US 12,148,311 B2
Systems and methods for managing energy use in automated vehicles
Aralakuppe Yogesha, Bangalore (IN); Shouvik Das, Bangalore (IN); Mohammed Ibrahim Mohideen, Bangalore (IN); Perumal Kumar, Bangalore (IN); Rafeek Sainudeen, Bangalore (IN); Parag Rao, Bangalore (IN); and Abhishek Alladi, Hyderabad (IN)
Assigned to Honeywell International Inc., Morris Plains, NJ (US)
Filed by Honeywell International Inc., Morris Plains, NJ (US)
Filed on Aug. 31, 2020, as Appl. No. 17/007,822.
Claims priority of application No. 202041027835 (IN), filed on Jun. 30, 2020.
Prior Publication US 2021/0407303 A1, Dec. 30, 2021
Int. Cl. B64U 30/20 (2023.01); G05D 1/00 (2006.01); G06N 20/00 (2019.01); G08G 5/00 (2006.01)
CPC G08G 5/0039 (2013.01) [G05D 1/0088 (2013.01); G05D 1/042 (2013.01); G05D 1/1062 (2019.05); G06N 20/00 (2019.01); G08G 5/0069 (2013.01); G08G 5/0091 (2013.01); B64U 30/20 (2023.01); B64U 2201/10 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for managing energy use in a vehicle, the computer-implemented method comprising:
receiving forecasted data from a first external source, the forecasted data comprising at least one forecasted weather parameter predicted along a first navigation path of the vehicle;
receiving real-time data comprising at least one real-time weather parameter, the real-time data being received from at least one of a second external source and/or a sensor connected to the vehicle;
receiving historical data including an energy drain profile comprising data that identifies an impact of historical weather parameters on battery charge and/or energy drain;
identifying, based at least in part on the at least one real-time weather parameter and the energy drain profile included in the received historical data, an effective mission duration model associated with the vehicle to determine whether an estimated battery charge level is sufficient to enable completion of a first trip of the vehicle defined along the first navigation path;
continuously determining whether to perform an adjustment to a control parameter of the vehicle by using a machine learning model that is based on the forecasted data comprising the at least one forecasted weather parameter, the real-time data comprising the at least one real-time weather parameter, the energy drain profile from the received historical data, the effective mission duration model, and at least one of a battery condition of the vehicle and an estimated amount of energy consumed by traveling along the first navigation path; and
in response to determining that an adjustment to a control parameter of the vehicle is to be performed, controlling the vehicle by automatically performing the adjustment to the control parameter of the vehicle;
wherein the adjustment to the control parameter comprises adjusting one or more of a number of rotors being supplied power and a number of propellers being supplied power.