Claims:We claim:
1. A device (100) to determine an energy optimized route for a vehicle (136), said device (100) comprises:
a receiver of a satellite based positioning system (134),
an input unit (132), to enter source and destination, and
a controller (130) connected to and receiving signals from said receiver and said input unit (132), characterized by, said controller (130) adapted to,
access an energy map (140) overlaid with virtual grids (138);
receive real-time parameters based on current location of said vehicle (136), and
determine a least energy consumption route from said source to said destination based on said energy map (140) and said real-time parameters.

2. The device (100) as claimed in claim 1, wherein said controller (130) configured to,
identify at least one route distributed over multiple virtual grids (138);
determine sequence of virtual grids (138) through which said at least one route traverses;
estimate energy consumption on each of said at least one route based on aggregate of energy consumption as per said respective sequence of virtual grids (138), and
select and navigate through one of said at least one route with least estimated energy consumption.

3. The device (100) as claimed in claim 1, wherein said controller (130) configured to,
compute energy consumption in adjacent virtual grids (138) of a current virtual grid (138), and
navigate, before end of said current virtual grid (138), through a next road segment of one of said adjacent virtual grid (138) having least estimated energy consumption.

4. The device (100) as claimed in claim 1, wherein said controller (130) configured to,
communicate with at least one of a cloud server (128), a sensor unit (142) traffic management server, vehicles on said road segment and infrastructure along said road segment,
obtain real-time information on at least one of weather, traffic status, accidents, speed limits, driving pattern and geographical information, and
navigate said vehicle (136) using said real-time information.

5. The device (100) as claimed in claim 1, wherein said energy map (140) comprises information of energy consumption for each category, sub-category, make and variant of said vehicles, and wherein said energy consumption is computed based on health status of vehicles with respective internal components selected from transmission, wheels, battery, engine, age and driving pattern.

6. A method for determining energy optimized route for a vehicle (136), said method comprising the steps of:
receiving a source and destination from an input unit (132), characterized by,
obtaining an energy map (140) overlaid with virtual grids (138), each of said virtual grids (138) comprising energy consumption data for each road segment within said virtual grids (138);
obtaining real-time parameters based on current location of said vehicle (136), and
determining a least energy consuming route from said source to said destination based on said energy map (140) and said real-time parameters.

7. The method as claimed in claim 1, wherein said step of determining comprises,
identifying at least one route distributed over multiple virtual grids (138);
identifying sequence of virtual grids (138) through which said at least one route traverses;
estimating, by aggregating energy consumption in respective sequence of virtual grids (138) based on said energy map (140) and said real-time parameters, and
selecting and navigating through a route with least estimated energy consumption.

8. The method as claimed in claim 1, wherein said step of determining comprises,
computing energy consumption in adjacent virtual grids (138) of a current virtual grid (138), and
navigating, before end of said current virtual grid (138), a next road segment of one of said adjacent virtual grids (138) having least estimated energy consumption based on said energy map (140) and said real-time parameters.

9. The method as claimed in claim 1, wherein said obtaining real-time parameters comprises
communicating with at least one of a cloud server (128), a sensor unit (142),a traffic management server, vehicles on said road segment and infrastructure along said road segment, and
receiving real-time information on at least one of weather, traffic status, accidents, speed limits, driving pattern and geographical information.

10. The method as claimed in claim 1, wherein said energy map (140) comprises information of energy consumption for each category, sub-category, make and variants of said vehicles, and wherein said energy consumption is calculated based on vehicle health status with respective internal components selected from transmission, wheels, battery, engine, age and driving pattern.
, 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 to determine and recommend an energy optimized route for a vehicle.

Background of the invention:
[0002] The maximum distance that a Battery Electric Vehicle (BEV) can travel, depends on the capacity and consumption / discharge rate of the battery. In addition to road condition, which determines the coefficient of friction between the wheels of vehicle and road, the battery consumption in turn depends on way the acceleration/ deceleration / braking patterns. These patterns in turn depend on the route taken. For e.g. certain routes will have a high gradient, high traffic etc., leading to high battery consumption and hence lesser range for the electric vehicle. In BEV it is important to select a route that optimizes battery performance. This is also equally applicable for a vehicle with Internal Combustion Engine (ICE), where instead of battery, it is fuel consumption which needs to be optimized. Hence, there is a need to determine the least energy route between any given source and destination.

[0003] According to a patent literature DE102010041616, a method for detecting power consumption i.e. fuel consumption, of motor vehicle, for track section e.g. urban track section, of driving route, involves storing normalized power consumption as property of track section is disclosed. The method involves normalizing detected power consumption based on a comparison value i.e. vehicle-specific comparison value, and storing the normalized power consumption as a property of a track section. The detected power consumption is averaged from multiple detected power consumption values. Averaged, standardized stretch-dependent and rider-specific power consumption is determined from the normalized power consumption values of multiple motor vehicles and/or riders. An evaluation and recording unit is formed as a computing unit and connected with a sensor.

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 device to determine energy optimized route for a vehicle, according to an embodiment of the present invention, and
[0006] Fig. 2 illustrates a method for determining energy optimized route for the vehicle, according to the present invention.

Detailed description of the embodiments:
[0007] Fig. 1 illustrates a device to determine energy optimized route for a vehicle, according to an embodiment of the present invention. The device 100 comprises, a receiver of a satellite based positioning system 134 and an input unit 132, to enter source and destination. The device 100 also comprises a controller 130 connected to and receiving signals from the receiver and the input unit 132. The device 100 is characterized by, the controller 130 adapted to, access an energy map 140 overlaid with virtual grids 138, receive a real-time parameters based on current location of the vehicle 136, and determine a least energy consumption route from the source to the destination based on the energy map 140 and the real-time parameters.

[0008] The controller 130 comprises a processor, memory elements (Random Access Memory (RAM), Read Only Memory (ROM), timers, clocks and other circuits such as Analog To Digital (ADC) and/or DAC, etc. all connected by communication channel such as Bus. In an embodiment, the device 100 is a navigation unit and the controller 130 is part of the same. The device 100 is shown to be having the controller 130 and the input unit 132. However, the device 100 comprises the receiver and other components commonly known such as an output unit, speaker (optional), etc. The construction and working of the controller 130 is skipped, as being known to a person skilled in the art.

[0009] In accordance to an embodiment of the present invention, a sensor unit 142 is provided in the vehicle 136. The sensor unit 142 is either retrofit to a power socket of the vehicle, such as cigarette lighter, an independent power source or is a part of the device 100 or the vehicle 136. The sensor unit 142 comprises at least one of the sensors such as an Inertial Measurement Unit (IMU), an accelerometer, gyroscope and the like. If used as retrofit, the sensor unit 142 also comprises a control unit connected to the sensors and communicates with the controller 130 through known wired or wireless communication means such as Bluetooth, Wi-Fi, etc. The sensor unit 142 is connectable to a smartphone of the driver/passenger through the wireless means. The driver connects to the sensor unit 142 through an application installed in the smartphone. The smartphone in turn is connected to a server 128. In one embodiment, the smartphone is the sensor unit 142. The sensors of the smartphone are used instead of the sensor unit 142.

[0010] In accordance to an embodiment of the present invention, the controller 130 is configured to, first identify at least one route, such as a first route 122, a second route 124, a third route 126, distributed over multiple virtual grids 138. The controller 130 then determines sequence of virtual grids 138 through which the at least one route traverses. Further, the controller 130 estimates the energy consumption for each of the at least one route based on the aggregate of energy consumption as per respective sequence of virtual grids 138. Finally, the controller 130 selects and navigates through the route out of the at least one route, with the least estimated energy consumption. Here, the route from source to destination is shown before the starting the navigation.

[0011] In Fig. 1, consider the vehicle 136 is travelling, and the driver enters the destination. The source is either entered or detected by the receiver of the satellite based position system 134. The destination and the source are entered through the input unit 132 such as touch interface, buttons and the like. Once entered, the controller 130 downloads the energy map 140 from the server 128. Alternatively, the energy map 140 is stored within a memory element of the controller 130 itself. The energy map 140 is created or formed by the server 128, over a base map by information received from multiple vehicles over a period of time using the sensing unit 142. Based on the collected information, the server 128 estimates energy consumption for a vehicle within a road segment accommodated in the virtual grid 138. This energy consumption is categorized with type of vehicles, make of vehicles and version of vehicles. The energy consumption is calculated based on average speed of vehicles, road conditions, weather conditions, average traffic conditions and the like. Based on the destination, the controller 130 determines the current virtual grid 138 based on location of the vehicle 136. The controller 130 identifies three routes, disclosed above (as an example only). The controller 130 determines the sequence of virtual grids 138 for each route. The first route 122 traverses through grids (102, 114), (104, 114), (106, 114), (106, 116), (108, 116), (110, 116), (110, 118) and (110, 120). The second route 124 is determined to traverse through (102, 114), (104, 114), (108, 114), (108, 116), (108, 118), (110, 118), and (110, 120). Similarly, the third route 126 is determined to traverse through grids (102, 114), (104, 114), (104, 116), (102, 116), (102, 118), (104, 118), (104, 120), (106, 120), (108, 120) and (110, 120). The controller 130 refers to the energy map 140 and estimates the energy consumption in advance, if each of the route is taken. During estimation, the controller 130 also considers real-time parameters as well, which might affect the energy consumption. In this example, the controller 130 estimates the first route 122 as the least energy consuming route. The controller 130 then selects the first route 122 and navigates the vehicle 136 through the same.

[0012] In accordance to another embodiment of the present invention, the controller 130 is configured to compute energy consumption in adjacent virtual grids 138 of a current virtual grid 138. The controller 130 then navigates, before the end of the current virtual grid 138, through a next road segment of one of the adjacent virtual grid 138 having least estimated energy consumption.

[0013] Referring back to Fig. 1, consider the controller 130 is fed with the source and destination. The controller 130 loads the energy map 140 and detects the current virtual grid 138 based on the satellite based positioning system 134. The current virtual grid 138 is identified to be the grid (104, 112). The controller 130 identifies the adjacent or surrounding virtual grids 138, i.e. (102, 112), (106, 112), (102, 114), (104, 114) and (106, 114). Before the vehicle 136 reaches the end of the grid (104, 112), the energy consumption of surrounding virtual grids 138 are calculated using the energy map 140 with or without the real-time parameters. The road segment of the least energy consuming grid (104, 114) is selected and navigated by the controller 130. Now the grid (104, 114) is the current virtual grid 138 and the adjoining virtual grids 138 are computed for the least energy consumption by the controller 130. In this example, the controller 130 selects the grid (104, 116) as least energy consuming in comparison to the other virtual grids 138, and navigates through the same. Thus, the navigation is performed grid-by grid dynamically till the destination.

[0014] In accordance to an embodiment of the present invention, the controller 130 is configured to, communicate with at least one of the cloud server 128, the sensor unit 142, a traffic management server, other vehicles on the road segment (within the same or adjoining virtual grids 138) and infrastructure along the road segment. The controller 130 also obtains real-time information on at least one of weather, traffic status, accidents, speed limits, driving pattern and geographical information, to navigate the vehicle 136 using the real-time information.

[0015] In accordance to another embodiment of the present invention, the energy map 140 comprises information of energy consumption for each category, sub-category, make and variant of the vehicles. The energy map 140 so formed or the energy consumption calculated is further based on health status of the vehicles with respect to internal components selected from transmission, wheels, battery, engine, age and driving pattern.

[0016] In accordance to yet another embodiment of the present invention, the sensing unit 142 is used to measure at least two parameters, a first parameter (drive heaviness) and the second parameter (driver heaviness). The first parameter corresponds to the condition of a road and an overall speed at which the driver takes a turn. The second parameter corresponds to a number of time the driver has accelerated or braked the vehicle 136. The first parameter and the second parameter are few of the real time parameters obtained from the vehicle 136. A use of real-time parameter is explained below.

[0017] Consider the vehicle 136 is travelling along the route 122. Assuming the vehicle 136 is at a grid (106, 114), the controller 130 has two options to recommend the driver, i.e. either proceed to grid (106, 116) or proceed to grid (108, 114). Further, based on information received from the server 128, in the grid (106, 116) less energy is consumed but has more traffic, and more traffic indicates frequent braking and acceleration. In the grid (108, 114), more energy is consumed but has less traffic, and less traffic indicates less braking and acceleration.

[0018] Now, considering a fact that braking and acceleration has impact on overall energy consumption, i.e. more braking / acceleration corresponds to higher energy consumed and vice-versa. The controller 130 measures, the first parameter and the second parameter through the sensing unit 142, the frequency of braking / acceleration when the driver is driving from source (start of the trip) until the grid (106, 114). If the frequency of braking / acceleration is detected to be high, the driver is recommended to proceed to grid (106, 116) in spite of heavy traffic. The grid (106, 116) is selected by the controller 130 because, it consumes less energy, since already a lot of energy has been consumed until reaching the grid (106, 114), and the controller 130 determines to compensate the excess consumption by recommending a least energy route.

[0019] In another example, if the frequency of the braking is low, as detected by the sensing unit 142, the driver is recommended to proceed to the grid (108, 114) at a decision point. This is because the grid (108, 114) has low traffic, and even if the energy consumption along the route is high, this is compensated by the low braking / acceleration requirement because of low traffic density.

[0020] In accordance to another embodiment, the device 100 determines the type of vehicle 136 and accordingly applies the corresponding energy map 140, with or without the real-time parameters. Further, the present invention is applicable for a battery operated vehicle 136, a fuel based vehicle 136 or a hybrid vehicle 136.

[0021] Fig. 2 illustrates a method for determining energy optimized route for the vehicle, according to the present invention. The method comprising multiple steps. A step 202 comprises receiving a source and destination from an input unit 132. The method is characterized by, a step 204 which comprises obtaining the energy map 140 overlaid with virtual grids 138, where each of the virtual grids 138 comprises energy consumption data for each road segment within the virtual grid 138. A step 206 comprises obtaining real-time parameters based on current location of the vehicle 136. The real-time parameters is obtained from the server 128, the sensing unit 142, or vehicle-to-vehicle or vehicle-to-infrastructure communication systems, etc. A step 208 comprises determining a least energy consuming route from the source to the destination based on the energy map 140 and the real-time parameters.

[0022] The step 208 comprises two alternatives, a first alternative comprises a step 210 which comprises identifying at least one route distributed over multiple virtual grids 138. A step 212 comprises identifying sequence of virtual grids 138 through which the at least one route traverses. A step 214 comprises estimating, by aggregating energy consumption along the at least one route as per the respective sequence of virtual grids 138, based on the energy map 140 with or without the real-time parameters. A step 216 comprises selecting and navigating through the route with least estimated energy consumption.

[0023] The second alternative of the step 208 comprises a step 218. The step 218 comprises computing energy consumption in adjacent or adjoining virtual grids 138 of a current virtual grid 138. A next step 220 comprises navigating, before end of the current virtual grid 138, a next road segment of one of the adjacent virtual grid 138 having least estimated energy consumption, calculated based on the energy map 140 and said real-time parameters.

[0024] The step of obtaining real-time parameters through the external source comprises communicating with at least one of a cloud server 128, the sensor unit 142, traffic management server, vehicles on the road segment and infrastructure along the road segment, and receiving real-time information on at least one of weather, traffic status, accidents, speed limits, driving pattern and geographical information.

[0025] The energy map 140 comprises information of energy consumption for each category, sub-category, make and variants of the vehicles. The energy consumption or the energy map 140 further comprises categorization or classification based on health status of the vehicles with respective internal components selected from transmission, wheels, battery, engine, age and driving pattern.

[0026] According to the present invention, a device 100 and method for determining energy efficient routing for a vehicle 136 is provided, whether it is a battery operated or fuel based. One of the object of the present invention is to provide improved route planning and map service. The device 100 enables navigation of the vehicle 136 based on a static route or dynamically created route. The static route is generated beforehand, whereas the dynamic route is created grid by grid.

[0027] 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