determined using the one or more parameters may vary over time.
[0030] In an embodiment, the recursive mass estimator (208) determines vehicle mass estimates at predefined intervals. For example, the recursive estimator (208) may determine the vehicle mass estimate every 100 ms. The recursive mass estimator (208) may use the relationship defined in the state space matrix to determine initial vehicle mass estimate. The initial vehicle mass estimate may be inaccurate due to noise parameter. In an embodiment, the noise parameter may be a function of road gradient and the vehicle mass. The noise in the vehicle mass may be due to inaccurate measurement of the one or more parameters or due to delay in measurement or processing the measured data.
[0031] In an embodiment, the prediction module (207) predicts the state variable (vehicle mass estimate) based on previous vehicle mass estimates. For example, the prediction module (207) may consider a mean value or a standard deviation of previous 100 vehicle mass estimate to predict the state variable. Also, a variance of the vehicle mass estimate may be used to predict the state variable. In an embodiment, the predicted state variable may be noisy due to the noise parameter.

[0032] In an embodiment, the recursive mass estimator (208) further filters the noise variable by recursively estimating the state variable, thereby recursively estimating the mass of the vehicle, which is among one of state variables. As the measured state variable like the vehicle speed is obtained from the ECU, the other state variables like mass and grade may also be obtained. In an embodiment, a Kalman gain is determined, which is a function of the noise parameter. A high Kalam gain indicates that latest measurements carry more weight and the estimates are based more on the latest measurements. Therefore, the recursive mass estimator (208) ensures that noise is reduced from the predicted state variable and the vehicle mass estimate is accurate by recursively filtering the vehicle mass estimate. The recursive loop may be stopped when the noise parameter is within a threshold range.
[0033] In an embodiment, the recursive estimator (208) and the prediction module (207) may be implemented using Extended Kalman Filter (EKF).
[0034] In an embodiment, the resulting vehicle mass estimate is then used to control the vehicle. In an embodiment, the other modules (209) may include control modules to control the vehicle. For example, the control module may be a throttle controller, a torque controller, or any other actuator of the vehicle. The other modules (209) may also include a Huan Machine Interface (HMI) for displaying the moving mass of the vehicle. A driver of the vehicle may then control the vehicle based on the moving mass. For example, when the moving mass is found to be more than an expected value, the driver may drive the vehicle slowly, especially in high gradient roads and curved roads.
[0035] Fig. 3 shows a flowchart illustrating a method for controlling the vehicle by estimating mass of the vehicle, in accordance with some embodiment of the present disclosure. The order in which the method (300) may be described is not intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the spirit and scope of the subject matter described herein. Furthermore, the method may be implemented in any suitable hardware, software, firmware, or combination thereof.

[0036] At the step (301), the control system (100) obtains the one or more parameters of the vehicle from the one or more existing sensors (101a, 101b, 101c, lOld) in real-time. As described, the one or more parameters includes, the velocity value, the torque value, the engine RMP value, the clutch switch value and the brake switch value. Further, the one or more parameters are obtained at regular intervals.
[0037] At step (302), the control system (100) determines the vehicle mass estimate using the one or more parameters. The control system (100) may use statistical model such as the EKF to determine the vehicle estimates. The EKF may require a state space representation to predict the state variable based on previous measurements. Hence, the state space model is used to generate the state space matrix based on a mathematical expression as given below:
Σ F = mv
[0038] Fig. 4 shows the different forces acting on a vehicle. Accordingly, equation (1) can be re-written as:
[0040] A general representation of state space matrix is given in below equation:
F'tractive - Fdrag - Froll - Fgravity = mv [0039] By considering the equation (2) and solving for all forces:



[0041] Upon substituting equations (3) to (6) in equation (7), a state space matrix for the vehicle is generated:

[0042] In the equation (10), Xi is the state variable (vehicle mass estimate) and the parameters in the right hand side of the equation are the measurements of the one or more parameters and the unknown parameter road gradient.
[0043] The control system (100) further predicts the state variable based on previously estimated vehicle mass estimate. As described before, the prediction can be made by considering a mean value or a standard deviation, and a variance of the previous vehicle mass estimates. The following equations represent the prediction of state variables (V,ξ m, ξ g ).
Xi =f(xi_1,ui_l,ai_1) Pi = FjPi-1FjT+ WjQWjT
[0044] Further, as the predicted vehicle mass is based on the previous vehicle mass estimates, an embodiment, the predicted state variable may be noisy due to the noise parameter. In an embodiment, the control system (100) filters the predicted state variable by recursively estimating

the state variable. As the number of estimates of the noise parameter is obtained, the noise parameter can be easily filtered. In an embodiment, a Kalman gain is determined, which is a function of the noise parameter. Therefore, the recursive mass estimator (208) ensures that noise is reduced from the predicted state variable and the vehicle mass estimate is accurate by recursively filtering the vehicle mass estimate. The recursive loop may be stopped when the noise parameter is within a threshold range. The following equations denote the updated state variable:

Xupdated = Xi + K (Yi - Cxi )
Pupdated = (I — KC ) Pi
[0045] The equations (14) and (15) are used to then determine the vehicle mass estimate. In an embodiment, 100 previous vehicle mass estimates may be considered for filtering the noise parameter. In an embodiment, the estimated moving mass of the vehicle is locked after a standard deviation of the vehicle mass estimate is within a predefined range or after a convergence time period. For example, when 600 vehicle mass estimates are within a standard deviation of certain range, the moving mass of the vehicle is locked and is considered for controlling the vehicle.
[0046] At step (303), the control system (100) controls the vehicle based on the locked mass of the vehicle. In one embodiment, the locked mass may be displayed on the HMI of the vehicle. In an embodiment, the control system (100) may control throttle of the vehicle, torque of the vehicle based on the locked mass.
[0047] 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. [0048] The terms "including", "comprising", "having" and variations thereof mean "including but not limited to", unless expressly specified otherwise.

[0049] The enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise. The terms "a", "an" and "the" mean "one or more", unless expressly specified otherwise.
[0050] A description of an embodiment with several components in communication with each 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.
[0051] When a single device or article is described herein, it may be readily apparent that more than one device/article (whether or not they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it may be readily apparent that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices, which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself.
[0052] The illustrated operations of Fig. 3 and Fig. 5 show certain events occurring in a certain order. In alternative embodiments, certain operations may be performed in a different order, modified, or removed. Moreover, steps may be added to the above described logic and still conform to the described embodiments. Further, operations described herein may occur sequentially or certain operations may be processed in parallel. Yet further, operations may be performed by a single processing unit or by distributed processing units.
[0053] 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. It 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 disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

[0054] While various aspects and embodiments have been disclosed herein, other aspects and embodiments may 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.
REFERRAL NUMERALS:

Reference number Description
100 Control system
101a, 101b, 101c, 101d Sensors
102 Communication network
103 ECU
201 I/O interface
202 Memory
203 Processor
204 Modules
205 Acceleration estimator
206 Gear estimator
207 Prediction Module
208 Recursive mass estimator
209 Other modules

We claim:
1. A method for controlling a vehicle, the method comprising:
obtaining, by an Electronic Control Unit (ECU), one or more parameters of the vehicle in real-time, wherein the one or more parameters comprises at least a engine Rotations Per Minute (RMP) value, a velocity value, a torque value, a clutch switch value, and a brake switch value;
determining, by the ECU, a vehicle mass estimate based on the one or more parameters using a statistical model, wherein determining the vehicle mass estimate comprises:
predicting a state variable of a state space model using the one or more parameters
and a noise parameter; and
updating the state variable based on recursive estimation, until the estimation of the
vehicle mass has standard deviation within a certain limit;
and
controlling, by the ECU, the vehicle based on the vehicle mass estimate.
2. The method as claimed in claim 1, wherein the statistical model is an Extended Kalman Filter (EKF).
3. The method as claimed in claim 1, wherein the noise parameter is a function of an estimated road grade value and an estimated vehicle mass value.
4. The method as claimed in claim 1, wherein the vehicle mass estimate is locked after a convergence time period, wherein the vehicle is controlled after the vehicle mass estimate is locked.
5. A control system for a vehicle, the system comprising:
a communication network configured to receive one or more parameters of the vehicle from one or more existing sensors in the vehicle in real-time, wherein the one or more parameters comprises at least an engine Rotations Per Minute (RPM) value, a velocity value, a torque value, a clutch switch value, and a brake switch value; and
an Electronic Control Unit (ECU) configured to:

obtain one or more parameters of the vehicle in real-time;
determine a vehicle mass estimate based on the one or more parameters using a mathematical model, wherein for determining the vehicle mass estimate, the ECU is configured to:
predict a state variable of a state space model using the one or more
parameters and a noise parameter;
update the state variable based on recursive estimation, until the estimation
of the vehicle mass has standard deviation within certain limit.;
and
control the vehicle based on the vehicle mass estimate.
6. The control system as claimed in claim 6, wherein the ECU at least one of, receives the one or more parameters and calculates the one or more parameters using measurements received from one or more existing sensors of the vehicle.
7. The control system as claimed in claim 6, wherein ECU implements an Extended Kalman Filter (EKF) as the statistical model.
8. The control system as claimed in claim 6, wherein the ECU locks the vehicle mass estimate after a convergence time period, wherein ECU controls the vehicle after the vehicle mass estimate is locked.
9. The control system as claimed in claim 6, wherein the ECU controls at least, a fuel supply to an engine of the vehicle, a torque of the vehicle, and a steering of the vehicle.