Power management in a hybrid electric vehicle
Described herein are devices, systems, and methods for managing the power consumption of an automotive vehicle, and thereby for optimizing the power consumption of the vehicle. The devices and systems for managing the power consumption of the vehicle typically include power management logic that can calculate an applied power for the vehicle engine based on information provided from the external environment of the vehicle, the operational status of the vehicle, one or more command inputs from a driver, and one or more operational parameters of the vehicle.
1. A hybrid electric vehicle comprising:
one or more wheels;
a power train comprising an electric motor, an internal combustion engine, and a drive train delivering power from at least one of the electric motor and the internal combustion engine to the one or more wheels;
a sensor interface configured to receive sensor data samples during operation of a vehicle;
a storage device configured to store said sensor data samples over a number of points in time; and
a processing device configured to (i) analyze said sensor data samples stored in said storage device to detect a historical pattern of said vehicle and (ii) manage energy usage by said electric motor and by said internal combustion engine in response to said historical pattern, wherein (a) said historical pattern is used to predict future conditions for said vehicle and (b) said energy usage is managed to improve power-resource-consumption efficiency of said power train as said vehicle encounters said predicted future conditions;
wherein said energy usage is further managed by controlling a consumable power source and a renewable power source; and
wherein said power-resource-consumption efficiency comprises reducing a usage of said consumable power source.
2. The vehicle according to claim 1 , wherein said energy usage is further managed by adjusting a ratio between said energy usage by said electric motor and said energy usage by said internal combustion engine.
3. The vehicle according to claim 2 , wherein said ratio is adjusted based on calculated power usage and an amount of energy stored in batteries for said electric motor.
4. The vehicle according to claim 2 , wherein said ratio is adjusted to use said electric motor while traveling uphill until a power source for said electric motor is empty and recharge said power source for said electric motor while traveling downhill.
5. The vehicle according to claim 1 , wherein (i) said consumable power source comprises gasoline for said internal combustion engine and (ii) said renewable power source comprises a battery for said electric motor.
6. The vehicle according to claim 1 , wherein (i) said processing device further comprises a classification module, (ii) said sensor data samples are implemented as arrays of data and (iii) said classification module is configured to classify said arrays of data into clusters associated with one or more conditions.
7. The vehicle according to claim 1 , wherein said sensor data samples are received from one or more sensors configured to monitor said vehicle.
8. The vehicle according to claim 1 , wherein said sensor data samples are received from one or more sensors configured to monitor an environment external to said vehicle.
9. The vehicle according to claim 1 , wherein said sensor data samples are received from one or more sensors configured to monitor data from a remote database.
10. The vehicle according to claim 1 , wherein (i) said sensor data samples are received from one or more driver-related sensors and (ii) said processing device is further configured to generate a likelihood that a particular driver is operating said vehicle based upon said historical pattern.
11. The vehicle according to claim 1 , wherein said future conditions comprise at least one of: drag, wind resistance, tire resistance, a location relative to a destination, traffic patterns, an amount of light energy, a position of the sun, geographical information, an elevation of said vehicle, a grade of a road, a location of stoplights, a timing of stoplights, weather, a wind direction, a wind velocity, a temperature, air pressure, moisture, visibility, an amount of vibration, a fuel supply, a battery supply, a condition of tires of said vehicle, a condition of the electric motor of said vehicle, a condition of said power train of said vehicle, a time of day, a day of the week, a date, and traction.
12. The vehicle according to claim 1 , wherein said future conditions are determined by deriving inferences from said sensor data samples.
13. The vehicle according to claim 1 , further comprising a communication device configured to connect to a telemetry system, wherein (i) said vehicle is configured to receive information about a plurality of route segments from said telemetry system and (ii) said processing device is configured to associate said future conditions with said route segments.
14. The vehicle according to claim 13 , wherein said route segments are used to predict said energy usage.
15. The vehicle of claim 1 , wherein said power-resource-consumption efficiency comprises increasing speed on a downhill section in order to reduce usage of said consumable power source on an uphill section that follows the downhill section.
16. The vehicle of claim 1 , wherein said ratio is adjusted so that the renewable power source is one of fully depleted at a crest of a hill and fully charged at a bottom of the hill.
17. A hybrid electric vehicle comprising:
one or more wheels;
a power train comprising an electric motor, an internal combustion engine, and a drive train delivering power from at least one of the electric motor and the internal combustion engine to the one or more wheels;
a sensor interface configured to receive sensor data samples during operation of a vehicle;
a storage device configured to store said sensor data samples over a number of points in time; and
a processing device configured to (i) analyze said sensor data samples stored in said storage device to detect a historical pattern of said vehicle and (ii) manage energy usage by said electric motor and by said internal combustion engine in response to said historical pattern, wherein (a) said historical pattern is used to predict future conditions for said vehicle and (b) said energy usage is managed to improve power-resource-consumption efficiency of said power train as said vehicle encounters said predicted future conditions;
wherein said energy usage is further managed by adjusting a ratio between said energy usage by said electric motor and said energy usage by said internal combustion engine; and
wherein said ratio is adjusted to use said electric motor while traveling uphill until a power source for said electric motor is empty.
18. The vehicle according to claim 17 , wherein (i) said processing device further comprises a classification module, (ii) said sensor data samples are implemented as arrays of data and (iii) said classification module is configured to classify said arrays of data into clusters associated with one or more conditions.
19. The vehicle of claim 17 , wherein said power-resource-consumption efficiency comprises increasing speed on a downhill section in order to reduce usage of said consumable power source on an uphill section that follows the downhill section.
20. A hybrid electric vehicle comprising:
one or more wheels;
a power train comprising an electric motor, an internal combustion engine, and a drive train delivering power from at least one of the electric motor and the internal combustion engine to the one or more wheels;
a sensor interface configured to receive sensor data samples during operation of a vehicle;
a storage device configured to store said sensor data samples over a number of points in time; and
a processing device configured to (i) analyze said sensor data samples stored in said storage device to detect a historical pattern of said vehicle and (ii) manage energy usage by said electric motor and by said internal combustion engine in response to said historical pattern, wherein (a) said historical pattern is used to predict future conditions for said vehicle and (b) said energy usage is managed to improve power-resource-consumption efficiency of said power train as said vehicle encounters said predicted future conditions;
wherein (i) said sensor data samples are received from one or more driver-related sensors and (ii) said processing device is further configured to generate a likelihood that a particular driver is operating said vehicle based upon said historical pattern.