Claims:We claim:
1. A device (100) to determine operational metrics of a vehicle, said device (100) comprises:
at least one controller (110) configured to,
receive input signals for parameters comprising at least engine speed, vehicle speed and a timestamp, said parameters are stored as datapoints in a memory element (106), wherein plurality of said datapoints form a dataset (112),
calculate a ratio of said engine speed to said vehicle speed for each datapoint and update said dataset (112), characterized in that, said at least one controller (110) further configured to
process said ratios through a statistical module (108) and determine at least one operational metrics.

2. The device (100) as claimed in claim 1, wherein said device (100) is at least one selected from a portable dongle inserted in an On-Board Diagnostics (OBD) port of said vehicle, a telematics unit, an Engine Control Unit (ECU) and a server (114).

3. The device (100) as claimed in claim 1, wherein said statistical module (108) is configured to,
categorize said dataset (112) into multiple data frames based on vehicle speed;
calculate, for each data frame, statistical parameters comprising mean (202), median (204), mode (206), first quartile (Q1), third quartile (Q3), Interquartile Range (IQR) (210), half of Q1 and Q3 (half Q13) and a median difference (216) of half Q13 and said median (204);
set a status of each data frame as “in range” when said median difference (216) is less than a threshold, else set said status as “out of range”;

iteratively process said “in range” and “out of range” data frames and conditionally merge with each other until number of datapoints with status as “out of range” is less than a threshold limit, and
assign each range a gear position/number (222) in manner that a highest value range to lower value range is assigned with lowest gear position to highest gear position respectively.

4. The device (100) as claimed in claim 1, wherein said operational metrics further comprises gear number (222), number of gears, distance travelled in each gear, duration of travel in each gear, distance travelled in half clutch condition, duration of travel in half clutch condition, distance travelled in coasting, duration travelled in coasting, fuel efficiency, failure prediction and the like.

5. The device (100) as claimed in claim 1, wherein said controller (110) filters out parameters from said dataset (112) which corresponds to idle condition and coasting condition of said vehicle, before being processed by said statistical module (108).

6. A method for determining operational metrics of a vehicle, said method comprising the steps of:
receiving input signals for parameters comprising at least engine speed, vehicle speed and a timestamp, said parameters are stored as datapoints in a memory element (106), wherein plurality of said datapoints form a dataset (112),
calculating a ratio of said engine speed to said vehicle speed for each datapoint and update said dataset (112), characterized by,
processing said ratios through a statistical module (108) and determining at least one operational metrics.

7. The method as claimed in claim 6, wherein said method is executed by at least one of a portable dongle inserted in an On-Board Diagnostics (OBD) port of said vehicle, a telematics unit, an Engine Control Unit (ECU) and a server (114).

8. The method as claimed in claim 6, wherein processing through said statistical module (108) comprises,
categorizing said dataset (112) into multiple data frames based on vehicle speed;
calculating, for each data frame, statistical parameters comprising mean (202), median (204), mode (206), first quartile (Q1), third quartile (Q3), Interquartile Range (IQR) (210), half of Q1 and Q3 (half Q13) and a median difference (216) of half Q13 and said median (204);
setting a status of each data frame as “in range” when said median difference (216) is less than a threshold, else setting said status as “out of range”;
iteratively processing said “in range” and “out of range” data frames and conditionally merging with each other until number of datapoints with status as “out of range” is less than a threshold limit, and
assigning each range, a gear position/number in manner that a highest value range to lower value range is assigned to lowest gear position to highest gear position respectively.

9. The method as claimed in claim 6, wherein said operational metrics is selected from a group comprising, a gear number (222), number of gears, distance travelled in each gear, duration of travel in each gear, distance travelled in half clutch condition, duration of travel in half clutch condition, distance travelled in coasting, duration travelled in coasting, fuel efficiency, failure prediction and the like.

10. The method as claimed in claim 6, wherein before being processed by said statistical module (108) said dataset (112) is filtered to remove datapoints corresponding to idle condition and coasting condition of said vehicle.
, 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 device and method for determining operational metrics of a vehicle.

Background of the invention:
[0002] The fleet management companies are looking at various ways to reduce the operating cost of their fleet. One of the main operating cost for fleet is fuel. There are various possibilities to reduce the fuel usage in fleet for e.g. reducing the engine idling time, preventing fuel theft, unwanted engine revving etc. Even improper usage of gear and also driving in lower gears results in increased fuel consumption. Currently there is no connected vehicles solution in aftermarket that has the capability to detect and analyze the gear usage by driver, for e.g. in which gear the driver drives the most.

[0003] A patent literature US2013079975 discloses a systems and methods for processing operational gear data of a vehicle. Systems and methods to process vehicle operation data are described. A data module associated with a vehicle can collect operation data relating to the gear operation of the vehicle. The data module can process the operation data to identify a top gear of the vehicle and determine the current gear at which the vehicle is operating. The data module can process the operation data to determine an amount of time that the vehicle operates in top gear. The data module can provide the data to an operator of the vehicle, or to a remote management center, for storage and/or further processing.

Brief description of the accompanying drawings:
[0004] An embodiment of the disclosure is described with reference to the following accompanying drawing,
[0005] Fig. 1 illustrates a block diagram of a device to determine operational metrics of a vehicle, according to an embodiment of the present invention;
[0006] Fig. 2 illustrates a block diagram of dataset for statistical analysis, according to an embodiment of the present invention, and
[0007] Fig. 3 illustrates a method for determining operational metrics of the vehicle, according to the present invention.

Detailed description of the embodiments:
[0008] Fig. 1 illustrates a block diagram of a device to determine operational metrics of a vehicle, according to an embodiment of the present invention. The device 100 comprises at least one controller 110 configured to, receive input signals for parameters comprising at least engine speed, vehicle speed and a timestamp of receiving the engine speed and the vehicle speed. The engine speed and the vehicle speed are detected by respective sensors such as engine speed sensor 102, vehicle speed sensor 104 or other means known in the art. It is also possible to derive the vehicle speed in dependence to other sensor inputs, such as geolocation position sensors for example. The parameters are stored as datapoints in a memory element 106. The plurality of the datapoints form a dataset 112. The controller 110 calculates a ratio of the engine speed to said vehicle speed for each datapoint and updates the dataset 112, characterized in that, the controller 110 further configured to process the ratios through a statistical module 108 and determine at least one operational metrics.

[0009] The controller 110 comprises memory element 106 such as Random Access Memory (RAM) and/or Read Only Memory (ROM), Analog-to-Digital Converter (ADC) and vice-versa Digital-to-Analog Convertor (DAC), clocks, timers and at least one processor (capable of implementing machine learning) connected with the 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 and/or threshold values, which is/are accessed by the at least one processor as per the defined routines. The internal components of the controller 110 are not explained for being state of the art, and the same must not be understood in a limiting manner. The controller 110 may also comprise communication units to communicate with a server 114 or cloud 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.

[0010] In accordance to an embodiment of the present invention, the device 100 is at least one of a portable dongle inserted in an On-Board Diagnostics (OBD) port of said vehicle, a telematics unit, an Engine Control Unit (ECU) and a server 114 (or cloud).

[0011] In accordance to an embodiment of the present invention, the vehicle is preferably a two-wheeler such as a motorcycle, a scooter, a moped, etc. However, the controller 110 is equally adaptable to be used for three-wheelers such as auto-rickshaws, four wheelers such as cars and the other existing and new vehicles (even snow mobiles) where there is use of gears.

[0012] The device 100, through at least one controller 110, broadly performs following functions, data collection, data cleaning, data preprocessing, data analysis and metrics estimation. In data collection, the data from a vehicle is taken as the raw data (timestamp, engine speed, vehicle speed). In data cleaning, the coasting range and the engine idling data range are identified and removed from the raw dataset 112. In data preprocessing, the ratio based on engine speed and the vehicle speed is computed. In data analysis, the exploratory/statistical analysis for identifying gear range for every gear is performed. In metrics estimation the gear numbers are assigned to each range and based on this assignment, each datapoint in the dataset 112 is further analyzed.

[0013] In accordance to the present invention, the device 100 uses the parameters from the vehicle, especially, engine speed and vehicle speed, to determine the condition of clutch/gearbox. For example, the engine transmits the power to the wheels via clutch and gearbox. The power transfer to the rear axle happens via the propeller shaft and/or a final drive unit. During this transmission from engine to wheels, the power is amplified at the gearbox and the final drive unit (differential). The formula to calculate this power transmitted in terms of vehicle speed is as follows
V = [(2*Pi*r) * N] / (Gt * Gd)
where,
V = Vehicle speed
r = Radius of tire
N = Engine Speed
Gt = Gear ratio at gearbox (if there are 5 gears then there will be 5 different gear ratios)
Gd = Gear ratio at differential

[0014] It is clear from the above formula that except for vehicle speed and engine speed, the remaining values are more or less constant. The gear ratio at gearbox may have discrete number of different values based on number of gears, however this is also limited, and each gear has a constant gear ratio. Therefore, for a five-speed gear box with an extra reverse gear, there should be only 6 possible ratios of engine and vehicle speed, corresponding to each gear. The device 100 enables to identify the pattern from the ratios of engine speed and vehicle speed, and also identify the number of gears in the vehicle, which in turn helps in determining operational metrics comprising the driver behavior with respect to gear usage. The working of the device 100, specifically the statistical module 108 is explained in Fig. 2.

[0015] Fig. 2 illustrates a block diagram of dataset for statistical analysis, according to an embodiment of the present invention. The dataset 112 collected comprises the engine speed, the vehicle speed, and the timestamp, all arranged in rows of an initial table (not shown). The initial table is then processed by the device 100 which adds lot of statistical parameters. A first table 200 shows the statistical parameters obtained/calculated from the dataset 112 collected or being collected from the vehicle. The rows are numerous and hence shown with dotted lines. The statistical module 108 is configured to, categorize/split the dataset 112 into multiple data frames based on vehicle speed. For example, the gear ratios calculated before, is taken as main part of the dataset 112 and is divided into multiple data frames of vehicle speed range of 5 kmph based on top speed of the vehicle. If top speed of vehicle is 100 kmph then the dataset 112 is divided into 19 small data frames starting from 5 – 10 kmph, 10 – 15 kmph till 95 – 100 kmph. Each of the data frame contains multiple datapoints. The statistical module 108 then calculates, for each data frame, statistical parameters comprising mean 202, median 204, mode 206, a standard deviation 208, a first quartile (Q1), third quartile (Q3), Interquartile Range (IQR) 210, a lower limit/ lower whisker 212, an upper limit/ upper whisker 214, a half/mean of Q1 and Q3 (half Q13) and a median difference 216 which is subtraction of half Q13 and the median 204, and a count 218 which is number of datapoints in the data frame. The statistical module 108 sets a status of each data frame as “in range” when the median difference 216 is less than a threshold value, else sets the status as “out of range”. For example, if this median difference 216 is less than 1, then the status is set as “In range” otherwise it is assigned as “Out of range”. The device 100 then iteratively processes and merges the “in range” and “out of range” data frames until the count 218 of the datapoints with status as “out of range” is less than a threshold value. The device 100 then assigns each range a gear position/number in manner that a highest value range to lower value range is assigned with lowest gear position to highest gear position respectively.

[0016] The iterative process and the merging performed by the statistical module 108 is elaborated below. Once the status i.e. “in range” or “Out of range”, is assigned, the next step is to merge similar data frames. For this, the median 204 from all the “in range” data frames are checked by the statistical module 108, to see if they fall between the lower limit 212 and the upper limit 214 of other “in range” data frames. If it is between the upper limit 214 and the lower 212 of other data frames, then these two data frames are merged with each other. This process is repeated and all the “In range” data frames that can be combined are merged. After the merge, the estimation of the statistical parameters is followed again and the new lower limit 212, the upper limit 214 ranges are calculated. After merging, the new median 204 values that falls between the new “in range” data frames are checked in data frames that have status “out of range”. If yes, then these values are then moved from the “out of range” data frames to the corresponding “in range” data frames. For remaining “Out of range” data frames (which cannot be combined/merged), the statistical analysis is repeated. Since the median 204 of ratio that belong to other data frames are removed, performing the statistical analysis again results in conversion of some of these “Out of range” data frames to “in range”. In cases, where the data frames are still “out of range”, it indicates a combination of datapoints for two different gears. The data frame is divided into two data frames considering mean as the mid-point for divide. This again gives multiple data frames with “In range” and “Out of range” status. The iteration is repeated by the statistical module 108 until the count 218 of datapoints in “out of range” data frames becomes less than a threshold count or percentage. For example, if the data points in “Out of range” data frame is more than 5% then the iteration and merging is continued again and again till it is lower than 5%. When the data points in “Out of range” data frame is lower than 5% then the analysis is stopped, and final result produces all the gear ranges. In the final result if any gear range contains less than 1% data points then it is removed from the final result. A second table 220 shows the final table when the ranges are finalized, and the gear numbers 222 are assigned to the ranges. Each range is assigned with the gear position/number in manner that a highest value range to lower value range is assigned to lowest gear position to highest gear position respectively.

[0017] The operational metrics is selected from a group comprising, a gear number, number of gears, distance travelled in each gear, duration of travel in each gear, distance travelled in half clutch condition, duration of travel in half clutch condition, distance travelled in coasting, duration travelled in coasting, fuel efficiency, failure prediction and the like.

[0018] In accordance to the present invention, the at least one controller 110 filters out parameters from the dataset 112 which corresponds to idle condition and coasting condition of the vehicle, before being processed by the statistical module 108. The engine idling data is identified based on the condition engine speed is greater than zero and the vehicle speed equals to zero. Based on this, the range of engine speed during idling is identified. This data is then filtered out from the dataset 112 using standard deviations 208 from the mean. Based on the idling speed range, the coasting range is detected. The coasting is the condition where the vehicle is driven by disengaging the clutch or by selecting neutral gear. This coasting range is then applied on the data with vehicle speed more than zero and two separate datasets are created from it (first dataset contains all coasting data for vehicle speed greater than zero i.e.- only coasting data and second dataset contains remaining data for vehicle speed greater than zero i.e.- only relevant gears data). The processing of second dataset 112 by the statistical module 108 is explained above, whereas the processing of first dataset is used for other metrics estimation related to coasting.

[0019] Further, the above processing by the statistical module 108 is done on the same vehicle or different vehicle of same make and type to find out stable gear ranges. When the gear ranges are finalized, they are deployed to carry out driver behavior analysis and failure predictions in the same vehicle model.

[0020] In an embodiment of the present invention, the device 100 is usable in different cases, i.e. as a edge device 100 or a cloud device 100 or combination thereof. The different possible cases with respective implementation and working is described below. In a first case, the device 100 is deployed as a dongle to be plugged in the OBD port of the vehicle. The owner of the vehicle starts receiving the operational metrics of the vehicle after a minimum distance or duration or combination thereof. The dongle is connected to a server 114 (or cloud) through wireless communication network established by telecommunication technology. The processing happens either in the dongle or in the server 114 and the notification of the operational metrics are sent to the owner on a user apparatus 116 such as mobile phone or a display screen of a computer, etc. Alternatively, the processing is distributed between the dongle and the server 114 either in a predetermined manner or based on processing load.

[0021] In a second case, the device 100 is the telecommunication unit or the ECU of the vehicle. The device 100 is flashed or installed with logics or instructions by an Original Equipment Manufacturer (OEM) or by an external vendor. The device 100 then either directly or indirectly communicates with the server 114. The indirect communication corresponds to connecting to an external/portable communication means such as mobile phone with internet connectivity.

[0022] In a third case, the device 100 is a mobile phone of the driver connected to the vehicle through wireless or wired means known in the art. The mobile phone receives the input signals from the vehicle. An application installed in the mobile phone in combination with the processor of the mobile phone helps in the statistical analysis and determination of the operational metrics. The information is either sent through the telecommunication unit of the vehicle or directly from the mobile phone to the server 114.

[0023] In a fourth case, the device 100 is a server 114 (or cloud) to determine operational metrics of a fleet of vehicle. The fleet of vehicles comprises vehicles of different make and variants and managed by fleet manager. The server 114 receives input signals from different connected vehicles for either predetermined distance, for example, 200 kms, 500 kms or continuously as required. The connected vehicles imply that the vehicle is connected to the server 114 either through built-in connectivity devices or external devices. The server 114 processes the inputs signals from all the connected vehicles of the fleet and categorizes dataset 112 for each vehicle of same make and model. For example, a vehicle of make A and vehicle of make B have respective datasets 112 different from each other. Similarly, two vehicles of make A of same model are grouped together. In yet another example, the datasets 112 of two vehicles of make A but different variant are kept separately. These examples are provided for clarity and must not be understood in limiting sense. The server 114 collates the data of the same make and variant and processes through the statistical module 108 and obtains a stable range to identify the gear range followed by remaining operational metrics of the vehicles of for that particular make. Thus, input signals from the vehicles of different make are obtained and analyzed before extending/providing the determination of operational metrics for that make of vehicle. The server 114 performs the statistical analysis through the statistical module 108 and stores the range values for each identified gear. Now, the statistical module 108 may use the same identified range directly to identify the gear and the other operational metrics without using the statistical module 108. Alternatively, the statistical module 108 may be continuously used to determine more robust range. Further, the server 114 performs the statistical analysis either at the end of a trip of the vehicle of interest, or during runtime.

[0024] In a fifth case, the device 100 is a combination of two or more selected from the group comprising the dongle, the ECU, the server 114 and the portable device. The tasks are distributed in the device 100 based on requirement.

[0025] In accordance to an embodiment of the present invention, the device 100 is implemented either for a single vehicle or a fleet of vehicle and the input signals are obtained during run time. The device 100 processes the data in real-time or run time and starts providing the operational metrics. The operational metrics keeps updating till the vehicle is driven. The runtime operational metrics is also displayed to the owner either on the mobile phone or on a display screen of a fleet operator, etc. Alternatively, the device 100 first collects the data for a trip of the vehicle, and then processes to obtain the operational metrics. Based on the operation metrics, necessary corrective actions are taken.

[0026] Fig. 3 illustrates a method for determining operational metrics of the vehicle, according to the present invention. The method comprises plurality of steps. A step 302 comprises receiving input signals for parameters comprising at least engine speed, vehicle speed and a timestamp. The parameters are stored as datapoints in the memory element 106. The plurality of said datapoints form the dataset 112. A second step 304 comprises calculating the ratio of the engine speed to the vehicle speed for each datapoint and update the dataset 112. The method is characterized by, a step 306 which comprises, processing the ratios through the statistical module 108 and determining at least one operational metrics. The operation metrics is displayed on the user apparatus 116 in step 308. The method is executed by at least one of a portable dongle inserted in the On-Board Diagnostics (OBD) port of the vehicle, the telematics unit, the Engine Control Unit (ECU) and the server 114.

[0027] The step 306, processing through said statistical module 108 comprises a step 310 of categorizing the dataset 112 into multiple data frames based on vehicle speed. A step 312 comprises calculating, for each data frame, statistical parameters comprising mean 202, median 204, mode 206, standard deviation 208, a first quartile (Q1), third quartile (Q3), Interquartile Range (IQR) 210, half of Q1 and Q3 (half Q13) and a median difference 216 of half Q13 and the median 204. A step 314 comprises setting the status of each data frame as “in range” when the median difference 216 is less than the threshold value, else setting the status as “out of range”. A step 316 comprises iteratively processing the “in range” and “out of range” data frames and conditionally merging with each other until number of datapoints with status as “out of range” is less than a threshold count. A step 318 comprises assigning each range the gear position/number in manner that a highest value range to lower value range is assigned to lowest gear position to highest gear position respectively. It is to be noted that, before being processed by the statistical module 108, the dataset 112 is filtered to remove datapoints corresponding to idle condition and coasting condition of the vehicle.

[0028] The operational metrics is selected from the group comprising, the gear number, the number of gears, the distance travelled in each gear, the duration of travel in each gear, the distance travelled in half clutch condition, the duration of travel in half clutch condition, the distance travelled in coasting, the duration travelled in coasting, the fuel efficiency, the failure prediction and the like.

[0029] According to the present invention, a self-learning method to detect transmission/gear ratio from connected vehicle data is provided. The present invention enables a fleet manager to know the driver behavior so that training can be provided for bad driving behaviors. Also, helps in identification of incorrect gear usage which affects the fuel economy of the vehicle thus leading to increased operating costs. The device 100 and method is able to detect the number of gears in the vehicle, identify the gear used when vehicle is being driven and provide a summary of gear usage at the end of the trip (distance travelled in each gear).

[0030] It should be understood that 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.