Description:Complete Specification:
The following specification describes and ascertains the nature of this invention and the manner in which it is to be performed.

Field of the invention:
[0001] The present invention relates to a computing device and method to estimate carbon dioxide (CO2) emission from vehicles.

Background of the invention:
[0002] Due to the climate change, emissions of greenhouse gases like carbon dioxide and methane has led to an average increase in the global temperatures. The carbon dioxide (CO2) released by the excess burning of fossil fuels is a major contributor for global warming. The fossil fuels are the main energy source in power, manufacturing and the transport sectors. The advent of electric vehicles, with zero emissions, is a promising avenue towards a more sustainable approach to curb excess CO2 in the atmosphere. However, this complete transition is predicted to take place in a few decades. Currently it is necessary to monitor the vehicular CO2 production in order to control/limit its impact on the climate.

[0003] According to state of the art, the current CO2 estimation and modeling methods rely mainly on speed, congestion data along with specialized sensors. The speed and acceleration, though highly corelated with CO2 emission, can only provide limited information into a vehicle’s emission characteristics. The special sensors for CO2 modeling also may be accurate but not very scalable. Thus, these are not suitable for large scale applications. Again, the methods of CO2 estimation in the literature are not able to monitor individual vehicle emissions effectively. Finally, telematics based prediction systems were considered in the literature like insurance risk prediction, driver identification, etc. Most emissions are from road transport, i.e. vehicular emissions. To control vehicular emission, first, an efficient emission monitoring system is required. A direct sensor installation in individual vehicles is neither cost effective nor the data is easy to collect. Further, there exists mileage and distance based CO2 estimation and speed and acceleration based CO2 estimation.

[0004] According to a prior art 202241026881 a design system of IoT driven to estimating CO2 gas emissions using machine learning for smart city is disclosed. Because of the fast spread of the Internet of Things (IoT) in the 5G era, the establishment of smart cities throughout the world is an important avenue for global low-carbon city research. Carbon dioxide emissions in small cities are frequently greater than in large and medium cities. Due to enormous disparities in data settings between small and medium-large cities, insufficient IoT hardware, and high input costs, the development of a small city smart carbon monitoring platform has yet to be completed. CO2 emissions from fossil fuel burning are a substantial contributor to atmospheric changes and climate change. General CO2 estimations can be estimated by considering the weight moved, distance travelled, time spent, and emissions factor of each individual vehicle, and (ii) by considering the weight moved, distance travelled, time spent, and emissions factor of each unique vehicle. These approaches are inefficient for measuring the influence of automobiles on CO2 in cities since vehicles emit CO2 in a variety of ways depending on their efficiency, producer, fuel, weight, driver style, road conditions, seasons, and other factors. Thanks to today's technology, it is now possible to collect real-time traffic data to obtain critical information that may be used to measure changes in carbon emissions. The research investigated the sources of CO2 emissions in the air under different traffic conditions. We present a model and method for computing CO2 emissions from traffic flow data in both non-congested and congested environments. These traffic scenarios each contribute a different amount of CO2 to the atmosphere, resulting in a different emissions factor. The system was tested in the city of Florence, where CO2 levels are measured by sensors at a few places with over 100 traffic flow sensors (data accessible on the Snap4City platform). The solution allowed for the assessment of seasonal fluctuations in the emissions factor and accuracy, as well as the computation of CO2 from the traffic flow. The provided model and solution might be used to calculate the CO2 distribution in the city.

Brief description of the accompanying drawings:
[0005] An embodiment of the disclosure is described with reference to the following accompanying drawings,
[0006] Fig. 1 illustrates a block diagram of a computing device to estimate carbon dioxide (CO2) emission from vehicles, according to an embodiment of the present invention;
[0007] Fig. 2 illustrates a block diagram for the estimation model in the computing device, according to an embodiment of the present invention, and
[0008] Fig. 3 illustrates a method for estimating carbon dioxide (CO2) emission from vehicles, according to the present invention.

Detailed description of the embodiments:
[0009] Fig. 1 illustrates a block diagram of a computing device to estimate carbon dioxide (CO2) emission from vehicles, according to an embodiment of the present invention. The computing device 120 configured to receive input signals comprising operational parameters 102 from an internal network 104 such as a Controller Area Network (CAN) bus of the vehicle 100 or sensors directly interfaced with the computing device 120. The computing device 120, characterized in that, configured to process the operational parameters 102 using an estimation model 116. The estimation model 116 is based on a Temporal Convolutional Network (TCN). The computing device 120 then estimates the CO2 emission from the vehicle 100. The computing device 120 further configured to compare the estimated CO2 with a threshold CO2 level 118 stored in a memory element 106. The computing device 120 then triggers an action 114 if the estimated CO2 exceeds the threshold CO2 level 118. Apart from TCN, Deep Neural Networks (DNN), Convolutional Neural Networks (CNN) and Long short Term Memory (LSTM) network are also usable.

[0010] According to an embodiment of the present invention, the operational parameters 102 is selected from engine load, fuel flow, mileage, vehicle speed, fuel flow and acceleration. Moreover, each of the operational parameters 102 are measured using respective sensors in the vehicle 100 or derived using measured parameters.

[0011] According to an embodiment of the present invention, the computing device 120 applies transfer learning to the TCN based estimation model 116 to scale the estimation model 116 to another type of vehicle 100 having limited data. The type of vehicle 100 corresponds to cars, trucks, buses, off-road vehicles 100 for agriculture , etc. The TCN based estimation model 116 predicts/estimates the present CO2 emissions which outperforms several other prediction techniques mentioned above. The transfer learning technique employed after initial training makes the estimation model 116 more scalable to be applied to different types of vehicle 100 and is done using very little data from the corresponding type of vehicle 100. The computing device 120 makes the prediction/estimation model 116 usable with different vehicles 100, i.e. it is easily scalable with the limited amount of data available, with the use of TCN based estimation model 116.

[0012] In accordance to an embodiment of the present invention, the computing device 120 is at least one selected from a group comprising an internal device comprising an Electronic Control Unit (ECU) 110 of the vehicle 100, and an external device comprising a cloud 108 based device and a communication device 112 and a combination thereof. The internal device denotes that the computing device 120 is internal or part of the vehicle 100. Similarly, the external device denotes that the computing device 120 is externally interfaced with the vehicle 100 and is generally not part of the vehicle 100. The ECU 110 (or controller) is at least one of an Engine Management System (EMS) controller, a Tire Pressure Monitoring System (TPMS) controller, a Telematics Control Unit (TCU) controller, Anti-lock Braking System (ABS) ECU 110, a Body Control Unit (BCU), a Human-Machine Interface (HMI) cluster unit, other vehicular controllers and a combination thereof. The communication device 112 corresponds to electronic computing devices such as smartphone, wearable electronics such as smart watch, intelligent HMI cluster (or connected cluster) in the vehicle 100 etc. The cloud 108 based device corresponds to cloud computing architecture having single or network of servers, databases connected with each other and vehicle 100 for processing of inputs and providing outputs.

[0013] According to the present invention, the computing device 120 is provided with necessary signal detection, acquisition, and processing circuits. The computing device 120 is a controller/ control unit which comprises memory element 106 such as Random Access Memory (RAM) and/or Read Only Memory (ROM), Analog-to-Digital Converter (ADC) and a Digital-to-Analog Convertor (DAC), clocks, timers, counters and at least one processor (capable of implementing machine learning) connected with each other and to other components through communication bus channels. The memory element 106 is pre-stored with logics or instructions or programs or applications or modules/models and/or threshold values (threshold CO2 level 118), which is/are accessed by the at least one processor as per the defined routines. The internal components of the computing device 120 are not explained for being state of the art, and the same must not be understood in a limiting manner. The computing device 120 may also comprise communication units to communicate with an external computing device such as the cloud 108, a remote server, etc., through wireless or wired means such as Global System for Mobile Communications (GSM), 3G, 4G, 5G, Wi-Fi, Bluetooth, Ethernet, serial networks, and the like. The computing device 120 is implementable in the form of System-in-Package (SiP) or System-on-Chip (SOC) or any other known types.

[0014] Fig. 2 illustrates a block diagram for the estimation model in the computing device, according to an embodiment of the present invention. The TCN based estimation model 116 is derived by modifying simple Convolutional Neural Networks (CNN) to improve its performance in tasks involving time series data. The TCN has some advantages over traditional Recurrent Neural Networks (RNN) like parallel operation and low memory requirements for training. The TCN comprises flexible receptive field size to have a better memory management. There are two main concepts behind the improvements dilated causal convolutions 202 and residual block 200. In the Dilated Causal Convolutions 202, the output dimension and the output (temporal) dimension is the same. To make the convolutions causal, zero padding is applied only at one side of the input sequence. One of the major features of the layers is that each output has some contribution from all the input till that time. But the receptive field size is proportional to the number of layers. To solve this problem, dilated causal convolutions 202 are applied. The dilation introduces a fixed step between two consecutive filter taps.

[0015] In residual block 200, two of the dilated causal convolutional 202 block is shown to be combined with respective weight normalization 204, activation ReLU 206 and dropout 208 (introduces regularization to prevent overfitting), and connected parallel to 1x1 convolution block 210.This enables in building deeper and larger model as well as increasing the receptive field. It also prevents the vanishing or exploding gradient problem during training. The residual layers 2142, 2144, .., 2146 can be stacked on top of each other forming the TCN layer 222. Each of the dilated causal convolution 202 layer is composed of 64 filters, each with kernel size 3 and dilation d at 4 consecutive layers given by the set {1, 2, 4, 8}. The input 212 is in the form of temporal sequence with a fixed window length k 228. A dense layer 214 (or fully connected layer 224) consists of 32 neurons followed by ReLU activation function which outputs 218 a single value. The terms used with reference to TCN is known in the art and understood by skilled person.

[0016] More the residual blocks 200 that are added to the estimation model 116, more parameters are introduced which need to be trained. This increases the complexity of the estimation model 116 and needs more data to train efficiently. The performance with just one residual block 200 performs better than the ones with a higher number of residual blocks 200. The data is not sufficient to train the increasing number of parameters. Hence, only one residual block 200 is selected for evaluation, but not limited to the same.

[0017] Further, the processing of inputs 212 and providing outputs 218 is explained using a diagram 230. The input feature 212 time series is captured in windows 228 of fixed length. The output 218 is computed for the input feature 212 in the window 228. As time passes, the sliding window 228 moves by one time unit to predict the instantaneous CO2 emission for the current timestamp 226. The data has been normalized to lie within 0 and 1. The TCN based estimation model 116 utilizes the fixed sliding window size 228. Using the data captured in this window width 228, the estimation model 116 determines the output 218 of instantaneous CO2 emission.

[0018] According to an embodiment of the present invention, when the external device is used as the computing device 120, the input signals are received either through Telematics Control Unit (TCU) of the vehicle 100 or through wireless transceiver connected to an On-Board Diagnostic (OBD) port of the vehicle 100 or both.

[0019] According to the present invention, the estimation model 116 based on TCN comprises the conventional steps of training such as preprocessing the collected data, feature formation and feature selection using but not limited to Pearson correlation and random forest. At the time of collecting and training, the sensors monitored by the computing device 120 also comprises an instantaneous CO2 emission measurement sensor. The readings from the CO2 sensor are used as true values for evaluation. Further, the estimation model 116 is trained with noise signal as well to perform against the noise present in real time data.

[0020] According to the present invention, the action 114 comprises sending an alert to a display screen in the vehicle 100, or to the communication device 112 of a concerned stakeholder, or to government/regulatory offices or private emission management servers of Original Equipment Manufacturers (OEMs) and the like. The action 114 may also comprise curtailing the CO2 emission from the vehicle 100.

[0021] According to the present invention, a working of the computing device 120 is envisaged. Consider the OBD II interface is used as the means of accessing the data of the vehicle 100. The data is collected and transferred through a Bluetooth wireless dongle based connection to the communication device 112, where the data is processed and registered. The sensor data are observed once every second or other frequency. For example, a first estimation model 116 for a first vehicle 100 is completely trained on the large quantity of training data, while the transfer learning is applied to update few parameters in a second estimation model 116 as it has lesser amount of data. The second estimation model 116 is for the second type of vehicle 100 different from the first type of vehicle 100. Consider the first estimation model 116 is located in the communication device 112. The first estimation model 116 processes the input signals of the operational parameters 102 based on the window length 228 and provides instantaneous output 218.

[0022] Fig. 3 illustrates a method for estimating carbon dioxide (CO2) emission from vehicles, according to the present invention. The method comprises plurality of steps, of which a step 302 comprises receiving, by the computing device 100, input signals comprising operational parameters 102 from an internal network 104 of the vehicle 100, such as from On-Board Diagnostic (OBD) port of the vehicle 100 or all the sensors directly interfaced with a computing device 120. The method is characterized by, a step 304 which comprises processing operational parameters 102 using the estimation model 116. The estimation model 116 is based on the Temporal Convolutional Network (TCN). A step 306 comprises estimating the CO2 emission from the vehicle 100 from the estimation model 116. A step 308 further comprises comparing the estimated CO2 with the threshold CO2 level 118. A step 310 comprises triggering the action 114 if the estimated CO2 exceeds the threshold CO2 level 118. A step 312 comprises applying transfer learning applied on the TCN based estimation model 116 and scaling the estimation model 116 to another type of vehicle 100 having limited data.

[0023] According to the present invention, the operational parameters 102 is selected from engine load, fuel flow, mileage, vehicle speed, fuel flow and acceleration. Further, each of the operational parameters 102 are measured using respective sensors in the vehicle 100 or derived using the measured parameters. In addition, the method is performed by the computing device 120. The computing device 120 is at least one of the internal device and the external device. The internal device is the Electronic Control Unit (ECU) in the vehicle 100, and the external device comprises the cloud 108 and the portable/communication device 112 in communication with the ECU of the vehicle 100.

[0024] According to the present invention, a deep transfer learning based CO2 emission prediction using real time vehicle telematics sensors data is provided. In the present invention, a scalable vehicle CO2 emission prediction/estimation model 116 is developed which uses vehicle data such as through OBD-II port data. Since, the OBD is a standardized protocol, the data is easily accessible for the vehicle 100 and in the same format everywhere. The computing device 120 exploits the OBD time series data judiciously to obtain causal predictions/estimation for CO2 emissions in real time. For this the computing device 120 is configured in manner to use Temporal Convolutional Network time series is involved. The present invention solves the scalability of the approach. The computing device 120 enables the use of transfer learning, with which one estimation model 116 trained extensively for one type of vehicle 100 is usable in another type of vehicle 100 with limited data available.

[0025] In an embodiment, an end-to end telematics solution to enable our customer to track and monitor respective machines, connect with authenticated dealer network, and reduce machine down time there by increasing their Return on Investment (ROI). As part of this solution, a telematics device (such as TCU) is fitted into the machines (or vehicles 100) which sends data continuously tracks machine location, also collects all this data in real time from machines and sends to the cloud 108. The estimation model 116 available in the cloud 108 analyses all these data at almost real time and provides useful and interactive insights to users through web and mobile applications. The computing device 120 and method provides Artificial Intelligence /Machine Learning (AI/ML) based data analytics approach for CO2 emission prediction using sensors data of the vehicle 100.

[0026] It should be understood that the embodiments explained in the description above are only illustrative and do not limit the scope of this invention. Many such embodiments and other modifications and changes in the embodiment explained in the description are envisaged. The scope of the invention is only limited by the scope of the claims.
, Claims:We claim:
1. A computing device (120) to estimate carbon dioxide (CO2) emission from vehicles (100), said computing device (120) configured to:
receive input signals (102, 104) comprising operational parameters (102) from an internal network (104) of said vehicle (100), characterized in that,
process said operational parameters (102) using an estimation model (116), said estimation model (116) is based on a Temporal Convolutional Network (TCN), and
estimate said CO2 emission from said vehicle (100) as an output from said estimation model (116).

2. The computing device (120) as claimed in claim 1 further configured to,
compare said estimated CO2 with a threshold CO2 level (118) stored in a memory element (106), and
trigger an action (114) if said estimated CO2 exceeds said threshold CO2 level (118).

3. The computing device (120) as claimed in claim 1, wherein said operational parameters (102) is selected from engine load, fuel flow, mileage, vehicle speed, fuel flow and acceleration, and wherein each of said operational parameters (102) are measured using respective sensors in said vehicle (100) or derived using measured parameters.

4. The computing device (120) as claimed in claim 1, wherein transfer learning is applied on said TCN based estimation model (116) to scale said estimation model (116) to another type of vehicle (100) having limited data.

5. The computing device (120) as claimed in claim 1 is at least one of an internal device and an external device, wherein said internal device is an Electronic Control Unit (ECU) in said vehicle (100), and said external device comprise a cloud (108) and a communication device (112) in communication with said ECU of said vehicle (100).

6. A method for estimating carbon dioxide (CO2) emission from vehicles (100), said method comprising the steps of:
receiving input signals comprising operational parameters (102) from an internal network (104) of said vehicle (100), characterized by,
processing operational parameters (102) using an estimation model (116), said estimation model (116) is based on a Temporal Convolutional Network (TCN), and
estimating said CO2 emission from said vehicle (100) as an output from said estimation model (116).

7. The method as claimed in claim 6 further comprises
comparing said estimated CO2 with a threshold CO2 level (118), and
triggering an action (114) if said estimated CO2 exceeds said threshold CO2 level (118).

8. The method as claimed in claim 6, wherein said operational parameters (102) is selected from engine load, fuel flow, mileage, vehicle speed, fuel flow and acceleration, and wherein each of said operational parameters (102) are measured using respective sensors in said vehicle (100) or derived using said measured parameters.

9. The method as claimed in claim 6, comprises applying transfer learning applied on said TCN based estimation model (116) and scaling said estimation model (116) to another type of vehicle (100) having limited data.

10. The method as claimed in claim 6 is performed by a computing device (120), wherein said computing device (120) is at least one of an internal device and an external device, wherein said internal device is an Electronic Control Unit (ECU) in said vehicle (100), and said external device comprise a cloud (108) and a communication device (112) in communication with said ECU of said vehicle (100).